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Simple and robust methods for remote sensing of canopy chlorophyll content: A comparative analysis of hyperspectral data for different types of vegetation

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Abstract

Canopy chlorophyll content (CCC) is an essential ecophysiological variable for photosynthetic functioning. Remote sensing of CCC is vital for a wide range of ecological and agricultural applications. The objectives of this study were to explore simple and robust algorithms for spectral assessment of CCC. Hyperspectral datasets for six vegetation types (rice, wheat, corn, soybean, sugar beet and natural grass) acquired in four locations (Japan, France, Italy and USA) were analysed. To explore the best predictive model, spectral index approaches using the entire wavebands and multivariable regression approaches were employed. The comprehensive analysis elucidated the accuracy, linearity, sensitivity and applicability of various spectral models. Multivariable regression models using many wavebands proved inferior in applicability to different datasets. A simple model using the ratio spectral index (RSI; R815, R704) with the reflectance at 815 and 704nm showed the highest accuracy and applicability. Simulation analysis using a physically based reflectance model suggested the biophysical soundness of the results. The model would work as a robust algorithm for canopy-chlorophyll-metre and/or remote sensing of CCC in ecosystem and regional scales. The predictive-ability maps using hyperspectral data allow not only evaluation of the relative significance of wavebands in various sensors but also selection of the optimal wavelengths and effective bandwidths.

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... e., C3, C4), in a wide range of agricultural conditions (e.g., different cultivars and N-fertilization regimes, rainfed, irrigated), acquired in different locations, and across different years. These datasets have been analyzed in multiple studies for different purposes (Ciganda et al., 2009;Gitelson et al., 2019Gitelson et al., , 2018Gitelson et al., , 2016Gitelson et al., 2005Gitelson et al., , 2008Gitelson et al., , 2006Inoue et al., 2016;Peng et al., 2017;Viña et al., 2011). A summary of the procedures employed for their acquisition follows. ...
... An additional dataset on maize was collected during the growing season of 2006 in a sprinkler irrigated site near Shelton, Nebraska, USA under five N treatments (0, 50, 100, 150, and 200 kg N ha − 1 ). The data collection campaign for rice was performed in 2009 at experimental fields of the National Institute for Agro-Environmental Studies (NIAES) in Tsukuba, Japan (Inoue et al., 2016). In addition to the standard level of N application (10 g m − 2 ), four different N levels (2, 6, 14 and 16 g m − 2 ) were further applied to induce a wide range of LAI and CCC. ...
... This allows obtaining representative measurements of canopy reflectance. Canopy reflectance factors were calculated as the ratio of the upwelling radiance to that of a Spectralon-Labsphere white reference to account for differences in atmospheric conditions (Inoue et al., 2016) (Table 1). ...
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Article
The green leaf area index (GLAI) has been widely used in agriculture, forestry, and environmental sciences for the analysis and modeling of many biophysical processes of vegetation, including the attenuation of light through the canopy, transpiration, photosynthesis, and carbon and nutrient cycles. Nevertheless, its usefulness is hampered by the uncertainty introduced through the lack of quantitative information on leaf biochemistry, particularly leaf chlorophyll content, in its computation. Thus far, this uncertainty has not been properly recognized nor quantified. The main goal of this study was to quantify the uncertainty of GLAI as used in the estimation of key photosynthetic canopy traits, namely canopy chlorophyll content (CCC). This uncertainty was assessed through the evaluation of the relationship between GLAI and CCC in structurally and functionally contrasting crop species (Zea mays L., Glycine max (L.) Merr., and Oryza sativa L). Results show that for the same GLAI value, CCC varied 2- to 3-fold due mainly to the variability of leaf chlorophyll content. Therefore, we suggest using the absorption coefficient in the red-edge region of the electromagnetic spectrum as an alternative to GLAI for the evaluation of CCC and other important photosynthetic canopy traits. The absorption coefficient in this spectral region is particularly suitable as it has been successfully related with the gross primary productivity of vegetation canopies, the quantum yield of photosynthesis, and is sensitive to the repositioning of chloroplasts within leaf cells in response to water stress.
... Like LAI and FCOVER, CCC is an intrinsic canopy property, so does not vary depending on illumination conditions. CCC corresponds to the product of LAI and leaf chlorophyll concentration (LCC), which describes the mass of chlorophyll per unit horizontal leaf area (Inoue et al., 2016). ...
... With its red-edge bands and high spatial resolution (10 m to 60 m), the Multispectral Instrument (MSI) on-board the Sentinel-2 missions is well-suited to vegetation biophysical and biochemical variable retrieval (Drusch et al., 2012). In particular, its spectral sampling opens up opportunities for the retrieval of CCC, to which the position of the red-edge is highly sensitive (Clevers et al., 2017;Clevers and Gitelson, 2013;Darvishzadeh et al., 2019;Frampton et al., 2013;Inoue et al., 2016). A considerable body of work related to the retrieval of vegetation biophysical and biochemical variables using MSI data has been published in recent years, although the majority of studies have been restricted to agricultural environments containing crop canopies. ...
... For example, in a recent study using INFORM to retrieve LCC from MSI data,Darvishzadeh et al. (2019) demonstrated increased retrieval accuracies when using a spectral subset of only MSI's red-edge bands as opposed to the full band set. Similarly,Inoue et al. (2016) observed that models utilising a large number of spectral bands did not improve the retrieval of CCC from hyperspectral data when compared to those incorporating just two optimally selected bands. It is worth noting that, unlikeDarvishzadeh et al. (2019), we did not observe large discrepancies between modelled and observed MSI spectra in the near-infrared shoulder. ...
Thesis
Accurate and timely information on vegetation status, in the form of biophysical and biochemical variables, is key to the effective management of vegetated environments. Using optical instruments capable of resolving the spectral characteristics of vegetation, satellite-derived vegetation products now provide users routine estimates of these variables at the regional and global scale. However, to ensure their fitness-for-purpose, quality assessment is required. Unfortunately, progress in the validation of these products has been restricted by temporally limited reference data, with periodic field campaigns providing few in situ reference measurements throughout the growing season or over multiple years. Information on how product performance varies over time is, therefore, scarce (resulting in uncertainty in models of crop yield, carbon exchange, and the weather and climate systems, for which vegetation seasonality is an important driver). In recent years, several techniques have emerged with the potential to provide temporally continuous in situ reference data, automating the data collection process and overcoming the logistical issues associated with periodic field campaigns. This thesis focuses on addressing challenges associated with these techniques, including those related to data processing methods, measurement assumptions, spatial representativeness, and upscaling approaches (which are a necessity for validating moderate spatial resolution products). Of various emerging techniques, above-canopy digital repeat photography was identified as being of particular interest due to its maturity and degree of spatial integration. However, a critical appraisal of the approach revealed that due to non-linear and seasonal effects, the resulting timeseries of colour indices were prone to asymptotic saturation and could not be easily linked to any one biophysical property. To overcome these limitations, a new technique based on automated below-canopy digital hemispherical photography was proposed and evaluated. Benchmarking against manually collected data provided confidence that the approach could deliver leaf area index measurements of comparable quality to traditional in situ measurement techniques (but with substantially improved temporal characterisation). Upscaling methods were then investigated, as existing approaches are not well-suited to dense temporal characterisation of a limited number of locations. It was concluded that radiative transfer model-based approaches, which incorporate physical knowledge and enable seasonal variations in sun-sensor geometry to be accounted for, were more robust than vegetation index-based multitemporal transfer functions. Overall, the thesis provides a framework for routine quality assessment of satellitederived vegetation products, from a cost-effective automated in situ measurement technique, through to an upscaling approach capable of deriving time-series of high spatial resolution reference maps suitable for product validation. By facilitating a temporally explicit quantification of product performance in future work, the framework will enable targeted product improvements to be made, ultimately reducing uncertainties in downstream applications.
... The knowledge of leaf biochemical traits is significant to understand the ecosystem functioning, environmental changes, plant growth, and physiological status Yi et al., 2014). Leaf chlorophyll content (C ab ) is a vital parameter for the characterization of plant photosynthesis and nitrogen (N) status (Inoue et al., 2016); leaf water content (C w ) and dry matter content (C m ) are the primary traits related Abbreviations: C ab , leaf chlorophyll content; C w , leaf water content; C m , dry matter content; VIS, visible; NIR, near-infrared; SWIR, shortwave infrared; CI 800,720 , red edge chlorophylls index ; MSI, moisture stress index; NDMI, normalized dry matter index; DLM, Dorsiventral Leaf Model; MDATT, modified DATT; DLARI, dorsiventral leaf adjusted ratio index; C ar , carotenoid content; fair, fraction of air spaces; β wdm , fraction of water and matter content; β pigm , fraction of pigment content in the palisade layer; δ, abaxial diffusion parameter; µ, roughness parameter; LAI, leaf area index; ALA, average leaf angle; hspot, hot spot parameters; psoil, soil brightness parameters; tts, solar zenith angle; tto, observed zenith angle; psi, observation relative azimuth; N, nitrogen; P, phosphate; K, potash; RSI, ratio of spectral index; NDSI, normalized difference spectral index; RDSI, ratio of difference spectral index; ARDSI, adjusted RDSI; RMSE, root mean square error; R 2 , coefficient of determination; rRMSE, relative RMSE. ...
... Among all reflectance-based methods, spectral indices are more favored for estimating leaf biochemical traits because they only require several bands, and are easily integrated with ground and aerial platforms. Numerous spectral indices have been developed to assess leaf biochemical traits from reflectance data collected at different scales (Sims and Gamon, 2002;Levizou et al., 2005;Ustin et al., 2009;Inoue et al., 2016;Wu et al., 2019;D'Odorico et al., 2020;Wong et al., 2020). At leaf level, spectral indices were generally established with the combination of several wavelengths, e.g., the regions of green, red, red edge, and near-infrared (NIR), showing the potential to quantify leaf pigments (Gamon and Surfus, 1999). ...
... While the soil background cannot be avoided for the canopy reflectance of oilseed rape that was measured at the seeding stage, suggesting that there may exist the background effect when using the canopy reflectance. Previous studies have demonstrated that the use of spectral indices can minimize the effect of the background noise (Sims and Gamon, 2002;Inoue et al., 2016), and thus this study used the spectral indices calculated from canopy reflectance to estimate leaf biochemical traits. The CV of the reflectance measurement of the canopy of oilseed rape with three repetitions is shown in Supplementary Fig. S1B. ...
Article
The knowledge of leaf biochemical traits is significant to understand the plant growth and physiological status. Spectral indices have been widely used to assess leaf biochemical traits, while the estimation accuracies at canopy level are frequently lower compared to those at leaf level in part due to the complexity of canopy structures and the variations in optical properties between leaf adaxial and abaxial surfaces. This study thus improved spectral indices with minimizing the effect of the difference between leaf adaxial and abaxial surfaces to assess leaf biochemical traits from leaf to canopy level. The datasets including leaf reflectance and canopy reflectance with corresponding leaf chlorophyll content (Cab), water content (Cw), and dry matter content (Cm) from a wide range of plant species were used. Results showed that there existed a significant difference between leaf abaxial and abaxial reflectance, causing the variation in the relationship between leaf biochemical traits and spectral indices. The published spectral indices exhibited the strong relationships with Cab, Cw, and Cm for either leaf adaxial or abaxial data, while the relationships of spectral indices with Cab, Cw, and Cm from leaf adaxial reflectance were inconsistent with those from leaf abaxial reflectance. The proposed adjusted ratio of difference spectral index (ARDSI) minimized the difference between leaf adaxial and abaxial reflectance for assessing Cab, Cw, and Cm at leaf level with the root mean square error of 9.32 μg cm⁻², 0.0050 g cm⁻², and 0.0053 g cm⁻², respectively. The application of the proposed ARDSI to the canopy level alleviated the effect of the variations of leaf adaxial and abaxial reflectance difference and canopy structures on the estimation of leaf biochemical traits, which thus improved the assessment of Cab, Cw, and Cm at canopy level in the simulated and measured datasets. The proposed approach would advance the applicability of spectral indices to monitor the physiological and functional traits of field crops at different scales.
... We applied the NDSI map to explore optimal indices for assessment of V cmax25 , J max25 , and leaf N content using hyperspectral data (500-2,400 nm) (Inoue et al., 2016). NDSI is calculated using the following equation: ...
... The discrepancy of the slope of regression line between measured and predicted values from unity (DS; Inoue et al., 2016) is calculated as follows: ...
... where s is the slope of the regression line between measured and predicted data. Consequently, these three indicators represent the overall scattering including bias, sensitivity (slope), and linearity of the model, respectively (Inoue et al., 2016). ...
Article
Traditional gas exchange measurements are cumbersome which makes it difficult to capture variation in biochemical parameters, namely the maximum rate of carboxylation measured at a reference temperature (Vcmax25) and the maximum electron transport at a reference temperature (Jmax25), in response to growth temperature over time from days to weeks. Hyperspectral reflectance provides reliable measures of Vcmax25 and Jmax25; however, the capability for this method to capture biochemical acclimations of the two parameters to high growth temperature over time has not been demonstrated. In this study, Vcmax25 and Jmax25 were measured over multiple growth stages during two growing seasons for field‐grown soybeans using from both gas exchange techniques and leaf spectral reflectance under ambient and four elevated canopy temperature treatments (ambient+1.5, +3, +4.5 and +6 °C). Spectral vegetation indices and machine learning methods were used to build predictive models for Vcmax25 and Jmax25, based on the leaf reflectance. Results showed that these models yielded an R2 of 0.57‐0.65 and 0.48‐0.58 for Vcmax25 and Jmax25, respectively. Hyperspectral reflectance captured biochemical acclimation of leaf photosynthesis to high temperature in the field, improving spatial and temporal resolution in the ability to assess the impact of future warming on crop productivity. This article is protected by copyright. All rights reserved.
... The study used multiple datasets of the three crop species evaluated (i.e., maize, soybean and rice) acquired for different studies (Ciganda et al. 2009;Gitelson et al. 2005Gitelson et al. , 2006Gitelson et al. , 2008Gitelson et al. , 2016Gitelson et al. , 2018Gitelson et al. , 2019Inoue et al. 2016;Peng et al. 2017;Viña et al. 2011) in separate sites, across different years and at scales ranging from individual leaves to entire plants. The following paragraphs summarize the procedures employed. ...
... Rice data collection campaigns were carried out during the growing season of 2009 in ten experimental plots in Tsukuba, Japan. To induce a wide range of canopy responses, four different nitrogen levels (2, 6, 14, and 16 g m −2 ) were applied (Inoue et al. 2016). ...
... In the rice fields, reflectance measurements were taken using a portable spectroradiometer (ASD FieldSpec-Pro, Analytical Spectral Devices, Inc., Longmont, CO) with a 25° field-of-view at a nadir-looking angle from 2 m above the plants (Table 1). Percent plant reflectance was calculated as the ratio of the upwelling radiance to that of a Spectralon-Labsphere white reference (Inoue et al. 2016). More than 30 spectra were averaged for each plot to derive the representative reflectance spectrum. ...
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Article
Non-invasive comparative analysis of the spectral composition of energy absorbed by crop species at leaf and plant levels was carried out using the absorption coefficient retrieved from leaf and plant reflectance as an informative metric. In leaves of three species with contrasting leaf structures and photosynthetic pathways (maize, soybean, and rice), the blue, green, and red fractions of leaf absorption coefficients were 48, 20, and 32%, respectively. The fraction of green light in the total budget of light absorbed at the plant level was higher than at the leaf level approaching the size of the red fraction (24% green vs. 25.5% red) and surpassing it inside the canopy. The plant absorption coefficient in the far-red region (700-750 nm) was significant reaching 7-10% of the absorption coefficient in green or red regions. The spectral composition of the absorbed light in the three species was virtually the same. Fractions of light in absorbed PAR remained almost invariant during growing season over a wide range of plant chlorophyll content. Fractions of absorption coefficient in the green, red, and far-red were in accord with published results of quantum yield for CO 2 fixation on an absorbed light basis. The role of green and far-red light in photosynthesis was demonstrated in simple experiments in natural conditions. The results show the potential for using leaf and plant absorption coefficients retrieved from reflectance to quantify photosynthesis in each spectral range.
... The number of spikelets per area sets the upper limit of rice grain yield because the grain size lacks elasticity owing to the rigid palea and lemma (Hoshikawa, 1989). However, an excessive number of spikelets limit grain filling and thus grain yield (Matsushima, 1980). ...
... They had much higher R 2 values than the other VIs, especially those based on red edge reflectance. RSIs and NDSIs calculated from NIR and red edge or green data have high coefficient of determination with green LAI in maize and soybean (Vina et al., 2011), plant nitrogen content in maize (Li et al., 2014), and canopy chlorophyll content in crops including rice (Inoue et al., 2016). In these reports; however, the RSI and NDSI show higher estimation accuracy when based on red edge than on green. ...
... The reason for this inconsistency may be the actual reflectance bands used for red edge and NIR. Whereas the Sequoia camera captures reflectance at 730-740 nm for red edge and 770-810 nm for NIR (instruction manual), other researchers used red edge/NIR bands of 704-710/771-786 nm (Vina et al., 2011), 720-740/760-780 nm (Li et al., 2014), and 704/815 nm (Inoue et al., 2016). Inoue et al. (2016) showed that coefficient of determination between VI and canopy chlorophyll content changes drastically with a small change in the band region used in both the NIR and red edge regions. ...
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Article
The objectives of this study were to find the best vegetation index (VI) associated with plant nitrogen content at the reproductive stage in rice, to associate the number of spikelets with this VI and solar radiation, and to estimate the number of spikelets. Rice cultivars Ishikawa 65 and Koshihikari were grown in the field in 2019 and 2020 at various nitrogen application rates and transplanting densities. From 30 days before heading to just after heading, the field was imaged with a multispectral camera. The images were processed with predefined ground control point data to create VI maps. From the maps, VI data were retrieved from the canopy area where plants were harvested for the determination of plant nitrogen content at the reproductive stage and of the number of spikelets at maturity. Among 6 VIs tested, the chlorophyll index green (CIgreen) had the highest coefficient of determination (R²) with plant nitrogen content at the reproductive stage and was the only VI with a linear relation with plant nitrogen content. The number of spikelets per unit area was well explained by multiple regression with CIgreen at 15 days before heading (CIG15) and cumulative solar radiation in the 15 days before heading (CSR15) as independent variables. A higher CIG15 would increase the number of spikelets differentiated and a higher CSR15 would reduce the rate of degeneration by increasing dry matter production.
... However, the use of single-band reflectance makes it difficult to quantify physiological and agronomic parameters directly because of the influences of canopy structure, growth stages, species, and the environment (Ustin and Gamon, 2010;Li et al., 2013Li et al., , 2014. Spectral indices can overcome these limitations, reducing the influence of external factors and providing a relatively simple, reliable method that can extract the N nutritional signal from complex crop canopy reflection (Hatfield et al., 2008;Viña et al., 2011;Inoue et al., 2016). The normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) are the most commonly used indices to monitor, analyze, and map the spatiotemporal distributions of physiological and biophysical vegetation characteristics (Jordan, 1969;Fitzgerald et al., 2010;Erdle et al., 2011;Ballester et al., 2017). ...
... For example, to estimate the biophysical characteristics of plant canopies, Viña et al. (2011) found that the MERIS terrestrial chlorophyll index (MTCI), the red-edge chlorophyll index (CIred-edge), the green chlorophyll index (CIgreen), and the simple ratio R NIR /R Red could be used for assessing the green leaf area index (LAI) for corn and soybean together under irrigated and rainfed conditions. Inoue et al. (2016) found that the simple ratio RSI (R 815 /R 704 ) allowed to predict the canopy chlorophyll content of different crops together (wheat, corn, rice, and soybean) with a coefficient of determination (R 2 ) of 0.89. In contrast, Li et al. (2014) found that the narrow and broad three-band spectral index, i.e., Nitrogen Planar Domain Index (NPDI), could be used to estimate the N uptake of Chinese and German winter wheat cultivars with R 2 values of 0.81 during the vegetative stage. ...
... However, there is limited information on the optimized selection of vegetation indices to assess the N uptake of corn and wheat under different conditions. Furthermore, previous studies have focused on specific cultivars, species, and local conditions (Viña et al., 2011;Inoue et al., 2016), whereas the performance of spectral indices in this study was tested to estimate the N uptake of corn and wheat together in China and Germany. This is of particular interest since remote sensing becomes more straightforward and applicable if it allows for applications in contrasting regions characterized also by differences in management. ...
Article
Nitrogen (N) fertilization management plays an important role in optimizing crop growth and yield. Concerns over environmental risk require a quick, accurate, non-destructive determination of the N status of crops. Hyperspectral remote sensing allows a timely monitoring of the in-season crop N status. Although many spectral indices for assessing the N status of crops have been proposed, it is still necessary to further optimize the central bands, since they often vary with plant cultivars and species. To improve this, we identified optimized three-band spectral indices for estimating the canopy N uptake of corn and wheat. Experiments were conducted from 2009 to 2011 by evaluating and testing optimized three-band spectral indices for estimating the N status of wheat cultivars grown in Germany and China and corn cultivars grown in China. The indices generally enabled more robust predictions compared to published indices. The central bands suitable for assessing the canopy N uptake were 768, 740, and 548 nm for corn; 876, 736 and 550 nm for wheat; and 846, 732 and 536 nm for corn and wheat combined. Both wheat and corn assessed individually as well as in combination where sharing similar wavebands reflected by the species-specific and interspecies-specific optimized three-band spectral indices, e.g. the wavelengths 740, 736 and 732 nm were identified as optimum for corn, wheat and their combination, respectively. The validation results suggest that predictions using the optimized three-band N planar domain index (NPDI) delivered the highest coefficient of determination (R 2 = 0.86) and the lowest root mean square error (RMSE, 20.1 kg N ha-1) and relative error (RE, 18.7 %). The optimized NPDI consistently estimated canopy N uptake of both corn and wheat alone and in combination. Therefore, the optimized three-band algorithm is an attractive tool for optimizing and identifying central bands. Our algorithm may allow the design of improved N diagnosis systems and enhance the application of ground-and satellite-based sensing.
... The study used multiple datasets of the three crop species evaluated (i.e., maize, soybean and rice) acquired for different studies (Ciganda et al., 2009;Gitelson et al., 2019;Gitelson et al., 2018;Gitelson and Gamon, 2015;Gitelson et al., 2016;Gitelson et al., 2005;Gitelson et al., 2008;Gitelson et al., 2006;Inoue et al., 2016;Nguy-Robertson et al., 2012;Peng et al., 2011;Peng et al., 2017;Viña and Gitelson, 2005;Viña et al., 2011) in separate sites, across different years and at scales ranging from individual leaves to entire fields. While there is some variation among datasets since the studies were performed across multiple scales, at different times, and in different places, the data collection techniques employed constitute standard procedures. ...
... Rice data collection campaigns were carried out during the growing season of 2009 in ten experimental plots in Tsukuba, Japan. To induce a wide range of canopy responses, four different nitrogen levels (2, 6, 14, and 16 g m − 2 ) were applied (Inoue et al., 2016). ...
... In the rice fields, reflectance measurements were taken using a portable FieldSpec-Pro ASD spectroradiometer with a 25 • field-of-view at a nadir-looking angle from 2 m above the canopy (Table 1). Percent canopy reflectance was calculated as the ratio of this upwelling radiance to that of a Spectralon-Labsphere white reference (Inoue et al., 2016). More than 30 spectra were averaged for each plot to derive the representative reflectance spectra. ...
Article
Absorption of radiation in the photosynthetically active radiation (PAR) region is significantly influenced by plant biochemistry, structural properties, and photosynthetic pathway. To understand and quantify the effects of these traits on absorbed PAR it is necessary to develop practical and reliable tools that are sensitive to these traits. Using a semi-analytical modeling framework for deriving the absorption coefficient of plant canopies from reflectance spectra, we quantify the effects of functional, structural and biochemical traits of vegetation on the relationship between the absorption coefficient in the PAR region (αpar) with canopy characteristics such as the fraction of PAR absorbed by photosynthetically active vegetation (fAPARgreen) and chlorophyll (Chl) content. The reflectance dataset used in the study included simulated data obtained from a canopy reflectance model (PROSAIL) and empirical data on three diverse crop species with different leaf structures, canopy architectures and photosynthetic pathways (rice, maize and soybean) acquired at proximal (i.e., using field spectroradiometers) and remote (i.e., Landsat TM and ETM+) distances. Results show the usefulness of αpar derived from reflectance data for assessing not only the photosynthetic status of vegetation, but also the effects of different functional, structural and biochemical traits on plant performance. Furthermore, these assessments can be made using data acquired by satellite sensor systems such as the Landsat series, which are available since the 1980s, thus facilitating the analysis of the photosynthetic status of terrestrial ecosystems throughout the world with a high temporal depth.
... However, the wavelengths and bandwidths are often specific to sensors, and predictive models are also sensor specific (e.g., Liu et al. 2003;Gitelson and Merzlyak 1997;Gitelson et al. 2005;Mutanga and Skidmore 2004). Thus, the generalized spectral index approach using hyperspectral data has been proposed (Inoue et al. 2008) and evaluated (Inoue et al. , 2016. ...
... Principal component regression (PCR) and partial least-squares regression (PLSR) are effective to reduce the impact of multi-collinearity. However, some theoretical and experimental considerations (Spiegelman et al. 1998;Inoue et al. 2008Inoue et al. , 2012Inoue et al. , 2016 suggested that PLSR method may not always provide the best solutions. Alternatively, the interval PLSR (iPLSR: PLS with iterative waveband selection) would be more feasible for predictive modeling using hyperspectral data (Norgaard et al. 2000;Leardi and Norgaard 2004;Inoue et al. 2016). ...
... However, some theoretical and experimental considerations (Spiegelman et al. 1998;Inoue et al. 2008Inoue et al. , 2012Inoue et al. , 2016 suggested that PLSR method may not always provide the best solutions. Alternatively, the interval PLSR (iPLSR: PLS with iterative waveband selection) would be more feasible for predictive modeling using hyperspectral data (Norgaard et al. 2000;Leardi and Norgaard 2004;Inoue et al. 2016). ...
Article
Present climate and socioeconomic issues would threaten the global food and environmental security. Smart farming (SF) based on advances in sensing, robotic, and information and communication technologies is a promising approach to support the efficient, sustainable, and profitable crop production. This paper discusses the background needs for SF and the role of remote sensing. Recent advances in remote sensing technology (platforms, sensors, algorithms) for diagnostic information of crops and soils are reviewed based on some leading case studies. The operation of a bundle of similar satellites (constellation) allows timely or frequent observations, and their spatial resolution (1 ~ 10 m) is applicable to agricultural regions of relatively small farmlands. The efficient use of high-resolution satellite sensors would strongly support the diagnostics and decision-making in SF on regional scales. Drone-based remote sensing would allow low-cost, high resolution, and flexible observations of crops and soils. Diagnostic information on crop growth, water stress, soil fertility, weed, disease, lodging, and 3D topography can be created from the optical, thermal and/or video images. The linkage between the remote sensing function and drone-based application of seeds, pesticides, fertilizes would greatly enhance the efficiency of labor and material applications and profitability.
... Its content is affected by anthropogenic and natural stressors, such as water or nutrient shortage or oversupply, insects, herbivores, pollution or diseases [1][2][3]. Therefore, plant chlorophyll content is typically used as a bioindicator of vegetation state [4][5][6][7]. Further, it is a crucial measure of ecosystem functioning and has therefore been suggested as an Essential Biodiversity Variable (EBV) to monitor progress towards the Aichi Biodiversity Targets [8,9]. ...
... Narrowband indices calculated from hyperspectral images can overcome this problem by allowing a more precise selection of specific spectral regions from an almost continuous spectrum which is sensitive to specific vegetation parameters such as chlorophyll [25,26]. Further, narrowband vegetation indices have been used to detect vegetation stress and estimate chlorophyll content [5,23,[26][27][28][29][30]. As such, [26] achieved an accurate assessment of grassland CCC (R 2 = 0.69) using a narrowband soil adjusted vegetation index. ...
... Haboudane et al. [27] integrated the Modified Chlorophyll Absorption in Reflectance Index (MCARI) and the Transformed Chlorophyll Absorption in Reflectance Index (TCARI) to predict crop CCC from PROSAIL simulated hyperspectral data. Inoue et al. [5] compared multivariable regression models and narrowband indices for CCC estimation of different vegetation types (crops and grasslands). They achieved the highest accuracy using a narrowband simple ratio index using wavelengths from the infrared and red-edge regions (815 nm and 704 nm). ...
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Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands; 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region; 3 m spatial resolution) on July 6, 2017. In situ leaf chlorophyll content and leaf area index measurements were sampled from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy.
... Remote sensing is becoming the most popular means to retrieve chlorophyll content over large areas, by establishing empirical relationships between different vegetation indices (VIs) and chlorophyll content, or through physical-based approaches relying on the inversion of canopy reflectance models. Remote sensing of CCC is mainly based on optical remote sensing, covering the visible to near-infrared (NIR) spectral region, and the short wave infrared region (SWIR) as well when LAI and other vegetation biophysical properties effect is high (Inoue et al., 2016;Darvishzadeh et al., 2008c). ...
... Concurrently with the evolution of satellite sensors, which bring increased resolution and sensitivity to biochemical variables, a number of robust algorithms have been developed that relate CCC and remote sensing data (e.g., Vincini et al., 2016;Dian et al., 2016;Li et al., 2015;Ma et al., 2014;Verrelst et al., 2012). Notably, the red edge region (680-760 nm) of the reflectance spectrum also known as Red Edge Position (REP), has been widely used to estimate chlorophyll content from reflectance spectra (e.g., Okuda et al., 2016;Li et al., 2016;Inoue et al., 2016) based on the fact that an increase in chlorophyll content will be reflected in the spectra by a shift in the absorption feature to longer wavelengths (Curran, 1989). ...
... This was expected as spectra in the red-edge region have proven strong sensitivity to chlorophyll (e.g., Ju et al., 2010;Curran et al., 1990). Our finding agrees with several studies that reported the superior sensitivity of simple ratio vegetation indices to subtle changes in chlorophyll content (Inoue et al., 2016;Cui and Zhou, 2017;Tong and He, 2017). ...
Article
The Sentinel-2 Multi-Spectral Imager (MSI) has three spectral bands centered at 705, 740, and 783 nm wavelengths that exploit the red-edge information useful for quantifying plant biochemical traits. This sensor configuration is expected to improve the prediction accuracy of vegetation chlorophyll content. In this work, we assessed the performance of several statistical and physical-based methods in retrieving canopy chlorophyll content (CCC) from Sentinel-2 in a heterogeneous mixed mountain forest. Amongst the algorithms presented in the literature, 13 different vegetation indices (VIs), a non-parametric statistical approach, and two radiative transfer models (RTM) were used to assess the CCC prediction accuracy. A field campaign was conducted in July 2017 to collect in situ measurements of CCC in Bavarian forest national park, and the cloud-free Sentinel-2 image was acquired on 13 July 2017. The leave-one-out cross-validation technique was used to compare the VIs and the non-parametric approach. Whereas physical-based methods were calibrated using simulated data and validated using the in situ reference dataset. The statistical-based approaches, such as the modified simple ratio (mSR) vegetation index and the partial least square regression (PLSR) outperformed all other techniques. As such the modified simple ratio (mSR3) (665, 865) gave the lowest cross-validated RMSE of 0.21 g/m2 (R2 = 0.75). The PLSR resulted in the highest R2 of 0.78, and slightly higher RMSE =0.22 g/m2 than mSR3. The physical-based approach-INFORM inversion using look-up table resulted in an RMSE =0.31 g/m2, and R2 = 0.67. Although mapping CCC using these methods revealed similar spatial distribution patterns, over and underestimation of low and high CCC values were observed mainly in the statistical approaches. Further validation using in situ data from different terrestrial ecosystems is imperative for both the statistical and physical-based approaches' effectiveness to quantify CCC before selecting the best operational algorithm to map CCC from Sentinel-2 for long-term terrestrial ecosystems monitoring across the globe.
... It was found that there was an increasing number of publications in recent years that used HSI for agricultural applications (Table 3). During 2013-2019, we searched papers on the 'Web of Science' website using keywords containing 'HRS' and 'crop' to find that HRS has been widely used in monitoring of various crops which includes corn (Essayed and Darwish, 2017), wheat (Zhang et al., 2018), rice (Krishna et al., 2019), cotton (Marshall et al., 2016), grapevine (Zovko et al., 2019), Crambe abyssinica Hochst (Viana et al., 2018), white bean, canola, peas (Pacheco et al., 2008), sugarcane (Mokhele and Ahmed, 2010), soybean (Yuan et al., 2017), sugar beet (Inoue et al., 2016), mustard (Kumar et al., 2013), barley (Lausch et al., 2015), blackgram (Prabhakar et al., 2013), and potato leave (Latorre-Carmona et al., 2014). A chart showing the application domain of HRS in crop monitoring is provided for clear description. ...
... However, there are relatively few studies on many other crops, and even many crops which are important for regional economic development have not been studied, e.g., tobacco, tomato, pepper, cucumber, Application of HRS in retrieving key crop parameters HRS provides an effective means for the extraction of plant parameters (Millan and Azofeifa, 2018;Yu et al., 2018). It also has been widely used in retrieving crop parameters including water content (Chou et al., 2017), weed management (Huang et al., 2016), evapotranspiration (Marshall et al., 2016), yield estimation (Elsayed and Darwish, 2017), heavy metal (Zhou et al., 2019), bioenergy potential (Udelhoven et al., 2013), stand density (Pacheco et al., 2008), crop residue (Chi and Crawford, 2014), gross photosynthesis (Yuan et al., 2017), disease diagnosis (Prasannakumar et al., 2014), phenology derivation (Lausch et al., 2015), species identification , nutrient concentration (Mahajan et al., 2017), biomass assessment, and pigment content (Inoue et al., 2016). In order to show the application of HRS in retrieving these parameters more clearly, a chronological diagram was drawn (Figure 1). ...
Article
Numerous technologies have contributed to the recent development of agriculture, especially the advancement in hyperspectral remote sensing (HRS) constituted a revolution in crop monitoring. The widespread use of HRS to obtain crop parameters suggests the need for a review of research advances in this area. HRS offers new theories and methods for studying crop parameters, but much work needs to be done both experimentally and theoretically before we can truly understand the physical and chemical processes that predict these crop parameters. The study focuses on the following elements: 1) The article provides a relatively comprehensive introduction to HRS and how it can be applied to crop monitoring; 2) Current state-of-the-art techniques are summarized and analyzed to inform further advances in crop monitoring; 3) Opportunities and challenges for crop monitoring applications using HRS are discussed, and future research is summarized. Finally, through a comprehensive discussion and analysis, the article proposes new directions for using HRS to study crop characteristics, such as new data mining techniques including deep learning provide opportunities for efficient processing of large amounts of HRS data; combining the temporal and dynamic characteristics of crop parameters and vegetation growth processes will greatly improve the accuracy of crop parameter detection and monitoring; multidata fusion and multiscale data assimilation will become HRS monitoring. Multidata fusion and multiscale data assimilation will become another research hotspot for HRS monitoring of crop parameters.
... First, we examined the accuracy of LAI estimation by the regression models based on each index. Since spectral information is affected by various factors, including plant morphology, soil background, and the shooting environment [14,49,50], the optimal index for LAI estimation depends on prior information [51][52][53]. Under the condition in this experiment, SR (NIR, Red) was the most favorable of the indices (R 2 = 0.976 and RMSE = 0.334), and VEG was the most accurate of the CIs (R 2 = 0.947 and RMSE = 0.401) ( Table 4, Figures 8 and 13). ...
... Compared to VEG, which showed the highest estimation First, we examined the accuracy of LAI estimation by the regression models based on each index. Since spectral information is affected by various factors, including plant morphology, soil background, and the shooting environment [14,50,51], the optimal index for LAI estimation depends on prior information [52][53][54]. Under the condition in this experiment, SR (NIR, Red) was the most favorable of the indices (R 2 = 0.976 and RMSE = 0.334), and VEG was the most accurate of the CIs (R 2 = 0.947 and RMSE = 0.401) ( Table 4, Figures 8 and 13). ...
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Article
Leaf area index (LAI) is a vital parameter for predicting rice yield. Unmanned aerial vehicle (UAV) surveillance with an RGB camera has been shown to have potential as a low-cost and efficient tool for monitoring crop growth. Simultaneously, deep learning (DL) algorithms have attracted attention as a promising tool for the task of image recognition. The principal aim of this research was to evaluate the feasibility of combining DL and RGB images obtained by a UAV for rice LAI estimation. In the present study, an LAI estimation model developed by DL with RGB images was compared to three other practical methods: a plant canopy analyzer (PCA); regression models based on color indices (CIs) obtained from an RGB camera; and vegetation indices (VIs) obtained from a multispectral camera. The results showed that the estimation accuracy of the model developed by DL with RGB images (R2 = 0.963 and RMSE = 0.334) was higher than those of the PCA (R2 = 0.934 and RMSE = 0.555) and the regression models based on CIs (R2 = 0.802-0.947 and RMSE = 0.401–1.13), and comparable to that of the regression models based on VIs (R2 = 0.917–0.976 and RMSE = 0.332–0.644). Therefore, our results demonstrated that the estimation model using DL with an RGB camera on a UAV could be an alternative to the methods using PCA and a multispectral camera for rice LAI estimation.
... By contrast, the discrepancy between calibration and validation accuracy was much larger for PLSR (18%) than for iPLSR (5.5%), presumably because of stronger multicollinearity in PLSR. Accordingly, iPLSR may be more powerful than PLSR for assessing SC values, as reported for hyperspectral assessment of plant ecophysiological variables (Inoue et al. 2016). The 412 bands selected in iPLSR were summarized into 15 latent variables that consisted of multiple wavebands in the VIS, NIR, and SWIR regions. ...
... The simplicity and wide applicability of spectral indices, particularly with broad wavebands, is of significant merit. The influence of disturbing factors such as moisture content and measuring environments would be reduced by spectral normalization using multiple wavebands (Inoue et al. 2016). In this preliminary case study, several spectral indices using VIS to NIR wavelength regions proved promising, but their close relationship with SC values was attributed mainly to changes in soil color associated with the decontamination procedures (removal of contaminated soil and dressing with new soil). ...
Article
Farmland in the Fukushima region of Japan experienced unprecedented radioactive contamination as a result of the Fukushima Nuclear Power Plant disaster in 2011. Many fields (4,950 ha; over 30,000 fields to date) have been decontaminated by replacing the top surface soil with non-contaminated soil. However, the fertility of these fields is quite low and within-field heterogeneity is marked. Accordingly, appropriate management of soil and fertilizer is required for recovery of crop productivity in these decontaminated fields. Remote sensing can play a critical role in rapid spatial assessment of soil fertility. This preliminary study investigated the potential of spectral sensing approaches based on hyperspectral reflectance measurements (400–2500 nm) of soil samples from a decontaminated paddy field in the Fukushima region. Spectral index algorithms (the ratio spectral index [RSI] and normalized difference spectral index [NDSI]) and multivariate regression methods (partial least-squares regression [PLSR] and interval PLSR [iPLSR]) were used to identify accurate, robust predictive models. The iPLSR and PLSR showed higher predictive accuracy than the other methods (r²val = 0.937 and 0.802, respectively). The best spectral indices explored using RSI and NDSI have good potential for assessing the spatial variability of soil carbon content (SC; r²val = 0.730 ~ 0.844), despite using only two wavebands. The results from the RSI map (or NDSI map) approach provided useful information for creating optimal algorithms for assessing SC values using various sensors, including high-resolution optical satellite sensors. Although we must note that optimal algorithms and their applicability are often site-specific depending on soil type and surface conditions, our results imply that these spectral sensing methods can contribute to the recovery of soil fertility of decontaminated fields in the Fukushima region through careful calibration/validation procedures with sufficient in situ data.
... increasing the CSRR), with comparable uncertainty to valid estimates obtained using SL2P alone, by reducing the dimensionality of the feature space used for inversion through active learning given local calibration data. The strategy of dimensionality reduction is supported by empirical and numerical experiments that indicate that locally calibrated regression models with a few (usually one or two) spectral VIs as independent variables are often able to predict canopy variables with comparable or better performance as complex globally calibrated inversion schemes (Garrigues et al., 2008;Canisius and Fernandes, 2011;Inoue et al., 2016;Djamai et al., 2019). Locally calibrated regression models are also likely to result in more predictions that fall within the calibration range (Houborg and McCabe, 2016). ...
... ALR was motivated by two well established results. Firstly, that relatively simple predictive models of canopy structure and biochemistry variables based on a few, sometime even one, VIs are able to make predictions that satisfy user requirements as long as they are calibrated over similar land cover / land use conditions with representative data (Canisius and Fernandes, 2014;Inoue et al., 2016;Morcillo-Pallarés et al., 2019). Secondly, that there is sufficient information within the spectral sampling of many multispectral imagers, including MSI, such that retrievals of variables meeting user requirements are feasible by inversion of accurate radiative transfer models with the caveat that the inversion is conditioned by representative priors (Baret and Buis, 2008;Berger et al., 2018). ...
Article
For typical cloud conditions, a clear sky retrieval rate (CSRR) >67% is required to meet the Global Climate Observing System temporal interval requirement of 10 days when mapping canopy biophysical variables ('variables'). Physically based algorithms suitable for global mapping of variables using multispectral satellite imagery, e.g. the Simplified Level 2 Prototype Processor (SL2P), typically have a CSRR between 25% and 75%. An Active Learning Regularization (ALR) approach was developed to increase the CSRR rate while satisfying uncertainty requirements. A local calibration database for each variable was produced from representative valid SL2P estimates and associated Sentinel-2 Multispectral Instrument surface reflectance estimates. Predictors for each variable were developed by i) using Least Absolute Shrinkage and Selection Operator regression to select a subset of spectral vegetation indices (VIs) from a provided library, ii) removing outliers from the calibration database by trimming the conditional distribution of each variable given a VI, and iii) calibrating a non-linear regression predictor of the variable given the selected VIs using the trimmed database. ALR was applied to MSI imagery acquired over the Canadian Prairies during the 2016 and 2018 growing seasons and validated with in-situ data collected over 50 fields by the SMAPVEX16-MB campaign. The mean CSRR during the 2018 growing season was ~98% (~70%) for ALR (SL2P) for all canopy variables except FCOVER and ~ 98% for FCOVER using both ALR and SL2P. In comparison to SL2P, ALR had increased agreement rates with in-situ leaf area index (86% versus 79%) and fraction cover (96% versus 79%) but not canopy water content (35% versus 53%). Intercom-parison with valid SL2P estimates from different MSI images acquired within ±2 days found that 90% [±5%] of ALR estimates fell within the uncertainty of the valid estimates. These findings support the hypothesis that, over croplands, ALR significantly increases CSRR over SL2P without appreciably increasing uncertainty for variables retrieved by SL2P within thematic performance requirements.
... Thorp et al. (2015) estimated leaf chlorophyll with the partial least squares regression (PLSR) approach, and the results showed that the performance was better than NDVI and the physiological reflectance index. Inoue et al. (2016) compared the canopy chlorophyll of different plant types and regional scales and found that the ratio of the spectral index with the reflectance at 815 nm and 704 nm was robust to predict canopy chlorophyll content. ...
Chapter
Breeding is thought to be one of the most effective ways to solve the problem of food crisis. However, traditional phenotyping in breeding is time consuming and laborious, and the database is insufficient to meet the requirements of plant breeders, hindering the development of breeding. Accordingly, innovations in phenotyping are urgent to solve this bottleneck. The morphometric and physiological parameters of plants are particularly of interest to breeders. Numerous sensors have been employed and novel algorithms have been proposed to collect data on such parameters. These sensors and sensing techniques used in phenotyping include color digital cameras, Lidar or laser sensors, range cameras, spectral sensors and cameras, thermography, fluorescence sensors, and X-ray computed tomography (CT) techniques. In addition, remote-sensing technologies, three-dimensional (3D) imaging techniques, reverse engineering, and virtual plant techniques can also provide the basis for phenotyping. Some parameters that have been measured in phenotyping include plant height, leaf parameters, in-plant space, chlorophyll, water stress, biomass, and characteristics of plant roots. In plant phenotyping, different types of platforms are employed to meet the requirements of different plant phenotyping scenarios. Indoor phenotyping equipment, in-field sensor networks, ground mobile platforms, phenotyping towers, field-scan platforms, unmanned aerial vehicles (UAVs), airplanes, and even satellites can all implement plant phenotyping. In order to extract features of plants and reveal corresponding traits of interests in plant phenotyping, different mathematical tools and algorithms are employed to process the data, including data preprocessing algorithms, traditional statistical tools, and machine learning algorithms.KeywordsPlant phenotypeMorphometric parametersPhysiological parametersSensorsLidarPhenotyping platform
... Such a method is simple, but its generality for other crops and other vegetation types is yet to be demonstrated. There are also empirical studies that relate VIs to either LCC (Croft et al., 2014;le Maire et al., 2008;Wu et al., 2008;) or canopy chlorophyll content (Gitelson et al., 2005;Inoue et al., 2016;. The empirical relationships developed without explicit consideration of the influence of LAI in these studies may be site and plant type specific and may not be reliable for regional and global applications. ...
Chapter
Land teleconnections refer to the supply–demand relationship of land between distant countries/regions and its socio-environmental impacts. Modeling land teleconnections is critical for understanding the environmental and social consequences arising from land use and consumption. In this chapter, we first explain a widely used analytical tool for the quantification of land teleconnections, and briefly describe several data sources of global/national trade. We then discuss three potential research themes of land teleconnections for future work, such as identifying the functional characteristics of land use, evaluating the land-related impacts of changing consumption patterns, and associating land teleconnections with sustainability.KeywordsLand teleconnectionsMRIO modelLand use changeSustainability
... The CCC (in g/m 2 ) was calculated by exploiting the product of LAI green and SPAD upper (Baret et al., 2007) to diagnose the canopy nitrogen content (Gitelson et al., 2005;Liu et al., 2017) and assess the total canopy-scale productivity of rice (Inoue et al., 2016). ...
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As a promising method, unmanned aerial vehicle (UAV) multispectral remote sensing (RS) has been extensively studied in precision agriculture. However, there are numerous problems to be solved in the data acquisition and processing, which limit its application. In this study, the Micro-MCA12 camera was used to obtain images at different altitudes. The piecewise empirical line (PEL) method suitable for predicting the reflectance of different ground objects was proposed to accurately acquire the reflectance of multi-altitude images by comparing the performance of the conventional methods. Several commonly utilized vegetation indices (VIs) were computed to estimate the rice growth parameters and yield. Then the rice growth monitoring and yield prediction were implemented to verify and evaluate the effects of radiometric calibration methods (RCMs) and UAV flying altitudes (UAV-FAs). The results show that the variation trends of reflectance and VIs are significantly different due to the change in component proportion observed at different altitudes. Except for the milking stage, the reflectance and VIs in other periods fluctuated greatly in the first 100 m and remained stable thereafter. This phenomenon was determined by the field of view of the sensor and the characteristic of the ground object. The selection of an appropriate calibration method was essential as a result of the marked differences in the rice phenotypes estimation accuracy based on different RCMs. There were pronounced differences in the accuracy of rice growth monitoring and yield estimation based on the 50 and 100 m-based variables, and the altitudes above 100 m had no notable effect on the results. This study can provide a reference for the application of UAV RS technology in precision agriculture and the accurate acquisition of crop phenotypes.
... Such a method is simple, but its generality for other crops and other vegetation types is yet to be demonstrated. There are also empirical studies that relate VIs to either LCC (Croft et al., 2014;le Maire et al., 2008;Wu et al., 2008;) or canopy chlorophyll content (Gitelson et al., 2005;Inoue et al., 2016;. The empirical relationships developed without explicit consideration of the influence of LAI in these studies may be site and plant type specific and may not be reliable for regional and global applications. ...
Chapter
The three-dimensional (3D) structure of forests has long been recognized to have profound effects on forest ecosystems. However, the use of spectral and radar remotely sensed data for forest structure quantification is insensitive to changes in forest vertical structure. LiDAR has emerged as a robust means to measure forest structures. Numerous studies have been devoted to accurately quantifying forest structures from LiDAR data at various scales (from tree branches level to global level) and revolutionized the way we consider forest structure in ecosystem studies. In this chapter, we outline how LiDAR sheds light on forest ecosystem studies and discuss current challenges and perspectives of LiDAR applications.
... Close relationships between total canopy chlorophyll content (Chl Canopy ) and GPP have been found in croplands (Gitelson et al., 2006;Peng et al., 2011;Peng and Gitelson, 2012;Wu et al., 2009). Chl Canopy , determined as the product of leaf chlorophyll content (Chl Leaf ) and total leaf area index (LAI), can be estimated by radiative transfer models (Darvishzadeh et al., 2008;Delloye et al., 2018;Weiss and Baret, 2016) or vegetation indices (Dash and Curran, 2004;Gitelson et al., 2005;Inoue et al., 2016) from canopy reflectance, which allows GPP to be estimated using chlorophyll-related indices and PAR Wu et al., 2009). The strong correlations between Chl Canopy and GPP can be explained in two ways. ...
Article
Recent advances in remotely sensed solar-induced chlorophyll fluorescence (SIF) have provided an exciting and promising opportunity for estimating gross primary production (GPP). Previous studies mainly focused on the linear correlation between SIF and GPP and the slope of the SIF-GPP relationship, both of which lack rigorous consideration of the seasonal trajectories of SIF and GPP. Here, we investigated the timing of seasonal peaks of far-red SIF and GPP in soybean fields by integrating tower data, satellite data, and process-based Soil Canopy Observation of Photosynthesis and Energy (SCOPE, v2.0) model simulations. We found inconsistency between the seasonal peak timing of far-red SIF and GPP in three of four soybean fields based on tower far-red SIF and eddy-covariance measurements. In particular, far-red SIF reached its seasonal maximum 14–17 days earlier than GPP. This far-red SIF-GPP difference in peak timing degraded the correlation between sunny-day far-red SIF and GPP at daily scale (Pearson r = 0.83–0.87 at the site with 14–17 days difference and Pearson r = 0.96 at the site with no difference), and it can be explained by a divergence in the seasonality between absorbed photosynthetic active radiation (APAR) and canopy chlorophyll content (ChlCanopy). We found that the seasonality of far-red SIF - a byproduct of the light reactions of photosynthesis - was primarily controlled by APAR, whereas GPP seasonality was dominated by ChlCanopy. Further, SCOPE model simulations showed that the seasonal patterns of leaf area index (LAI), leaf chlorophyll content (ChlLeaf) and leaf angle distribution (LAD) could affect the different peak timing of SIF and GPP and consequently the seasonal relationship between far-red SIF and GPP. A further increase in LAI after the fraction of light absorption (FPAR) saturates and a later peak of ChlLeaf compared to LAI results in a later peak of GPP compared to far-red SIF. More horizontal leaf angles can further exacerbate this difference. Our results advance mechanistic understanding of the SIF-GPP relationships and combining chlorophyll content information with SIF could potentially improve remote-sensing-based GPP estimation.
... where ρ NIR , ρ green , and ρ red are spectral reflectance values (0-1.0) in the NIR (840 ± 26 nm), red (650 ± 16 nm), and green (560 ± 16 nm) bands. CI green is correlated with the canopy chlorophyll content, which is the amount of chlorophyll per unit area (Wu et al. 2012;Schlemmer et al. 2013;Inoue et al. 2016;Clevers et al. 2017) or nitrogen concentration in plant leaves (Cai et al. 2019). CI green is widely used as a remote sensing-based indicator useful for estimating photosynthetic carbon assimilation in various fields (Peng and Gitelson 2011;Zhang et al. 2015). ...
Article
Vegetation indices (VIs), such as the green chlorophyll index and normalized difference vegetation index, are calculated from visible and near-infrared band images for plant diagnosis in crop breeding and field management. The DJI P4 Multispectral drone combined with the Agisoft Metashape Structure from Motion/Multi View Stereo software is some of the most cost-effective equipment for creating high-resolution orthomosaic VI images. However, the manufacturer's procedure results in remarkable location estimation inaccuracy (average error: 3.27–3.45 cm) and alignment errors between spectral bands (average error: 2.80–2.84 cm). We developed alternative processing procedures to overcome these issues, and we achieved a higher positioning accuracy (average error: 1.32–1.38 cm) and better alignment accuracy between spectral bands (average error: 0.26–0.32 cm). The proposed procedure enables precise VI analysis, especially when using the green chlorophyll index for corn, and may help accelerate the application of remote sensing techniques to agriculture.
... Alguns satélites como o Sentinel-2 vem sendo bastante utilizado na Europa e outros países para quantificar as concentrações de Chl-a de ecossistemas aquáticos. No Brasil os estudos vem sendo feitos apenas para ambientes terrestres (INOUE et al., 2016;ELHAG et al., 2019;POMPÊO et al., 2021), já para ambientes aquáticos ainda é necessário gerar e validar as equações, pois o hemisfério é diferente, o clima, relevo, altitudes, temperatura, entre outras questões que alteram o resultado, necessitando assim de um estudo para haver essa reformulação e, assim, facilitar o cálculo das concentrações de Chl-a em ambientes aquáticos no Brasil. Assim é possível verificar a heterogeneidade espacial, que permite melhor compreensão de aspectos da modelagem, estrutura e dinâmica dos ecossistemas aquáticos. ...
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Chapter
Pollution is a threat to the aquatic ecosystem because different chemicals, such as metals and drugs, are discarded and can interfere with the metabolism and physiology of aquatic organisms. Among these environments affected by anthropic action, municipal water reservoirs can be highlighted. Faced with this problem, this chapter presents different ecotoxicological tools (biomarkers) to assess the impact of pollution on freshwater fish from municipal water reservoirs. We highlight the following biomarkers: biotransformation, metal sequestration, oxidative stress, genotoxic effects, cellular integrity, cell/tissue damage, neurotoxicity, and reproductive (endocrine disruption and seminal quality). It is observed that some of these biomarkers are widely used in field studies to evaluate the impact of the mixture of pollutants on different physiological processes, such as the generation of oxidative stress. However, other biomarkers are still little explored in these studies, such as cell integrity and seminal quality analysis.
... Alguns satélites como o Sentinel-2 vem sendo bastante utilizado na Europa e outros países para quantificar as concentrações de Chl-a de ecossistemas aquáticos. No Brasil os estudos vem sendo feitos apenas para ambientes terrestres (INOUE et al., 2016;ELHAG et al., 2019;POMPÊO et al., 2021), já para ambientes aquáticos ainda é necessário gerar e validar as equações, pois o hemisfério é diferente, o clima, relevo, altitudes, temperatura, entre outras questões que alteram o resultado, necessitando assim de um estudo para haver essa reformulação e, assim, facilitar o cálculo das concentrações de Chl-a em ambientes aquáticos no Brasil. Assim é possível verificar a heterogeneidade espacial, que permite melhor compreensão de aspectos da modelagem, estrutura e dinâmica dos ecossistemas aquáticos. ...
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Chapter
A toxicologia é uma ciência muito antiga, já que as plantas e os animais desde os primórdios já apresentavam substâncias tóxicas em suas estruturas e o ser humano necessitava compreender suas ações como medidas de precaução. Com o surgimento da problemática gerada pela degradação exacerbada e poluição ambiental decorrente da revolução industrial do pós-guerra e das transformações científico-tecnológicas, agravadas por fatores como o elevado crescimento populacional e pressão sobre os recursos naturais, houve a necessidade de incorporar os aspectos ecológicos ao estudo dos efeitos tóxicos das substâncias sintetizadas por ações antropogênicas e que invariavelmente afetam o meio ambiente e organismos não-alvo. Assim, surge a ecotoxicologia com suas ferramentas metodológicas de avaliação da qualidade ambiental e crítica sobre a relação predatória com o ambiente natural. A ecotoxicologia é uma ferramenta útil para criar políticas com a implantação de diretrizes e regulamentações mais rígidas para combater a poluição. Recentemente, contaminantes que não eram encontrados frequentemente no ambiente ou que não haviam sido relatados como oferecendo potencial perigo aos organismos vivos passaram a ser relatados em diversos habitats e seu risco passou a ser investigado; essa classe de compostos é chamada de contaminantes emergentes. Assim, este capítulo objetiva contextualizar a ecotoxicologia, explorando as suas metodologias de investigação da saúde ambiental com foco nos ecossistemas aquáticos e principalmente poluentes emergentes.
... The Visible and near-infrared region (VNIR, 400-1,000 nm) can be better correlated with chlorophyll and N in fresh leaves (Afandi et al., 2016;Li D. et al., 2018). Vegetation Indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Red Edge Index (NDRE), the Red Edge Chlorophyll Index (CI rededge ), and the Green Chlorophyll Index (CI green ), have been used for chlorophyll and N estimations in maize, wheat, and rice (Fitzgerald et al., 2010;Shiratsuchi et al., 2011;Cao et al., 2013;Schlemmer et al., 2013;Inoue et al., 2016;Klem et al., 2018;Wen et al., 2018). However, the accuracy of estimation is influenced by soil backgrounds, genetic differences, and climate change, and varies across seasons and locations. ...
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Identification of high Nitrogen Use Efficiency (NUE) phenotypes has been a long-standing challenge in breeding rice and sustainable agriculture to reduce the costs of nitrogen (N) fertilizers. There are two main challenges: (1) high NUE genetic sources are biologically scarce and (2) on the technical side, few easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD). To overcome the challenges, we captured a unique higher NUE phenotype in rice as a dynamic time-series N variation curve through the entire GD analysis by canopy reflectance data collected by Unmanned Aerial Vehicle Remote Sensing Platform (UAV-RSP) for the first time. LY9348 was a high NUE rice variety with high Nitrogen Uptake Efficiency (NUpE) and high Nitrogen Utilization Efficiency (NUtE) shown in nitrogen dosage field analysis. Its canopy nitrogen content (CNC) was analyzed by the high-throughput UAV-RSP to screen two mixed categories (51 versus 42 varieties) selected from representative higher NUE indica rice collections. Five Vegetation Indices (VIs) were compared, and the Normalized Difference Red Edge Index (NDRE) showed the highest correlation with CNC ( r = 0.80). Six key developmental stages of rice varieties were compared from transplantation to maturation, and the high NUE phenotype of LY9348 was shown as a dynamic N accumulation curve, where it was moderately high during the vegetative developmental stages but considerably higher in the reproductive developmental stages with a slower reduction rate. CNC curves of different rice varieties were analyzed to construct two non-linear regression models between N% or N% × leaf area index (LAI) with NDRE separately. Both models could determine the specific phenotype with the coefficient of determination ( R ² ) above 0.61 (Model I) and 0.86 (Model II). Parameters influencing the correlation accuracy between NDRE and N% were found to be better by removing the tillering stage data, separating the short and long GD varieties for the analysis and adding canopy structures, such as LAI, into consideration. The high NUE phenotype of LY9348 could be traced and reidentified across different years, locations, and genetic germplasm groups. Therefore, an effective and reliable high-throughput method was proposed for assisting the selection of the high NUE breeding phenotype.
... The empirical methods, such as spectral indices, also used derivatives and the quotient of two difference based on three wavelengths to reduce the specular reflection effect on LCC estimation [18,24,30,31]. Although the physical methods have the potential advantage to be more widely applied in different plant species than empirical methods [36] and the use of 2-3 wavelengths may not obtain a similar output as a well-defined radiative transfer model (such as PROSPECT), the spectral indices are still the popular and useful means for estimating LCC due to their simplicity and efficiency in local studies [37][38][39][40][41][42][43][44]. Previous studies have used the leaf reflectance from nadir, one direction (leaf clip) near the nadir, or integrating sphere to avoid the influence specular reflection from the leaf surface on LCC estimation based on spectral indices [45][46][47][48]. ...
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Article
Plant leaf chlorophyll content (LCC) plays a key role in the assessment of plant stress and plant functioning. To date, accurate estimation of LCC over a wide range of plant species (trees, bushes and lianas) under different measurement conditions is still challenging for non-destructive methods. Based on multi-angular hyperspectral reflection of 706 leaves (10 plant species), several popular spectral indices were evaluated for a general estimation of LCC. The modified difference ratio index (MDRI) had the strongest linear relationship (R2=0.92) to LCC among all the tested spectral indices. The regression algorithm was then used to estimate LCC in other datasets from different regions across the globe. Comparing with the machine learning techniques and PROSPECT model, validation results from 2024 leaves (114 plant species) confirmed that the linear algorithm derived from the MDRI was the most effective for estimating LCC (RMSE=6.72 μg/cm2) across a wide range of plant species under different measurement conditions. The MDRI does not require parameterization for each plant species and has the potential to estimate LCC from simple handheld laboratory or field instrument at any arbitrary direction. The generality of the approach makes it convenient for botanical and ecological studies under different measurement conditions that need accurate LCC estimates.
... However, low robustness and easy saturation confine the further application of these methods (Colombo et al., 2008;Eitel et al., 2006). Unlike multi-spectral observations, hyperspectral data containing fullbands radiation information could describe various characteristics associated with the biochemical and physiological traits of targets (Inoue et al., 2016;Sytar et al., 2017;Wahabzada et al., 2016). The hyperspectral inversion methods based on lookup tables and machine learning algorithms have been trialed and obtained (Croft et al., 2013;Danner et al., 2017;Houborg et al., 2015;Verrelst et al., 2014Verrelst et al., , 2012. ...
Article
Leaf chlorophyll, as a key factor for carbon circulation in the ecosystem, is significant for the photosynthetic productivity estimation and crop growth monitoring in agricultural management. Hyperspectral remote sensing (RS) provides feasible solutions for obtaining crop leaf chlorophyll content (LCC) by the advantages of its repeated and high throughput observations. However, the data redundancy and the poor robustness of the inversion models are still major obstacles that prevent the widespread application of hyperspectral RS for crop LCC evaluation. For winter wheat LCC inversion from hyperspectral observations, this study described a novel hybrid method, which is based on the combination of amplitude- and shape- enhanced 2D correlation spectrum (2DCOS) and transfer learning. The innovative feature selection method, amplitude- and shape- enhanced 2DCOS, which originated from 2DCOS, additionally considered the relationships between external perturbations and hyperspectral amplitude and shape characteristics to enhance the dynamic spectrum response. To extract the representative LCC featured wavelengths, the amplitude- and shape- enhanced 2DCOS was conducted on the leaf optical PROperties SPECTra (PROSPECT) + Scattering from Arbitrarily Inclined Leaves (SAIL) (PROSAIL) simulated dataset, which covered most possible winter wheat canopy spectra. Nine wavelengths (i.e., 455, 545, 571, 615, 641, 662, 706, 728, and 756 nm) were then extracted as the sensitive wavelengths of LCC with the amplitude- and shape- enhanced 2DCOS. These wavelengths had specificity to LCC and showed good correlation with LCC from the aspect of photosynthesis mechanism, molecular structure, and optical properties. The transfer learning techniques based on the deep neural network was then introduced to transfer the knowledge learned from the PROSAIL simulated dataset to the inversion tasks of field measured LCC. Parts of the labeled samples in field observations were used to finetune the model pre-trained by the simulated dataset to improve the inversion accuracy of the winter wheat LCC in different field scenes, aiming to reduce the need for the field measured and labeled sample size. To further ascertain the universality, transferability and predictive ability of the proposed hybrid method, field samples collected from different locations at different phenological phases, including the jointing and heading stages in 2013, 2014, and 2018, were utilized as target tasks to validate the proposed hybrid method. Moreover, the LCC of winter wheat estimated with the proposed method was evaluated with the ground-based platform and the UAV-based platform to verify the model versatility for different monitoring platforms. Various validations demonstrated that the hybrid inversion method combining the amplitude- and shape- enhanced 2DCOS and the fine-tuned transfer learning model could effectively estimate winter wheat LCC with good accuracy and robustness, and can be extended to the detection and inversion of other key variables of crops.
... The results 25 of this study support future vegetation indices design and model 26 development. 27 Index Terms-Leaf chlorophyll content (LCC), spectral 28 indices, polarimetric measurements, non-polarized information. 29 ...
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Leaf chlorophyll content (LCC) is a key indicator of plant photosynthesis and can be estimated by the optical properties of leaves. Due to the random distribution of leaf angles and the change of incident light angle, it is necessary to reduce the effects of specular reflection when estimating LCC under different measurement geometries. Because the polarized reflectance factor can account for specular reflection, which does not relate to LCC, it is possible to improve LCC estimation using spectral indices when the polarized reflectance is removed from the total reflectance. In this study, polarimetric measurements of leaves from three different plant species were performed with different measurement geometries in both laboratory and field conditions. We tested all possible waveband combinations in the 400-1000 nm range with two types of spectral indices: simple ratio (SR) ( $R_{λ1}$ / $R_{λ 2})$ and normalized difference vegetation index (NDVI) ( $R_{λ {i}} - R_{λ {j}})$ /( $R_{λ {i}} + R_{λ {j}})$ , using both total intensity [defined as I parameter reflectance factor (IpRF)] and non-polarized [defined as non-polarized reflectance factor (NpRF)] information. By comparing the LCC estimation accuracy based on IpRF with that based on NpRF, we found that NpRF increased the number of bands that can estimate LCC with relatively high accuracy. These results indicate that the simple two-band indices based on the NpRF are robust and accurate for estimating LCC at leaf scale, and the broad effective wavelength range of NpRF may have the ability to overcome bandwidth limitations. The results of this study support future vegetation indices design and model development.
... Many scholars used radiative transfer models to simulate vegetation canopy spectrum [36][37][38]67,68]. Studies explored the best predictive model and vegetation indexes and used the converted spectrum to estimate the ecophysiological functioning of vegetation [69]. This study will also focus on these methods. ...
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Using reflectance spectroscopy to monitor vegetation pigments is a crucial method to know the nutritional status, environmental stress, and phenological phase of vegetation. Defining cities as targeted areas and common greening plants as research objects, the pigment concentrations and dust deposition amounts of the urban plants were classified to explore the spectral difference, respectively. Furthermore, according to different dust deposition levels, this study compared and discussed the prediction models of chlorophyll concentration by correlation analysis and linear regression analysis. The results showed: (1) Dust deposition had interference effects on pigment concentration, leaf reflectance, and their correlations. Dust was an essential factor that must be considered. (2) The influence of dust deposition on chlorophyll—a concentration estimation was related to the selected vegetation indexes. Different modeling indicators had different sensitivity to dust. The SR705 and CIrededge vegetation indexes based on the red edge band were more suitable for establishing chlorophyll-a prediction models. (3) The leaf chlorophyll concentration prediction can be achieved by using reflectance spectroscopy data. The effect of the chlorophyll estimation model under the levels of “Medium dust” and “Heavy dust” was worse than that of “Less dust”, which meant the accumulation of dust had interference to the estimation of chlorophyll concentration. The quantitative analysis of vegetation spectrum by reflectance spectroscopy shows excellent advantages in the research and application of vegetation remote sensing, which provides an important theoretical basis and technical support for the practical application of plant chlorophyll content prediction.
... The importance of the residual N effect arises when fertilizer application approaches suboptimal levels, and so it is particularly relevant in countries seeking a reduction in N fertilizer use due to environmental concerns [8]. A lot of research has been devoted to using sensors to identify crop differences between various levels of fertilizer application [9], [10]; however, detecting the residual N effect with proximal or remote sensors remains a challenge. ...
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Adjusting nitrogen (N) fertilization and accounting for the legacy of past N fertilizer application (i.e., residual N) based on remote sensing estimation of crop nutritional status may increase resource efficiency and promote sustainable management of cropping systems. Our main goal was to evaluate the potential of hyperspectral airborne imagers and ground-level sensors for identifying N fertilizer rates and the residual N effect from the previous crop fertilization in a maize/wheat rotation. A two-season field trial that provided various combinations of N rates and residual N response was established in central Spain. Ground-level sensors and aerial hyperspectral images were used to calculate vegetation indices (VIs). In addition, the solar-induced chlorophyll fluorescence (SIF $_{760}$ ) was estimated by the Fraunhofer line-depth method using high-resolution hyperspectral imagery, and together with biophysical modeling, biochemical and biophysical constituents at canopy scales were retrieved. N uptake, N output, grain N concentration, and proximal sensors discriminated between different N fertilizer rates and identified the residual effect when it was relevant. Structural, photosynthetic pigments and short-wave infrared region (SWIR)-based VIs, together with SIF $_{760}$ and the chlorophyll a + b ( $C_{ab}$ ), biomass, and the leaf area index (LAI), performed similarly on N rate detection. However, the residual effect of nitrification inhibitors was only detected by the structural (NDVI and OSAVI), chlorophyll (CCCI and NDRE), blue/green, NIR-SWIR (N $_{850,1510}$ ) indices, SIF $_{760}$ , $C_{ab}$ , biomass, and the LAI. This study confirmed the ability of remote sensing to identify N rates at early growth stages and highlighted its potential to detect residual N in crop rotation.
... Also, a reference wavelength in the near-infrared (~760 nm or ~940-950 nm, according to the instrument) is used to determine the contribution of leaf structures such as cell walls [41,67,88]. However, some of these leaf structures, such as vein distribution, leaf anatomy, or water content, do not scale linearly and therefore chlorophyll quantification must be validated with an extractive method [21,85,[87][88][89][90][91][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106]. Table 3 shows correlations between different handheld meters and extraction methods in the most important crops. ...
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Crop yields have increased substantially during the last 50 years, but the traits that drove these remarkable improvements, such as plant architecture, have a little remaining potential for improvement. New traits such as photosynthesis, as the ultimate determinant of yield, must be explored to support future demands. However, improving photosynthetic efficiency has played only a minor role in improving crop yield to date. Chlorophylls are the pigments allowing light to be transformed into carbohydrates, and therefore help to maintain crop yield under stress. Chlorophyll content correlates with higher yields in diverse conditions. In this review, we discuss using chlorophyll content as the basis of screens for drought tolerance. We review chlorophyll-related responses to drought in different plants and summarize the advantages and disadvantages of current methods to measure chlorophyll content, with the ultimate goal of improving the efficiency of crop breeding for drought tolerance.
... Zhang et al. [31] selected two bands of 680 nm and 935 nm according to the most sensitive hyperspectral bands of Suaeda chlorophyll fluorescence parameters and ultimately proved that (R680-R935)/(R680+R935) and R680/R935 have a higher determination coefficient (R 2 ) and lower root mean square error (RMSE). Yoshio et al. [34] explored models for predicting the chlorophyll content of the canopy based on the spectral data set of six planting types (rice, wheat, corn, soybean, sugar beet, and natural grass) and found that that the simple ratio spectrum model was more accurate and applicable than that of the multivariate regression model. Accordingly, we chose D690/D1320 and D725/D1284, which have the highest correlation coefficients, to retrieve the chlorophyll fluorescence parameters / and (Table 3, Figure 11). ...
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The stomata of Suaeda salsa are closed and the photosynthetic efficiency is decreased under conditions of water–salt imbalance, with the change to photosynthesis closely related to the chlorophyll fluorescence parameters of the photosystem PSII. Accordingly, chlorophyll fluorescence parameters were selected to monitor the growth status of Suaeda salsa in coastal wetlands under conditions of water and salt. Taking Suaeda salsa in coastal wetlands as the research object, we set up five groundwater levels (0 cm, –5 cm, –10 cm, –20 cm, and –30 cm) and six NaCl salt concentrations (0%, 0.5%, 1 %, 1.5%, 2%, and 2.5%) to carry out independent tests of Suaeda salsa potted plants and measured the canopy reflectance spectrum and chlorophyll fluorescence parameters of Suaeda salsa. A polynomial regression method was used to carry out hyperspectral identification of Suaeda salsa chlorophyll fluorescence parameters under water and salt stress. The results indicated that the chlorophyll fluorescence parameters Fv/Fm, Fm', and ΦPSII of Suaeda salsa showed significant relationships with vegetation index under water and salt conditions. The sensitive canopy band ranges of Suaeda salsa under water and salt conditions were 680–750 nm, 480–560 nm, 950–1000 nm, 1800–1850 nm, and 1890–1910 nm. Based on the spectrum and the first-order differential spectrum, the spectral ratio of A/B was constructed to analyze the correlation between it and the chlorophyll fluorescence parameters of Suaeda salsa. We constructed thirteen new vegetation indices. In addition, we discovered that the hyperspectral vegetation index D690/D1320 retrieved Suaeda chlorophyll fluorescence parameter Fv/Fm with the highest accuracy, with a multiple determination coefficient R2 of 0.813 and an RMSE of 0.042, and that D725/D1284 retrieved Suaeda chlorophyll fluorescence parameter ΦPSII model with the highest accuracy, with a multiple determination coefficient R2 of 0.848 and an RMSE of 0.096. The hyperspectral vegetation index can be used to retrieve the chlorophyll fluorescence parameters of Suaeda salsa in coastal wetlands under water and salt conditions, providing theoretical and technical support for future large-scale remote sensing inversion of chlorophyll fluorescence parameters.
... These low relationships were attributed to differences in canopy cover and soil background. Indices that estimated chlorophyll activity at the canopy level were able to provide a better relationship with crop status and yield in Australia, Italy, and the US [19,57,69]. In the current study, a uniform ground cover was observed in all plots, probably because they were all irrigated. ...
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Remote sensing allows fast assessment of crop monitoring over large areas; however, questions regarding uncertainty in crop parameter prediction and application to nitrogen (N) fertilization remain open. The objective of this study was to optimize of remote sensing spectral information for its application to grain yield (GY), biomass, grain N concentration (GNC), and N output assessment, and decision making on spring wheat fertilization. Spring wheat (Triticum turgidum L.) field experiments testing two tillage treatments, two irrigation levels and six N treatments were conducted in Northwest Mexico over four consecutive years. Hyperspectral images were acquired through 27 airborne flight campaigns. At harvest, GY, biomass, GNC and N output were determined. Spectral exploratory analysis was used to identify the best wavelength combinations, the most suitable vegetation indices (VIs) and the best growth stages to assess the agronomic variables. The relationship between the spectral information and the agronomic measurements was evaluated by the coefficient of determination (R2) and the root mean square error (RMSE). The ability of the indices to guide fertilizer recommendation was assessed through an error analysis based on the N sufficiency index. GY was better assessed from the end of flowering to the early milk stage by VIs based on the combination of bands from near infrared radiation/visible and from near infrared radiation/red-edge regions (R2 > 0.6; RMSE < 700 kg ha−1). N output was efficiently assessed by a combination of bands from near infrared radiation/red-edge at booting (R2 > 0.7; RMSE < 9 kg N ha−1). The GNC was better estimated by VIs combining bands in near infrared radiation/red-edge at early milk, but with great variability among the years studied. Some VIs were promising for guiding fertilizer recommendation for increasing GNC, but there was not a single index providing reliable recommendations every year. This study highlights the potential of remote sensing imagery to assess GY and N output in spring wheat, but the identification of GNC responsive sites needs to be improved.
... One interesting comparison between factor loads shows that the macronutrient, whether nitrogen or potassium, did not drastically reorder the wavelengths of each component, probably because they are the two macronutrients most required by cotton, among various other crops, and because they play roles that maintain the activity of chlorophyll, which acts both in photosynthetic metabolism and in greening the leaves, recognised by PC1 and PC3 generated by the PCA. In general, reflectances in the NIR (800 nm to 1300 nm) and in the visible (400 nm to 700 nm) are continually associated in the literature with changes in the cell structure of the mesophile (MIPHOKASAP et al., 2012) and in the levels of chlorophyll (INOUE et al., 2016). The relevance of the infrared to PC1 was also reported by Yang et al. (2016) in rice. ...
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The detailed study of hyperspectral data that optimise the management of agricultural inputs can be a powerful ally in the nutrient diagnosis of plants. This study characterised variations in the reflectance factors of cotton leaves (Gossypium hirsutum L.) of the BRS 293 cultivar, submitted to different levels of N and K fertilisation. A total of 166 plants were submitted to four doses of N and K, with twenty replications and six controls. Each treatment represents one level of fertilisation: 50, 75, 100 and 125% of the recommended amount of both macronutrients at each stage of the phenological cycle. The spectroradiometer used in the laboratory was the FieldSpec Pro FR 3® with a spectral resolution of 1 nm and an operating range that extends from 350 to 2500 nm. In both treatments, PCA allowed wavelengths to be idenbtified grouped by such parameters as brightness, chlorophyll and leaf moisture. The N and K fertilisation caused significant changes in the factors, where the greatest difference between doses was seen at 790 and 1198 nm. The wavelengths between 550 and 700 nm and at 1390 and 1880 nm were, respectively, the most promising for explaining the variance in nutrient levels of N and K in cotton.
... Thorp et al. (2015) estimated leaf chlorophyll with the partial least squares regression (PLSR) approach; the results showed the performance was better than NDVI and the physiological reflectance index. Inoue et al. (2016) compared the canopy chlorophyll contents of different plant types and regional scales and found the ratio spectral index with the reflectance at 815 nm and 704 nm was robust to predict canopy chlorophyll content. Recently, fluorescence sensors became another promising method for measuring plant chlorophyll and nitrogen content. ...
Chapter
Nitrogen (N) is crucial for plant nutrition and is often a limiting factor for biomass production that feeds humans and animals and contributes to energy and material use. N fertilization aims at a high N use efficiency, with a maximum of N input taken up by the crop. Otherwise, N losses to water and air will harm the environment and the climate. Farmers should adopt a well-balanced crop N management, and variable-rate fertilization enables adjusting N fertilization according to plant status and needs. For more than 20 years, sensor technologies have involved handheld devices supplemented with satellite technology. Today, the launch of the two twin satellites Copernicus Sentinel-2A/B has yielded a higher spectral and spatial resolution. These data can help improve N fertilization. In this chapter, we describe our steps to evaluate sensor and satellite technologies using field data collected under optimal conditions and including different stages of N fertilization. The close cooperation between remote sensing experts, soil scientists, agronomists, and practitioners was crucial for the success of the field experiments. Our findings yielded productivity maps that serve as a basis for N fertilization maps for use by commonly applied farm machinery.
... Thorp et al. (2015) estimated leaf chlorophyll with the partial least squares regression (PLSR) approach; the results showed the performance was better than NDVI and the physiological reflectance index. Inoue et al. (2016) compared the canopy chlorophyll contents of different plant types and regional scales and found the ratio spectral index with the reflectance at 815 nm and 704 nm was robust to predict canopy chlorophyll content. Recently, fluorescence sensors became another promising method for measuring plant chlorophyll and nitrogen content. ...
Chapter
Agricultural crops require nutrients to support their growth and to produce abundant, nutritious food for humans and animals. Soil fertility researchers evaluate the nutrient status of soils before selecting the right source and amount of fertilizer to apply to crops. They also consider the best time and place to add fertilizers so the nutrients will be used efficiently by the crop. Technological advances make it possible to apply nutrient-rich fertilizers to crops in smaller doses and with greater precision at the field scale, which avoids nutrient losses from agroecosystems to surrounding environments. Low-cost sensors should improve our understanding of nutrient availability in slow-release fertilizers such as animal manure, which will lead to better storage and handling and precise application. Increasing the precision of soil fertility management will sustain the agricultural production of farms while protecting the health of ecosystems and populations in the surrounding environment.
... Two new best vegetation indices (D_RVI and D_NDVI) were found. A combination of bands in the 500-900 nm based on the first derivatives was selected to construct the vegetation indices using the MATLAB 2019b software [42]. The correlation coefficient and significance level matrices were obtained based on the correlation analysis. ...
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This paper reports on monitored Suaeda salsa spectral response to salt conditions in coastal wetlands, using spectral measurements and remotely sensed algorithms. Suaeda salsa seedlings were collected from the Dafeng Elk National Nature Reserve (DENNR) in Jiangsu Province, China. We treated 21 Suaeda salsa seedlings planted in pots with 7 different salt concentrations (n = 3 for each concentration) to assess their response to varying salt conditions. Various plant growth indicators, including chlorophyll content, fresh weight, dry weight, and canopy hyperspectral reflectance, were measured. One-way analysis of variance (ANOVA) and post hoc multiple comparisons of least-significant difference (LSD) were used to explore the physiological indicators of sensitivity to salt treatment. Red edge parameters and spectral reflectance indices were used to analyze spectral response to salt conditions and to investigate the potential for remotely sensing physiological parameters which are sensitive to salt conditions. The results indicated that among these physiological indicators, the total chlorophyll content differed significantly with salt conditions, being highest at 50 mmol/L, whereas the differences observed for the morphological parameters were highest at 200 mmol/L. In addition, new vegetation indices were significantly more responsive to salt concentrations than were traditional red edge parameters. The two vegetation indices, D854/D792 and (D792 − D854)/(D792 + D854), were the most sensitive to the total chlorophyll content, and these also strongly correlated with salt concentrations. An analysis of the responses of plant growth indicators to salt treatment showed that soil having a salt concentration of 50~200 mmol/L is most suitable for the growth of Suaeda salsa. These results suggest the potential for using remote sensing to effectively interpret the causes of salt-induced spectral changes in Suaeda salsa. This methodology also provides a new reference for the inversion model of estimating the total chlorophyll content of Suaeda salsa under different salt concentrations in similar coastal wetlands, whether in China or elsewhere.
... Among the available technologies, imaging spectroscopy has been identified as a mature and adequate way for plant phenotyping (Li, Zhang, & Huang, 2014). The reflectance spectrum of a leaf is influenced in the visible range (400e700 nm) by pigment concentrations (Gitelson, Gritz, & Merzlyak, 2003;Inoue et al., 2016), in the near-infrared (700e1300 nm) by leaf cellular structure (Peñuelas & Filella, 1998) and in the mid infrared (1300e3000 nm) mainly by radiation absorption by water but also by protein, lignin and cellulose (Downing, Carter, Holladay, & Cibula, 1993;Koch, Ammer, Schneider, & Wittmeier, 1990;Zhao et al., 2016). Multispectral imagery in the visible and near-infrared domains carries a lot of information on plant disease state (Sankaran, Mishra, Ehsani, & Davis, 2010). ...
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During its growth, winter wheat (Triticum aestivum L.) can be impacted by multiple stresses involving fungal diseases that are responsible for high yield losses. Enhancing the breeding and the identification of resistant cultivars could be achieved by collecting automated and reliable information at the plant level. This study aims to estimate the severity of stripe rust (SR), brown rust (BR) and septoria tritici blotch (STB) in natural conditions and to highlight wavebands of interest, based on images acquired through a multispectral camera embedded on a ground-based platform. The severity of the three diseases has been assessed visually in an agronomic trial involving five wheat cultivars with or without fungicide treatment. An acquisition system using multispectral imagery covering the visible and near-infrared range has been set up at the canopy level. Based on spectral and textural features, estimations of area under disease progress curve (AUDPC) were performed by means of artificial neural networks (ANN) and partial least squares regression (PLSR). Supervised classification was also implemented by means of ANN. The ANN performed better at estimating disease severity with R² of 0.72, 0.57 and 0.65 for STB, SR and BR respectively. Discrimination in two classes below or above 100 AUDPC reached an accuracy of 81% (κ = 0.60) for STB. This study, which combined the effect of date, cultivar and multiple disease infections, managed to highlight a few wavebands for each disease and took a step further in the development of a machine vision-based approach for the characterisation of fungal diseases in natural conditions.
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Recent advancements in hyperspectral remote sensing bring exciting opportunities for various domains. Precision agriculture is one of the most widely-researched examples here, as it can benefit from the non-invasiveness and enormous scalability of the Earth observation solutions. In this paper, we focus on estimating the chlorophyll level in leaves using hyperspectral images—capturing this information may help farmers optimize their agricultural practices and is pivotal in planning the plants’ treatment procedures. Although there are machine learning algorithms for this task, they are often validated over private datasets; therefore, their performance and generalization capabilities are virtually impossible to compare. We tackle this issue and introduce an open dataset including the hyperspectral and in situ ground-truth data, together with a validation procedure which is suggested to follow while investigating the emerging approaches for chlorophyll analysis with the use of our dataset. The experiments not only provided the solid baseline results obtained using 15 machine learning models over the introduced training-test dataset splits but also showed that it is possible to substantially improve the capabilities of the basic data-driven models. We believe that our work can become an important step toward standardizing the way the community validates algorithms for estimating chlorophyll-related parameters, and may be pivotal in consolidating the state of the art in the field by providing a clear and fair way of comparing new techniques over real data.
Chapter
Shortwave remote sensing signals acquired from vegetation contain information not only for vegetation structure, such as leaf area index and clumping index, but also for leaf biochemical parameters, such as pigments, nitrogen content, water content, dry matter, etc. However, the retrievals of these two types of parameters are generally carried out separately without considering the influence of one type of parameters on the spectral signals used to retrieve the other type of parameters. Since green leaves would be very different from brown leaves in performing photosynthesis and transpiration, we suggest that a next step in vegetation remote sensing be directed towards synergetic retrievals of these two types of parameters for the purpose of improving regional and global carbon and water cycle estimation.KeywordsVegetation structural parametersLeaf biochemical parametersShortwave remote sensingPhotosynthesisTranspiration
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Background The chlorophyll content is a vital indicator for reflecting the photosynthesis ability of plants and it plays a significant role in monitoring the general health of plants. Since the chlorophyll content and the soil–plant analysis development (SPAD) value are positively correlated, it is feasible to predict the SPAD value by calculating the vegetation indices (VIs) through hyperspectral images, thereby evaluating the severity of plant diseases. However, current indices simply adopt few wavelengths of the hyperspectral information, which may decrease the prediction accuracy. Besides, few researches explored the applicability of VIs over rice under the bacterial blight disease stress. Methods In this study, the SPAD value was predicted by calculating the spectral fractal dimension index (SFDI) from a hyperspectral curve (420 to 950 nm). The correlation between the SPAD value and hyperspectral information was further analyzed for determining the sensitive bands that correspond to different disease levels. In addition, a SPAD prediction model was built upon the combination of selected indices and four machine learning methods. Results The results suggested that the SPAD value of rice leaves under different disease levels are sensitive to different wavelengths. Compared with current VIs, a stronger positive correlation was detected between the SPAD value and the SFDI, reaching an average correlation coefficient of 0.8263. For the prediction model, the one built with support vector regression and SFDI achieved the best performance, reaching R ² , RMSE, and RE at 0.8752, 3.7715, and 7.8614%, respectively. Conclusions This work provides an in-depth insight for accurately and robustly predicting the SPAD value of rice leaves under the bacterial blight disease stress, and the SFDI is of great significance for monitoring the chlorophyll content in large-scale fields non-destructively.
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Remote sensing is an important technology to map land-surface parameters, with many studies demonstrating land-surface parameter estimation, but fewer testing model transferability and quantifying model parameter uncertainty. In this study, we evaluated the uncertainty across time, space, and spatial scales of shrub willow health characterization based on canopy chlorophyll content (CCC) derived from unmanned aerial systems (UAS) data. Shrub willow is a short-rotation woody crop used to produce biomass for bioproducts and CCC is a popular biophysical parameter used to indicate shrub willow health status. We analyzed time-series UAS data at three spatial scales (5 m, 10 m, and 20 m) and stratified field-observed CCC levels. Since scale effects are often related to spatial heterogeneity, we implemented a nested analysis of variance (ANOVA) to evaluate the spatial heterogeneity within different pixel sizes and found that 5 m pixels were the most homogeneous, followed by 10 m and 20 m. Results from regression modeling of shrub willow CCC as a function of red-edge normalized difference vegetation index (NDVIre) at 5 m, 10 m, and 20 m scales showed that the models built at 5 m, 10 m, and 20 m could be applied across time, space, and scales. We also quantified the uncertainty for model parameters using two different inferential frameworks, confidence interval (CI) widths from the frequentist framework and credible interval (CrI) widths from the Bayesian framework. Unlike predictive root mean square error (RMSE), which showed model output uncertainty decreased as pixel size increased, CI and CrI widths indicated that the related model parameter uncertainty increased as pixel size increased. We calculated CI and CrI widths based on time-series model building and different stratified model building to analyze model parameter uncertainty and quantified predictive RMSE for all sampling date combinations. The results showed that, in terms of model complexity and the range of ground observations, both frequentist and Bayesian regression have advantages and disadvantages and demonstrated that the uncertainty quantified by CrI widths is able to guide future experimental design to save resources.
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Sugarcane is a good source of renewable energy and helps reduce the emission of greenhouse gases. Nitrogen has a critical role in plant growth; therefore,estimating nitrogen levels is essential, and remote sensing can improve fertilizer management. This field study selects wavelengths from hyperspectral data on a sugarcane canopy to generate models for estimating leaf nitrogen concentrations. The study was carried out in the municipalities of Piracicaba, Jaú, and Santa Maria da Serra, state of São Paulo, in the 2013/2014 growing season. The experiments were carried out using a completely randomized block design with split plots (three sugarcane varieties per plot [variety SP 81-3250 was common to all plots] and four nitrogen concentrations [0, 50, 100, and 150 kgha-1] per subplot) and four repetitions. The wavelengths that best correlated with leaf nitrogen were selected usingsparse partial least square regression. The wavelength regionswere combinedby stepwise multiple linear regression. Spectral bands in the visible (700-705 nm), red-edge (710-720 nm), near-infrared (725, 925, 955, and 980 nm), and short-wave infrared (1355, 1420, 1595, 1600, 1605, and 1610 nm) regions were identified. The R² and RMSE of the model were 0.50 and 1.67 g.kg-1, respectively. The adjusted R² and RMSE of the models for Piracicaba, Jaú, and Santa Maria were 0.31 (unreliable) and 1.30 g.kg-1, 0.53 and 1.96 g.kg-1, and 0.54 and 1.46 g.kg-1, respectively. Our results showed that canopy hyperspectral reflectance can estimate leaf nitrogen concentrations and manage nitrogen application in sugarcane.
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Background The chlorophyll content is a vital indicator for reflecting the photosynthesis ability of plants and it plays a significant role in monitoring the general health of plants. Since the chlorophyll content and the soil-plant analysis development (SPAD) value are positively correlated, it is feasible to estimate the SPAD value by calculating the vegetation indices (VIs) through hyperspectral images, thereby estimating the chlorophyll content. However, current indices simply adopted few wavelengths of the hyperspectral information, which may decrease the estimation accuracy. Besides, few researches explored the applicability of VIs over plant leaves under disease stress.Methods In this study, the SPAD value was estimated by calculating the fractal dimension of hyperspectral curves, ranging from 420 to 950 nm. The correlation between the SPAD value and wavelengths under disease stress was analyzed. In addition, a SPAD prediction model was built upon the combination of selected indices and 4 machine learning methods, including decision tree (DT), partial least square regression (PLSR), support vector regression (SVR), and back propagation neural network (BPNN). The performance of these models was compared through the correlation of determination, root mean square error, and relative error.ResultsThe results suggested that the SPAD value of rice leaves under different disease levels were sensitive to different wavelengths, meaning that the fixed wavelength selection in current indices may achieve poor estimation results. Compared with current VIs, a stronger positive correlation was detected between the SPAD value and our proposal, reaching an average correlation coefficient of 0.8263. For the prediction model, the one built with our proposal and SVR achieved the best performance, reaching R ² , RMSE, and RE at 0.8752, 3.7715, and 7.8614%, respectively.Conclusions This work provides an in-depth insight for accurately and robustly estimating the SPAD value of rice leaves under disease stress, and our proposal is of great significance for monitoring the chlorophyll content in large-scale fields non-destructively.
Article
Oxidase activities (OA) are highly correlated with the nitrogen concentration in crop leaves. To improve the universality and inter-annual repeatability of the model for estimating cotton leaf nitrogen concentration (LNC), a method for model construction was proposed based on the combination of the bands sensitive to LNC with the bands sensitive to OA. In this plot experiment, 320 and 250 sets of hyperspectral data of cotton leaves in seedling stage, bud stage, initial flowering stage, full flowering stage, and boll setting stage were collected in 2019 and 2020, respectively by using hyperspectral technology, and the LNC and OA were also measured in indoor biochemical experiments. Then, successive projection algorithm (SPA) was used to analyze the LNC and OA-sensitive bands in the original spectrum and five kinds of spectral conversions in 2019, to construct the partial least squares regression (PLSR) and principal component regression (PCR) models. Finally, the accuracy of the models were verified using the spectral data in 2020. The results showed that the selection of LNC and OA-sensitive bands could greatly reduce the collinearity and redundant information among bands. The accuracy of the models based on the LNC and OA-sensitive bands in all stages were higher than those of the models based on the LNC-sensitive bands. The optimal was the model based on the malondialdehyde (MDA), peroxidase (POD), and LNC sensitive bands in full flowering stage, with determination coefficients (R²) of 0.846, root mean squared error (RMSE) of 3.081, and residual prediction deviation (RPD) of 2.975. The universality and inter-annual repeatability of the optimal model were significantly improved, with R² increasing by 12.24%-79.89% and RMSE reducing by 19.80%-72.52%, compared with those of the model based on the LNC-sensitive bands. Besides, the accuracy and stability of PLSR models were significantly higher than those of PCR models. In conclusion, the combination of LNC and OA-sensitive bands could obviously improve the accuracy and universality of the LNC estimation model. This study provides a new method for improving the accuracy and universality of crop nitrogen estimation model.
Article
The objective of this study was to clarify a vegetation index (VI) that could be the best measure for leaf nitrogen content of soybean canopy and identify the stages or periods at which the change in the VI positively correlates with seed yield. We investigated the relation between the nitrogen contents and VI in 2018 and 2019 and the relation between the VI and seed yield in 2019. In both years, plants were grown conventionally and in narrow rows, both at 2 or 3 planting densities. Soy fields were photographed with a multispectral camera and visible camera on an unmanned aerial vehicle. The images were reconstructed to generate ortho-images using the location information of the ground control points and VI maps of the fields were created. Nitrogen content of each organ, leaves, pods and others, were determined within 3 days after taking the photo. After seed maturation, we determined seed yield and VIs of the harvested areas. Among the VIs, GNDVI showed the highest coefficients of determination with the total nitrogen content and leaf nitrogen content but CIGreen was linearly related with those until the nitrogen content became high. Although CIGreen did not correlate with seed yield at any stage, we found highly significant positive correlations between seed yield and the change in CIGreen, delta CIGreen, in the periods of reproductive stages; the highest was the period between R1 and R5. Delta CIGreen (R1-R5) also showed highly significant positive correlations between the number of seeds and between 100-seed weight. These results suggest that seed yield of soybean could be speculated from delta CIGreen (R1-R5).
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Surface reflectance data acquired in red and near-infrared spectra by remote sensing sensors are traditionally applied to construct various vegetation indices (VIs), which are related to vegetation biophysical parameters. Most VIs use pre-defined weights (usually equal to 1) for the red and NIR reflectance values, therefore constraining particular weights for red and NIR during the VI design phase, and potentially limiting capabilities of the VI to explain an independent variable. In this paper, we propose an approach to estimate biophysical variables, such as Leaf Area Index (LAI), Canopy Chlorophyll Content (CCC) and Fraction of Photosynthetically Active Radiation (FPAR) absorbed by green vegetation, represented as linear combinations of the red and NIR reflectances with weights determined empirically from observations and radiative transfer model (PROSAIL) simulations. The proof of concept is first tested on available close-range observations over maize and soybean crops in Nebraska, USA. The empirical results compare well with those from PROSAIL model simulations. The proposed LAI model is then used with data from Landsat 8, Sentinel-2 and Planet/Dove, and the results are validated with in situ LAI measurements in Ukraine. We show that the weights on red and NIR reflectances are vegetation-specific and stable in time. The approach is further tested on crops and forests in the conterminous USA and on a global scale using MODIS LAI and FPAR products as proxies for “ground observations”. These LAI and FPAR, however, are not independently measured but derived from the corresponding remotely sensed reflectances, which precludes recommending a final set of the weights/coefficients for the users, and, thus, should be considered mostly for demonstrating the concept. The results for crop types, other than maize and soybean, and for all forests are conceptual and need to be tested with real ground data. It was, however, encouraging to see that the derived maps of coefficients/weights exhibit regular patterns over the globe compatible with those of vegetation classes and crop types. Tedious and thorough work on compiling available in situ measurements on various crops and forests needs to be accomplished prior to large-scale applications, and the method needs to be further tested and proven that it works at a large scale. The proposed parameterization may be attractive for global studies of various sub-classes of vegetation, once the parameter coefficients are established, validated, tabulated and their stability verified. Ultimately, this approach may provide quantification of vegetation traits for the past decades and be a useful asset for climate models that include satellite-derived land cover classifications and vegetation variables for simulating surface fluxes. This is a conceptual paper, with a proof-of-concept supported by observations over two crops, for which we had close-range observations. It is not a technical note, which would provide users with a recommended set of coefficients for global applications. Our intent was to develop a paradigm, which could ultimately be useful in global models.
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Frost during stem elongation is one of the most destructive disasters in China, which has a significant impact on the production of winter wheat. Automatic monitoring of frost injury to canopy is of vital importance to the early prediction of yield loss. This study investigated the potential of hyperspectral techniques in predicting the Percent Yield Difference (PYD) of frost-damaged winter wheat. Three artificial frost experiments were conducted to obtain hyperspectral reflectance and grain yield for winter wheat subjected to sub-freezing temperature treatments. The PYD was used to evaluate the level of frost damage. Nine new indices based on the best combination of wavelengths were selected through the contour mapping approach. All new and published indices were used to establish the linear regression models with PYD. The results showed that as the value of PYD increased, the NIR reflectance within 760–1140 nm decreased, whereas the red and SWIR reflectance increased. The most significant change was found in the NIR region, where the water absorption bands within 930–970 nm almost disappeared. The cross-validation results indicated that the three water-sensitive spectral indices NWI-2, RDSI ((R958-R545)/(R826-R545)), and NDSI ((R962-R829)/(R962+R829)) demonstrated the best prediction accuracy and outperformed the partial least square regression (PLSR) and support vector regression (SVR) models. NWI-2 and NDSI represented a relatively simple waveband combination similar to NDVI, which could be referenced for developing satellite multispectral products to predict PYD at a large spatial scale. GS and PYD range had a significant impact on the spectral indices. The prediction accuracy of PYD for a single GS improved as development advanced before heading. When the PYD value was above 10 %, no significant differences between the subfreezing treatments and the unfrosted controls was detected until the PYD value exceeded 30–40%. It was difficult to predict the relatively low PYD level due to the hybrid response of the spectral reflectance to frost damage.
Chapter
Consistent and near-real-time crop growth monitoring over a large scale is a very crucial step for digital agriculture. An efficient tool for accurate retrieval of different biophysical parameters is the basic requirement for crop growth monitoring. Quantitative estimation of various crop biochemical and biophysical variables with reliable accuracy is very useful for different applications related to agriculture, ecology, and climate. This chapter briefly describes different methods and models for the retrieval of various crop biophysical parameters using remote sensing (RS) approaches. Leaf area index (LAI) is a vital attribute in many land-surface vegetation and climate models which have many important applications. Leaf chlorophyll and leaf water content are key parameters in many ecological processes, such as photosynthesis, respiration, transpiration, and they also provide stress information. The fraction of absorbed photosynthetically active radiation (fAPAR) by crop vegetation is used as an essential climate variable (ECVs) and critical input in many land-surface, crop growth and climate, ecological, water, and carbon cycle models. This chapter highlights various retrieval methods of crop biophysical parameters, including empirical, semiempirical, hybrid, physically based models with various inversion algorithms like look-up table, neural network, genetic algorithms, Bayesian networks, support vectors, etc.
Chapter
This chapter begins by introducing the concept of precision agriculture and the impact of new technologies on the development and deployment of precision agriculture technologies. It then extensively reviews the sensing technologies commonly used in precision agriculture applications for crop, root, and soil monitoring. The chapter also reviewed platforms developed to implement field sensing tasks, including ground-based static platforms, ground-based mobile platforms, and aerial-based platforms. Two case studies using precision agriculture sensing technologies are finally presented.
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The enormous increase of remote sensing data from airborne and space-borne platforms, as well as ground measurements has directed the attention of scientists towards new and efficient retrieval methodologies. Of particular importance is the consideration of the large extent and the high dimensionality (spectral, temporal and spatial) of remote sensing data. Moreover, the launch of the Sentinel satellite family will increase the availability of data, especially in the temporal domain, at no cost to the users. To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited. Among these, greater attention is devoted to machine learning methods due to their flexibility and the capability to process large number of inputs and to handle non-linear problems. The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities (vegetation biomass and soil moisture) from remote sensing data using machine learning methods.
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As part of the EnMAP preparation activities this study aims at estimating the uncertainty in the EnMAP L2A ground reflectance product using the simulated scene of Barrax, Spain. This dataset is generated using the EnMAP End-to-End Simulation tool, providing a realistic scene for a well-known test area. Focus is set on the influence of the expected radiometric calibration stability and the spectral calibration stability. Using a Monte-Carlo approach for uncertainty analysis, a larger number of realisations for the radiometric and spectral calibration are generated. Next, the ATCOR atmospheric correction is conducted for the test scene for each realisation. The subsequent analysis of the generated ground reflectance products is carried out independently for the radiometric and the spectral case. Findings are that the uncertainty in the L2A product is wavelength-dependent, and, due to the coupling with the estimation of atmospheric parameters, also spatially variable over the scene. To further illustrate the impact on subsequent data analysis, the influence on two vegetation indices is briefly analysed. Results show that the radiometric and spectral stability both have a high impact on the uncertainty of the narrow-band Photochemical Reflectance Index (PRI), and also the broad-band Normalized Difference Vegetation Index (NDVI) is affected.
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Efficient use of N fertilizer has become crucial due to fertilizer costs and the impact of excessive Non the environment. Diagnostic tools for estimating plant N status have an important role in reducing N inputs while maintaining yield. The objective of our study was to quantify corn (Zea mays L.) leaf greenness with a digital camera and image-analysis software and establish the relationship with yield, leaf N concentration, and chlorophyll meter (or SPAD, soil plant analysis development) values. In 2008 and 2009, field experiments were conducted at five sites with N treatments ranging from 0 to 336 kg N ha(-1). At tasseling, the ear leaf was sampled for color analysis and SPAD measurements, and then analyzed for total N. Hue, saturation, and brightness (HSB) values from digital images were processed into a dark green color index (DGCI), which combines HSB values into one composite number. Including calibration disks in images and changing the background color in photographs to pink greatly improved DGCI precision in 2009 over 2008. There was a close relationship (typically r(2) >= 0.70) of SPAD and DGCI with leaf N concentration. Within a location, yield increased linearly in most cases with both SPAD (average r(2) = 0.79) and DGCI (average r(2) = 0.78). Digital-image analysis was a simple method of determining corn N status that has potential as a diagnostic tool for determining crop N needs.
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The machine learning method, random forest (RF), is applied in order to derive biophysical and structural vegetation parameters from hyperspectral signatures. Hyperspectral data are, among other things, characterized by their high dimensionality and autocorrelation. Common multivariate regression approaches, which usually include only a limited number of spectral indices as predictors, do not make full use of the available information. In contrast, machine learning methods, such as RF, are supposed to be better suited to extract information on vegetation status. First, vegetation parameters are extracted from hyperspectral signatures simulated with the radiative transfer model, PROSAIL. Second, the transferability of these results with respect to laboratory and field measurements is investigated. In situ observations of plant physiological parameters and corresponding spectra are gathered in the laboratory for summer barley (Hordeum vulgare). Field in situ measurements focus on winter crops over several growing seasons. Chlorophyll content, Leaf Area Index and phenological growth stages are derived from simulated and measured spectra. RF performs very robustly and with a very high accuracy on PROSAIL simulated data. Furthermore, it is almost unaffected by introduced noise and bias in the data. When applied to laboratory data, the prediction accuracy is still good (Cab: R2 = 0.94/ LAI: R2 = 0.80/BBCH (Growth stages of mono-and dicotyledonous plants) : R2 = 0.91), but not as high as for simulated spectra. Transferability to field measurements is given with prediction levels as high as for laboratory data (Cab: R2 = 0.89/LAI: R2 = 0.89/BBCH: R2 = �0.8). Wavelengths for deriving plant physiological status based on simulated and measured hyperspectral signatures are mostly selected from appropriate spectral regions (both field and laboratory): 700–800 nm regressing on Cab and 800–1300 nm regressing on LAI. Results suggest that the prediction accuracy of vegetation parameters using RF is not hampered by the high dimensionality of hyperspectral signatures (given preceding feature reduction). Wavelengths selected as important for prediction might, however, vary between underlying datasets. The introduction of changing environmental factors (soil, illumination conditions) has some detrimental effect, but more important factors seem to stem from measurement uncertainties and plant geometries.
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Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research. Hyperspectral Remote Sensing of Vegetation integrates this knowledge, guiding readers to harness the capabilities of the most recent advances in applying hyperspectral remote sensing technology to the study of terrestrial vegetation. Taking a practical approach to a complex subject, the book demonstrates the experience, utility, methods and models used in studying vegetation using hyperspectral data. Written by leading experts, including pioneers in the field, each chapter presents specific applications, reviews existing state-of-the-art knowledge, highlights the advances made, and provides guidance for the appropriate use of hyperspectral data in the study of vegetation as well as its numerous applications, such as crop yield modeling, crop and vegetation biophysical and biochemical property characterization, and crop moisture assessment. This comprehensive book brings together the best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, vegetation classification, biophysical and biochemical modeling, crop productivity and water productivity mapping, and modeling. It provides the pertinent facts, synthesizing findings so that readers can get the correct picture on issues such as the best wavebands for their practical applications, methods of analysis using whole spectra, hyperspectral vegetation indices targeted to study specific biophysical and biochemical quantities, and methods for detecting parameters such as crop moisture variability, chlorophyll content, and stress levels. A collective "knowledge bank," it guides professionals to adopt the best practices for their own work.
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In situ optical meters are widely used to estimate leaf chlorophyll concentration, but non-uniform chlorophyll distribution causes the optical measurement to vary widely among species for the same chlorophyll concentration. Over 30 studies have sought to quantify the in situ/in vitro (optical/absolute) relationship, but neither chlorophyll extraction nor measurement techniques for in vitro analysis have been consistent among studies. Here we: 1) review standard procedures for measurement of chlorophyll, 2) estimate the error associated with non-standard procedures, and 3) implement the most accurate methods to provide equations for conversion of optical to absolute chlorophyll for 22 species grown in multiple environments. Tests of five Minolta (model SPAD-502) and 25 Opti-Sciences (model CCM-200) meters, manufactured from 1992 to 2013, indicate that differences among replicate models are less than 5 %. We thus developed equations for converting between units from these meter types. There was no significant effect of environment on the optical/absolute chlorophyll relationship. We derive the theoretical relationship between optical transmission ratios and absolute chlorophyll concentration and show how non-uniform distribution among species causes a variable, non-linear response. These results more rigorously link in situ optical measurements with in vitro chlorophyll concentration and provide insight to strategies for single-leaf radiation capture among diverse species.
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The long wavelength edge of the major chlorophyll absorption feature in the spectrum of a vegetation canopy moves to longer wavelengths with an increase in chlorophyll content. The position of this red-edge has been used successfully to estimate, by remote sensing, the chlorophyll content of vegetation canopies. Techniques used to estimate this red-edge position (REP) have been designed for use on small volumes of continuous spectral data rather than the large volumes of discontinuous spectral data recorded by contemporary satellite spectrometers. Also, each technique produces a different value of REP from the same spectral data and REP values are relatively insensitive to chlorophyll content at high values of chlorophyll content. This paper reports on the design and preliminary evaluation of a surrogate REP index for use with spectral data recorded at the standard band settings of the Medium Resolution Imaging Spectrometer (MERIS). This index, termed the MERIS Terrestrial Chlorophyll Index (MTCI) was evaluated indirectly using model spectra, field spectra and MERIS data. It was easy to calculate (and so can be automated), was correlated strongly with REP and unlike REP, was sensitive to high values of chlorophyll content. Further evaluation of the MTCI is proposed, using both greenhouse and field data. Its utility for a regional scale application in southern Vietnam is also discussed.
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Spatial distribution of canopy N status is the primary information needed for precision management of N fertilizer. This study demonstrated the feasibility of a simple spectral index (SI) using the first derivative of canopy reflectance spectrum at 735 nm (dR/d lambda vertical bar(735)) to assess N concentration of rice (Oryza sativa L.) plants, and then validated the applicability of a simplified imaging system based on the derived spectral model from the dR/d lambda vertical bar(735) relationship in mapping canopy N status within field. Results showed that values of dR/d lambda vertical bar(735) were linearly related to plant N concentrations measured at the panicle formation stage. The leaf N accumulation per unit ground area was better fitted than other ratio-based SIs, such as simple ratio vegetation index (SRVI), normalized difference vegetation index (NDVI), R810/R560, and (R1100 - R660)/(R1100 + R660), and remained valid when pooling more data from different cropping seasons in varied years and locations. A simplified imaging system was assembled and mounted on a mobile lifter and a helicopter to take spectral imageries for mapping canopy N status within fields. Results indicated that the imaging system was able to provide field maps of canopy N status with reasonable accuracy (r = 0.465-0-912, root mean standard error [RMSE] = 0.100-0-550) from both remote sensing platforms.
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Hyperspectral/multiangular data allow the retrieval of important vegetation properties at canopy level, such as the Leaf Area Index (LAI) and Leaf Chlorophyll Content. Current methods are based on the relationship between biophysical properties and retrievals from those spectral bands (from the complete hyperspectral/multiangular information) where specific absorption features are present within the considered spectral range. Furthermore, new sensors such as PROBA/CHRIS provide continuous hyperspectral reflectance measurements that can be considered as a continuous function of wavelength. The mathematical analysis of these continuous functions allows a new way of exploiting the relationships between spectral reflectance and biophysical variables by more powerful and stable mathematical tools, in particular for the retrieval of LAI and chlorophyll content. Within the overall context of the European Space Agency (ESA) Spectra Barrax Campaign (SPARC) experiment, an extensive field study was carried out in La Mancha, Spain, simultaneously to the overflight of airborne imaging spectrometers (AHS, HyMAP, ROSIS) and the overpass of CHRIS‐PROBA and MERIS sensors. During the SPARC‐2003 and SPARC‐2004 campaigns, numerous ground measurements were made in the Barrax study area (covering LAI, fCover, leaf chlorophyll a+b, leaf water content and leaf biomass), together with other complementary data, and a total of 17 CHRIS‐PROBA images were acquired. Representative points have been selected from a total of nine different crops, and also retrieved from the CHRIS‐PROBA images acquired within the days of the field campaign. About 250 reflectance spectra from five different observation angles have been analysed. Hyperspectral reflectance spectra have been adjusted by means of third‐degree polynomial functions between 500 nm and 750 nm, and correlations observed between LAI values and the coefficients of these polynomials yielded LAI as a result of the mathematical fitting. On the other hand, the area under the spectral reflectance curves has been calculated in the interval from 600 nm to 700 nm, the region of the red spectral interval where strong absorption features for chlorophyll have been observed, though areas under the curves are also strongly correlated to the chlorophyll content of the crops. Furthermore, a linear relationship between these areas and the chlorophyll content is proposed in this work. This relationship allows the retrieval of leaf chlorophyll by satellite data, based on the spectral information. Both of the proposed methods are almost independent of the observation angles employed. The high number of in situ measurements acquired simultaneously to satellite overpasses, and the broad available range of data, have allowed validation of both methods, with a large number of data and in a statistically consistent manner.
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The estimation of soil moisture from reflectance measurements in the solar spectral domain (400-2500 nm) was investigated. For this purpose, 18 soils representing a large range of permanent characteristics was considered. Reflectance data were measured in the laboratory during the soil drying process with a high spectral resolution spectroradiometer. Five approaches were compared. The first one was based on single-band reflectance and on the normalization of reflectance data by the reflectance of the corresponding soil under dry conditions. The second and the third approaches were based on either reflectance derivatives or absorbance derivatives. The fourth and fifth approaches were based on the differences of reflectance and absorbance observed in two non-consecutive bands. In the first step, the relationships were calibrated over half the dataset (nine soils) with emphasis on the selection of the most pertinent spectral bands. Results showed that, for the first approach, the bands corresponding to the highest water absorption capacities (1944 nm) yielded the best soil moisture retrieval performances. For the second and third approaches, the bands corresponding to sharp edges of the water absorption features performed better (1834 nm for the reflectance derivatives and 1622 nm for the absorbance derivatives). The fourth and fifth approaches that can be considered as a generalization of the derivative approach when bands are no longer consecutive, the best performances were achieved when the bands were not separated too much. The best overall retrieval performances were achieved with the absorbance derivatives and the difference of absorbance, confirming the non-linear character of the relationship between soil moisture and reflectance. The previously calibrated relations were tested over the evaluation dataset made of the nine remaining soils. It showed additionally that the normalization of reflectance values by that observed under dry conditions was only partly minimizing soil type effects. The best performances for the lowest soil moisture values (
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In this paper, we develop, present and evaluate a refined, statistical index of model performance. This ‘new’ measure (d r) is a reformulation of Willmott's index of agreement, which was developed in the 1980s. It (d r) is dimensionless, bounded by − 1.0 and 1.0 and, in general, more rationally related to model accuracy than are other existing indices. It also is quite flexible, making it applicable to a wide range of model‐performance problems. The two main published versions of Willmott's index as well as four other comparable dimensionless indices—proposed by Nash and Sutcliffe in 1970, Watterson in 1996, Legates and McCabe in 1999 and Mielke and Berry in 2001—are compared with the new index. Of the six, Legates and McCabe's measure is most similar to d r. Repeated calculations of all six indices, from intensive random resamplings of predicted and observed spaces, are used to show the covariation and differences between the various indices, as well as their relative efficacies. Copyright © 2011 Royal Meteorological Society
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1] Leaf pigment content and composition provide important information about plant physiological status. Reflectance measurements offer a rapid, nondestructive technique to estimate pigment content. This paper describes a recently developed three-band conceptual model capable of remotely estimating total of chlorophylls, carotenoids and anthocyanins contents in leaves from many tree and crop species. We tuned the spectral regions used in the model in accord with pigment of interest and the optical characteristics of the leaves studied, and showed that the developed technique allowed accurate estimation of total chlorophylls, carotenoids and anthocyanins, explaining more than 91%, 70% and 93% of pigment variation, respectively. This new technique shows a great potential for noninvasive tracking of the physiological status of vegetation and the impact of environmental changes. (2006), Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves, Geophys. Res. Lett., 33, L11402, doi:10.1029/ 2006GL026457.
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The photochemical reflectance index (PRI), derived from narrow-band reflectance at 531 and 570 nm, was explored as an indicator of photosynthetic radiation use efficiency for 20 species representing three functional types: annual, deciduous perennial, and evergreen perennial. Across species, top-canopy leaves in full sun at midday exhibited a strong correlation between PRI and ΔF/Fm′, a fluorescence-based index of photosystem II (PSII) photochemical efficiency. PRI was also significantly correlated with both net CO2 uptake and radiation use efficiency measured by gas exchange. When species were examined by functional type, evergreens exhibited significantly reduced midday photosynthetic rates relative to annual and deciduous species. This midday reduction was associated with reduced radiation use efficiency, detectable as reduced net CO2 uptake, PRI, and ΔF/Fm′ values, and increased levels of the photoprotective xanthophyll cycle pigment zeaxanthin. For each functional type, nutrient deficiency led to reductions in both PRI and ΔF/Fm′ relative to fertilized controls. Laboratory experiments exposing leaves to diurnal courses of radiation and simulated midday stomatal closure demonstrated that PRI changed rapidly with both irradiance and leaf physiological state. In these studies, PRI was closely correlated with both ΔF/Fm' and radiation use efficiency determined from gas exchange at all but the lowest light levels. Examination of the difference spectra upon exposure to increasing light levels revealed that the 531 nm Δ reflectance signal was composed of two spectral components. At low irradiance, this signal was dominated by a 545-nm component, which was not closely related to radiation use efficiency. At progressively higher light levels above 100 μmol m−2 s−1, the 531-nm signal was increasingly dominated by a 526-nm component, which was correlated with light use efficiency and with the conversion of the xanthophyll pigment violaxanthin to antheraxanthin and zeaxanthin. Further consideration of the two components composing the 531-nm signal could lead to an index of photosynthetic function applicable over a wide range of illumination. The results of this study support the use of PRI as an interspecific index of photosynthetic radiation use efficiency for leaves and canopies in full sun, but not across wide ranges in illumination from deep shade to full sun. The discovery of a consistent relationship between PRI and photosynthetic radiation use efficiency for top-canopy leaves across species, functional types, and nutrient treatments suggests that relative photosynthetic rates could be derived with the “view from above” provided by remote reflectance measurements if issues of canopy and stand structure can be resolved.
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Soil-Vegetation-Atmosphere Transfer Models (SVAT) and Crop Simulation Models describe physical and physiological processes occurring in crop canopies. Remote sensing data may be used through assimilation procedures for constraining or driving SVAT and crop models. These models provide continuous simulation of processes such as evapotranspiration and, thus, direct means for interpolating evapotranspiration between remote sensing data acquisitions (which is not the case for classical evapotranspiration mapping methods). They also give access to variables other than evapotranspiration, such as soil moisture and crop production. We developed the coupling between crop, SVAT and radiative transfer models in order to implement assimilation procedures in various wavelength domains (solar, thermal and microwave). Such coupling makes it possible to transfer information from one model to another and then to use remote sensing information for retrieving model parameters which are not directly related to remote sensing data (such as soil initial water content, plant growth parameters, physical properties of soil and so on). Simple assimilation tests are presented to illustrate the main techniques that may be used for monitoring crop processes and evapotranspiration. An application to a small agricultural area is also performed showing the potential of such techniques for retrieving evapotranspiration and information on irrigation practices over wheat fields.
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This study systematically evaluated linear predictive models between vegetation indices (VI) derived from radiometrically corrected airborne imaging spectrometer (HyMap) data and field measurements of biophysical forest stand variables (n=40). Ratio-based and soil-line-related broadband VI were calculated after HyMap reflectance had been spectrally resampled to Landsat TM channels. Hyperspectral VI involved all possible types of two-band combinations of ratio VI (RVI) and perpendicular VI (PVI) and the red edge inflection point (REIP) computed from two techniques, inverted Gaussian Model and Lagrange Interpolation. Cross-validation procedure was used to assess the prediction power of the regression models. Analyses were performed on the entire data set or on subsets stratified according to stand age. A PVI based on wavebands at 1088 nm and 1148 nm was linearly related to leaf area index (LAI) (R2=0.67, RMSE=0.69 m2 m−2 (21% of the mean); after removal of one forest stand subjected to clearing measures: R2=0.77, RMSE=0.54 m2 m−2 (17% of the mean). A PVI based on wavebands at 885 nm and 948 nm was linearly related to the crown volume (VOL) (R2=0.79, RMSE=0.52). VOL was derived from measured biophysical variables through factor analysis (varimax rotation). The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify LAI and VOL with good accuracy. For broadband multispectral data, the accuracy was generally lower. It can be stated that the hyperspectral data set contains more information relevant to the estimation of the forest stand variables LAI and VOL than multispectral data. When the pooled data set was analysed, soil-line-related VI performed better than ratio-based VI. When age classes were analysed separately, hyperspectral VI performed considerably better than broadband VI. Best hyperspectral VI in relation with LAI were typically based on wavebands related to prominent water absorption features. Such VI are related to the total amount of canopy water; as the leaf water content is considered to be relatively constant in the study area, variations of LAI are retrieved.
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Hyperspectral reflectance data representing a wide range of canopies were simulated using the combined PROSPECT+SAIL model. The simulations were used to study the stability of recently proposed vegetation indices (VIs) derived from adjacent narrowband spectral reflectance data across the visible (VIS) and near infrared (NIR) region of the electromagnetic spectrum. The prediction power of these indices with respect to green leaf area index (LAI) and canopy chlorophyll density (CCD) was compared, and their sensitivity to canopy architecture, illumination geometry, soil background reflectance, and atmospheric conditions were analyzed. The second soil-adjusted vegetation index (SAVI2) proved to be the best overall choice as a greenness measure. However, it is also shown that the dynamics of the VIs are very different in terms of their sensitivity to the different external factors that affects the spectral reflectance signatures of the various modeled canopies. It is concluded that hyperspectral indices are not necessarily better at predicting LAI and CCD, but that selection of a VI should depend upon (1) which parameter that needs to be estimated (LAI or CCD), (2) the expected range of this parameter, and (3) a priori knowledge of the variation of external parameters affecting the spectral reflectance of the canopy.
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This latest Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) will again form the standard reference for all those concerned with climate change and its consequences, including students, researchers and policy makers in environmental science, meteorology, climatology, biology, ecology, atmospheric chemistry and environmental policy.