No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Effect of Vertical Distribution of Crop Structure and Biochemical Parameters of Winter Wheat on Canopy Reflectance Characteristics and Spectral Indices
Abstract
Vertical heterogeneity of the canopy is being increasingly recognized in remote estimates of vegetative properties. Given the current limited knowledge of this issue, this paper investigated the effects of different vertical distributions of crop structure [e.g., leaf angle (LA)] and leaf area index (LAI)] and biochemical parameters [e.g., chlorophyll a and b content (Chl
a+b
) and water content (W
c
)] on canopy reflectance and vegetation indices (VIs). A recently developed multiple-layer canopy reflectance model (MRTM) was tested for winter wheat and used to run a simulation analysis of different canopy scenarios. The results showed that the MRTM performed well to model winter wheat canopy reflectance with regard to spikes and vertical distributions of leaf properties. The vertical profiles of LA and LAI influenced canopy reflectance at almost all wavelengths, whereas the vertical profile of Chl
a+b
mainly affected reflectance in the visible region, and that of W
c
only affected reflectance in the near-infrared region. Changes in vertical distribution of the LA resulted in clear variations in VIs related to the LA, LAI, and Chl
a+b
estimates. The vertical LAI and Chl
a+b
profiles mainly influenced the VIs related to the LAI and Chl
a+b
estimates. The W
c
vertical profile primarily affected the VIs used to estimate crop water properties. The sensitivities of the VIs were mainly associated with the spectral responses and penetration characteristics of the bands they used. These findings suggest that the sensitivity of VIs to the vertical distributions of crop parameters should be considered when establishing models for remote crop monitoring.
... The distribution of N within a plant canopy is spatially heterogeneous, since plant canopies are three-dimensional (3D) structures [6,7]. This vertical distribution pattern matches the available radiation along the plant height to achieve the maximum canopy photosynthesis [8], since about 75% of leaf nitrogen is involved in the plant photosynthesis process [2], and previous studies have shown that shaded leaves in the canopy bottom have lower N than upper leaves that are exposed to sunlight [9,10]. ...
... The optical remote sensing technique, based on the strong correlation between plant biochemical parameters and reflected spectral properties, has been widely used in N estimation in several fields of research [16][17][18][19]. However, the heterogeneous nature of N distributions affects the monitored reflectance spectra and, thus, the accuracy of N estimation [7,20,21]. Despite both the top view [6,7,22] and multi-angle observations [23,24] having been used for detecting the vertical properties of biochemical parameters, these indirect methods have limited effectiveness and still lack plant structural information. ...
... However, the heterogeneous nature of N distributions affects the monitored reflectance spectra and, thus, the accuracy of N estimation [7,20,21]. Despite both the top view [6,7,22] and multi-angle observations [23,24] having been used for detecting the vertical properties of biochemical parameters, these indirect methods have limited effectiveness and still lack plant structural information. The structure from motion (SFM) method, building plant point clouds with multi-angle two-dimensional (2D) images, has been used to characterize plant three-dimensional (3D) biochemical properties [25]; however, its data-collection process is laborious, and the spectral information of the constructed point cloud was limited. ...
Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R2 > 0.6) than the single spectral index (R2 > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R2 = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture.
... The absorption of Chl in the red-edge region is lower than that in visible light, which can effectively reduce the saturation problem, and the reflection of the red-edge region still maintains a high sensitivity to Chl absorption at medium to high values (Ciganda et al.2009;Shiratsuchi et al. 2011). With the increase of canopy depth, the attenuation caused by scattering is less than that caused by absorption, which allows light to penetrate the canopy to the lower leaves (Huang et al.2011;Li et al. 2013;Huang et al. 2014;He et al. 2016;Zhao et al. 2017). Zhao et al. (2017) also showed that the red-edge region has stronger canopy penetration ability than the visible light region. ...
... With the increase of canopy depth, the attenuation caused by scattering is less than that caused by absorption, which allows light to penetrate the canopy to the lower leaves (Huang et al.2011;Li et al. 2013;Huang et al. 2014;He et al. 2016;Zhao et al. 2017). Zhao et al. (2017) also showed that the red-edge region has stronger canopy penetration ability than the visible light region. ...
... Leaf N content, as one of the most important nutrient elements, exhibits a pronounced vertical heterogeneity in its vertical distribution within vegetation canopies (Ciganda et al. 2008;Huang et al. 2014;Guo et al. 2015;Zhao et al. 2017). Canopy architecture will affect the interception and utilization of solar radiation, which indirectly leads to the vertical heterogeneity of light distribution within the maize canopy (Hansen and Schjoerring, 2003). ...
Real-time monitoring of leaf nitrogen (N) content by remote sensing can accurately diagnose crop nutrient status and facilitate precision N management. However, the methods used to estimate of vertically integrated leaf N content do not consider different cropping systems, in which the maize growth stages are not synchronized, resulting in decreased practical value of the results. The purpose of this study was to propose an optimized red-edge absorption area (OREA) index in which the prediction accuracy of vertically integrated leaf N content is improved within spring- and summer-sown maize canopies. The results showed that vertical distributions of N existed regardless of variations in the maize growth stages, that is, the leaf N density of the upper and middle layers was higher than that of the lower layers. These published vegetation indices (VIs) provided relatively good correlations with leaf N density at different layers across all of the datasets. When predicting leaf N density of each leaf layer, an optimal VI is generated, and inconsistent VIs will limit its practical application. To further overcome the drawbacks of the inconsistency of each VI when estimating the leaf N density at different layers, a new OREA index was proposed based on red-edge absorption area parameter. The OREA index showed the highest prediction accuracy with leaf N density for entire canopies (r² = 0.811, RMSE = 0.374, RE = 13.17%) and canopies without top first leaves (r² = 0.795, RMSE = 0.269, RE = 15.20%) compared with the other published VIs. It is concluded that the vertically integrated leaf N content under different field experiments can be accurately estimated by the OREA index.
... Therefore, the influence of vertical heterogeneity on spectral response is not negligible [5][6][7]. Furthermore, vertical heterogeneity may greatly influence the accuracy of actual growth detection and remote estimation of nutrient characteristics of crops [8]. Zhao et al. [8] also found that the chlorophyll, leaf moisture, and leaf area index (LAI) of winter wheat have vertical heterogeneity. ...
... Furthermore, vertical heterogeneity may greatly influence the accuracy of actual growth detection and remote estimation of nutrient characteristics of crops [8]. Zhao et al. [8] also found that the chlorophyll, leaf moisture, and leaf area index (LAI) of winter wheat have vertical heterogeneity. Moreover, the physiological and biochemistry indices of crops and canopy are correlated with the spectra at visible light, near-infrared, and middle-infrared bands [8,9]. ...
... Zhao et al. [8] also found that the chlorophyll, leaf moisture, and leaf area index (LAI) of winter wheat have vertical heterogeneity. Moreover, the physiological and biochemistry indices of crops and canopy are correlated with the spectra at visible light, near-infrared, and middle-infrared bands [8,9]. ...
Vertical heterogeneity of the biochemical characteristics of crop canopy is important in diagnosing and monitoring nutrition, disease, and crop yield via remote sensing. However, the research on vertical isomerism was not comprehensive. Experiments were carried out from the two levels of simulation and verification to analyze the applicability of this recently development model. Effects of winter wheat on spectrum were studied when input different structure parameters (e.g., leaf area index (LAI)) and physicochemical parameters (e.g., chlorophyll content (Chla+b) and water content (Cw)) to the mSCOPE (Soil Canopy Observation, Photochemistry, and Energy fluxes) model. The maximum operating efficiency was 127.43, when the winter wheat was stratified into three layers. Meanwhile, the simulation results also proved that: the vertical profile of LAI had an influence on canopy reflectance in almost all bands; the vertical profile of Chla+b mainly affected the reflectivity of visible region; the vertical profile of Cw only affected the near-infrared reflectance. The verification results showed that the vegetation indexes (VIs) selected of different bands were strongly correlated with the parameters of the canopy. LAI, Chla+b and Cw affected VIs estimation related to LAI, Chla+b and Cw respectively. The Root Mean Square Error (RMSE) of the new-proposed NDVIgreen was the smallest, which was 0.05. Sensitivity analysis showed that the spectrum was more sensitive to changes in upper layer parameters, which verified the rationality of mSCOPE model in explaining the law that light penetration in vertical nonuniform canopy gradually decreases with the increase of layers.
... In recent years, the non-uniform vertical N distribution has been reported for various plant canopies (Archontoulis et al., 2011;Hikosaka et al., 2016;Zhao et al., 2017;Gara et al., 2018;Ye et al., 2018), and the influencing factors could be roughly divided into two categories: environmental factors and vegetation factors. In terms of environmental factors, the light condition is one of the key factors that causes the non-uniform distribution of N. A number of studies have shown that the specific leaf nitrogen (SLN) in different leaf layers is closely related to the light conditions (Dreccer et al., 2000;Archontoulis et al., 2011;Hikosaka, 2014;Coble and Cavaleri, 2015). ...
... As a rapid and non-destructive method, remote sensing has been widely used to determine the vegetation N status (Mitchell et al., 2012;Oerke et al., 2016;Song et al., 2016;Chemura et al., 2018;Prey and Schmidhalter, 2019); however, such analyses assume that the vegetation canopy is uniform and seldom consider the N distribution. Vertical heterogeneity of the canopy is being increasingly recognized in remote estimates of vegetative properties (Jia et al., 2013;Li et al., 2013;Liu et al., 2015;Li et al., 2016;Zhao et al., 2017). Ciganda et al. (2012) reported that the red edge chlorophyll index (CI red-edge ) could effectively monitor the chlorophyll content of the top 7 to 9 leaves in the maize canopy. ...
... In this study, the temporal and spatial distribution characteristics of the LNC in the rice canopy at different growth stages were investigated. Our study results showed that the LNC distribution followed a declining trend from the top layer to the bottom layer in rice canopy (Figure 3), which is consistent with the findings in wheat Zhao et al., 2017), winter oilseed rape (Li et al., 2018), and maize (Ye et al., 2018). Light conditions are the main limiting factors for vegetation photosynthesis, with more N allocated to leaves receiving higher light intensities (Wyka et al., 2012); thus, the top of the canopy, which has the best light conditions in the rice canopy, is assigned more. ...
Accurate estimations of the vertical leaf nitrogen (N) distribution within a rice canopy is helpful for understanding the nutrient supply and demand of various functional leaf layers of rice and for improving the predictions of rice productivity. A two-year field experiment using different rice varieties, N rates, and planting densities was performed to investigate the vertical distribution of the leaf nitrogen concentration (LNC, %) within the rice canopy, the relationship between the LNC in different leaf layers (LNCLi, i = 1, 2, 3, 4), and the relationship between the LNCLi and the LNC at the canopy level (LNCCanopy). A vertical distribution model of the LNC was constructed based on the relative canopy height. Furthermore, the relationship between different vegetation indices (VIs) and the LNCCanopy, the LNCLi, and the LNC vertical distribution model parameters were studied. We also compared the following three methods for estimating the LNC in different leaf layers in rice canopy: (1) estimating the LNCCanopy by VIs and then estimating the LNCLi based on the relationship between the LNCLi and LNCCanopy; (2) estimating the LNC in any leaf layer of the rice canopy by VIs, inputting the result into the LNC vertical distribution model to obtain the parameters of the model, and then estimating the LNCLi using the LNC vertical distribution model; (3) estimating the model parameters by using VIs directly and then estimating the LNCLi by the LNC vertical distribution model. The results showed that the LNC in the bottom of rice canopy was more susceptible to different N rates, and changes in the LNC with the relative canopy height could be simulated by an exponential model. Vegetation indices could estimate the LNC at the top of rice canopy. R705/(R717+R491) (R² = 0.763) and the renormalized difference vegetation index (RDVI) (1340, 730) (R² = 0.747) were able to estimate the parameter “a” of the LNC vertical distribution model in indica rice and japonica rice, respectively. In addition, method (2) was the best choice for estimating the LNCLi (R² = 0.768, 0.700, 0.623, and 0.549 for LNCL1, LNCL2, LNCL3, and LNCL4, respectively). These results provide technical support for the rapid, accurate, and non-destructive identification of the vertical distribution of nitrogen in rice canopies.
... 3 of 25 suffered by the crop will affect the growth and development of the crop, making the biochemical composition of the crop different, and the reflectance spectrum of the crop with chlorophyll content, nutrient content, water content and other biochemical components will show different patterns in different bands. Numerous studies have shown that the spectral reflectance characteristics of crops are closely related to their morphological structure, pigment content and nutrient content [15][16][17], so it is feasible to monitor the nutrient and biochemical information of crops using hyperspectral techniques. Figure 1 shows a schematic diagram of the reflectance spectral properties of plant leaves [18,19]. ...
... Soil moisture content, soil nutrients and the degree of pests and diseases suffered by the crop will affect the growth and development of the crop, making the biochemical composition of the crop different, and the reflectance spectrum of the crop with chlorophyll content, nutrient content, water content and other biochemical components will show different patterns in different bands. Numerous studies have shown that the spectral reflectance characteristics of crops are closely related to their morphological structure, pigment content and nutrient content [15][16][17], so it is feasible to monitor the nutrient and biochemical information of crops using hyperspectral techniques. Figure 1 shows a schematic diagram of the reflectance spectral properties of plant leaves [18,19]. ...
Crop nutrient biochemical information (mainly including chlorophyll class and nutrient elements mainly nitrogen, phosphorus and potassium) is an important basis for revealing crop growth and development patterns and their relationship with the environment. Hyperspectral technology has been rapidly developed and applied in crop nutrient biochemical information monitoring research. This paper firstly describes the theoretical basis of hyperspectral technology for monitoring crop nutrients and biochemical information. Then, the research progress of hyperspectral technology in monitoring nutrient and biochemical information of crops in different growth periods or different growth environments is outlined. Meanwhile, the shortcomings of the current technology in these research directions and the future research trends are discussed. Finally, the modeling methods for building crop nutrient biochemical information monitoring models by applying hyperspectral data are systematically outlined. And the effects of different spectral pre-processing methods, spectral effective information extraction methods and modeling algorithms on the accuracy of monitoring models are analyzed. On this basis, the challenges and prospects of hyperspectral technology in monitoring crop nutrient biochemical information are presented, aiming to provide relevant theoretical basis and technical reference for the research related to monitoring and inversion of crop physiological parameters based on hyperspectral technology.
... understanding the effects of climate change. In addition to horizontal heterogeneity, the biochemical components of vegetation such as nitrogen, chlorophyll, and water present nonuniform vertical profiles due to their 3-D structures and vertical heterogeneity [1]- [3]. Monitoring the 3-D distribution of the biochemical parameters can improve the accuracy of remote estimates of vegetation properties and is necessary to accurately assess energy, water, and nutrient flows [3], [4]. ...
... In addition to horizontal heterogeneity, the biochemical components of vegetation such as nitrogen, chlorophyll, and water present nonuniform vertical profiles due to their 3-D structures and vertical heterogeneity [1]- [3]. Monitoring the 3-D distribution of the biochemical parameters can improve the accuracy of remote estimates of vegetation properties and is necessary to accurately assess energy, water, and nutrient flows [3], [4]. Plant structural variables can complement biochemical information, thus comprehensively characterizing vegetation properties. ...
Hyperspectral light detection and ranging (LiDAR) (HSL) can be used to acquire backscattered full-waveform data with abundant spectral information, providing a new technique for the remote sensing of plant 3-D properties monitoring. HSL 3-D point cloud of two Kniphofia uvaria plants was acquired by HSL scanning and data processing. Then, chlorophyll concentration could be retrieved at any 3-D position using a constructed partial least squares regression (PLSR) model. At the same time, the vegetation canopy structural parameters (canopy height, maximum canopy width, and projected canopy area) were retrieved based on the geometric information of the HSL data. The results of this study showed that the HSL system could be used to realize the simultaneous extraction of both the 3-D biochemical and structural parameters, which has great potential in the field of quantitative remote sensing.
... Plants with a shortage of Chl show deficiencies in growth and carbon fixation. Remote sensing has become an efficient means of biochemical content detection and a standard for its nondestructive measurement [2]. The vertical distribution of vegetation properties has gained an increasing attention because plants generally have vertical heterogeneity, and this property affects the reflectance observed by remote sensing [2], [3]. ...
... Remote sensing has become an efficient means of biochemical content detection and a standard for its nondestructive measurement [2]. The vertical distribution of vegetation properties has gained an increasing attention because plants generally have vertical heterogeneity, and this property affects the reflectance observed by remote sensing [2], [3]. Besides, the lower leaves of crops with insufficient nutrition turn yellow before higher ones [4] . ...
The detection of vertical heterogeneity in vegetation has attracted an increasing attention as it has a great significance for precise agriculture. The hyperspectral light detection and ranging (LiDAR) (HSL) can obtain the spectral and spatial information simultaneously. However, its ability to monitor the vertical distribution of biochemical parameters in plants has not been fully explored. In this article, the applicability of empirical ratio and normalized spectral indices for HSL channels in chlorophyll (Chl) detection was investigated using three data sets: the PROSPECT-5 synthetic data set, the ANGERS public data set, and an HSL-measured data set. A linear regression model of the best performing index against measured Chl values was constructed so as to build 3-D Chl point clouds of maize. The performance of HSL in Chl detection at the upper and lower layers was also tested based on the selected spectral index. The result showed that the CIred edge index was most compatible with the HSL channels. The estimated Chl concentrations of the upper and lower layers showed the close relationships with HSL measurements (R² = 0.73 and 0.91, respectively). The vertical Chl profiles in maize were also presented, indicating that the HSL system has a strong ability to monitor the vertical distribution of maize Chl concentrations. This article provides a basis for the vertical detection of vegetation biochemical parameters directly from HSL measurements.
... The conventional approach of sampling foliar material exclusively from the sunlit upper canopy has recently become a contentious approach in remote sensing vegetation canopies. Recent studies demonstrate that the vertical heterogeneity in leaf chlorophyll, water and dry matter content have a significant effect on canopy reflectance measured by remote sensing instruments Yang et al., 2017;Zhao et al., 2017). The vertical heterogeneity in leaf traits is known to affect reabsorption and scattering of radiation within vegetation canopies, and subsequently, the top of canopy reflectance measured by remote sensing instruments (Verhoef and Bach, 2007). ...
... The traditional and widely accepted approach of sampling foliar material exclusively from the sunlit upper canopy for remote sensing canopy traits has lately been subjected to scrutiny. Recent studies have also demonstrated that the vertical heterogeneity in leaf traits is a source of variation in canopy reflectance measured by remote sensing instruments Zhao et al., 2017). Canopy traits expression has a strong implication in earth system models such as the Community Land Model that require accurate characterization of key input parameters such as LMA and foliar nitrogen. ...
Understanding spatial and temporal dimension of leaf traits is key in monitoring ecosystem function, processes and services. Plant traits provides an insight in improved understanding of ecosystem services across biomes. Tracking changes in foliar nutrient content within the earth system is vital in assessing the effects and adaptation capacity of vegetation communities to climate change. Remote sensing provides a cost effective and practical means of charactering plants traits from spectra over large spatial extents.
We sought out to understand the role of vertical heterogeneity in leaf traits across canopy in estimating foliar traits using in-situ hyperspectral measurements and Sentinel-2. Results presented in this thesis demonstrated that leaf spectral reflectance mirror variation in trait content across canopy. Leaf spectral reflectance shifted to longer wavelengths in the 'red edge' spectrum (685 - 701 nm) in the order of lower > middle > upper canopy positions. Key wavebands that enhance leaf samples discrimination have been reported to be sensitive to variation in chlorophyll, EWT, N, carbon and SLA. These leaf traits exhibited significant variation across the canopy vertical profile.
Our results at field level showed that reflectance spectra of leaf samples collected from the lower canopy matched PROSPECT simulated reflectance spectra better compared to reflectance spectra measured from upper canopy across the growing season. Leaf chlorophyll and Equivalent Water Thickness for leaf samples collected from the lower canopy were retrieved with higher accuracy compared to leaf samples collected from the upper canopy. This observation imply that variation in leaf biochemistry and morphology through the canopy vertical profile potentially affects the performance of the PROSPECT model.
Results obtained using in-situ canopy hyperspectral measurements and simulated Sentinel-2 data showed that leaf-to-canopy upscaling approaches that consider the contribution of leaf traits from the exposed upper canopy layer together with the shaded middle canopy layer yield significantly (p < 0.05) lower error as well as high explained variance (R2 > 0.71) in the estimation of canopy leaf mass per area, nitrogen and carbon. At landscape level, canopy leaf mass per area, nitrogen and carbon estimated based on the weighted canopy expression yielded stronger correlations and higher prediction accuracy from Sentinel-2 MSI data compared to the top-of-canopy traits expression across all seasons. This observation imply that remote sensing instruments sense leaf traits beyond the sunlit upper canopy. These results have a strong implication in modelling leaf traits using remote sensing. We also demonstrated the capability of the newly launched Sentinel-2 to map seasonal changes in leaf traits at landscape level.
This thesis demonstrated the importance of vertical heterogeneity of leaf traits in estimating plants traits at leaf, canopy and landscape level. We showed that incorporating the leaf traits content of foliage material from the shaded canopy improves the estimation accuracy of plants traits at canopy and landscape level using in-situ hyperspectral measurements in the laboratory and Sentinel-2 multispectral data at field level. We also demonstrate that the performance of the PROSPECT model and retrieval of chlorophyll, equivalent water thickness and leaf mass per area is likely to be affected by the leaf biochemistry and morphological changes through the vertical canopy profile over the growing season. These results are important in canopy reflectance modelling and retrieval of canopy traits for various application ranging from forestry to agriculture.
... It is well known that spectral light composition changes vertically within a canopy, becoming gradually less rich in the blue to red bands due to selective leaf absorption [25]. In addition, bands in the red edge region have been shown, using physically-based radiative transfer models, to have much higher penetration depth than green light inside the canopy [26]. Bands at longer wavelengths, e.g., in the SWIR, have an even higher canopy penetration, though they are not sensitive to chlorophyll. ...
... Because attenuation with depth by scattering is weaker than by absorption [25], a larger amount of light penetrates to and is backscattered from the lower layers. In addition, the light in the red edge bands has proved to have much deeper penetration inside the canopy than in the green bands [26]. Optimized spectral indices that combined the red edge and NIR bands in our study, therefore, were applicable to estimate leaf Chl content in the lower layers, rather than the same green spectral indices optimized in the upper leaf Chl assessment. ...
Monitoring the vertical profile of leaf chlorophyll (Chl) content within winter wheat canopies is of significant importance for revealing the real nutritional status of the crop. Information on the vertical profile of Chl content is not accessible to nadir-viewing remote or proximal sensing. Off-nadir or multi-angle sensing would provide effective means to detect leaf Chl content in different vertical layers. However, adequate information on the selection of sensitive spectral bands and spectral index formulas for vertical leaf Chl content estimation is not yet available. In this study, all possible two-band and three-band combinations over spectral bands in normalized difference vegetation index (NDVI)-, simple ratio (SR)- and chlorophyll index (CI)-like types of indices at different viewing angles were calculated and assessed for their capability of estimating leaf Chl for three vertical layers of wheat canopies. The vertical profiles of Chl showed top-down declining trends and the patterns of band combinations sensitive to leaf Chl content varied among different vertical layers. Results indicated that the combinations of green band (520 nm) with NIR bands were efficient in estimating upper leaf Chl content, whereas the red edge (695 nm) paired with NIR bands were dominant in quantifying leaf Chl in the lower layers. Correlations between published spectral indices and all NDVI-, SR- and CI-like types of indices and vertical distribution of Chl content showed that reflectance measured from 50°, 30° and 20° backscattering viewing angles were the most promising to obtain information on leaf Chl in the upper-, middle-, and bottom-layer, respectively. Three types of optimized spectral indices improved the accuracy for vertical leaf Chl content estimation. The optimized three-band CI-like index performed the best in the estimation of vertical distribution of leaf Chl content, with R² of 0.84–0.69, and RMSE of 5.37–5.56 µg/cm² from the top to the bottom layers, while the optimized SR-like index was recommended for the bottom Chl estimation due to its simple and universal form. We suggest that it is necessary to take into account the penetration characteristic of the light inside the canopy for different Chl absorption regions of the spectrum and the formula used to derive spectral index when estimating the vertical profile of leaf Chl content using off-nadir hyperspectral data.
... However, in reality, canopies generally exhibit large vertical heterogeneity of both biophysical and biochemical properties (Dreccer et al., 2000;Valentinuz and Tollenaar, 2004;Ciganda et al., 2008). Vertical heterogeneity of chlorophyll and leaf water has been found in winter wheat (Liu et al., 2015;Zhao et al., 2017), corn (Ciganda et al., 2008) and beech tree (Wang and Li, 2013). A multi-layer structure of vegetation canopies is very common, for example, forests with a grass or bush layer, field crops with a weed layer and vegetation in the senescent stage (Kuusk, 2001;Verhoef and Bach, 2007;Ciganda et al., 2008;Liu et al., 2015). ...
... The modelled results from the six synthetic scenarios showed noticeable effect of vertical heterogeneity on canopy TOC reflectance, which confirms findings in previous modelling studies (Kuusk, 2001;Widlowski et al., 2007;Wang and Li, 2013;Zhao et al., 2017). Furthermore, significant effect of canopy heterogeneity on simulated TOC fluorescence and photosynthesis was demonstrated in this study (Fig. 3c, 3d). ...
... However, due to the limited penetration capability of optical remote sensing techniques, the contribution of crop stems to the canopy spectrum is far less than that of leaves under vertical observation conditions in cases of high canopy coverage [21,22]. As a consequence, the canopy spectrum is mainly derived from the contribution of the canopy leaf [23]. In general, leaf dry biomass (LDB) is defined as the product of the leaf dry matter content and the leaf area index. ...
The timely and precise estimation of stem biomass is critical for monitoring the crop growing status. Optical remote sensing is limited by the penetration of sunlight into the canopy depth, and thus directly estimating winter wheat stem biomass via canopy spectra remains a difficult task. There is a stable linear relationship between the stem dry biomass (SDB) and leaf dry biomass (LDB) of winter wheat during the entire growth stage. Therefore, this study comprehensively considered remote sensing and crop phenology, as well as biomass allocation laws, to establish a novel two-component (LDB, SDB) and two-parameter (phenological variables, spectral vegetation indices) stratified model (Tc/Tp-SDB) to estimate SDB across the growth stages of winter wheat. The core of the Tc/Tp-SDB model employed phenological variables (e.g., effective accumulative temperature, EAT) to correct the SDB estimations determined from the LDB. In particular, LDB was estimated using spectral vegetation indices (e.g., red-edge chlorophyll index, CIred edge). The results revealed that the coefficient values (β0 and β1) of ordinary least squares regression (OLSR) of SDB with LDB had a strong relationship with phenological variables. These coefficient (β0 and β1) relationships were used to correct the OLSR model parameters based on the calculated phenological variables. The EAT and CIred edge were determined as the optimal parameters for predicting SDB with the novel Tc/Tp-SDB model, with r, RMSE, MAE, and distance between indices of simulation and observation (DISO) values of 0.85, 1.28 t/ha, 0.95 t/ha, and 0.31, respectively. The estimation error of SDB showed an increasing trend from the jointing to flowering stages. Moreover, the proposed model showed good potential for estimating SDB from UAV hyperspectral imagery. This study demonstrates the ability of the Tc/Tp-SDB model to accurately estimate SDB across different growing seasons and growth stages of winter wheat.
... The brown pigment content, which has a minimal impact on the spectral reflectance [44], was set to 0. The range values for leaf chlorophyll content were obtained from field measurements conducted during different wheat growth stages. To represent realistic cases, the leaf structure index, leaf carotenoid content, leaf equivalent water thickness, and leaf dry matter content were derived from related studies [45,46]. ...
Quantifying the vertical distribution of leaf chlorophyll content (LCC) is integral for a comprehensive understanding of the physiological status and function of winter wheat crops, having significant implications for crop management and yield optimization. In this study, we investigated the vertical LCC trait of winter wheat during two consecutive field growth seasons using proximal multispectral imaging measurements to evaluate vertical variations of LCC within winter wheat canopies. The results revealed the non-uniform vertical LCC distribution varied across the entire growth season. The effects of nitrogen fertilization rate on LCC among vertical layers increased gradually from upper to lower layers of canopy. To enhance LCC prediction accuracy, this study proposes a deep transfer learning network model for leaf trait estimation (LeafTNet). It integrates the advantages of physical radiative transfer simulations with deep neural network through transfer learning. The results demonstrate that the LeafTNet achieved remarkable predictive performance and strong robustness. Furthermore, the proposed method exhibits superior estimation accuracy compared to empirical statistical method and traditional machine learning method. This study highlights the performance of LeafTNet in accurately and efficiently quantifying LCC from proximal multispectral data, which provide technical support for the estimation of the vertical distribution of leaf traits and improve crop management.
... Since the 3D radiative transfer model takes into account the multi-scale canopy structure, it is more suitable for the radiative transfer simulation of heterogeneous canopy with a large number of shadows and branching changes [7]. Zhao et al. used the multiple-layer canopy reflectance model (MRTM) to simulate the vertical distribution of spike characteristics and leaf characteristics of winter wheat canopy reflectance [8]. Malenovsky et al. successfully used DART to invert leaf chlorophyll content from imaging spectra [9]. ...
The canopy of perennial evergreen fruit trees in southern China has a unique Bidirectional Reflectance Factor (BRF) due to its complex multi-branch structure and density changes. This study aimed to address the lack of clarity regarding the changes in BRF of evergreen fruit trees in southern China. Litchi, a typical fruit tree in this region, was chosen as the subject for establishing a three-dimensional (3D) real structure model. The canopy BRF of litchi was simulated under different leaf components, illumination geometry, observed geometry, and leaf area index (LAI) using a 3D radiation transfer model. The corresponding changes in characteristics were subsequently analyzed. The findings indicate that the chlorophyll content and equivalent water thickness of leaves exert significant influences on canopy BRF, whereas the protein content exhibit relatively weak effects. Variation in illumination and observation geometry results in the displacement of hotspots, with the solar zenith angle and view zenith angle exerting significant influence on the BRF. As the LAI of the litchi orchard increases, the distribution of hotspots becomes more concentrated, and the differences in angle information are relatively smaller when observed from multiple angles. With the increase in LAI in litchi orchards, the BRF on the principal plane would be saturated, but observation at hotspots could alleviate this phenomenon. The above analysis provides a reference for quantitative inversion of vegetation parameters using remote sensing monitoring information of typical perennial evergreen fruit trees.
... The security mechanisms that are already in place, meanwhile, could be too costly and resource-intensive for IoT devices. This security architecture is still being used, despite the fact that it is not recommended for use with Internet of Things devices (Hu et al.2016;Tang et al. 2020;Pal et al. 2022;Zhao et al. 2016). This makes the use of privacy and safety measures that are both light and distributed is necessary for intelligent farming. ...
Smart agriculture has become one of the most popular technologies for farmers due to its simplicity, ease of deployment, high efficiency, and low overheads. But due to an exponential increase in smart-farming data generation, it is necessary to design secure storage interfaces, that can be scaled for multiple farms. Existing storage models either showcase high security, or high storage efficiency, but a very few models enhance both these parameter sets. Such models are highly complex, and reduce the scalability when applied to large-scale scenarios. To overcome these limitations, this text proposes design of a highly efficient and secure agriculture-record-storage model via reconfigurable blockchains. The proposed model initially uses a multiple crop pattern prediction system via Binary Cascaded Convolutional Neural Network (BC CNN), and deploys a single chained Proof-of-Trust (PoT) based blockchain, that is tuned w.r.t. context of the farms. The prediction is done via weather conditions and soil types. This assists in identification of different crop types, and selection of high trust miner nodes, that can preserve privacy during communication and storage operations. As the blockchain is scaled, a Grey Wolf Optimization (GWO) based model is deployed, which assists in splitting the underlying chain into multiple sidechains. This split is done based on QoS and Security optimizations, which is estimated via temporal miner performance under different farm types. The GWO Model also assists in estimating long-term and high-capacity storage chains, which can be used for archival operations. Due to which, the proposed model is able to improve mining speed by 9.4%, while reducing the energy consumption by 3.5% for different mining operations. The model also defines an indexing strategy for different shards, which assists in increasing data access speed by 12.8% for long-term data sets. Due to these enhancements, the proposed model is capable of deployment for large-scale scenarios.
... Turbid medium RTMs represent a canopy as a single layer with absorbing and scattering particles dissolved uniformly in it. Thus, the alignment of leaves in vertically-structured layers with different optical properties found in winter wheat cannot be fully represented (Zhao et al., 2017). Significant changes in trait retrieval accuracy might only be possible using more sophisticated models, such as RTMs with multiple leaf layers (Verhoef and Bach, 2007) or three-dimensional RTMs (Jiang et al., 2022). ...
Timely knowledge of phenological development and crop growth is pivotal for evidence-based decision making in agriculture. We propose a near real-time approach combining radiative transfer model inversion with physiological and phenological priors from multi-year field phenotyping. Our approach allows to retrieve Green Leaf Area Index (GLAI), Canopy Chlorophyll Content (CCC) and hence Leaf Chlorophyll Content (Cab) from Sentinel-2 optical satellite imagery to quantify winter wheat growth conditions in a physiologically sound way. Phenological macro stages are based on accumulated growing degree day thresholds obtained from multi-year field phenotyping covering more than 2400 ratings from roughly 300 winter wheat varieties and reflect important physiological transitions. These include the transition from vegetative to reproductive growth and the onset of flowering, which is important information for agricultural decision support. Validation against a large data set of on-farm trials in Switzerland collected in 2019 and 2022 revealed high accuracy of our approach that produced spatio-temporally consistent results. Phenological macro stages were predicted for 970 Sentinel-2 observations reaching a weighted F1-score of 0.96. Sentinel-2 derived GLAI and CCC explained between 77 to 84% and between 79 to 84% of the variability in in-situ measurements, respectively. Here, the incorporation of phenological priors clearly increased trait retrieval accuracy. Besides, this work highlights that physiological priors, e.g., obtained by field phenotyping, can help enhancing landscape scale observations and hold potential to advance the retrieval of remotely sensed vegetation traits and in-season phenology.
... Vertical leaf area profiles (LAP) impact light interception (Ciganda et al. 2012;Gitelson et al. 2014b), radiation use efficiency (RUE) (Cabrera-Bosquet et al., 2016;Gitelson et al. 2021), nutrient transport (Parker et al. 2001) and primary production (Gitelson et al. 2014a). Leaf area dynamics monitoring could guide nutritional management (Singh et al. 2006) and canopy phenotyping studies (Zhao et al. 2016). From a breeding point of view, breeders improved RUE and increased crop yield by optimizing leaf area distribution (Drewry et al. 2014;Li et al. 2021). ...
Leaf area profiles (LAP) represent the green leaf area per unit ground area distributed with the vertical leaf layer, which is a key trait for guiding nutrition diagnosis, crop management and crop breeding. However, passive mono-angle optical sensors don't have direction information on vertical LAP, which makes spectral remote sensing can't capture the canopy-scale vertical leaf area information from top to bottom. To meet this challenge, we present a modeling framework to decipher maize vertical LAP from spectral imagery data. It first employed a hybrid method to derive LAI from the spectral imagery, and then configured a bell-shaped function to decipher the vertical LAP. We conducted a five-year field experiment in critical growth stages to test the ability of the proposed method. Results showed great disagreements between vegetative and reproductive stages. Such differences impacted the leaf area development and the largest leaf layer for LAP modeling. The proposed method considered these two phenological stage to improve the LAP estimation. The performance of this method was assessed by comparing the derived vertical LAP with measurements over different planting years and maize grain production fields. Results showed robust canopy-level modeling for vertical LAP (RMSE = 0.083-0.094 m 2 /m 2). This study highlights that this method extends the ability of passive optical remote sensing to derive vertical information. This method is a valuable and effective remote-sensing approach for deriving vertical LAP over maize canopy scale, also has potential reference value for other vegetation with similar vertical structure.
... Two completely expanded leaves were selected arbitrarily from the middle part of a single plot and a total of 6 leaves were taken from the 3 replications. LA (Leaf area) was determined by CI-202 Leaf area meter as cited in Zhao et al. (2016). Then, dry weight estimation was done by digital sensitive balance after leaves were dried for 24 hours at 70 0 C. Finally, SLA was estimated as leaf area (cm 2 )/Leaf dry weight (g). ...
Maize (Zea mays L.) variety has diverse genetic characters; hence this study was led to assess drought tolerance of maize variety subjected to well water and water stress environments. Drought is one of major factor that results thoughtful influence plant development as well as productivity. The outcome becomes severe where rain fed agriculture is practiced. Evaluation of variety that can better tolerate such condition can help for selection and are important in reducing associated crop loss. The study was performed in greenhouse using CRD with three replications. Six maize varieties MH-140, Melkassa-2 (MH-2), Melkassa-3 (MH-3), Melkassa-4 (MH-4), Melkassa-6Q and MHQ138 developed by Melkassa Agricultural Research Center (MARC) were used and grown in normal and water scarce condition. The collected data were leaf proline, chlorophyll content, soluble sugar content, relative water content, leaf nitrogen content, protein content specific leaf area, specific leaf weight. Data were analyzed by ANOVA using SAS 9.4 software. Mean comparisons were implemented using LSD test at P < 0.01. The study outcomes revealed that high SLA and SLW value was recorded for, Melkassa-4 (MH-4) whereas, MHQ138 is recorded lower value. Leaf proline, chlorophyll content, soluble sugar content, relative water content, leaf nitrogen (N) content and protein content, value had revealed Melkassa-4 the most drought tolerant variety whereas MHQ138 is the least drought tolerant variety among the maize varieties used in this research study.
... Due to the transferability of N (Wang et al., 2005), the vertical distribution of N within the plant canopy is non-uniform (Li et al., 2015;Hikosaka et al., 2016;Zhao et al., 2017aZhao et al., ,2017bYe et al., 2018;Gara et al., 2018;Li et al., 2018). Under sufficient N fertilizer supply, plants tend to adjust the distribution of N to maximize the photosynthetic efficiency of the canopy (Dreccer et al., 2000;Bertheloot et al., 2012;D'Odorico et al., 2019). ...
Understanding the effect of nitrogen (N) vertical distribution within the rice canopy at different growth stages and development of critical nitrogen dilution curves for upper leaf layers will not only help accurate estimations of the spatial and temporal variation in N status, but will also provide a basis for analyses of N nutrition in rice. Based on a three-year field experiment with 2 rice varieties, 3 N levels and 4 planting densities, the effect of heterogeneity in N vertical distribution on the nitrogen nutrition index (NNI) was then determined, and for the first time, critical N dilution curves of upper leaf layers were constructed based on a Bayesian statistical model. The relationship for NNI entire canopy and its relationship with the other tested leaf layers was investigated. The leaf layer suitable for analyses of N nutrition and directly inversion of NNI by remote sensing is determined. The results revealed that the main contributor to plant N concentration (PNC) of entire canopy (PNCCanopy) was found to be the lower leaf layer of the rice canopy, and may lead to misdiagnosis under the non-N-limiting condition by using the canopy level based NNI. The PNC of top half leaf layer of the canopy was more stable in terms of short-term environmental changes, and more accurately representing the overall N status. The top half leaf layer was the main contributor to canopy reflectance. Thus, compared with the NNI of other upper leaf layers, the NNI of the top half leaf layer is more suitable for direct inversion by using CI (1113, 743) with the R² = 0.70. Overall, these findings suggest that the NNI of the top half leaf layer is the optimal remote sensing indicator of N nutrition in rice.
... The interrelationship of vertical leaf N distribution within the canopy was simulated by a statistical method and used for the estimation of leaf N content for the whole canopy. In addition, a physically based multiple-layer canopy reflectance model was proposed by Wang et al. [21], and was successfully tested in depicting vertical profiles of leaf variables for winter wheat [26]. They suggested that the penetration characteristics and sensitivity of spectral bands used in the spectral indices should also be considered. ...
Monitoring vertical profile of leaf water content (LWC) within wheat canopies after head emergence is vital significant for increasing crop yield. However, the estimation of vertical distribution of LWC from remote sensing data is still challenging due to the effects of wheat spikes and the efficacy of sensor measurement from the nadir direction. Using two-year field experiments with different growth stages after head emergence, N rates, wheat cultivars, we investigated the vertical distribution of LWC within canopies, the changes of canopy reflectance after spikes removal, the relationship between spectral indices and LWC in the upper-, middle- and bottom-layer. The interrelationship among vertical LWC were constructed, and four ratio of reflectance difference (RRD) type of indices were proposed based on the published WI and NDWSI indices to determine vertical distribution of LWC. The results indicated a bell shape distribution of LWC in wheat plants with the highest value appeared at the middle layer, and significant linear correlations between middle-LWC vs. upper-LWC and middle-LWC vs. bottom-LWC (r ≥ 0.92) were identified. The effects of wheat spikes on spectral reflectance mainly occurred in near infrared to shortwave infrared regions, which then decreased the accuracy of LWC estimation. Spectral indices at the middle layer outperformed the other two layers in LWC assessment and were less susceptible to wheat spikes effects, in particular, the newly proposed narrow-band WI-4 and NDWSI-4 indices exhibited great potential in tracking the changes of middle-LWC (R2 = 0.82 and 0.84, respectively). By taking into account the effects of wheat spikes and the interrelationship of vertical LWC within canopies, an indirect induction strategy was developed for modeling the upper-LWC and bottom-LWC. It was found that the indirect induction models based on the WI-4 and NDWSI-4 indices were more effective than the models obtained from conventional direct estimation method, with R2 of 0.78 and 0.81 for the upper-LWC estimation, and 0.75 and 0.74 for the bottom-LWC estimation, respectively.
... Winter wheat leaves are sparse in the early stages therefore the LAI and CCD are relatively low, then the leaves grow larger and turn green which leads to an increase in parameter values. The yellow and senescent leaves come after the emergence of stems and spikes which result in a decrease of the parameters (Zhao et al., 2016). To better assess the growth conditions of crops, it is crucial to estimate variable dynamics accurately at different development stages. ...
Leaf area index (LAI) and canopy chlorophyll density (CCD) are key indicators of crop growth status. In this study, we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth stages. The indices were calculated with Sentinel-2 MSI data and hyperspectral data. Their performances were validated against ground measurements using R², RMSE, and bias. The results suggest that indices computed with hyperspectral data exhibited higher R² than multispectral data at the late jointing stage, head emergence stage, and filling stage. Furthermore, red-edge modified indices outperformed the traditional indices for both data genres. Inversion models indicated that the indices with short red-edge wavelengths showed better estimation at the early jointing and milk development stage, while indices with long red-edge wavelength estimate the sought variables better at the middle three stages. The results were consistent with the red-edge inflection point shift at different growth stages. The best indices for Sentinel-2 LAI retrieval, Sentinel-2 CCD retrieval, hyperspectral LAI retrieval, and hyperspectral CCD retrieval at five growth stages were determined in the research. These results are beneficial to crop trait monitoring by providing references for crop biophysical and biochemical parameters retrieval.
... Effective and accurate mapping tools of crop assessment with precision location information is hence the key and essential approaches [24]. The information from wheat canopy has been implemented to study precision agriculture questions to evaluate indices about crop growth status across the cultivation zones, such as yellow rust and fusarium head blight [25], chlorophyll fluorescence and nitrogen nutrition [26], canopy temperature [27], spikes in wheat canopies, and different vertical distributions of leaf properties [28]. For canopy estimation using proximal plant sensing, instruments are placed within 2 m of the targets [29], for example, on a ground-based mobile platform, in order to provide a rapid and reliable signal that can be used in creating accurate near-surface maps [30]. ...
Estimation of plant canopy using low-altitude imagery can help monitor the normal growth status of crops and is highly beneficial for various digital farming applications such as precision crop protection. However, extracting 3D canopy information from raw images requires studying the effect of sensor viewing angle by taking into accounts the limitations of the mobile platform routes inside the field. The main objective of this research was to estimate wheat (Triticum aestivum L.) leaf parameters, including leaf length and width, from the 3D model representation of the plants. For this purpose, experiments with different camera viewing angles were conducted to find the optimum setup of a mono-camera system that would result in the best 3D point clouds. The angle-control analytical study was conducted on a four-row wheat plot with a row spacing of 0.17 m and with two seeding densities and growth stages as factors. Nadir and six oblique view image datasets were acquired from the plot with 88% overlapping and were then reconstructed to point clouds using Structure from Motion (SfM) and Multi-View Stereo (MVS) methods. Point clouds were first categorized into three classes as wheat canopy, soil background, and experimental plot. The wheat canopy class was then used to extract leaf parameters, which were then compared with those values from manual measurements. The comparison between results showed that (i) multiple-view dataset provided the best estimation for leaf length and leaf width, (ii) among the single-view dataset, canopy, and leaf parameters were best modeled with angles vertically at −45° and horizontally at 0° (VA −45, HA 0), while (iii) in nadir view, fewer underlying 3D points were obtained with a missing leaf rate of 70%. It was concluded that oblique imagery is a promising approach to effectively estimate wheat canopy 3D representation with SfM-MVS using a single camera platform for crop monitoring. This study contributes to the improvement of the proximal sensing platform for crop health assessment.
... Substantial differences exist in the reflection and scattering of electromagnetic waves in different canopy layers due to different light conditions of the leaves. Leaves in different layers have different contributions to the canopy hyperspectral reflectance, affecting the remote sensing assessments of the crop's biochemical properties (e.g., LCC) [27]. It is crucial to define the contribution of the leaves in different layers to the canopy's spectral reflectance to increase the accuracy of estimating the vertical distribution of the LCC. ...
Heterogeneity exists in the vertical distribution of the biochemical components of crops. A leaf chlorophyll deficiency occurs in the bottom- and middle-layers of crops due to nitrogen stress and leaf senescence. Some studies used multi-angular remote sensing data for estimating the vertical distribution of the leaf chlorophyll content (LCC). However, these studies performed LCC inversion of different vertical layers using a fixed view zenith angle (VZA), but rarely considered the contribution of the components of the non-target layers to the spectral response. The main goal of this work was to determine the LCC of different vertical layers of the canopy of winter wheat (Triticum aestivum L.), using multi-angular remote sensing and spectral vegetation indices. Different combinations of VZAs were used for obtaining the LCC of different layers. The results revealed that the responses of the transformed chlorophyll in reflectance absorption index (TCARI) and modified chlorophyll absorption in reflectance index (MCARI)/optimized soil-adjusted vegetation index (OSAVI) to the upper-layer LCC were strongest at VZA 10°. For the middle-layer LCC, the response was strongest at 30°, but the response was significantly lower than that of the upper-layer. For the bottom-layer LCC, the responses were weak due to the obscuring effect of the upper- and middle-layer; thus, the LCC inversion of the bottom-layer data was not optimal for a single VZA. The optimal VZA or VZA combinations for LCC estimation were VZA 10° for the upper-layer LCC (TCARI with coefficient of determination (R2) = 0.69, root mean square error (RMSE) = 4.80 ug/cm2, MCARI/OSAVI with R2 = 0.73, RMSE = 4.17 ug/cm2), VZA 10° and 30° for the middle-layer LCC (TCARI with R2 = 0.17, RMSE = 4.81 ug/cm2, MCARI/OSAVI with R2 = 0.17, RMSE = 4.76 ug/cm2), and VZA 10°, 30°, and 50° for the bottom-layer LCC (TCARI with R2 = 0.40, RMSE = 6.29 ug/cm2, MCARI/OSAVI with R2 = 0.40, RMSE = 6.36 ug/cm2). The proposed observation strategy provided a significantly higher estimation accuracy of the target layer LCC than the single VZA approach, and demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the wheat canopy chlorophyll content vertical distribution.
... Remote Sens. 2020, 12, 3534 2 of 27 environment assessment, and understanding how to more accurately estimate this index has become a popular topic in the field of quantitative remote sensing [3,4,[7][8][9][10][11][12][13]. ...
The leaf area index (LAI) is an essential indicator used in crop growth monitoring. In the study, a hybrid inversion method, which combined a physical model with a statistical method, was proposed to estimate the crop LAI. The simulated compact high-resolution imaging spectrometer (CHRIS) canopy spectral crop reflectance datasets were generated using the PROSAIL model (the coupling of PROSPECT leaf optical properties model and Scattering by Arbitrarily Inclined Leaves model) and the CHRIS band response function. Partial least squares (PLS) was then used to reduce the dimension of the simulated spectral data. Using the principal components (PCs) of PLS as the model inputs, the hybrid inversion models were built using various modeling algorithms, including the backpropagation artificial neural network (BP-ANN), least squares support vector regression (LS-SVR), and random forest regression (RFR). Finally, remote sensing mapping of the CHRIS data was achieved with the hybrid model to test the inversion accuracy of LAI estimates. The validation result yielded an accuracy of R2 = 0.939 and normalized root-mean-square error (NRMSE) = 6.474% for the PLS_RFR model, which indicated that the crops LAI could be estimated accurately by using spectral feature extraction and a hybrid inversion strategy. The results showed that the model based on principal components extracted by PLS had a good estimation accuracy and noise immunity and was the preferred method for LAI estimation. Furthermore, the comparative analysis results of various datasets showed that prior knowledge could improve the precision of the retrieved LAI, and using this information to constrain parameters (e.g., chlorophyll content or LAI), which make important contributions to the spectra, is the key to this improvement. In addition, among the PLS, BP-ANN, LS-SVR, and RFR methods, RFR was the optimal modeling algorithm in the paper, as indicated by the high R2 and low NRMSE in various datasets.
... The red-edge chlorophyll index RMI was calculated to quantitatively estimate the chlorophyll content. The RMI distribution results are shown in figure 4. On the whole, RMI presented a decreasing tendency from top to bottom in a rice plant, although abnormal behaviour occurred for some rice varieties, which was found to be consistent with the known vertical distribution of leaf nitrogen content [44,45]. Besides, with the increase of applied nitrogen levels, the chlorophyll content gradient was somewhat decreasing. ...
Nitrogen is one of the most important nutrient indicators for the growth of crops, and is closely related to the chlorophyll content of leaves and thus influences the photosynthetic ability of the crops. In this study, five hybrid rice varieties were cultivated during one entire growing period in one experimental field supplied with six nitrogen fertilizer levels. Visible and near infrared (vis/NIR) reflectance spectroscopy combined with multivariate analysis was used to identify hybrid rice varieties and nitrogen fertilizer levels, as well as to detect chlorophyll content associated with nitrogen levels. The support vector machine (SVM) algorithm was applied to identify five varieties of hybrid rice and six levels of nitrogen fertilizer. The results demonstrated that different varieties of hybrid rice for each nitrogen level can be well distinguished except for the highest nitrogen level, and no nitrogen level for each rice variety can be completely identified from the other five nitrogen levels. Further, 12 spectral indices combined with partial least square (PLS) analysis were applied for estimating chlorophyll content of rice leaves from plants subjected to different nitrogen levels, and a root mean square error of cross-validation (RMSECV) of 0.506, a coefficient of determination (R2) of 97.8% and a ratio of performance to deviation (RPD) of 4.6 for all rice varieties indicated this as a preferable procedure. This study demonstrates that Vis/NIR spectroscopy can have a great potential for identification of rice varieties and evaluation of nitrogen fertilizer levels.
... 1,2 Vegetation usually presents a three-dimensional (3-D) structure, and biochemical contents vary greatly in the vertical direction. 3,4 Monitoring vertical distribution of vegetation biochemical contents is significant for plant growth and carbon estimation. 5 But at the same time, it also puts forward new requirements for remote sensing technology. ...
... The conventional approach of sampling foliar material exclusively from the sunlit upper canopy has recently become a contentious approach in remote sensing vegetation canopies. Recent studies demonstrate that the vertical heterogeneity in leaf chlorophyll, water and dry matter content have a significant effect on canopy reflectance measured by remote sensing instruments Wang and Li, 2013;Zhao et al., 2017). The vertical heterogeneity in leaf traits is known to affect re-absorption and scattering of radiation within vegetation canopies, and subsequently, the top of canopy reflectance measured by remote sensing instruments (Verhoef and Bach, 2007). ...
Leaf traits and subsequently leaf spectral properties depend on the leaf phenological stage and light conditions within a canopy. The PROSPECT radiative transfer model has been extensively and successfully used to retrieve leaf traits for mature, sunlit leaves at peak vegetation growth, i.e. summer. However, research on the quanti-fication of leaf traits using PROSPECT across the canopy vertical profile throughout the growing season is still lacking. Therefore, this study aims at examining the effect of leaf position on the performance of the PROSPECT model in modelling leaf optical properties and retrieving leaf chlorophyll content (C ab), equivalent water thickness (EWT), and leaf mass per area (LMA) throughout the growing season. To achieve this objective, we collected 588 leaf samples from the upper and lower canopies of deciduous stands over three seasons (i.e., spring, summer and autumn) in Bavaria Forest National Park, Germany. Leaf traits including C ab , EWT and LMA, were measured for all the samples, and their reflectance spectra were obtained using an ASD FieldSpec-3 Pro FR spectroradiometer coupled with an Integrating Sphere. We initially assessed the performance of the PROSPECT model by comparing reflectance spectra generated in forward mode against reflectance spectra measured on leaf samples collected in the field. We subsequently inverted the PROSPECT model to retrieve C ab , EWT and LMA using the look-up-table (LUT) approach. Our results consistently demonstrated that the measured reflectance of leaf samples collected from the lower canopy had a stronger match with PROSPECT simulated reflectance spectra, especially in the NIR spectrum compared to leaf samples collected from the upper canopy throughout the growing season. This observation concurred with the pattern of C ab and EWT retrieval accuracies across the canopy i.e. the retrieval accuracy for the lower canopy was consistently higher (NRMSE = 0.1-0.2 for C ab ; NRMSE = 0.125-0.16 for EWT) when compared to the upper canopy (NRMSE = 0.122-0.269 for C ab ; NRMSE = 0.162-0.0.258 for EWT) across all seasons. In contrast, LMA retrieval accuracies for the upper canopy (NRMSE = 0.146-0.184) were higher compared to the lower canopy (NRMSE = 0.162-0.239) for all seasons except for the spring season. For all the leaf traits examined in this study, the range in retrieval accuracy between the upper and lower canopy was greater in summer (compared to other seasons). We report for the first time that although the PROSPECT model provides reasonable retrieval accuracy of C ab , EWT and LMA, variations in leaf biochemistry and morphology through the vertical canopy profile affects the performance of the model over the growing season. Findings of this study have important implications on field sampling protocols and upscaling leaf traits to canopy and landscape level using multi-layered physical models coupled with PROSPECT.
... Different leaf layers make different contributions to the canopy hyperspectral reflectance, and, in turn, affect remote assessments of crop biochemical properties (e.g., LNC) (Jia et al., 2013;Zhao et al., 2017). It is crucial to define the contribution of specific leaf layers to the overall canopy reflectance spectra in order to increase the accuracy of the LNC estimation. ...
Timely estimation of the vertical heterogeneity of leaf nitrogen concentration (LNC) from canopy reflectance using hyperspectral sensing is important for precision N management during winter oilseed rape productivity. However, current research pays little attention to LNC assessments by only taking LNC's vertical distribution into consideration, leading to limited accuracy and reduced applied value of the results. The main goal of this work was to quantitatively define the contributions of LNC in different layers to winter oilseed rape canopy raw (R) hyperspectra and to its transformation technique (i.e., first derivative reflectance, FDR), and develop a monitoring model considering the vertical LNC gradient using spectral data. Two field experiments were conducted for two consecutive years (2015–2017) with different N rates, cultivars and growth stages. At seedling and budding stage, canopy hyperspectral reflectance and LNC were measured in situ. Canopies of each treatment were divided into three layers of equal vertical (upper, middle, lower). Partial least square (PLS), lambda-lambda r² (LL r²) and support vector machine (SVM) models were used to analyze the relationships between LNC in different layers and the hyperspectral reflectance measured from above the canopy. Field sampling revealed that a vertical distribution pattern of LNC existed, presenting an evident decline from the upper to lower layer. The FDR-PLS model for LNC prediction in different layers yielded a relatively higher accuracy compared to the R-PLS based on the full range hyperspectra, the coefficient of determination (r²val) was 0.872 for LNC in the upper layer, 0.903 in the middle layer, and 0.837 in the lower layer, with a relative percent deviation (RPD val) of 2.794, 3.052, and 2.328, respectively. Finally, seven (437, 565, 667, 724, 993, 1084 and 1189 nm), six (423, 570, 598, 659, 725 and 877 nm), and five bands (420, 573, 597, 667 and 718 nm) were identified as effective wavelengths for assessing the vertical LNC distribution in the upper, middle and lower layer, respectively. The newly-developed SVM-FDR regression model using the effective wavelengths also performed well for upper (r²val = 0.828, RPD val = 2.358), middle (r²val = 0.844, RPD val = 2.556), and lower (r²val = 0.781, RPD val = 2.029) layer LNC prediction. Our results indicate that estimation of LNC using hyperspectral reflectance data is most effective for the upper and middle layers of oilseed rape canopies. Moreover, the calibration model developed in this study has great potential to assess the N status of the whole oilseed rape canopy.
The accuracy of leaf nitrogen accumulation (LNA) estimation is often compromised by the vertical heterogeneity of crop nitrogen. In this study, an estimation model of LNA considering vertical heterogeneity of wheat was developed based on unmanned aerial vehicle (UAV) multispectral data and near-ground hyperspectral data, both collected at different view zenith angles (e.g., 0°, −30°, and −45°). Winter wheat plants were evenly divided into 3 layers from top to bottom, and LNA was obtained for the upper, middle, and lower leaf layers, as well as for various combinations of these layers (upper and middle, middle and lower, and the entire canopy, referred to as LNACanopy). The linear regression (LR) and random forest regression (RF) models were constructed to estimate the LNA for each individual leaf layer. Subsequently, models for estimating LNACanopy that considered the impact of vertical heterogeneity (namely, LR-LNASum and RF-LNASum) were established based on the relationships between LNACanopy and LNA in different leaf layers. Meanwhile, LNA models that did not consider the effect of vertical heterogeneity (LR-LNAnon and RF-LNAnon) were used for comparative validation. The validation datasets consisted of UAV-simulated data from hyperspectral reflectance and UAV-measured data. Results showed that LNASum models had markedly higher accuracy compared to LNAnon. The optimal scheme for estimating LNACanopy was the combination of the upper, middle, and lower layers based on the normalized difference red edge index. Among these models, RF-LNASum demonstrated higher accuracy than LR-LNASum, with a validation relative root mean square error of 19.3% and 17.8% for the UAV-measured and simulated dataset, respectively.
Crop growth models (CGMs) commonly simulates the response of photosynthetic rate to nitrogen (N) dynamic by calculating critical N concentration. However, critical N concentration makes it hard to describe the physiological effect of N dynamic on photosynthesis. Meanwhile, the effect of diffuse light on photosynthesis was limited in previous studies. In this study, we introduced a Two-leaf Photosynthetic Model Sensitive to Chlorophyll Content (TPMSCC) and coupled it with the crop growth model (WheatGrow) to enhance our understanding of how N dynamic and diffuse light affects photosynthesis. By coupling the Farquhar-von Caemmerer-Berry (FvCB) C3 photosynthesis model with a canopy radiative transfer model (PROSAIL), TPMSCC simulated the light interception of direct and diffuse light, and employed leaf chlorophyll content (LCC) to simulate the light absorption and electron transfer rate of leaves. Result showed that TPMSCC well simulated the light absorption of the wheat canopy and found the canopy photosynthetic rate benefited from the increase of diffuse radiation fraction (DRF) except for when the condition of a dense canopy at a high solar zenith angle was present. Research found the logarithmic and linear relationships of LCC to the initial light use efficiency (α) and the maximum photosynthetic rate (Amax), respectively, which followed the field measurements. Additionally, the optimized WheatGrow model outperforms its predecessor in describing the response of N application rate on photosynthesis.
Canopy scattering coefficient (CSC) is the ratio of bidirectional reflectance factor (BRF) to directional area scattering coefficient (DASF), and has been successfully applied to correct the effect of canopy structure. The key to calculate CSC is to calculate DASF, which is determined by the intercept b and slope k of the linear relationship between BRF and BRFλ(Ω) ωλ (the ratio of hyperspectral BRF in certain view direction to leaf albedo (ω λ). However, due to the limitation of multispectral bands, how to accurately calculate crop DASF during the whole growth period using multispectral UAV data is still an urgent problem to be solved. In this study, wheat canopy multi-angular (0 • , − 30 • , − 45 •) datasets including near-ground hyperspectral data, UAV multispectral data, and PROSAIL simulation data were obtained for three consecutive years. The DASF k-b model was proposed to estimate b and k based on multispectral sensors by relative accumulated growing degree days (RAGDD) and BRF. The previously developed DASF g-NIR model and vegetation index (VI) model were compared with DASF k-b model under different view angles (VZAs), to evaluate their performances in correcting canopy structural effect, estimating leaf nitrogen content (LNC) and leaf chlorophyll content (LCC) based on generalized additive model (GAM). The results showed that the hyperspectral band range suitable for estimating wheat DASF was 710-760 nm. Parameter b decreased with increased N application rates, and activated first and then inhibited with increased RAGDD; the tendency of k, DASF Hy were opposite. Compared with CSC g-NIR (calculated from DASF g-NIR model) and VIs, CSC k-b (calculated from DASF k-b model) had the best correction effect on canopy structure under different VZAs. The estimation accuracy of LNC and LCC using CSC k-b was improved compared with CSC g-NIR and VIs, with RRMSE values of 9.2% and 7.0%, respectively, and the recommended VZA was − 45 • for both models.
The vertical leaf nitrogen (N) distribution in summer maize is believed to be an important adaptive reaction to crop physiology and ecosystem functions. Therefore, it is of great significance to understand the vertical characteristics of leaf N concentration (LNC) across summer maize canopies for crop growth and production. However, the effect of vertical canopy position on LNC and subsequently leaf hyperspectral characteristics of the entire spectrum (350–2500 nm) for summer maize is poorly understood. The purpose of this work was to quantitatively study the effects of the N nutrition on vertical distribution of LNC, identify the sensitive layer and effective wavelength of leaves, and establish a in situ leaf spectrum monitoring model considering vertical distribution of LNC. Six N field trials were conducted for four consecutive years (2017–2020). In addition, the data of 30 farmers' conventional farmland management in 2020 were collected to check the robustness of the constructed optimal estimation model for LNC prediction. Leaf spectral measurements together with LNC were studied at three vertical leaf positions on the crop stem: upper, middle and lower. Spectral reflectance was processed by continuous wavelet transform (CWT); partial least square (PLS) was used to analyze the relationships between LNC of different layers and spectral reflectance. Field sampling indicated that LNC had a vertical distribution pattern and an evident decline from the upper to lower layer. CWT technique can significantly increase the prediction accuracy of LNC at different layers, and the best decomposition scale is CWT-1. The CWT-1-PLS model for predicting vertical LNC distribution had achieved relatively higher accuracy than that based on the full range of the raw hyperspectral reflectance (R), the LNC determination coefficient (R²val) of the validation dataset was 0.832, 0.857 and 0.811 for the upper, middle and lower layer, and the relative percentage deviations (RPDval) were 2.444, 2.432, and 2.181, respectively. Eventually, ten bands were selected as the effective wavelengths for predicting the vertical LNC distribution in the upper, middle and lower layer, respectively. Newly developed CWT-1-PLS model using the effective wavelengths for LNC prediction in different layers also performed well (RPDval>2.0) based on the field experiments validation. Moreover, the validation at the farmers’ fields also showed fine precision for upper (RPDval=2.012), middle (RPDval=2.137) and lower (RPDval=1.881) layer LNC prediction. These results are of great significance for the study of summer maize leaf reflectance modelling, especially for the studies of integrating hyperspectral measurements and leaf traits data.
Normalized Difference Vegetation Index (NDVI) time series data are used by agricultural agencies for many essential operational crop monitoring programmes. But optical sensors often miss key growth stages due to cloud cover interference, impacting the performance of operational activities. Although the synergistic use of optical and Synthetic Aperture Radar (SAR) imagery can provide time series data, any SAR-optical integration necessitates building a relationship between these two data sources. The objective of this study was to use a semi-empirical Canopy Structure Dynamics Model (CSDM), Growing Degree Days (GDD), and SAR parameters calibrated to optical NDVI to derive daily estimates of canola crop condition over an entire growing season. RADARSAT-2 Fine Quad-pol and RapidEye images were collected over three years for a study site in western Canada. Object-based image analysis was applied to study the relationship between the optical and SAR time series data. Significant correlations were documented between a number of SAR parameters and optical NDVI, specifically a ratio of backscatter intensities (HH-HV)/(HH+HV), a ratio of volume to surface scattering extracted from the Freeman Durden decomposition, and Entropy from the Cloude-Pottier decomposition. Correlations (r-values) between these SAR parameters and optical NDVI ranged from 0.63 to 0.84 for the three years of data. Based on this analysis, a simple statistical model was used to relate SAR parameters to optical NDVI, creating a SAR-calibrated NDVI (SARcal-NDVI). A CSDM was fit to the SARcal-NDVI for each canola field, constructing a temporal vegetation index curve which captured canopy development from emergence to senescence. Coefficients of determination (R²) were 0.87 0.86 and 0.82 for entropy, the volume-surface scattering ratio, and the ratio of backscatter intensities (HH-HV)/(HH+HV), respectively, demonstrating a good model fit. The CSDM describes well the temporal evolution of SARcal-NDVI. Using the CSDM, SARcal-NDVI and GDD, the canola condition can be estimated for any given day in the growing season. In fact when the CSDM was used to estimate SARcal-NDVI for the exact days of RapidEye acquisitions, correlations with optically derived NDVI were high. The strongest correlations with RapidEye NDVI were reported for the volume-surface scattering ratio (R² of 0.69 and RMSE of 0.15). The SARcal-NDVI estimated from the CSDM was also physically meaningful. Field-based biomass was significantly correlated (R² of 0.79) with the SARcal-NDVI calculated using the volume-surface scattering ratio. Although further research is needed to extend this method to other crops, these results demonstrate that SAR data can be used to estimate vegetation conditions and when coupled with a CSDM, integrated into current monitoring operations based on optical NDVI. As a next step, the research team will be assessing SARcal-NDVI in a national operational programme which reports on crop yields using modelling with optical-based NDVI.
The water content of potato leaves in different leaf positions was studied using hyperspectral technique in this research. According to leaf position, the leaves were divided into upper, middle and lower 3 groups. The experiment was conducted, in which the fresh leaves were collected and dried, and the reflectance spectra and water content were measured. The relationship between the spectral characteristics of different leaf positions and water content distribution of potato plants was analyzed. It was found that the reflectance and water content of fresh leaves in different positions were different. With the leaf position from top to bottom, the reflectance increased successively (R upper <R middle<R lower) in 862.9-1311.9 nm; the reflectance reduced in 1311.9-1403.6 nm; the reflectance at the middle and upper leaves was significantly higher than lower leaves (R upper >R middle >R lower) in 1403.6-1704.2 nm and the average value of water content increased in turn. After drying, with the leaf position from top to bottom, the spectral reflectance of leaves decreased successively (R upper<R middle<R lower) in 862.9-1704.2 nm and the average water content increased in turn. It also showed that the reflection spectra of 862.9-1311.9 nm was sensitive to the difference of leaf age and tissue structure for dry processing; the correlation coefficient between the reflectance and water content was greater than 0.93 in 1400.3-1500.7 nm. Combining the correlation analysis and PCA, 866.4 nm and 1406.8 nm were selected to establish the multiple linear regression (MLR) model for the water content detection of potato leaves, the model calibration coefficient (Rc²) was 0.9507 and the validation coefficient (Rv²) was 0.8189.The validation coefficients in the upper, middle, and lower leaf position were 0.9481, 0.7900 and 0.7673.
Understanding how the physical structure of forest canopies influences light absorption is a long-standing area of inquiry fundamental to the physical sciences, including the modeling and interpretation of biogeochemical cycles. Conventional measures of forest canopy structure used to infer canopy light absorption are often limited to leaf or vegetation area indexes. However, LiDAR-derived measures of canopy structural complexity (CSC) that describe the arrangement of vegetation may improve prediction of canopy light absorption by providing novel information on canopy-light interactions not regulated by leaf area alone. We measured multiple indexes of CSC, vegetation area index (VAI), and the fraction of photosynthetically active radiation absorbed (fPAR) across the eastern United States using portable canopy LiDAR to evaluate how different canopy structural attributes relate to fPAR. Our survey included sites from the National Ecological Observation Network and university field stations. Measures of CSC were more strongly coupled with fPAR under high light (>1,000 μmol m⁻² s⁻¹ PAR). Under low light conditions, when diffuse light predominates, light scattering weakens the dependency of fPAR on CSC. A multivariate model including CSC parameters and VAI explains ~89% of the intersite variance in fPAR, an improvement of over a VAI only linear model (r² = 0.73). The inclusion of CSC metrics in canopy light absorption models could increase confidence in predictions of biogeochemical cycles and energy balance.
Timely and nondestructive monitoring of leaf area index (LAI) using remote
sensing techniques is crucial for precise and efficient management of crops. In this paper, a
new spectral index (SI) for estimating LAI of winter wheat (Triticum aestivum L.) is
proposed on the basis of field hyperspectral measurements. A simple index based on the
empirical relationships between LAIs and SIs of all available two-waveband combinations
from hyperspectral data is developed by considering the difference between reflectance
values at 760 and 739 nm (DSIR760–R739 = R760 – R739). Among published and newly
developed SIs, DSIR760–R739 exhibited a significant and strong linear relationship with LAI and showed outstanding performance in LAI assessments. The permissible bandwidths for
broad-band DSIR760–R739 investigated using simulated reflectance were 5 nm for both 760
and 739 nm center wavelengths. The results indicate that the linear regression model based
on the narrow-band and broad-band DSIR760–R739 is a simple but accurate method for timely and nondestructive monitoring of LAI.
This study analyzed the vertical distribution of gravimetric water content (GWC), relative water content (RWC), and equivalent water thickness (EWT) in winter wheat during heading and early ripening stages, and evaluated the position of leaf number at which Vegetation Indexes (VIs) can best retrieve canopy water-related properties of winter wheat. Results demonstrated that the vertical distribution of these properties followed a near-bell-shaped curve with the highest values at the intermediate leaf position. GWC of the top three or four leaves during the heading stage and the top two or three leaves during the early ripening stage can represent the GWC of the whole canopy, but the RWC and EWT of the whole canopy should be calculated based on the top four leaves. At leaf level, the analysis demonstrated strong relationships between EWT and VIs for the top leaf layer, but for GWCD, GWCF, and RWC, the strongest relationships with VIs were found in the intermediate leaf layers. At canopy level, VIs provided the most accurate estimation of GWCfor the top three or four leaves. Water absorption-based VIs could estimate canopy EWT of winter wheat for the top four leaves, but the suitable bands sensitive to water absorptions should be carefully selected for the studied species.
The main objective of this study was to evaluate the spectral reflectance in the vertical profile of spring barley canopy at the booting growth stage and to determine how the reflectance gradient changes in relation to crop density and nitrogen (N) nutrition. Vertical gradients of spectral reflectance were studied in field trials with three sowing densities (2, 4 and 6 million of germinating seeds/ha) and two levels of N nutrition (0 and 90 kg/ha). It was found that differences in vegetation indices caused by N nutrition are most pronounced in the second and third leaf from the top, and these increase with increasing sowing density.
The vertical gradient of reflectance, specifically the ratio between the leaves F-3/F-1 for vegetation indices based on red-edge reflectance, represents a reliable indicator of number of ears per area unit (R = –0.87 for Normalised Red Edge-Red Index (NRERI) and –0.93 for Zarco-Tejada and Miller Simple Ratio Index (ZM)). A close relationship to ear productivity was found almost for all observed vegetation indices and any leaf in vertical profile (R = 0.79–0.97). In contrast, the prediction of protein content in barley grain was the most reliable when the red-edge reflectance indices (ZM and NRERI) particularly from upper three leaves were used (R = 0.81–0.88). The results show that the knowledge of reflectance heterogeneity in the vertical profile of canopy can significantly contribute to the interpretation of the measured data, to the differentiation of the N nutrition effect from the response to canopy density, and finally to a more accurate estimation of yield parameters and protein content in grain.
Leaf nitrogen content (LNC) and leaf mass per unit area (LMA) were assessed by near-infrared spectroscopy (NIRS) on fresh and dried plants of durum wheat (Triticum turgidum ssp). Individual leaves were scanned with a portable spectrometer and reference analyses of LNC and LMA were then carried out. Partial least squares (PLS) regression was used for calibration and cross-validation. LNC was accurately predicted for both fresh and dry leaves whatever the phenologic stage (correlation coefficient of calibration R2cal ranging from 0.932 to 0.958, standard error of cross-validation SECV ranging from 0.215 to 0.320% (dry matter)). LMA was predicted with R2cal = 0.942 and SECV = 4.84 g m−2. The combination of these two calibrations made it possible to predict leaf nitrogen per unit area (R2cross-validation = 0.94, SECV = 0.248 gN m−2) and provides a relevant and non-destructive tool for following the dynamics of three major leaf parameters.
Evaluation of phenotypic traits of crop plants on a large scale could provide important information to understand their responses to the environment. In this regard, remote sensing methods have shown much promise. However, the effect of the different contributions to spectral reflectance of the different leaf levels of plant canopies remains poorly investigated, despite their potential to improve the precision of canopy information estimation. In this study, we investigated the efficacy of sensor measurements in determining the vertical leaf nitrogen uptake of maize (Zea mays L.). We examined how nitrogen is distributed in the plant canopy, whether or not differences exist among fertilizer application rates, and how deep does the passive reflectance sensor meaningfully provide insight into the plant canopy. Our results, derived from either a sensor system with an oblique and multi view optic as well as SPAD measurements, indicated a convex (bulging outward) distribution of the relative chlorophyll content in maize plants. Similarly, both leaf nitrogen uptake and leaf biomass presented vertical bell shape distribution, although only the former showed qualitative differences among the fertilization treatments in the intermediate canopy leaf levels. By contrast, vertical nitrogen content presented a vertically decreasing gradient from top to bottom and one that was steeper at reduced nitrogen application. The spectral index R780/R740 was positively and curvilinearly related (R2 = 1.00) to the nitrogen uptake profile of the maize foliage and was able to detect the nitrogen uptake of each leaf level, even at the lowest levels. Yet, despite more than half of the total nitrogen being stored in the stem, the index values were influenced mainly by the foliage. Altogether, our results should help improve nitrogen fertilization recommendations in crop management as well as being useful in precision phenotyping and in improving in crop growth simulation models for architectural modeling.
Accurate estimation of spatially distributed chlorophyll content (Chl) in crops is of great importance for regional and global studies of carbon balance and responses to fertilizer (e.g., nitrogen) application. In this paper a recently developed conceptual model was applied for remotely estimating Chl in maize and soybean canopies. We tuned the spectral regions to be included in the model, according to the optical characteristics of the crops studied, and showed that the developed technique allowed accurate estimation of total Chl in both crops, explaining more than 92% of Chl variation. This new technique shows great potential for remotely tracking the physiological status of crops, with contrasting canopy architectures, and their responses to environmental changes.
In this paper, we focused on the retrieval of the LAI in an alpine wetland located in western part of China in late August and early July 2011. A two-layer canopy reflectance model (ACRM) was used to establish the relationships between the LAI and the reflectance of near-infrared (NIR) and red (RED) wavebands. The reflectance data were derived from Landsat TM L1T product and the Terra and Aqua MODIS 16-day and 8-day composite reflectance products (MOD/MYD09) at 250 m resolution. Due to the lack of the information about some major input parameters for ACRM, which are sensitive to model outputs in the reflectance of NIR and RED wavebands, the inverse problem was ill-posed. To overcome this problem, a method of increasing the sensitivity of the LAI while reducing the influence of other model free parameters based on the study of free parameters’ sensitivity to the ACRM outputs and the region's features was studied. The area of interest was divided into two parts using the approximately statistic normalized difference vegetation index (NDVI) value around 0.5. One part was sparse vegetation (0.1 < NDVI < 0.5), which is more sensitive to soil background effects and less sensitive to the canopy biophysical and biochemical variables. The other part was dense vegetation (0.5 ≤ NDVI < 1.0), which is less sensitive to soil background effects and more sensitive to plant canopies and leaf parameters. Then, the relationships of ρnir–LAI and ρred–LAI were established using a look-up table algorithm for the two parts. Furthermore, a regularization technique for fast pixel-wise retrieval was introduced to reduce the elements of LUT sets while maintaining a relatively high accuracy. The results were very promising compared to the field measured LAI values that the correlation (R2) of the measured LAI values and retrieved LAI values reached 0.95, and the root-mean-square deviation (RMSD) was 0.33 for late August, 2011, while the R2 reached 0.82 and RMSD was 0.25 for early July 2011.
Spectra of Spartina alterniflora were measure under different salinity, growth form and moisture conditions.
Remote sensing is a promising tool that provides quantitative and timely information for crop stress detection over large areas. Nitrogen (N) is one of the important nutrient elements influencing grain yield and quality of winter wheat (Triticum aestivum L.). In this study, canopy spectral parameters were evaluated for N status assessment in winter wheat. A winter wheat field experiment with 25 different cultivars was conducted at the China National Experimental Station for Precision Agriculture, Beijing, China. Wheat canopy spectral reflectance over 350–2500 nm at different stages was measured with an ASD FieldSpec Pro 2500 spectrometer (Analytical Spectral Devices, Boulder, CO, USA) fitted with a 25° field of view (FOV) fibre optic adaptor. Thirteen narrow-band spectral indices, three spectral features parameters associated with the absorption bands centred at 670 and 980 nm and another three related to reflectance maximum values located at 560, 920, 1690 and 2230 nm were calculated and correlated with leaf N concentration (LNC) and canopy N density (CND). The results showed that CND was a more sensitive parameter than LNC in response to the variation of canopy-level spectral parameters. The correlation coefficient values between LNC and CND, on the one hand, and narrow-band spectral indices and spectral features parameters, on the other hand, varied with the growth stages of winter wheat, with no predominance of a single spectral parameter as the best variable. The differences in correlation results for the relationships of CND and LNC with narrow-band spectral indices and spectral features parameters decreased with wheat plant developing from Feekes 4.0 to Feekes 11.1. The red edge position (REP) was demonstrated to be a good indicator for winter wheat LNC estimation. The absorption band depth (ABD) normalized to the area of absorption feature (NBD) at 670 nm (NBD670) was the most reliable indicator for winter wheat canopy N status assessment.
Water Index WI (R900/R970) was used for the estimation of plant water concentration (PWC) by ground-based, reflectance measurements. Reflectance and PWC were measured for adult plants growing in the field throughout an annual cycle and in potted seedlings submitted to progressive desiccation. The species studied were characteristicly Mediterranean: Pinus halepensis, Quercus ilex, Quercus coccifera, Arbutus unedo, Cistus albidus, Cistus monspeliensis, Phillyrea angustifolia, Pistacia lentiscus and Brachypodium retusum . WI was significantly correlated with PWC when all the species were considered together, and with almost all the species considered individually, especially when a wider range of PWC was obtained by extreme dessication of experimental plants. The correlations increased when normalizing WI by NDVI. The wavelength of the trough corresponding to water absorption band tended to shift from 970-980 nm to lower wavelengths 930-950 nm with decreasing PWCs. Infrared measurement of plant temperature and T leaf - T air provided worse assessment of PWC. A simple radiometer measuring plant reflectance at 680, 900, and 970nm could speed up the measurement of PWC, and be useful in wildfire risk evaluation and drought assessment.
Recent studies have demonstrated the usefulness of optical indices from hyperspectral remote sensing in the assessment of vegetation biophysical variables both in forestry and agriculture. Those indices are, however, the combined response to variations of several vegetation and environmental properties, such as Leaf Area Index (LAI), leaf chlorophyll content, canopy shadows, and background soil reflectance. Of particular significance to precision agriculture is chlorophyll content, an indicator of photosynthesis activity, which is related to the nitrogen concentration in green vegetation and serves as a measure of the crop response to nitrogen application. This paper presents a combined modeling and indices-based approach to predicting the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil (background) effects. This combined method has been developed first using simulated data and followed by evaluation in terms of quantitative predictive capability using real hyperspectral airborne data. Simulations consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative transfer models. In this modeling study, we developed an index that integrates advantages of indices minimizing soil background effects and indices that are sensitive to chlorophyll concentration. Simulated data have shown that the proposed index Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI) is both very sensitive to chlorophyll content variations and very resistant to the variations of LAI and solar zenith angle. It was therefore possible to generate a predictive equation to estimate leaf chlorophyll content from the combined optical index derived from above-canopy reflectance. This relationship was evaluated by application to hyperspectral CASI imagery collected over corn crops in three experimental farms from Ontario and Quebec, Canada. The results presented here are from the L'Acadie, Quebec, Agriculture and Agri-Food Canada research site. Images of predicted leaf chlorophyll content were generated. Evaluation showed chlorophyll variability over crop plots with various levels of nitrogen, and revealed an excellent agreement with ground truth, with a correlation of r2=.81 between estimated and field measured chlorophyll content data.
The normalized difference vegetation index (NDVI) has been widely used for remote sensing of vegetation for many years. This index uses radiances or reflectances from a red channel around 0.66 μm and a near-IR channel around 0.86 μm. The red channel is located in the strong chlorophyll absorption region, while the near-IR channel is located in the high reflectance plateau of vegetation canopies. The two channels sense very different depths through vegetation canopies. In this article, another index, namely, the normalized difference water index (NDWI), is proposed for remote sensing of vegetation liquid water from space. NDWI is defined as , where ϱ represents the radiance in reflectance units. Both the 0.86-μm and the 1.24-μm channels are located in the high reflectance plateau of vegetation canopies. They sense similar depths through vegetation canopies. Absorption by vegetation liquid water near 0.86 μm is negligible. Weak liquid absorption at 1.24 μm is present. Canopy scattering enhances the water absorption. As a result, NDWI is sensitive to changes in liquid water content of vegetation canopies. Atmospheric aerosol scattering effects in the 0.86–1.24 μm region are weak. NDWI is less sensitive to atmospheric effects than NDVI. NDWI does not remove completely the background soil reflectance effects, similar to NDVI. Because the information about vegetation canopies contained in the 1.24-μm channel is very different from that contained in the red channel near 0.66 μm, NDWI should be considered as an independent vegetation index. It is complementary to, not a substitute for NDVI. Laboratory-measured reflectance spectra of stacked green leaves, and spectral imaging data acquired with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) over Jasper Ridge in California and the High Plains in northern Colorado, are used to demonstrate the usefulness of NDWI. Comparisons between NDWI and NDVI images are also given.
The sensitivity of the normalized difference vegetation index (NDVI) to soil background and atmospheric effects has generated an increasing interest in the development of new indices, such as the soil-adjusted vegetation index (SAVI), transformed soil-adjusted vegetation index (TSAVI), atmospherically resistant vegetation index (AR VI), global environment monitoring index (GEMI), modified soil-adjusted vegetation index (MSAVI), which are less sensitive to these external influences. These indices are theoretically more reliable than NDVI, although they are not yet widely used with satellite data. This article focuses on testing and comparing the sensitivity of NDVI, SAVI, TSAVI, MSAVI and GEMI to soil background effects. Indices are simulated with the SAIL model for a large range of soil reflectances, including sand, clay, and dark peat, with additional variations induced by moisture and roughness. The general formulation of the SAVI family of indices with the form VI = (NIR - R) / (NIR + R + X) is also reexamined. The value of the parameter X is critical in the minimization of soil effects. A value of X = 0.16 is found as the optimized value. Index performances are compared by means of an analysis of variance.
Leaf chlorophyll content provides valuable information about physiological status of plants. Reflectance measurement makes it possible to quickly and non-destructively assess, in situ, the chlorophyll content in leaves. Our objective was to investigate the spectral behavior of the relationship between reflectance and chlorophyll content and to develop a technique for non-destructive chlorophyll estimation in leaves with a wide range of pigment content and composition using reflectance in a few broad spectral bands. Spectral reflectance of maple, chestnut, wild vine and beech leaves in a wide range of pigment content and composition was investigated. It was shown that reciprocal reflectance (R lambda)-1 in the spectral range lambda from 520 to 550 nm and 695 to 705 nm related closely to the total chlorophyll content in leaves of all species. Subtraction of near infra-red reciprocal reflectance, (RNIR)-1, from (R lambda)-1 made index [(R lambda)(-1)-(RNIR)-1] linearly proportional to the total chlorophyll content in spectral ranges lambda from 525 to 555 nm and from 695 to 725 nm with coefficient of determination r2 > 0.94. To adjust for differences in leaf structure, the product of the latter index and NIR reflectance [(R lambda)(-1)-(RNIR)-1]*(RNIR) was used; this further increased the accuracy of the chlorophyll estimation in the range lambda from 520 to 585 nm and from 695 to 740 nm. Two independent data sets were used to validate the developed algorithms. The root mean square error of the chlorophyll prediction did not exceed 50 mumol/m2 in leaves with total chlorophyll ranged from 1 to 830 mumol/m2.
The fundamental basis for applying remote sensing methods to crop assessments is the interrelationships between crop features and canopy spectral characteristics. Thus, a thorough understanding of the differences in the distributions of crop variables among different vertical layers and modules, as well as the effects of these layers and modules on canopy spectral characteristics and derived vegetation indices, is vital to improving the performance of crop monitoring by remote sensing. The main goal of this work was to provide insight into the above issues based on field measurements of winter wheat under different treatments during anthesis. The results demonstrated that the leaf area index (LAI), leaf chlorophyll a and b content (Chla+b), nitrogen (N) content per unit leaf area (NL), N concentration on a mass basis (NM), dry matter accumulation (DM), N accumulation (NA), and water content (Wc) in leaves and stems generally exhibited great vertical differences within wheat canopies during anthesis. There were also significant differences in NM, DM, NA, and Wc among the different plant modules (i.e., leaves, stems, and spikes) and the entire canopy. Moreover, the removal of wheat spikes and different vertical leaf layers had evident effects on the canopy spectral reflectance, and there were differences in the types and degrees of these effects at different wavelengths and treatments. In most cases, the vegetation indices decreased after removing wheat spikes and different leaf layers, although a few indices increased. There were also some vegetation indices that responded weakly to removal events. In comparison, the vegetation indices mostly showed stronger responses to the removal of the top 1st leaves than the spikes. To a certain extent, the responses to the removal of the top 2nd leaves were comparable to those of the top 1st leaves. The responses of most vegetation indices to the top 3rd leaves' removal were less than the responses to the top 2nd leaves' removal. In the case of the removal of the top 4th leaves and below, many of the RV (i.e., relative variation rate) absolute values of vegetation indices were greater than those of the top 3rd leaves' removal but smaller than or close to those of the top 2nd leaves' removal; this may partly be because all the withered and dead leaves covering the soil surface were also removed in this case. The above findings indicated that crop features may not be well characterized when they are based solely on leaves, especially only the upper leaves, as the lower leaves, stems, and spikes also have important effects. Moreover, wheat spikes, not just leaves, and even bottom-layer leaves, could have evident effects on canopy reflectance characteristics and derived vegetation indices. More effort is needed in future research to obtain a thorough understanding of the effects of canopy heterogeneity on canopy spectral characteristics and the relationships between crop variables and spectral vegetation indices.
The author maintains that effective use of remote sensing data requires thorough knowledge and understanding of the spectral characteristics of the various Earth surface features, and the factors that influence the fundamental energy-matter interactions that control and influence the spectral characteristics of vegetation, soil, water and snow features and the temporal and spatial effects on the spectral characteristics.-R.Harris
Leaf chlorophyll content (mu g cm(-2)) is an important variable for agricultural remote sensing because of its close relationship to leaf N content. The objectives of this study were to develop and test a new index, based on red, green and blue bands, that is sensitive to differences in leaf chlorophyll content at leaf and canopy scales. We propose the triangular greenness index (TGI), which calculates the area of a triangle with vertices: (lambda r, Rr), (lambda g, Rg), and (lambda b, Rb), where lambda is the wavelength (nm) and R is the reflectance for bands in red (r), green (g), and blue (b) wavelengths. The TGI was correlated with chlorophyll content using a variety of leaf and plot reflectance data. Generally, indices using the chlorophyll red-edge (710-730 nm) had higher correlations with chlorophyll content compared to TGI. However, correlations between TGI and chlorophyll content were equal to or higher than broad-band indices, when leaf area index (LAI) was >2. Simulations using the Scattering by Arbitrarily Inclined Leaves (SAIL) canopy model indicate an interaction among TGI, LAI, and soil type at low LAI, whereas at high LAI, TGI was only affected by leaf chlorophyll content. The TGI could be used with low-cost sensors, such as commercially-available digital cameras, for N management by remote sensing.
Remote sensing is proving to be a rapid non-destructive method for crop nitrogen (N) status assessment. In this study, quantitative relationships between leaf N concentration (LNC) and ground-based canopy hyperspectral reflectance in winter wheat (Triticum aestivum L.) were investigated. Winter wheat field experiments were conducted over three years at different sites (Zhengzhou, Jiaozuo and Kaifeng) in Henan, China. Different N rates and wheat cultivars were tested, and a novel double-peak area index was developed to improve the prediction accuracy and stability of LNC measurement. The common optimal red-edge spectral indices were used to monitor the LNC models. Analysis of the relationship between existing vegetable indices and LNC indicated that red-edge spectral parameters were the most sensitive in this case. Integrated linear regression of LNC with mND705 and REPle was performed to describe the dynamic nature of the LNC patterns, giving coefficients (R2) of 0.83 and 0.82, and the standard errors (SE) of 0.414 and 0.424, respectively. These novel double-peak area parameters were constructed based on analysis of the red-edge characteristics, and the optimal normalized difference of the double-peak areas based on REPig division (NDDAig), in the form of (R755 + R680 − 2 × RREPig)/(R755 − R680), were calculated and found to be highly correlated with LNC (highest R2 = 0.85; lowest SE = 0.385). When independent data were fit into the derived equations, the average relative error (RE) values were 14.1%, 13.7% and 11.5% between measured and estimated LNC using mND705, REPle and NDDAig, respectively, indicating a superior fit and better performance for NDDAig. These results suggest that the models can accurately estimate LNC in wheat, and the novel double-peak area index is more effective for modeling LNC than previously reported red-edge indices.
The objectives of the present study are to determine the relationships of equivalent water thickness (EWT), fuel moisture content (FMC) and specific leaf weight (SLW) of cotton leaves with leaf spectra reflectance, and to find sensitive spectral bands and best spectral indices to establish quantitative models for the quick and accurate estimation of EWT, FMC and SLW in cotton plants under different salinity levels. Plot experiments were conducted at different levels of salinity and cotton cultivars during three consecutive growing seasons. Time-course measurements of the leaf spectral reflectance, leaf fresh weight, leaf dry weight, leaf area, and leaf ion content of cotton were recorded under various treatments. Then, the normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the leaf spectrum were obtained within 350–2500 nm, and their correlation with EWT, FMC and SLW were quantified. The results show that the EWT, FMC and SLW of cotton leaves increased with increasing soil salinity levels and that the changes in leaf spectral reflectance under varied salinity levels are highly significant, with consistent patterns across the two cultivars tested. As the soil salinity levels increased, the Na+, Cl−, and SO42− content in the cotton leaves increased, whereas K+ and Ca2+ decreased at the same growth stage. In addition, the relationships among ion content, EWT, FMC and SLW were significant (P < 0.01). The sensitive spectral bands for EWT, FMC and SLW occurred mainly within the near infrared (NIR) and short-wave infrared (SWIR) ranges. The best spectral indices for estimating EWT, FMC and SLW in cotton were found to be NDSI (R1347, R2307), RSI (R2307, R1347); NDSI (R1650, R1801), RSI (R1801, R1650); NDSI (R1300, R2308), RSI (R2307, R1347) and 1650/2220 nm ratio, and the regression models based on the above spectral indices were identified as the best equations for the effective estimation of EWT and SLW in cotton. After testing these derived equations, the models for EWT and SLW estimation based on NDSI and RSI yielded an R2 of over 0.73, with more satisfactory performance under different ecological conditions, but the estimated accuracies of FMC models were very low and may not be suitable for estimating vegetation water content in saline conditions from leaf-level reflectance. The high fit between the measured and estimated values indicates that the EWT and SLW models based on new spectral indices from leaf-level reflectance could be used for the indirect estimation of plant salinity status by monitoring the changes in EWT and SLW caused by soil salinity in cotton plants.
Crop growth and production are dependent not only on the amount of total nitrogen (N) absorbed by plants, but also on the vertical leaf N distribution within canopies. The non-uniform leaf N distribution has been reported for various plant canopies. Remote sensing has been widely used for determination of crop N status, but such analysis seldom takes N distribution into consideration, ultimately leading to limited accuracy and decreased practical value of the related results. This paper has reviewed the results of previous studies that investigated the ecophysiological aspects of non-uniform N distribution, and the remote sensing methods that have been proposed to monitor this phenomenon. Additionally, this study used field data to analyze the differences in leaf N distribution in wheat canopies with different plant types (i.e. spread type, semi-spread type, and erect type), and provided insights into the estimation of vertical leaf N distribution by means of remote sensing.
Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. The triangular greenness index (TGI) was developed based on the area of a triangle surrounding the spectral features of chlorophyll with points at (670 nm, R670), (550 nm, R550), and (480 nm, R480), where Rλ is the spectral reflectance at wavelengths of 670, 550 and 480, respectively. The equation is TGI = −0.5[(670 − 480)(R670 − R550) − (670 − 550)(R670 − R480)]. In 1999, investigators funded by NASA's Earth Observations Commercialization and Applications Program collaborated on a nitrogen fertilization experiment with irrigated maize in Nebraska. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and Landsat 5 Thematic Mapper (TM) data were acquired along with leaf chlorophyll meter and other data on three dates in July during late vegetative growth and early reproductive growth. TGI was consistently correlated with plot-averaged chlorophyll-meter values at the spectral resolutions of AVIRIS, Landsat TM, and digital cameras. Simulations using the Scattering by Arbitrarily Inclined Leaves (SAIL) canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high LAI and canopy closure, TGI was only affected by leaf chlorophyll content. Therefore, TGI may be the best spectral index to detect crop nitrogen requirements with low-cost digital cameras mounted on low-altitude airborne platforms.
An explicit analytical model for calculating vegetation canopy reflectance, the multiple-layer canopy radiative transfer model (MRTM), has been developed in this paper. The model is based on radiative transfer theory by separating the roles of incident direct and diffuse radiation and radiation singly and multiply scattered by foliage. Specifically, the vertical heterogeneity of biophysical and biochemical parameters within the canopy was carefully treated in the model. This model was validated with field measurements from a deciduous forest canopy. The results proved that the model could reproduce the measured reflectance quite well. In addition, the performance of MRTM was found to be superior in comparison to other canopy models such as PROSAIL, ACRM and FRT. The significant effect of vertical heterogeneity on the canopy reflectance was clearly identified by different scenarios, which indicates that the influence of vertical variation in leaf area density and leaf chlorophyll, water, and dry matter contents cannot be neglected, especially when the total LAI is large. If such influences are ignored, significant biases in the estimated canopy reflectance can be expected. Since this multiple-layer model is a hybrid one that offers efficient calculation, it could serve as a primary model to develop more accurate reflectance models for inhomogeneous forests at plot and regional scales in future studies.
Remote sensing offers a unique perspective of plant vigor based on reflectance of the crops' canopy. The goal of this study was to determine how deep into the maize canopy red-edge chlorophyll index, CIred edge, was affected by foliar chlorophyll (Chl) content and leaf area. Reflectance in the range 400 to 900 nm was measured at both the leaf and canopy levels and was used to determine foliar Chl and total canopy Chl content using CIred edge. Statistical techniques, a hierarchical regression and three Aikaike Information Criteria, were used to determine how many leaf layers are sensed by the CIred edge. All statistical techniques showed that the CIred edge senses the chlorophyll content of the upper 7 to 9 leaf layers in a maize canopy and that remote sensing technique is able to accurately estimate maize canopy Chl content.
Future remote sensing satellite missions exploring the earth will feature advanced hyperspectral and directional optical imaging instruments. Given the complex nature of the data to be expected from these missions, a thorough preparation for them is essential and this can be accomplished by realistic simulation of the imagery data, years before the actual launch. Based on given spectral and directional capabilities of the instrument, and in combination with biophysical land surface properties obtained from existing imagery, the spectral and directional responses of several types of vegetation and bare soil have been simulated pixel by pixel using the radiative transfer models PROSPECT (for hyperspectral leaf reflectance and transmittance), GeoSAIL (for two-layer canopy bidirectional spectral reflectance), and MODTRAN4 (for atmospheric hyperspectral and directional effects). In this way, one obtains realistically simulated hyperspectral and directional top-of-atmosphere spectral radiance images, with all major effects included, such as heterogeneity of the landscape, non-Lambertian reflectance of the land surface, the atmospheric adjacency effect, and the limited spatial resolution of the instrument. The output of the image simulations can be used to demonstrate the capabilities of future earth observation missions. In addition, instrument specifications and image acquisition strategies might be optimized on the basis of simulated image analysis results, and new advanced data assimilation procedures could be validated with realistic inputs under controlled circumstances. This paper describes the applied methodology, the study area with the input images, the set-up of the actual image simulations, and discusses the final results obtained.
Accurate estimates of vegetation biophysical variables are valuable as input to models describing the exchange of carbon dioxide and energy between the land surface and the atmosphere and important for a wide range of applications related to vegetation monitoring, weather prediction, and climate change. The present study explores the benefits of combining vegetation index and physically based approaches for the spatial and temporal mapping of green leaf area index (LAI), total chlorophyll content (TCab), and total vegetation water content (VWC). A numerical optimization method was employed for the inversion of a canopy reflectance model using Terra and Aqua MODIS multi-spectral, multi-temporal, and multi-angle reflectance observations to aid the determination of vegetation-specific physiological and structural canopy parameters. Land cover and site-specific inversion modeling was applied to a restricted number of pixels to build multiple species- and environmentally dependent formulations relating the three biophysical properties of interest to a number of selected simpler spectral vegetation indices (VI). While inversions generally are computationally slow, the coupling with the simple and computationally efficient VI approach makes the combined retrieval scheme for LAI, TCab, and VWC suitable for large-scale mapping operations. In order to facilitate application of the canopy reflectance model to heterogeneous forested areas, a simple correction scheme was elaborated, which was found to improve forest LAI predictions significantly and also provided more realistic values of leaf chlorophyll contents.
This paper presents a physically-based approach for estimating critical variables describing land surface vegetation canopies, relying on remotely sensed data that can be acquired from operational satellite sensors. The REGularized canopy reFLECtance (REGFLEC) modeling tool couples leaf optics (PROSPECT), canopy reflectance (ACRM), and atmospheric radiative transfer (6SV1) model components, facilitating the direct use of at-sensor radiances in green, red and near-infrared wavelengths for the inverse retrieval of leaf chlorophyll content (Cab) and total one-sided leaf area per unit ground area (LAI). The inversion of the canopy reflectance model is constrained by assuming limited variability of leaf structure, vegetation clumping, and leaf inclination angle within a given crop field and by exploiting the added radiometric information content of pixels belonging to the same field. A look-up-table with a suite of pre-computed spectral reflectance relationships, each a function of canopy characteristics, soil background effects and external conditions, is accessed for fast pixel-wise biophysical parameter retrievals. Using 1 m resolution aircraft and 10 m resolution SPOT-5 imagery, REGFLEC effectuated robust biophysical parameter retrievals for a corn field characterized by a wide range in leaf chlorophyll levels and intermixed green and senescent leaf material. Validation against in-situ observations yielded relative root-mean-square deviations (RMSD) on the order of 10% for the 1 m resolution LAI (RMSD = 0.25) and Cab (RMSD = 4.4 μg cm− 2) estimates, due in part to an efficient correction for background influences. LAI and Cab retrieval accuracies at the SPOT 10 m resolution were characterized by relative RMSDs of 13% (0.3) and 17% (7.1 μg cm− 2), respectively, and the overall intra-field pattern in LAI and Cab was well established at this resolution. The developed method has utility in agricultural fields characterized by widely varying distributions of model variables and holds promise as a valuable operational tool for precision crop management. Work is currently in progress to extend REGFLEC to regional scales.
Live fuel moisture content (FMC) is a key factor required to evaluate fire risk and its operative and accurate estimation is essential for allocating pre-fire resources as a part of fire prevention. This paper presents an operative and accurate procedure to estimate FMC though MODIS (moderate resolution imaging spectrometer) data and simulation models. The new aspects of the method are its consideration of several ecological criteria to parameterize the models and consistently avoid simulating unrealistic spectra which might produce indetermination (ill-posed) problems when inverting the model. The methodology was operatively applicable to 12 shrubland plots located in different provinces of the Mediterranean region of Spain and tested with field data collected in those areas. The results showed that the proposed method efficiently tracks changes of FMC with average errors around 15%. However the model under-estimates FMC values higher than 135.68% since those situations were not included in the simulation scheme and the inversion precision is also dependent on an accurate estimation of LAI. These limitations will be overcome in future work mainly by including spectral signatures of vegetation with FMC values higher than 135.68% in the simulations, and by exploring new methods for LAI retrieval. Further efforts will also be devoted to extend this approach to other ecosystems.
Vegetation water content (VWC) is one of the most important parameters for the successful retrieval of soil moisture content from microwave data. Normalized Difference Infrared Index (NDII) is a widely-used index to remotely sense Equivalent Water Thickness (EWT) of leaves and canopies; however, the amount of water in the foliage is a small part of total VWC. Sites of corn (Zea mays), soybean (Glycine max), and deciduous hardwood woodlands were sampled to estimate EWT and VWC during the Soil Moisture Experiment 2005 (SMEX05) near Ames, Iowa, USA. Using a time series of Landsat 5 Thematic Mapper, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Wide Field Sensor (AWiFS) imagery, NDII was related to EWT with R2 of 0.85; there were no significant differences among land-cover types. Furthermore, EWT was linearly related to VWC with R2 of 0.87 for corn and 0.48 for soybeans, with a significantly larger slope for corn. The 2005 land-cover classification product from the USDA National Agricultural Statistics Service had an overall accuracy of 92% and was used to spatially distribute VWC over the landscape. SMEX05 VWC versus NDII regressions were compared with the regressions from the Soil Moisture Experiment 2002 (SMEX02), which was conducted in the same study area. No significant difference was found between years for corn (P = 0.13), whereas there was a significant difference for soybean (P = 0.04). Allometric relationships relate the size of one part of a plant to the sizes of other parts, and may be the result from the requirements of structural support or material transport. Relationships between NDII and VWC are indirect, NDII is related to canopy EWT, which in turn is allometrically related to VWC.
Retrieval of leaf biochemical parameters from reflectance measurements using model inversion generally faces “ill-posed” problems, which dramatically decreases the estimation accuracy of an inverse model. While the standard approach for model inversion retrieves various parameters simultaneously, usually only based on one merit function, the new approach proposed in this paper assigns a specific merit function for each retrieved parameter. Each merit function is specified in terms of the wavelength domains that the given parameter was found to be specifically sensitive to in an earlier sensitivity analysis. The approach has been validated with both in situ measured data sets and an artificial data set of 10 000 spectra simulated by the PROSPECT model. Results indicate that the new approach greatly improves the performance of inversion models, with root-mean-square error (rmse) values for chlorophyll content (Chl), equivalent water thickness (EWT), and leaf mass per area (LMA), based on the simulated data, of 7.12 μg/cm2, 0.0012 g/cm2 , and 0.0019 g/cm2, respectively, compared with 11.36 μg/cm2, 0.0032 g/cm2, and 0.0040 g/cm2 when using the standard approach. As for field-measured data sets, the proposed approach also greatly outperformed the standard approach, with respective rmse values of 8.11 μg/cm2, 0.0012 g/cm2, and 0.0008 g/cm2 for Chl, EWT, and LMA when all data are pooled, compared with 11.84 μg/cm2, 0.0020 g/cm2, and 0.0027 g/cm2 when using the standard approach. Hence, the proposed approach for model inversion can largely alleviate the “ill-posed” problem, and it could be widely applied for retrieving leaf biochemical parameters.
The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring, analyzing, and mapping temporal and spatial distributions of physiological and biophysical characteristics of vegetation. It is well documented that the NDVI approaches saturation asymptotically under conditions of moderate-to-high aboveground biomass. While reflectance in the red region (rho(red)) exhibits a nearly flat response once the leaf area index (LAI) exceeds 2, the near infrared (NIR) reflectance (PNIR) continue to respond significantly to changes in moderate-to-high vegetation density (LAI from 2 to 6) in crops. However, this higher sensitivity of the rho(NIR) has little effect on NDVI values once the rho(NIR) exceeds 30%. In this paper a simple modification of the NDVI was proposed. The Wide Dynamic Range Vegetation Index, WDRVI = (a * rho(NIR-rho(red))/(a * rho(NIR) + rho(red)), where the weighting coefficient a has a value of 0.1-0.2, increases correlation with vegetation fraction by linearizing the relationship for typical wheat, soybean, and maize canopies. The sensitivity of the WDRVI to moderate-to-high LAI (between 2 and 6) was at least three times greater than that of the NDVI. By enhancing the dynamic range while using the same bands as the NDVI, the WDRVI enables a more robust characterization of crop physiological and phenological characteristics. Although this index needs further evaluation, the linear relationship with vegetation fraction and much higher sensitivity to change in LAI will be especially valuable for precision agriculture and monitoring vegetation status under conditions of moderate-to-high density. It is anticipated that the new index will complement the NDVI and other vegetation indices that are based on the red and NIR spectral bands.
Monitoring the leaf water content and specific leaf weight of cotton (
- L Zhang
- Z G Zhou
- G W Zhang
- Y L Meng
- B L Chen
- Y H Wang
Green surface area, chlorophyll content and dry matter accumulation and translocation in the late growth period of wheat
- qiu
Identification of Crop Canopy Geometry and Inversion of Biochemical Indicators Based on Bidirectional Canopy Hyperspectrum
- W J Huang
Estimation of the surface area of glumes and awns of spike module for the common wheat
- qiu