No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages
... Application of VIs to estimate crop biomass has been recognized as an effective approach to obtain crop biomass with simplicity and availability, especially at different stages of plant growth and development [2,32]. Several previous studies found there were significant variations in estimating crop biophysical parameters by using VIs at different growth stage [33]. For example, [32] reported that different VIs exhibited various importance for maize biomass estimation at different growth stages. ...
... For example, [32] reported that different VIs exhibited various importance for maize biomass estimation at different growth stages. The differences of crop biomass estimated by using VIs at various plant growth stages were mainly attributed to the potential saturation of spectral indices after plant canopy closure and the influence of the soils at early plant growth stages [33]. Thus, VIs derived from hyperspectral images were employed in the current study to estimate cotton biomass at different plant growth stages, including early budding, full budding, early blooming, full blooming, full-boll, and boll opening stages. ...
... As a result, plant growth stage should be considered when using plant canopy structure indexes as the variables for biomass estimation. Introducing spectral VIs information at different plant growth stage into traditional VIs-based models could improve the estimation accuracy of rice yields [33]. ...
The hyperspectral vegetation index was defined based on the distinctive features of the spectral curve. In alignment with the growth and development characteristics of cotton, the spectral reflectivity of the cotton canopy was computed at different growth stages. The aim of this study was to clarify the association between cotton yield and canopy spectral indices and to develop yield estimation models utilizing hyperspectral imaging. The ASD Field Spec Pro VNIR 2500 spectrometer radiometer was employed to collect spectral reflectance data from cotton canopies at various growth stages. Using spectral analysis techniques, quantitative models were developed based on hyper-spectral vegetation indices, including Normalized Difference Vegetation Index (NDVI) (NDVI) and Radar Vegetation Index (RVI) to extract characteristic information from the cotton canopies. Following thorough testing and precise monitoring of the estimation models, the most optimal models representing cotton canopy structure parameters were identified. Results demonstrate that the power function model, relying on NDVI, provides the most accurate forecasting for Leaf Area Index (LAI)). Additionally, the exponential function model based on RVI proves to be the most effective for predicting cotton unit area above-ground fresh biomass, while another exponential function model based on RVI is identified as the best for predicting cotton unit area above-ground fresh biomass. Clearly, the application of hyper-spectral remote sensing technology enables the analysis, simulation, assessment, and providing scientific basis for precision cotton planting and cotton field management strategies.
... The application of narrow-band VIs derived from hyperspectral data can provide additional and implicit information, and reduce the influences of atmospheric and water absorption, soil background, and the saturation problem of broadband VIs (Chen et al. 2009;Gnyp et al. 2014a;Zandler et al. 2015). Furthermore, the VIs constructed by the derivative reflectance were successfully applied in many studies, such as monitoring of the leaf nitrogen content (Liang et al. 2018;Wen et al. 2019), estimation of AGB (Gnyp et al. 2014b;Marshall and Thenkabail 2015), and evaluation of yield prediction models (Kanke et al. 2016). Derivative spectral analysis is another technique that can provide positive effects such as separating overlapping peaks and suppressing background signals (Demetriades-Shah et al. 1990). ...
... These results confirmed that the derivative of the raw reflectance (i.e. FDR spectra) is a useful way to reduce the effect of low-frequency noise (soil background) and enhance subtle and weak canopy spectral features, thereby producing more straightforward and better correlations (Demetriades-Shah et al. 1990;Chen et al. 2010;Gnyp et al. 2014b;Liang et al. 2018). In addition, six relatively significant correlation regions between FDR and AGB for S2 site involve all visible wavelengths, except for the near-infrared wavelengths, indicating that for less dense vegetation canopies the sensitive fluctuation of the correlation between FDR in the multiple scattering NIR bands and AGB was greater than that in the visible absorption bands. ...
... Overall, the estimation performances of the FDR-VIs-SMLR models were comparable with that of RR-VIs-SMLR models. Gnyp et al. (2014b) developed the optimum multiple narrow band reflectance models using the SMLR method for estimating the rice AGB at later growth stage with high canopy closure. They also found that the FDR-based and the RR-based regressions produced nearly the same degree of performance. ...
In this paper, field spectroradiometer and aboveground biomass (AGB) data were acquired at the harvest stage at two sites in semiarid grasslands in Inner Mongolia, China. Four forms of commonly used vegetation indices (VIs) using all possible combinations of narrow-band first derivative (FDR) and raw reflectance (RR) were calculated, and the best FDR-VIs and RR-VIs were chosen by a linear regression analysis against AGB. The stepwise multiple linear regression (SMLR) models using the optimal FDR-VIs, RR-VIs, and both FDR-VIs and RR-VIs as input variables were developed for estimating the AGB. Results demonstrated that the estimation performance using the best FDR-VIs were comparable with the best RR-VIs, while the accuracy has been further improved by combining the best FDR-VIs and RR-VIs (maximum decrease in RMSE of 44% and minimum RMAE of 4.7%). The approach was found to be an important step for more accurate and effective grassland AGB estimation.
... The most frequent journals were Precision agriculture and Computers and Electronics in Agriculture. Articles from this cluster focused on the characterization of on-farm variability to refine new technologies or models (e.g., hyperspectral canopy sensing of paddy, Gnyp et al. 2014), or proposed plans for on-farm precision experiments (Alesso et al. 2021). ...
... -Profitability at farm level or interactions with other practices are not assessed -Mentioned: simulation models, databases on broad range of environments Peoples et al. 1995, Witt et al. 1999, Savary et al. 2000, Chen et al. 2011, Van Ittersum et al. 2013, Gnyp et al. 2014, Dominant in clusters "grain yields and fertilizer management" and "laboratory and samples" -Quantitative performances -Profitability in farm contexts -Adoption of the tested technology is the main stated objective -Mentioned: models for calculation of fertilizer quantities; sensors such as SPAD or field sensors to calculate gaseous emissions Tabbal et al. 2002, Dobermann et al. 2002, Fox et al. 2005, Peng et al. 2006, Cui et al. 2008, Sapkota et al. 2015 In clusters "small holder and food security" and "grain yields and fertilizer management" ...
... For instance, Khan et al. (2008), in their on-farm evaluation of the "pushpull" technology for the control of stemborers and striga weed on maize in western Kenya, explained that "farmers are guided by the Ministry of Agriculture and ICIPE [International Centre of Insect Physiology and Ecology] field staff [in order] to ensure that the 'push-pull' plots are properly laid out and companion plots properly established and managed since the effectiveness of the technology is dependent on these two." Digital tools were seldom mentioned unless the tested technology corresponded to a set of practices resulting from a model (Chen et al. 2011) or when on-farm data contributed to the calibration of models based on specific sensors (e.g., hyperspectral canopy sensors, Gnyp et al. 2014). Cooper et al. 1987, Herweg et al. 1999, Carberry et al. 2002, Drewry 2006 In clusters "grain yields and fertilizer management" and "knowledge and innovations" ...
The convergence among the rise of digital technologies, the attention paid to the localized issues of transitions in practices toward agroecology, and the emergence of new open innovation models are renewing and reviving the scientific community’s interest in on-farm experimentation (OFE). This form of experimentation is claimed to be enhanced by digital tools as well as being an enabler of production of credible, salient, and legitimate science insofar as it embraces a farmer-centric perspective. However, the forms of research in which some experimental activities on farms are anchored vary greatly, notably with regard to the actual forms that interventions on farms take, the legitimacy of the actors involved and their roles, or the observations and instruments applied for interpretation. We propose a systematic review of the literature and an analytical framework in order to better understand this diversity of practices behind on-farm experimentation. Our analysis segregated six major publication clusters based on themes appearing in titles and abstracts. These themes guided a more in-depth analysis of representative articles, from which we identified seven types of OFE practices that are described and discussed here with regard to the knowledge targeted, roles of the various actors, and on-farm experimental space. Our typology provides an original basis for supporting reflexivity and building alignment between the above-mentioned dimensions and the ways in which new tools can support the experimental process.
... Optical sensing techniques provide a non-invasive and efficient way for evaluating both abiotic and biotic stresses in plants [14]. Non-imaging reflectance spectroscopy has proven effective in assessing both the crop's N status [16,17] and plant diseases [18,19]. However, this method averages spectral data over a field of view, without providing information on the spatial distribution of the measured parameters [20]. ...
... Remote Sens. 2024,16, 939 ...
Mineral nitrogen (N) supply reportedly increases rice susceptibility to the fungal pathogen Magnaporthe oryzae causing blast disease. These biotic and abiotic factors cause changes in spectral reflectance of leaves; however, the effects of N × pathogen interactions on spectral characteristics of rice have not been studied. In this study, hyperspectral imaging was used to assess the effect of N supply on symptoms of rice leaf blast under greenhouse conditions. Three rice genotypes differing in blast susceptibility grown at low, medium, and high N supply were inoculated at the four-leaf stage with three M. oryzae isolates differing in virulence. The reflectance spectra (400 to 1000 nm) of healthy and symptomatic leaves were analyzed using the spectral angle mapper algorithm for supervised classification. Mineral N supply increased the contents of chlorophyll and total N. The number and area of lesions and total blast severity varied depending on rice genotype—M. oryzae isolate interactions and the amount of mineral N applied. The reflectance spectra of healthy tissue and of blast symptom subareas differed with N supply; rice genotypes differed in the response to N supply. Infected plants at high mineral N supply could be distinguished from those at low N supply due to higher differences in the spectra of symptom subareas. Results reveal the potential (and limitations) of hyperspectral imaging for quantifying N effects on rice leaves, disease severity, and symptom expression. The impact of these findings on plant phenotyping and remote sensing under field conditions is discussed.
... Currently, NDVI is the most widely used vegetation index for crop yield estimation. However, because of the characteristics of NDVI, it has significant saturation under a high vegetation coverage level, thereby affecting estimation accuracy [47][48][49][50]. According to the spectral reflectance characteristics of the plant, the absorption of chlorophyll on the red-edge waveband is weaker than that of red band, with the red-edge region having stronger transmission ability with the crop canopy [51]. ...
... Currently, NDVI is the most widely used vegetation index for crop yield estimation. However, because of the characteristics of NDVI, it has significant saturation under a high vegetation coverage level, thereby affecting estimation accuracy [47][48][49][50]. According to the spectral reflectance characteristics of the plant, the absorption of chlorophyll on the rededge waveband is weaker than that of red band, with the red-edge region having stronger transmission ability with the crop canopy [51]. ...
Winter wheat is a major food source for the inhabitants of North China. However, its yield is affected by drought stress during the growing period. Hence, it is necessary to develop drought-resistant winter wheat varieties. For breeding researchers, yield measurement, a crucial breeding indication, is costly, labor-intensive, and time-consuming. Therefore, in order to breed a drought-resistant variety of winter wheat in a short time, field plot scale crop yield estimation is essential. Unmanned aerial vehicles (UAVs) have developed into a reliable method for gathering crop canopy information in a non-destructive and time-efficient manner in recent years. This study aimed to evaluate strategies for estimating crop yield using multispectral (MS) and hyperspectral (HS) imagery derived from a UAV in single and multiple growth stages of winter wheat. To accomplish our objective, we constructed a simple linear regression model based on the single growth stages of booting, heading, flowering, filling, and maturation and a multiple regression model that combined these five growth stages to estimate winter wheat yield using 36 vegetation indices (VIs) calculated from UAV-based MS and HS imagery, respectively. After comparing these regression models, we came to the following conclusions: (1) the flowering stage of winter wheat showed the highest correlation with crop yield for both MS and HS imagery; (2) the VIs derived from the HS imagery performed better in terms of estimation accuracy than the VIs from the MS imagery; (3) the regression model that combined the information of five growth stages presented better accuracy than the one that considered the growth stages individually. The best estimation regression model for winter wheat yield in this study was the multiple linear regression model constructed by the VI of ‘’ derived from HS imagery, incorporating the five growth stages of booting, heading, flowering, filling, and maturation with r of 0.84 and RMSE of 0.69 t/ha. The corresponding central wavelengths were 782 nm, 874 nm, 762 nm, and 890 nm, respectively. Our study indicates that the multiple temporal VIs derived from UAV-based HS imagery are effective tools for breeding researchers to estimate winter wheat yield on a field plot scale.
... The NDVI is one of the most influential parameters for characterizing changes in vegetation greenness, and it is often employed in studies of land cover [44]. RVI is widely used to estimate and monitor the biomass of green plants [45]. Pedicularis is a water-loving plant, often growing next to rivers [4,46]. ...
... Therefore, this experiment calculated the NDWI to extract Pedicularis [47]. Numerous studies have verified that the three indices are efficient for land use/land cover and target extraction [44,45,[48][49][50][51]. ...
The accurate identification and monitoring of invasive plants are of great significance to sustainable ecological development. The invasive Pedicularis poses a severe threat to native biodiversity, ecological security, socioeconomic development, and human health in the Bayinbuluke Grassland, China. It is imperative and useful to obtain a precise distribution map of Pedicularis for controlling its spread. This study used the positive and unlabeled learning (PUL) method to extract Pedicularis from the Bayinbuluke Grassland based on multi-period Sentinel-2 and PlanetScope remote sensing images. A change rate model for a single land cover type and a dynamic transfer matrix were constructed under GIS to reflect the spatiotemporal distribution of Pedicularis. The results reveal that (1) the PUL method accurately identifies Pedicularis in satellite images, achieving F1-scores above 0.70 and up to 0.94 across all three datasets: PlanetScope data (seven features), Sentinel-2 data (seven features), and Sentinel-2 data (thirteen features). (2) When comparing the three datasets, the number of features is more important than the spatial resolution in terms of use in the PUL method of Pedicularis extraction. Nevertheless, when compared with PlanetScope data, Sentinel-2 data demonstrated a higher level of accuracy in predicting the distribution of Pedicularis. (3) During the 2019–2021 growing season, the distribution area of Pedicularis decreased, and the distribution was mainly concentrated in the northeast and southeast of Bayinbuluke Swan Lake. The acquired spatiotemporal pattern of invasive Pedicularis could potentially be used to aid in controlling Pedicularis spread or elimination, and the methods proposed in this study could be adopted by the government as a low-cost strategy to identify priority areas in which to concentrate efforts to control and continue monitoring Pedicularis invasion.
... Therefore, this distinctive spectral characteristic of vegetation has motivated many researchers to explore developing vegetation indices (VIs) and quantitative estimation of vegetation using remote sensing images [18][19][20][21][22][23][24]. Using spectral VIs calculated by mathematical combinations of reflectance within several bands is an efficient approach to the biomass estimation of rice [25][26][27], winter wheat [28], and maize [29]. However, the relationship between VI and biomass is nonlinear, with VI tending to saturate when biomass is high [18,30]. ...
... Ten plot-level VIs were constructed from the plot-level canopy reflectance according to the equations in Table 2. These VIs have been found to be effective in estimating crop growth parameters [16,23,26,[51][52][53] and consist of different bands and different combinations, including ratio-based VIs (CI green and CI red edge ), normalized VIs (NDVI, NDRE, and GNDVI), and VIs commonly used to estimate rice growth parameters in recent years (MTCI, WDRVI, OSAVI, and EVI2). ...
The effective and accurate aboveground biomass (AGB) estimation facilitates evaluating crop growth and site-specific crop management. Considering that rice accumulates AGB mainly through green leaf photosynthesis, we proposed the photosynthetic accumulation model (PAM) and its simplified version and compared them for estimating AGB. These methods estimate the AGB of various rice cultivars throughout the growing season by integrating vegetation index (VI) and canopy height based on images acquired by unmanned aerial vehicles (UAV). The results indicated that the correlation of VI and AGB was weak for the whole growing season of rice and the accuracy of the height model was also limited for the whole growing season. In comparison with the NDVI-based rice AGB estimation model in 2019 data ( R ² = 0.03, RMSE = 603.33 g/m ² ) and canopy height ( R ² = 0.79, RMSE = 283.33 g/m ² ), the PAM calculated by NDVI and canopy height could provide a better estimate of AGB of rice ( R ² = 0.95, RMSE = 136.81 g/m ² ). Then, based on the time-series analysis of the accumulative model, a simplified photosynthetic accumulation model (SPAM) was proposed that only needs limited observations to achieve R ² above 0.8. The PAM and SPAM models built by using 2 years of samples successfully predicted the third year of samples and also demonstrated the robustness and generalization ability of the models. In conclusion, these methods can be easily and efficiently applied to the UAV estimation of rice AGB over the entire growing season, which has great potential to serve for large-scale field management and also for breeding.
... The crop biomass using remotely sensed data can be estimated using physiological proxies captured by satellite sensors. Most of these proxies include vegetation indices (VIs) derived from multispectral satellite imageries (Naghdyzadegan Jahromi et al., 2022c;Kross et al., 2015;Gnyp et al., 2014). For example, Kross et al. (2015) used VIs including Normalized Difference Vegetation Index (NDVI), Green-NDVI, Ratio Vegetation Index (RVI), and Modified Triangular Vegetation Index 2 (MTVI2) calculated from the SPOT and Landsat imageries to predict corn biomass. ...
... The relationship between VIs and crop biomass varies throughout the growing season due to crop phenological changes. Gnyp et al. (2014) reported that satellite-based VIs provided more accurate proxies for estimating rice biomass at the jointing stage compared to the booting stage. The resulting relationships between the VIs and crop biomass would also differ with crop type, as Ferchichi et al. (2022) reported. ...
Timely and accurate crop yield estimation is important for adjusting agronomic management and ensuring agricultural sustainability. Machine learning (ML) algorithms provide new opportunities to integrate agronomic information with ground-based and satellite data and develop flexible yield predictive models. In particular, satellite-based vegetation indices and evapotranspiration provide robust proxies for crop yield estimations in the absence of measurements; nevertheless, most prior model development efforts have focused on using only vegetation indices due to the simplicity of the process. Additionally, the contribution of input categories (i.e., field, meteorological, and satellite data) and the use of appropriate proxies, aligned with the crop growth stages, in developing yield predictive models have not been adequately investigated. To address these challenges, we employed two ML techniques, Random Forest (RF) and extreme gradient boosting algorithm (XGB), to estimate wheat yield using meteorological variables, satellite-driven actual evapotranspiration (ETa), and vegetation indices (VIs). ETa was separately computed using the surface energy balance concept and the METRIC model. The models were first trained and tested in the study area using three input combinations: i) meteorological variables, ii) satellite data, and iii) an ensemble of meteorological and satellite data. Then, the best-performing model was further evaluated using two independent datasets. We found ETa to be particularly important in improving the accuracy of the model predictions. Among the vegetation indices, EVI, EVI2, and NDVI during May, and among the meteorological data, growing degree days during the grain filling stage plus minimum temperature in the stem elongation stage had the highest contributions to yield predictions. Both ML algorithms generated relatively accurate results, where XGB was marginally more accurate than RF, considering an average mean absolute error of 0.39 t ha-1 for XGB and 0.50 t ha-1 for RF. Normalized root-mean-square errors of the ensemble, satellite-derived and meteorological-derived models in XGB were 0.05, 0.07, and 0.10, respectively. Nevertheless, both algorithms’ performances deteriorated in predicting the yield values beyond the range of the training set, though XGB could handle the extrapolation process more efficiently than RF.
... To accurately estimate crop's AGB, various spectral pre-processing methods including first-order derivative (FD) (Liu et al., 2021a;Xing et al., 2017), band depth analysis (Marabel and Alvarez-Taboada, 2014;Fu et al., 2014), and continuous wavelet transform (CWT) (Liu et al., 2021b), are applied to extract potential features for modeling optimization. Gnyp et al. (2014) used FD features to estimate rice AGB with R 2 of 0.32-0.59 and 0.55 at four single-growth stages and all growth stages, and the raw spectra obtained R 2 of 0.22-0.36 ...
Accurately estimating potato above-ground biomass (AGB), which is closely associated with the growth and yield
of crops, carries significant importance for guiding field management practices. Hyperspectral techniques have
emerged as a powerful and efficient tool for quickly and non-invasively acquiring information about AGB due to
its capability to provide rich spectral data closely related to crop physiology and biochemistry. However, using
spectral features obtained from hyperspectral data, such as spectral reflectance and vegetation indices (VIs),
often leads to inaccurate estimations of crop AGB at multiple growth stages due to spectral saturation effects and
dynamic changes in spectral responses. To enhance the robustness of AGB estimation models, this study proposed
a harmonic decomposition (HD) method derived from Fourier series to extract energy features. The ground
(referred to as ASD) and unmanned aerial vehicle hyperspectral (referred to as UHD185) remote sensing data
from three growth stages of potatoes in 2018 (validation set) and 2019 (calibration set) were utilized in the
study. Firstly, a comparison was made between the spectral reflectance of the potato canopy measured by the
ASD and UHD185 sensors. Subsequently, the correlation between spectral reflectance, VIs, and harmonic components obtained from ASD and UHD185 sensors was analyzed in relation to AGB at both the individual and
whole growth stage. Then, sensitive bands selected through CARS (competitive adaptive reweighted sampling),
the entire spectral reflectance, VIs, and harmonic components, were utilized to construct AGB estimation models
by partial least squares regression (PLSR). Finally, the optimal model performance was validated across different
years, growth stages, and treatment conditions. The results showed there were differences in spectral reflectance
stage was notably higher than that observed for entire acquired by ASD and UHD185 sensors across various wavelengths, but overall, there was a high level of consistency between the two. The correlation of spectral reflectance and VIs with potato AGB at individual growth
growth stages. The accuracy of AGB estimation using VIs
obtained from ASD (the R2
, RMSE and NRMSE of validation sets were 0.52, 592 kg/hm2 and 26.91 %, respectively) and UHD185 (the R2
, RMSE and NRMSE of validation sets were 0.46, 612 kg/hm2 and 27.82 %,
respectively) sensors were low. Utilizing sensitive bands and full spectral reflectance separately improved the
precision of models, although the enhancement was somewhat limited. The HD-PLSR models from ASD (the R2
,RMSE and NRMSE of validation sets were 0.69, 477 kg/hm2 and 21.69 %, respectively) and UHD185 (the R2
,
RMSE and NRMSE of validation sets were 0.66, 481 kg/hm2 and 21.86 %, respectively) achieved the best AGB
estimation results. Using the HD-PLSR model to estimate AGB for two years, the R2 values were 0.79 and 0.76 for
ASD and UHD185, with RMSE values of 381 kg/hm2 and 386 kg/hm2 and NRMSE values of 22.35 % and 22.70
%, respectively. The capability of the HD-PLSR model was confirmed at various growth stages and treatments.
This work offers valuable remote sensing technical support for implementing potato growth monitoring and yield
assessment in the field.
... Due to the need for destructive sampling and laboratory analysis, traditional N diagnostic methods are laborious, time-consuming, and unsuitable for large-scale operations (Gnyp et al., 2014). The emergence of remote sensing platforms and sensors offers an efficient approach to achieve non-destructive, real-time diagnosis of crop conditions. ...
A R T I C L E I N F O Keywords: Nitrogen dynamic Time series curve Unmanned aerial vehicle Normalized difference red-edge index Nitrogen diagnosis A B S T R A C T Context: Wheat and rice are the main food crops in China. Appropriate nitrogen (N) fertilizer can effectively promote the crop growth, whereas excessive use has repercussions on yield formation and environmental preservation. Therefore, timely assessment of crop N status and precise N application management are of paramount importance. Objective: The study aims to assess the impacts of N fertilizer on N accumulation and spectral dynamics in wheat and rice, and investigate the feasibility of real-time crop N status diagnosis using unmanned aerial vehicle (UAV) spectra and devise subsequent managements. Methods: Ten experiments were conducted in Xinghua City and Lianyungang City from 2017 to 2020, involving different N fertilizer rates (0-405 kg N ha − 1) and various cultivars. Field sampling was carried out simultaneously with UAV image acquisition, and the crop dry matter and N concentration were obtained by indoor analysis. Results: Normalized difference red-edge index (NDRE) and N nutrition index (NNI) demonstrated a robust power function relationship (R 2 > 0.70). The time series N diagnosis curves established by critical NDRE values achieved recognition accuracy of over 89%. The validation accuracy of critical NDRE values achieved 93.84%. The probability of the calculated topdressing rate (N) falling between the agronomic optimal N rate (AONR) and economic optimal N rate (EONR) is 86%. Conclusions: The time series N diagnosis curve offered possibility to real-time judgement of plant N status. In addition, the subsequent N topdressing design was proved through the improvement of existing sufficiency index (SI) algorithm. Significance: The time series N diagnosis curve will provide valuable decision support for the optimal N fertilizer management of wheat and rice. Moreover, the UAV platform holds promising potential for regional-scale application in the future.
... One of the most crucial applications of agricultural remote sensing is estimating plant N status throughout crop growth by analyzing the reflectance properties of the canopy spectrum (Li et al., 2014). By examining the sensitive bands of the canopy reflectance spectrum, a number of vegetation indices (ratio, normalization, and derivative) and crop growth indices (e.g., crop N/chlorophyll concentration, crop N/chlorophyll accumulation, leaf area index (LAI), and biomass) have been developed for diagnosing crop N status globally (Schlemmer et al., 2013;Gnyp et al., 2014;Jay et al., 2016). ...
... The characteristic of plant chlorophyll is that it absorbs blue (450-485 nm) and red (625-740 nm) waves while reflecting very strong near-infrared (500-1,400 nm) waves. The green leaf color (500-565 nm) is obtained from the nature of the wave which is slightly reflected by chlorophyll [21]. This causes the generative stage (booting and heading), where the reflectance of the near infrared reaches its peak [22]. ...
... The red-blue ratio index (RBRI) extracted from UAV RGB images by Schirrmann et al. [23] was strongly associated with biomass, with a coefficient of determination (R 2 ) ranging from 0.72 to 0.99. In addition, some studies have shown that spectral data from the red edge and near-infrared bands also have good applications in biomass estimation [24,25]. For example, commonly used VIs such as the green optimum soil adjusted vegetation index (GOASVI), the modified soil adjusted vegetation index (MSAVI), and the normalized difference vegetation index (NDVI) have been shown to give satisfactory results in estimating the agronomic traits (e.g., AGB and nitrogen status) of crops such as wheat, maize, and rice in many studies [26][27][28][29]. ...
Wheat is one of the most important food crops in the world, and its high and stable yield is of great significance for ensuring food security. Timely, non-destructive, and accurate monitoring of wheat growth information is of great significance for optimizing cultivation management, improving fertilizer utilization efficiency, and improving wheat yield and quality. Different color indices and vegetation indices were calculated based on the reflectance of the wheat canopy obtained by a UAV remote sensing platform equipped with a digital camera and a hyperspectral camera. Three variable-screening algorithms, namely competitive adaptive re-weighted sampling (CARS), iteratively retains informative variables (IRIVs), and the random forest (RF) algorithm, were used to screen the acquired indices, and then three regression algorithms, namely gradient boosting decision tree (GBDT), multiple linear regression (MLR), and random forest regression (RFR), were used to construct the monitoring models of wheat aboveground biomass (AGB) and leaf nitrogen content (LNC), respectively. The results showed that the three variable-screening algorithms demonstrated different performances for different growth indicators, with the optimal variable-screening algorithm for AGB being RF and the optimal variable-screening algorithm for LNC being CARS. In addition, using different variable-screening algorithms results in more vegetation indices being selected than color indices, and it can effectively avoid autocorrelation between variables input into the model. This study indicates that constructing a model through variable-screening algorithms can reduce redundant information input into the model and achieve a better estimation of growth parameters. A suitable combination of variable-screening algorithms and regression algorithms needs to be considered when constructing models for estimating crop growth parameters in the future.
... [4].Boron deficiency in paddy crops has several negative impacts on plant growth and development. Studies have shown that boron deficiency can result in stunted growth, reduced tillering, and poor panicle development [61].Seed treatment is an effective method to prevent boron deficiency symptoms in the early stages of crop growth [52]. Table XI shows the Critical levels and optimum ranges for Boron deficiency in rice plant tissue. ...
... Conventional methods for crop biomass monitoring require extensive field sampling, which not only takes a lot of time and effort but also harms local vegetation (Gnyp et al., 2014;Reynolds et al., 2000). In recent years, the prediction of crop yield using remote sensing (RS) image data has developed into a research hotspot in precision agriculture due to its short period, large range, and hyperspectral characteristics (Ferencz et al., 2010;Haboudane et al., 2002;Ma et al., 2022;Shanahan et al., 2001). ...
Early and accurate prediction and simulation of grain crop yield can help maximize the revision and development of regional food policy, which is crucial for ensuring national food security. The development of unmanned aerial vehicle (UAV) technology is gradually gaining an advantage over satellite remote sensing at the field scale. In this study, we predicted maize yield using canopy vegetation indices (VIs) and crop phenology metrics obtained through UAV with ordinary least squares (OLS), stepwise multiple linear regression (SMLR) and gradient‐boosted regression tree (GBRT). The results reveal that the VIs extracted from UAV imagery had a high correlation with yield ( R = 0.92), facilitating crop yield estimation. Additionally, coupling crop phenology significantly improved the prediction accuracy of SMLR, with the highest R ² and lowest RMSE of 0.894, 1.238 × 10 ³ kg ha ⁻¹ , respectively. But, the enhancement of GBRT by this method was slender. Its simulation outperformed OLS and SMLR with dramatic R ² , RMSE, and MAE of 0.892, 1.189 × 10 ³ kg ha ⁻¹ , and 9.150 × 10 ² kg ha ⁻¹ , respectively. Moreover, the blister stage was deemed the optimal stage for maize yield prediction with an accuracy rate exceeding 81%. These demonstrated the feasibility of using UAV images to predict crop yields, providing an important reference at the field scale.
... The training and validation datasets were randomly divided in a ratio of 3:1. The coefficient of determination (R 2 ), root mean square error (RMSE), and relative root mean square error (RRMSE) were used to evaluate the quality of the model (Gnyp et al., 2014). ...
... Jenal et al. (2020) investigated the potential of a UAS multispectral VNIR/SWIR camera for forage dry matter (DM) yield estimation with simple linear regression (SLR) models. The system acquired four bands (910 nm, 980 mn, 1100 nm, and 1200 nm); the selection of these bands was based on the vegetation indices NRI (Koppe et al., 2010) and GnyLi (Gnyp et al., 2014). Honkavaara et al. (2016) studied the potential of VNIR and SWIR hyperspectral frame cameras based on a tuneable Fabry-Pérot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area, and Tuominen et al. (2018) in the estimation of species distribution in a highly diverse forest area. ...
Miniaturised hyperspectral cameras are becoming more easily accessible and smaller, enabling efficient monitoring of agricultural crops using unoccupied aerial systems (UAS). This study’s objectives were to develop and assess the performance of UAS-based hyperspectral cameras in the estimation of quantity and quality parameters of grass sward, including the fresh and dry matter yield, the nitrogen concentration (Ncont) in dry matter (DM), the digestibility of organic matter in DM (the D-value), neutral detergent fibre (NDF), and water-soluble carbohydrates (WSC). Next-generation hyperspectral cameras in visible-near-infrared (VNIR, 400–1000 nm; 224 bands) and shortwave-infrared (SWIR; 900–1700 nm; 224 bands) spectral ranges were used, and they were compared with commonly used RGB and VNIR multispectral cameras. The implemented machine-learning framework identified the most informative predictors of various parameters, and estimation models were then built using a random forest (RF) algorithm for each camera and its combinations. The results indicated accurate estimations; the best normalised root-mean-square errors (NRMSE) were 8.40% for the quantity parameters, and the best NRMSEs for the quality parameters were 7.44% for Ncont, 1% for D-value, 1.24% for NDF, and 12.02% for WSC. The hyperspectral datasets provided the best results, whereas the worst accuracies were obtained using the crop height model and RGB data. The integration of the VNIR and SWIR hyperspectral cameras generally provided the highest accuracies. This study showed for the first time the performance of novel SWIR range hyperspectral UAS cameras in agricultural application.
... The coefficient of determination (R 2 ) between predicted value and measured value is used to summarize the performance of NNI model. Root mean square error (RMSE), relative root mean square error (RRMSE) and bias were also calculated (Cheng et al., 2014;Gnyp et al., 2014). Modeling set R 2 (Rc 2 ) and validation set R 2 (Rv 2 ) represent the R 2 of the modeling data set and validation data set, respectively. ...
... Among the numerous VIs currently used in agricultural research, the Normalized Difference Vegetation Index (NDVI) is the most studied. Over the past two decades, numerous reports have demonstrated strong correlations between the NDVI and N status across a wide range of crops, including rice [17][18][19]. Several others have also utilized NDVI to predict rice grain yields [20,21]. ...
Accurately detecting nitrogen (N) deficiency and determining the need for additional N fertilizer is a key challenge to achieving precise N management in many crops, including rice (Oryza sativa L.). Many remotely sensed vegetation indices (VIs) have shown promise in this regard; however, it is not well-known if VIs measured from different sensors can be used interchangeably. The objective of this study was to quantitatively test and compare the ability of VIs measured from an aerial and proximal sensor to predict the crop yield response to top-dress N fertilizer in rice. Nitrogen fertilizer response trials were established across two years (six site-years) throughout the Sacramento Valley rice-growing region of California. At panicle initiation (PI), unmanned aircraft system (UAS) Normalized Difference Red-Edge Index (NDREUAS) and GreenSeeker (GS) Normalized Difference Vegetation Index (NDVIGS) were measured and expressed as a sufficiency index (SI) (VI of N treatment divided by VI of adjacent N-enriched area). Following reflectance measurements, each plot was split into subplots with and without top-dress N fertilizer. All metrics evaluated in this study indicated that both NDREUAS and NDVIGS performed similarly with respect to predicting the rice yield response to top-dress N at PI. Utilizing SI measurements prior to top-dress N fertilizer application resulted in a 113% and 69% increase (for NDREUAS and NDVIGS, respectively) in the precision of the rice yield response differentiation compared to the effect of applying top-dress N without SI information considered. When the SI measured via NDREUAS and NDVIGS at PI was ≤0.97 and 0.96, top-dress N applications resulted in a significant (p < 0.05) increase in crop yield of 0.19 and 0.21 Mg ha−1, respectively. These results indicate that both aerial NDREUAS and proximal NDVIGS have the potential to accurately predict the rice yield response to PI top-dress N fertilizer in this system and could serve as the basis for developing a decision support tool for farmers that could potentially inform better N management and improve N use efficiency.
... The reflectance was higher at 67-82 days after sowing (which comprises 2 phases, the final days of the reproductive phase and the beginning of the ripening phase) compared to the other sowing dates, which coincides with the results of [48], in tests performed on the Kongyu rice variety in China. The presence of the panicle also increases reflectance in the near-infrared range, which is consistent with the results of He et al. [30] for the japonica rice variety. ...
This work presents a remote sensing application to estimate the leaf area index (LAI) in two rice (Oryza sativa L.) varieties (IDIAP 52-05 and IDIAP FL 137-11), as a proxy for crop performance. In-field, homogeneous spectroradiometric measurements (350–1050 nm) were carried in two campaigns (June–November 2017 and January–March 2018), on a private farm, TESKO, located in Juan Hombrón, Coclé Province, Panama. The spectral fingerprint of IDIAP 52-05 plants was collected in four dates (47, 67, 82 and 116 days after sowing), according to known phenological stages of rice plant growth. Moreover, true LAI or green leaf area was measured from representative plants and compared to LAI calculated from normalized PlanetScope multi-spectral satellite images (selected according to dates close to the in-field collection). Two distinct estimation models were used to establish the relationships of measured LAI and two vegetational spectral indices (NDVI and MTVI2). The results show that the MTVI2 based model has a slightly higher predictive ability of true LAI (R2 = 0.92, RMSE = 2.20), than the NDVI model. Furthermore, the satellite images collected were corrected and satellite LAI was contrasted with true LAI, achieving in average 18% for Model 2 for MTVI2, with the NDVI (Model 1) corrected model having a smaller error around 13%. This work provides an important advance in precision agriculture, specifically in the monitoring of total crop growth via LAI for rice crops in the Republic of Panama.
... For this reason, 14 VIs derived from UAV-based multispectral images were employed. In addition, some studies demonstrated that the growth stage plays a key role in the sensitivity and performance of VIs when predicting crop biophysical parameters [23]; there is also a lack of comprehensive high-frequency UAV observation for maize. Therefore, in our study, the UAV campaign was carried out nine times during all main growth stages of maize. ...
The accurate, timely, and non-destructive estimation of maize total-above ground biomass (TAB) and theoretical biochemical methane potential (TBMP) under different phenological stages is a substantial part of agricultural remote sensing. The assimilation of UAV and machine learning (ML) data may be successfully applied in predicting maize TAB and TBMP; however, in the Nordic-Baltic region, these technologies are not fully exploited. Therefore, in this study, during the maize growing period, we tracked unmanned aerial vehicle (UAV) based multispectral bands (blue, red, green, red edge, and infrared) at the main phenological stages. In the next step, we calculated UAV-based vegetation indices, which were combined with field measurements and different ML models, including generalized linear, random forest, as well as support vector machines. The results showed that the best ML predictions were obtained during the maize blister (R2)- Dough (R4) growth period when the prediction models managed to explain 88–95% of TAB and 88–97% TBMP variation. However, for the practical usage of farmers, the earliest suitable timing for adequate TAB and TBMP prediction in the Nordic-Baltic area is stage V7–V10. We conclude that UAV techniques in combination with ML models were successfully applied for maize TAB and TBMP estimation, but similar research should be continued for further improvements.
... The study site is in the southeastern Heilongjiang province and covers five districts: Xiangyang, Qianjin, Jianshan, Taoshan, and Jiguan in Northeast China (Figure 1). The region is characterized by a sub-humid continental monsoon climate with warm summers and cold winters [33], an annual mean temperature of 2 • C, and annual mean precipitation of 550 mm [34]. Heilongjiang is popularly known as a paddy rice agriculture hub in the region. ...
Rice is a globally significant staple food crop. Therefore, it is crucial to have adequate tools for monitoring changes in the extent of rice paddy cultivation. Such a system would require a sustainable and operational workflow that employs open-source medium to high spatial and temporal resolution satellite imagery and efficient classification techniques. This study used similar phenological data from Sentinel-2 (S2) optical and Sentinel-1 (S1) Synthetic Aperture Radar (SAR) satellite imagery to identify paddy rice distribution with deep learning (DL) techniques. Using Google Earth Engine (GEE) and U-Net Convolutional Neural Networks (CNN) segmentation, a workflow that accurately delineates smallholder paddy rice fields using multi-temporal S1 SAR and S2 optical imagery was investigated. The study′s accuracy assessment results showed that the optimal dataset for paddy rice mapping was a fusion of S2 multispectral bands (visible and near infra-red (VNIR), red edge (RE) and short-wave infrared (SWIR)), and S1-SAR dual polarization bands (VH and VV) captured within the crop growing season (i.e., vegetative, reproductive, and ripening). Compared to the random forest (RF) classification, the DL model (i.e., ResU-Net) had an overall accuracy of 94% (three percent higher than the RF prediction). The ResU-Net paddy rice prediction had an F1-Score of 0.92 compared to 0.84 for the RF classification generated using 500 trees in the model. Using the optimal U-Net classified paddy rice maps for the dates analyzed (i.e., 2016–2020), a change detection analysis over two epochs (2016 to 2018 and 2018 to 2020) provided a better understanding of the spatial–temporal dynamics of paddy rice agriculture in the study area. The results indicated that 377,895 and 8551 hectares of paddy rice fields were converted to other land-use over the first (2016–2018) and second (2018–2020) epochs. These statistics provided valuable insight into the paddy rice field distribution changes across the selected districts analyzed. The proposed DL framework has the potential to be upscaled and transferred to other regions. The results indicated that the approach could accurately identify paddy rice fields locally, improve decision making, and support food security in the region.
... Bao et al. [13] reported that the monoterpene emission rate under standard conditions (30 • C leaf temperature and 1000 µmol m −2 s −1 PPFD) was 0.40 µg gDW −1 h −1 . The dry matter of the above-ground parts of rice at the mature stage was reported to be~1760 g m −2 [18],~1600 g m −2 in Japan [13], and 680 g m −2 in China [38]. Assuming that the representative value is~1500 g m −2 , the monoterpene emission rate reported by Bao et al. [13] can be converted to 600 µg m −2 h −1 (1.2 nmol m −2 s −1 ) on a land-area basis. ...
The global cultivation area of rice is equivalent to 4% of the world’s forest area and may be an important sink and source of trace gases. To produce a precise terpenoid emission inventory, it is essential to obtain reliable data of terpenoid emission from rice plants. In the present study, terpenoid emissions from various rice species were measured using flow-through chamber and tower flux measurement methods. In the flow-through chamber measurement, linalool was emitted from the above-ground parts of the three rice cultivars “Koshihikari”, “Nipponbare” and “IR72”. The emission rates gradually decreased (<0.1 µg gDW−1 h−1) within two days during the measurement periods. As the touching stimulus might have enhanced linalool emission, a non-contact measurement method, i.e., the tower flux measurement method, was applied to a “Koshihikari” paddy. Linalool was not detected, but α-pinene was detected in the atmosphere above the rice paddy. The α-pinene flux (mean ± 95% confidence interval) was 0.006 ± 0.004 nmol m−2 s−1 on a land-area basis. The flux was 1/200 of the previously reported monoterpene emission rate of the rice plants measured in a commercial chamber, but was not largely different from three other reports. We provide terpenoid flux data above a rice paddy for the first time, which is more reliable because the tower flux measurement method can avoid stimuli to rice leaves and stems. Although the obtained terpenoid emission rate is very low, the obtained values can contribute to the establishment of a precise BVOC inventory in Asia.
... VIs are applied as a convenient tool to evaluate crop growth status and monitor agronomic indicators because the reflectance characteristics are related to the crop's physiological status and growth, such as LAI, AGB, and grain yield (Gnyp et al. 2014), and are easily obtained (Miao et al. 2010). ...
Nitrogen (N) fertilizer management plays a crucial role in high-yield rice production. To choose a well-performing rice N nutrient diagnosis indicator for developing rice production management strategies, this research conducted five field experiments under various N treatments. The results showed that machine learning and stepwise multiple linear regression suggested a strong relationship between vegetation indexes and agronomic indicators (0.70 > R² > 0.51). A strong correlation was obtained between red-edge based vegetation indexes and agronomic indicators (R² > 0.40). Additionally, the all-subset regression method results demonstrated that the red-edge basis vegetation indexes were generally applied during different vegetation index combinations. The red-edge basis vegetation indexes reached an approximately 40% contribution in nitrogen nutrient index prediction and an approximately 48% contribution in leaf area index monitoring. Furthermore, this study combined the normalized difference red-edge (NDRE) basis dynamic model to calculate the N dose, which ranged from 106 to 134 kg per hectare in large-scale N management according to the NDRE from Sentinel-2B images, a decrease of approximately 46 kg N ha⁻¹ fertilizer compared with farmers’ practices. Nevertheless, more refinements are needed to ensure that this strategy can be applied to farmers’ yield- and income-enhancing production.
... The hot spots of RRS from the perspective of spectral variables are shown in Table 6. The reflectance and vegetation index are the most important and frequently used spectral variables in RRS [122,123], with 361 and 272 published papers and intensities of 1.61 and 2.83, respectively. Reflectance is the basic variable of optical remote sensing [124]. ...
Rice is one of the most important food crops around the world. Remote sensing technology, as an effective and rapidly developing method, has been widely applied to precise rice management. To observe the current research status in the field of rice remote sensing (RRS), a bibliometric analysis was carried out based on 2680 papers of RRS published during 1980–2021, which were collected from the core collection of the Web of Science database. Quantitative analysis of the number of publications, top countries and institutions, popular keywords, etc. was conducted through the knowledge mapping software CiteSpace, and comprehensive discussions were carried out from the aspects of specific research objects, methods, spectral variables, and sensor platforms. The results revealed that an increasing number of countries and institutions have conducted research on RRS and a great number of articles have been published annually, among which, China, the United States of America, and Japan were the top three and the Chinese Academy of Sciences, Zhejiang University, and Nanjing Agricultural University were the first three research institutions with the largest publications. Abundant interest was paid to “reflectance”, followed by “vegetation index” and “yield” and the specific objects mainly focused on growth, yield, area, stress, and quality. From the perspective of spectral variables, reflectance, vegetation index, and back-scattering coefficient appeared the most frequently in the frontiers. In addition to satellite remote sensing data and empirical models, unmanned air vehicle (UAV) platforms and artificial intelligence models have gradually become hot topics. This study enriches the readers’ understanding and highlights the potential future research directions in RRS.
... The measurement of spectral reflectance characteristics of crop canopies is largely proposed as a quick, cheap, reliable and non-destructive method for estimating plant above ground biomass production in small-grain cereals (Cao et al. 2013;Aparicio et al. 2002) and individual plant level (Álvaro et al. 2007). Near-infrared (NIR) reflectance of rice is directly related to green biomass (Gnyp et al. 2014;Niel and McVicar 2001). High NDVI values are indicative of high chlorophyll content. ...
Handheld optical sensor was used to measure canopy reflectance at red region (656 nm) and near-infrared region (774 nm) to generate NDVI data for monitoring rice productivity under soil amendment with combinations of fertilizers at two levels of water regime in smallholder Irrigation Scheme, in Lower Moshi, North Tanzania. The study was carried out in an experimental design which consisted of two irrigation water levels (flooding and system of rice intensification) with multi-nutrients (NPK) and single nutrient (urea) application replicated three times in a randomized complete block design. Flood irrigation water was applied at 7 cm height throughout the growing season, while SRI treatment irrigation water was applied at 4 cm height under alternate wetting and drying conditions. The annual rates of fertilizers applied was 120 kg N/ha, 20 kg P/ha, and 25 kg K/ha. The variety SARO-5 was used in this experiment. Simple correlation coefficient (r) was used to measure the degree of association between field crop performance parameters (plant height, number of tillers, biomass, yield) and NDVI across growth stages and three positions of the sensor above the canopy in the tested fertilizer combinations and water regimes. Results show that at any given fertiliser combinations and water levels, there was no significant correlation between plant height and NDVI except for the plant height at a vegetative stage for 0.6 m above the crop canopy and booting stage at 0.3 m and 0.6 m above the canopy, respectively (P < 0.05). A good correlation was also observed between NDVI at booting and full booting stage regardless of the position of the sensor above the canopy and the number of tillers at full booting growth stage (P < 0.05). A significant relationship was observed between rice grain yield and NDVI at the vegetative, booting, and full booting stage. The simple linear regression models explained only slightly < 30% of the yield predictions by NDVI at the early stage of the crop growth, decreasing gradually to 5% at the full booting growth stage. Results demonstrate a positive linear relationship between rice grain yield and NDVI for the tested soil fertiliser amendments and irrigation water regimes. Thus, we conclude that handheld NDVI-based sensor can be used in smallholder rice yield predictions for optimising soil fertiliser use and irrigation water management. This allows future multi-functional land management within the soil–water-food nexus.
... Spectral vegetation indices derived from in situ ground-based remotely sensed data have been shown in prior studies to be useful for identifying stressed vegetation in a wide range of agricultural crops. These include, for example: the determination of aerial plant biomass [34][35][36][37]; chlorophyll a concentration [38][39][40][41][42]; crop grain yield [43,44]; leaf area index [45][46][47]; nitrogen content [48,49]; water stress [31,50]; pest injuries; and plant diseases [25,51,52]. Many earlier research studies have shown that ground-based remotely sensed data can be utilized to evaluate growth parameters and crop health status; however, most of the studies concentrated on detecting moisture shortage stress, whereas potassium deficiency has received comparatively less attention in the literature. ...
Moisture and potassium deficiency are two of the main limiting variables for squash crop performance in many water-stressed places worldwide. If major output decreases are to be avoided, it is critical to detect signs of crop stress as early as possible in the growth cycle. Proximal remote sensing can be a reliable technique for offering a rapid and precise instrument and localized management tool. This study tested the ability of proximal hyperspectral remotely sensed data to predict squash traits in two successive seasons (spring and fall) with varying moisture and potassium rates. Spectral data were collected from drip-irrigated squash that had been treated to varied rates of irrigation and potassium fertilization over both investigated seasons. To forecast potassium-use efficiency (KUE), chlorophyll meter (Chlm), water-use efficiency (WUE), and seed yield (SY) of squash, different commonly used and newly-introduced spectral index values for three bands (3D-SRIs), as well as a Decision Tree (DT) model, were evaluated. The results revealed that the newly constructed three-band SRIs based on the wavelengths of the visible (VIS), near-infrared (NIR), and red-edge regions were sensitive enough to measure the four tested parameters of squash in this study. For instance, NDI558,646,708 presented the highest R 2 of 0.75 for KUE, NDI744,746,738 presented the highest R 2 of 0.65 for Chlm, and NDI670,628,392 presented the highest R 2 of 0.64 for SY of squash. The results further demonstrated that the principal component analysis (PCA) demonstrated the ability to distinguish moisture stress from potassium deficiency stress at the flowering stage onwards. Combining 3D-SRIs, DT-based bands (DT-b), and the aggregate of all spectral characteristics (ASF) with DT models would be an effective strategy for estimating four observed parameters with appropriate accuracy. For example, the model's approximately 30 spectral characteristics were extremely important for predicting KUE. Its outputs with R 2 were, for the training and validation datasets, 0.967 (RMSE = 0.175) and 0.818 (RMSE = 0.284), respectively. For measuring Chlm, the DT-DT-b-20 model demonstrated the best. In the training and validation datasets, the R 2 value was 0.993 (RMSE = 0.522) and 0.692 (RMSE = 2.321), respectively. The overall outcomes showed that proximal-reflectance-Citation: Sharaf-Eldin, M.A.; Elsayed, S.; Elmetwalli, A.H.; Yaseen, Z.M.; Moghanm, F.S.; Elbagory, M.; El-Nahrawy, S.; Omara, A.E.-D.; Tyler, A.N.; Elsherbiny, O. Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress. Horticulturae 2023, 9, 79. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/license s/by/4.0/). Horticulturae 2023, 9, 79 2 of 19 sensing-based 3D-SRIs and DT models based on 3D-SRIs, DT-b, and ASF could be used to evaluate the four tested parameters of squash under different levels of irrigation regimes and potassium fertilizer .
... NDVI is recognized as one of the most effective parameters to characterize vegetation change and can reflect vegetation greenness change well [43]. RVI is widely used to estimate and monitor the biomass of green plants [44], and NDRE is mainly used to analyze vegetation health [45]. Numerous experiments have shown that these three vegetation indices are widely used for land use classification and target extraction and can improve classification accuracy [46][47][48][49][50][51]. ...
Pedicularis has adverse effects on vegetation growth and ecological functions, causing serious harm to animal husbandry. In this paper, an automated detection method is proposed to extract Pedicularis and reveal the spatial distribution. Based on unmanned aerial vehicle (UAV) images, this paper adopts logistic regression, support vector machine (SVM), and random forest classifiers for multi-class classification. One-class SVM (OCSVM), isolation forest, and positive and unlabeled learning (PUL) algorithms are used for one-class classification. The results are as follows: (1) The accuracy of multi-class classifiers is better than that of one-class classifiers, but it requires all classes that occur in the image to be exhaustively assigned labels. Among the one-class classifiers that only need to label positive or positive and labeled data, the PUL has the highest F score of 0.9878. (2) PUL performs the most robustly to change features in one-class classifiers. All one-class classifiers prove that the green band is essential for extracting Pedicularis. (3) The parameters of the PUL are easy to tune, and the training time is easy to control. Therefore, PUL is a promising one-class classification method for Pedicularis extraction, which can accurately identify the distribution range of Pedicularis to promote grassland administration.
Aboveground biomass (AGB) is regarded as a critical variable in monitoring crop growth and yield. The use of hyperspectral remote sensing has emerged as a viable method for the rapid and precise monitoring of AGB. Due to the extensive dimensionality and volume of hyperspectral data, it is crucial to effectively reduce data dimensionality and select sensitive spectral features to enhance the accuracy of rice AGB estimation models. At present, derivative transform and feature selection algorithms have become important means to solve this problem. However, few studies have systematically evaluated the impact of derivative spectrum combined with feature selection algorithm on rice AGB estimation. To this end, at the Xiaogang Village (Chuzhou City, China) Experimental Base in 2020, this study used an ASD FieldSpec handheld 2 ground spectrometer (Analytical Spectroscopy Devices, Boulder, Colorado, USA) to obtain canopy spectral data at the critical growth stage (tillering, jointing, booting, heading, and maturity stages) of rice, and evaluated the performance of the recursive feature elimination (RFE) and Boruta feature selection algorithm through partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM) and ridge regression (RR). Moreover, we analyzed the importance of the optimal derivative spectrum. The findings indicate that (1) as the growth stage progresses, the correlation between rice canopy spectrum and AGB shows a trend from high to low, among which the first derivative spectrum (FD) has the strongest correlation with AGB. (2) The number of feature bands selected by the Boruta algorithm is 19~35, which has a good dimensionality reduction effect. (3) The combination of FD-Boruta-PCR (FB-PCR) demonstrated the best performance in estimating rice AGB, with an increase in R² of approximately 10% ~ 20% and a decrease in RMSE of approximately 0.08% ~ 14%. (4) The best estimation stage is the booting stage, with R² values between 0.60 and 0.74 and RMSE values between 1288.23 and 1554.82 kg/hm². This study confirms the accuracy of hyperspectral remote sensing in estimating vegetation biomass and further explores the theoretical foundation and future direction for monitoring rice growth dynamics.
Aboveground biomass (AGB) is a key parameter reflecting crop growth which plays a vital role in agricultural management and ecosystem assessment. Real-time and non-destructive biomass monitoring is essential for accurate field management and crop yield prediction. This study utilizes a multi-sensor-equipped unmanned aerial vehicle (UAV) to collect remote sensing data during critical growth stages of millet, including spectral, textural, thermal, and point cloud information. The use of RGB point cloud data facilitated plant height extraction, enabling subsequent analysis to discern correlations between spectral parameters, textural indices, canopy temperatures, plant height, and biomass. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models were constructed to evaluate the capability of different features and integrated multi-source features in estimating the AGB. Findings demonstrated a strong correlation between the plant height derived from point cloud data and the directly measured plant height, with the most accurate estimation of millet plant height achieving an R2 of 0.873 and RMSE of 7.511 cm. Spectral parameters, canopy temperature, and plant height showed a high correlation with the AGB, and the correlation with the AGB was significantly improved after texture features were linearly transformed. Among single-factor features, the RF model based on textural indices showcased the highest accuracy in estimating the AGB (R2 = 0.698, RMSE = 0.323 kg m−2, and RPD = 1.821). When integrating two features, the RF model incorporating textural indices and canopy temperature data demonstrated optimal performance (R2 = 0.801, RMSE = 0.253 kg m−2, and RPD = 2.244). When the three features were fused, the RF model constructed by fusing spectral parameters, texture indices, and canopy temperature data was the best (R2 = 0.869, RMSE = 0.217 kg m−2, and RPD = 2.766). The RF model based on spectral parameters, texture indices, canopy temperature, and plant height had the highest accuracy (R2 = 0.877, RMSE = 0.207 kg m−2, and RPD = 2.847). In this study, the complementary and synergistic effects of multi-source remote sensing data were leveraged to enhance the accuracy and stability of the biomass estimation model.
Aboveground biomass (AGB) is a crucial physiological parameter for monitoring crop growth, assessing nutrient status, and predicting yield. Texture features (TFs) derived from remote sensing images have been proven to be crucial for estimating crops AGB, which can effectively address the issue of low accuracy in AGB estimation solely based on spectral information. TFs exhibit sensitivity to the size of the moving window and directional parameters, resulting in a substantial impact on AGB estimation. However, few studies systematically assessed the effects of moving window and directional parameters for TFs extraction on rice AGB estimation. To this end, this study used Unmanned aerial vehicles (UAVs) to acquire multispectral imagery during crucial growth stages of rice and evaluated the performance of TFs derived with different grey level co-occurrence matrix (GLCM) parameters by random forest (RF) regression model. Meanwhile, we analyzed the importance of TFs under the optimal parameter settings. The results indicated that: (1) the appropriate window size for extracting TFs varies with the growth stages of rice plant, wherein a small-scale window demonstrates advantages during the early growth stages, while the opposite holds during the later growth stages; (2) TFs derived from 45° direction represent the optimal choice for estimating rice AGB. During the four crucial growth stages, this selection improved performance in AGB estimation with R ² = 0.76 to 0.83 and rRMSE = 13.62% to 21.33%. Furthermore, the estimation accuracy for the entire growth season is R ² =0.84 and rRMSE =21.07%. However, there is no consensus regarding the selection of the worst TFs computation direction; (3) Correlation (Cor), Mean, and Homogeneity (Hom) from the first principal component image reflecting internal information of rice plant and Contrast (Con), Dissimilarity (Dis), and Second Moment (SM) from the second principal component image expressing edge texture are more important to estimate rice AGB among the whole growth stages; and (4) Considering the optimal parameters, the accuracy of texture-based AGB estimation slightly outperforms the estimation accuracy based on spectral reflectance alone. In summary, the present study can help researchers confident use of GLCM-based TFs to enhance the estimation accuracy of physiological and biochemical parameters of crops.
Crops’ above-ground biomass (AGB) is a crucial indicator that reflects crop health and predicts crop yield.
However, using only optical vegetation indices (VIs) can produce inaccurate AGB estimates due to differences in
crop varieties, growth stages, and measurement environments. Given the advantages of unmanned aerial vehicle
(UAV) RGB and hyperspectral image fusion, this study evaluated the performance of multi-source remote sensing
data for estimating potato AGB at multiple growth stages. In 2019, this study conducted potato trials with
different varieties, fertilization levels, and planting densities at the Xiaotangshan Experiment Base (Beijing). UAV
image and AGB data of potato three main stages were obtained from ground survey work. High-frequency information of the potato canopy was extracted from RGB images using discrete wavelet transform (DWT). VIs and
wavelet energy coefficients were extracted from hyperspectral images using continuous wavelet transform
(CWT). The linear relationships between potato AGB with VIs, high-frequency information, and wavelet coefficients were analyzed. Potato AGB estimation models were constructed based on single and multiple types of
variables using multiple stepwise regression (MSR) and random forest (RF) models, respectively. This work
showed the following results: (i) High-frequency information and wavelet coefficients were more sensitive to
potato multi-growth stage AGB than VIs, and the latter were the most sensitive. (ii) Using VIs, high-frequency
information, or wavelet coefficients separately to estimate the potato multi-growth stage AGB resulted in
higher error and lower model accuracy. (iii) Combining VIs with either high-frequency information or wavelet
coefficients improved the accuracy of AGB estimation, which was further improved by combining high-frequency
information with wavelet coefficients. (iv) Combining VIs with both high-frequency information and wavelet
coefficients provided the highest estimation accuracy using the MSR method. This combined AGB estimation
model reduced the RMSE by 27%, 21%, and 16%, respectively, relative to VIs, high-frequency information, or
wavelet coefficients alone. This result shows that the complementary advantages of multi-source UAV data can
solve the challenge of insufficient AGB estimation by optical remote sensing. The work in this study provides
remote sensing technology support to achieve potato crop growth monitoring and improve yield predictions.
Tobacco Mosaic Virus (TMV) and Potato Virus Y (PVY) pose significant threats to crop production. Non-destructive and accurate surveillance is crucial to effective disease control. In this study, we propose the adoption of hyperspectral and machine learning technologies to discern the type and severity of tobacco leaves affected by PVY and TMV infection. Initially, we applied three preprocessing methods – Multivariate Scattering Correction (MSC), Standard Normal Variate (SNV), and Savitzky-Golay smoothing filter (SavGol) – to corrected the leaf full-length spectral sheet data (350-2500nm). Subsequently, we employed two classifiers, support vector machine (SVM) and random forest (RF), to establish supervised classification models, including binary classification models (healthy/diseased leaves or PVY/TMV infected leaves) and six-class classification models (healthy and various severity levels of diseased leaves). Based on the core evaluation index, our models achieved accuracies in the range of 91–100% in the binary classification. In general, SVM demonstrated superior performance compared to RF in distinguishing leaves infected with PVY and TMV. Different combinations of preprocessing methods and classifiers have distinct capabilities in the six-class classification. Notably, SavGol united with SVM gave an excellent performance in the identification of different PVY severity levels with 98.1% average precision, and also achieved a high recognition rate (96.2%) in the different TMV severity level classifications. The results further highlighted that the effective wavelengths captured by SVM, 700nm and 1800nm, would be valuable for estimating disease severity levels. Our study underscores the efficacy of integrating hyperspectral technology and machine learning, showcasing their potential for accurate and non-destructive monitoring of plant viral diseases.
Rapid and non-destructive potato above ground biomass (AGB) monitoring is a crucial step in the development of
smart agriculture because AGB is closely related to crop growth, yield, and quality. Compared to time-consuming
and laborious field surveys, unmanned aerial vehicle (UAV) remote sensing provides a new direction for large scale AGB monitoring. However, estimating AGB using an optical remote sensing technique usually does not
work well because of spectral saturation, but multi-source remote sensing feature fusion (e.g., fusing spectral and
structural features) can mitigate that problem. Due to potato crop canopy structure and AGB change greatly
during growth, the potential of fusing optical, textural (TFs), and structural features (SFs) for calculating potato
AGB at multiple growth stages was unknown. In addition, the ability of optical features, TFs, and SFs and their
combinations to estimate potato AGB had not been examined. Vegetation indices (RGB-VIs), TFs, and SFs were
extracted from ultra-high spatial resolution RGB images and compared their performances for estimating potato
AGB with those of hyperspectral vegetation indices (H-VIs) obtained from UAV hyperspectral images. The results
revealed that each type of feature had its own advantages and limitations for potato AGB estimation. Except for
canopy volume (CV) in SFs, the best H-VI, RGB-VI, and TF for estimating AGB in both single growth stages and
the entire growth period were inconsistent. When estimating AGB with only a single type of feature, the model
accuracy in descending order was SFs, TFs, H-VIs, and RGB-VIs. The fusion of any two types of remote sensing
features improved AGB estimation model accuracy. Among them, TFs combined with SFs provided the best
estimation performance. The fusion of RGB-VIs, TFs, and SFs produced the best AGB estimates precision (R2 =
0.81, RMSE = 207 kg/hm2
, NRMSE = 17.40%). Since AGB was effectively estimated under different treatments
in the field, the model applicability was confirmed. Using different types of remote sensing features, the Gaussian
process regression method produced better estimation results than the partial least squares regression method
did. This study provides an economic and effective method for monitoring the potato growth in the field, and
thus helps improve farmland production and guide fertilization management
An accurate assessment of vegetable yield is essential for agricultural production and management. One approach to estimate yield with remote sensing is via vegetation indices, which are selected in a statistical and empirical approach, rather than a mechanistic way. This study aimed to estimate the dry matter of Choy Sum by both a causality-guided intercepted radiation-based model and a spectral reflectance-based model and compare their performance. Moreover, the effect of nitrogen (N) rates on the radiation use efficiency ( RUE ) of Choy Sum was also evaluated. A 2-year field experiment was conducted with different N rate treatments (0 kg/ha, 25 kg/ha, 50 kg/ha, 100 kg/ha, 150 kg/ha, and 200 kg/ha). At different growth stages, canopy spectra, photosynthetic active radiation, and canopy coverage were measured by RapidScan CS-45, light quantum sensor, and camera, respectively. The results reveal that exponential models best match the connection between dry matter and vegetation indices, with coefficients of determination ( R ² ) all below 0.80 for normalized difference red edge (NDRE), normalized difference vegetation index (NDVI), red edge ratio vegetation index (RERVI), and ratio vegetation index (RVI). In contrast, accumulated intercepted photosynthetic active radiation ( Aipar ) showed a significant linear correlation with the dry matter of Choy Sum, with root mean square error ( RMSE ) of 9.4 and R ² values of 0.82, implying that the Aipar -based estimation model performed better than that of spectral-based ones. Moreover, the RUE of Choy Sum was significantly affected by the N rate, with 100 kg N/ha, 150 kg N/ha, and 200 kg N/ha having the highest RUE values. The study demonstrated the potential of Aipar -based models for precisely estimating the dry matter yield of vegetable crops and understanding the effect of N application on dry matter accumulation of Choy Sum.
UAV-based multispectral imagery was used to characterize and associate the canopy features of the Moriche palm with the maturity state of its fruits, by correlating variations in the palm’s reflectance at different wavelengths throughout the phenological cycle. Several approaches for feature extraction were compared based on vegetation indices and graph-based models. A comprehensive dataset was collected and labeled, containing spatial–temporal variations in the features. Experimental results reported an accuracy of 72% in the estimation of the fruit maturity state, applying the proposed system to the dense forests of Colombia Amazonian region. Also, this UAV-based vision system enables monitoring, inventorying, palm identification, and fruit maturity identification, providing support to the local indigenous organizations of the Amazon.
Nitrogen management is an essential parameter for monitoring wheat plant growth, yield, and grain quality. Advance control management in wheat production generally requires non-destructive and rapid nitrogen determination. This experiment aimed to examine the relationship between spectral data and nitrogen contents in winter wheat (Triticum aestivum L.) at different growth periods, and develop a regression equation to predict nitrogen status. In this study, quantitative correlations between nitrogen content and canopy hyperspectral reflectance were examined under different nitrogen doses. This study was carried out in 2017 and 2018 at the Daxing experimental research base of the National Water Saving Irrigation Engineering Research Center in Beijing, China. The prediction accuracy of sensitive band 539 nm was maximum at the jointing-booting period with the maximum coefficient of determination(R 2) lowest RMSE (Root mean square error) and RE% (Relative error %) (0.546, 0.424, 12.234). The prediction model accuracy of band 697 nm was the highest with maximum R2 and lowest RMSE and RE% (0.609, 0.406, 16.033) at the booting-heading period. The prediction model of band 700nm has the maximum R 2 and lowest RMSE and RE% (0.875, 0.489, 22.445) value at the heading-maturity period. The prediction accuracy of spectral index NDV1canste (normalized difference vegetation index caste) showed the highest prediction accuracy with the maximum R 2 and lowest RMSE and RE% (0.741, 0.411, 13.013) at the jointing-booting period, mND705 (modified normalized difference 705) showed the highest accuracy with the lowest RMSE and RE% (0.741, 0.41, 13.013) at the booting-heading period. At heading-maturity, NDVI2 showed the highest accuracy with R 2 , RMSE and RE % value (0.824, 0.356, 16.376) in 2018. At the whole growth period, ND705 (normalized difference 705) performed the best accuracy with maximum R 2 , RMSE, and RE% (0.90, 0.389, 16.453). In conclusion, these hyperspectral prediction models are the best for predicting nitrogen in winter wheat at various growth periods.
UAV-captured multispectral imagery was used to characterize and associate Moriche’s palm 1 canopy features with the maturity stage of the corresponding fruits. Deep learning models based on 2 convolutional neural networks (CNN) were trained in order to determine correlations between the 3 photosynthetic radiation of the palms with the fruit. Here, we compare several approaches for feature 4 extraction based on vegetation indices and graph-based models. Also, a comprehensive dataset has 5 been collected and labeled, containing plant data for an entire phenological cycle of the Moriche 6 palms. Experimental results reported an average estimation accuracy of 72%, by using the proposed 7 method in dense forests of the Amazonian region.
Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R2 = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale.
Mapping and predicting crop yield on a large scale is increasingly important for use cases such as policy-making, risk insurance and precision agriculture applications at farm and field scale. The higher spatial resolution of Sentinel-2 compared to Landsat allows for satellite-based crop yield mapping even in relatively small scaled agricultural settings such as found in Switzerland and other central European regions. In this study, five years (2017–2021) of cereal crop yield data from a combine harvester were used to model crop yield within-field, on a spatial scale corresponding to the Sentinel-2 pixel level. Three established methods from literature using (i-ii) spectral indices and (iii) raw satellite reflectance as well as (iv) a recurrent neural network (RNN) were chosen for analysis. Although the RNN approach did not outperform the other methods, it was more efficient because of the comparatively simple end-to-end training of the model, resulting in much less time spent on data cleaning and feature extraction needed for spectral index time series analysis. The RNN was also able to discriminate cloudy data by itself, reaching similar performance levels as if using pre-processed, cloud-free data. Modelling was performed on individual years, all years combined and on unseen years using leave-one-year-out cross-validation. The models performed best when using data from all years (R2 up to 0.88, relative RMSE up to 10.49 %) and showed poor performance when predicting on unseen data years, especially for years with previously unknown weather patterns. This highlights the importance of yearly model calibration and the need for continuous data collection enabling long time series for future crop yield models.
Above-ground biomass (AGB) is one of the most important indicators for evaluating potato growth and yield. Rapid and accurate biomass estimation is of great significance to potato breeding and agricultural production. However, high cost, large data volume, and poor model scalability are the main problems of hyperspectral remote sensing and LiDAR in existing AGB measurement methods, especially in small-scale farmland. One of the important methods for solving the above problems is extracting canopy structure features through RGB images. In this study, a new AGB estimation method for potatoes at the field scale was proposed by using canopy leaf detection and digital images. First, using the improved feature fusion network and the soft intersection over union (soft-IoU) layer, an improved detection network of dense leaves, DenseNet-potato, was developed to detect canopy leaves. Second, the detection network was used to extract the canopy structural features, and the corrected number and total area of canopy leaves were obtained. Finally, multilayer perceptron (MLP) regression was introduced to build prediction models for AGB using canopy features. It was found that the DenseNet-potato network had excellent detection effects on dense canopy leaves. The mAP50 and mAP75 of the two detection pipelines reached 76.63% and 64.35%, respectively, which were 9.17% and 6.05% higher than the state-of-the-art RetinaNet method. In addition, the results indicated a strong correlation between the estimated and field-observed AGB using the MLP method from the digital camera dataset (R2 = 0.83, RMSE = 0.039 kg/plot, NRMSE = 12.16%), while the unmanned aerial vehicle (UAV) dataset was unsatisfactory (R2 = 0.62, RMSE = 0.051 kg/plot, NRMSE = 15.32%). This study can provide a reference for efficiently estimating potato AGB using RGB images.
China consumes more than 1/3 of global N fertilizers for rice with less than 1/5 of the world rice planting area. As a consequence, N efficiency is low and nitrate pollution risk is high. Developing efficient N management strategies and technologies for rice are therefore needed. Here, we developed an active canopy sensor-based precision N management strategy for rice in Northeast China. Four site-years of field N rate experiments were conducted in 2008 and 2009 in Sanjiang Plain, Heilongjiang, China. The GreenSeeker active sensor was used to collect rice canopy reflectance data at different growth stages. Three on-farm experiments were conducted in 2011 to evaluate the performance of the developed strategy. The results show that the crop sensor can be used to calculate rice yield potential without additional topdressing N application at stem elongation or booting stage. The GreenSeeker-based precision N management strategy has a regional optimum N rate of 90–110 kg N ha−1 as initial total amount and 45 and 20 % as basal and tillering N application. It uses the crop sensor to estimate the topdressing N rate at stem elongation stage. GreenSeeker-based precision management and chlorophyll meter-based site-specific N management increased the partial factor productivity of farmers by 48 and 65 %, respectively, without significant change in grain yield. The crop sensor-based N management strategy can therefore improve N use efficiency of rice. It is more suitable for practical on-farm applications, and will contribute to the sustainable development of rice farming.
Understanding spatial variability of indigenous nitrogen (N) supply (INS) is important to the implementation of precision N management (PNM) strategies in small scale agricultural fields of the North China Plain (NCP). This study was conducted to determine: (1) field-to-field and within-field variability in INS; (2) the potential savings in N fertilizers using PNM technologies; and (3) winter wheat (Triticum aestivum L.) N status variability at the Feekes 6 stage and the potential of using a chlorophyll meter (CM) and a GreenSeeker active crop canopy sensor for estimating in-season N requirements. Seven farmer’s fields in Quzhou County of Hebei Province were selected for this study, but no fertilizers were applied to these fields. The results indicated that INS varied significantly both within individual fields and across different fields, ranging from 33.4 to 268.4 kg ha−1, with an average of 142.6 kg ha−1 and a CV of 34%. The spatial dependence of INS, however, was not strong. Site-specific optimum N rates varied from 0 to 355 kg ha−1 across the seven fields, with an average of 173 kg ha−1 and a CV of 46%. Field-specific N management could save an average of 128 kg N ha−1 compared to typical farmer practices. Both CM and GreenSeeker sensor readings were significantly related to crop N status and demand across different farmer’s fields, showing a good potential for in-season site-specific N management in small scale farming systems. More studies are needed to further evaluate these sensing technology-based PNM strategies in additional farmer fields in the NCP.
Real-time and nondestructive monitoring of crop nitrogen (N) status is of significant importance for precision N management in rice and wheat production. In eight field experiments with different N rates, water regimes and cultivars in rice and wheat crops, a new form of three-band vegetation indices was constructed to reduce saturation in two-band vegetation indices, and the optimal common three-band vegetation index was selected to establish models for canopy leaf N concentration (LNC) monitoring in rice and wheat. The results showed that the linear models for LNC monitoring with (R924 − R703 + 2 × R423)/(R924 + R703 − 2 × R423) were stable and accurate, with coefficient of determination (R2) of 0.870 and 0.857, and SE of 0.052 and 0.148 in rice and wheat, respectively. Testing of the models with independent data gave R2 of 0.866 and 0.883, RRMSE of 13.1% and 16.9%, and slope of 0.741 and 0.980 in rice and wheat, respectively. Further analysis of the influence of bandwidth change on LNC accuracy indicated that the allowable bandwidths for the central bands were 36 nm for 924 nm, 15 nm for 703 nm and 21 nm for 423 nm. The new three-band vegetation indices with narrow bands and broad bands in the present study are generally more effective for LNC monitoring compared with the other published vegetation indices.
The reflectance from rice (Oryza sativa L.) leaves and canopy damaged by rice leaf folder (RLF), Cnaphalocrocis medinalis (Guenée) was studied at the booting stage in order to establish a monitoring method for RLF based on hyperspectral data. The results showed that reflectance from rice leaves significantly decreased in the green (530–570 nm) and near infrared (700–1000 nm) regions, and significantly increased in the blue (450–520 nm) and red (580–700 nm) regions as the leaf-roll rate of rice increased. Reflectance from rice canopy significantly decreased in the spectral regions from 737 to 1000 nm as the infestation scale of RLF increased, and the most correlation appeared at 938 nm. Seven spectral regions 503–521, 526–545, 550–568, 581–606, 688–699, 703–715, and 722–770 nm at leaf-level, and one region 747–754 nm at canopy-level were found to be sensitive bands to exhibit the damage severity in rice by RLF. The position of the red edge peak remarkably moved to blue region, and the amplitude and area of the red edge significantly decreased when rice leaves were severely infected by RLF. Thirty-eight spectral indices at leaf-level and 29 indices at canopy-level were found to be sensitive to leaf-roll rate and infestation scale in rice, respectively. The linear regression models were built to detect the leaf-roll rate (0.0–1.0) and infestation scale (0–5) in rice using leaf- and canopy-level reflectance data. The root mean square error of the model was only 0.059 and 0.22 for the leaf-roll rate and infestation scale, respectively. These results suggested that the hyperspectral reflectance was potential to detect RLF damage severity in rice.
This paper contributes an assessment
for estimating rice (Oryza sativa L., irrigated lowland
rice) biomass by canopy re%ectance in the
Sanjiang Plain, China. Hyperspectral data were
captured with 'eld spectroradiometers in experimental
'eld plots and farmers’ 'elds and then accompanied
by destructive aboveground biomass
(AGB) sampling at different phenological growth
stages. Best single bands, best two band-combinations,
optimised simple ratio (SR), and optimised
normalized ratio index (NRI), as well as multiple
linear regression (MLR) were calculated from the
re%ectance for the non-destructive estimation of
rice AGB. Experimental 'eld data were used as the
calibration dataset and farmers’ 'eld data as the
validation dataset. Re%ectance analyses display
several sensitive bands correlated to rice AGB, e.g.
550, 670, 708, 936, 1125, and 1670 nm, which
changed depending on the phenological growth
stages. These bands were detected by correlograms
for SR, NRI, and MLR with an offset of approximately
± 10 nm. The assessment of the three methods
showed clear advantages of MLR over SR and
NRI in estimating rice AGB at the tillering and
stem elongation stages by 'tting and evaluating the
models. The optimal band number forMLR was set
to four to avoid over'tting. The best validatedMLR
model (R2 = 0.82) at the tillering stage was using
four bands at 672, 696, 814 and 707 nm. Overall,
the optimized SR, NRI, and MLR have a great potential
in non-destructive estimation of rice AGB at
different phenological stages. The performance
against the validation dataset showed R2 of 0.69 for
SR and R2 of 0.70 for NRI, respectively
Estimation of rice disease using spectral reflectance is important to non-destructive, rapid, and accurate monitoring of rice
health. In this study, the rice reflectance data and disease index (DI) were determined experimentally and analyzed by single
wave correlation, regression model and neural network model. The result showed that raw spectral reflectance and first derivative
reflectance (FDR) difference of the rice necks under various disease severities is clear and obvious in the different spectral
regions. There was also significantly negative or positive correlation between DI and raw spectral reflectance, FDR. The regression
model was built with raw and first derivative spectral reflectance, which was correlated highly with the DI. However, due
to rather complicated non-linear relations between spectral reflectance data and DI, the results of DI retrieved from the
regression model was not so ideal. For this reason, an artificial neural network model (BP model) was constructed and applied
in the retrieval of DI. For its superior ability for solving the non-linear problem, the BP model provided better accuracy
in retrieval of DI compared with the results from the statistic model. Therefore, it was implied that the rice neck blasts
could be predicted by remote sensing technology.
Improvements of nitrogen use efficiency (NUE) maybe achieved through the use of sensing tools for N status determination. Leaf and canopy chlorophyll, as well as leaf polyphenolics concentrations, are characteristics strongly affected by N availability that are often used as a surrogate to direct plant N status estimation. Approaches with near-term operational sensors, handheld and tractor-mounted, for proximal remote measurements are considered in this review. However, the information provided by these tools is unfortunately biased by factors other than N. To overcome this obstacle, normalization procedures such as the well-fertilized reference plot, the no-N reference plot, and relative yield are often used. Methods to establish useful relationships between sensor readings and optimal N rates, such as critical NSI (nitrogen sufficiency index), INSEY (in-season estimated yield), and the relationship between chlorophyll meter readings, grain yield, and sensor-determined CI (chlorophyll index) are also reviewed. In a few cases, algorithms for translating readings into actual N fertilizer recommendation have been developed, but their value still seems limited to conditions similar to the ones where the research was conducted. Near-term operational sensing can benefit from improvements in sensor operational characteristics (size and shape of footprint, positioning) or the choice of light wavebands more suitable for specific conditions (i.e., genotype, growth stage, or crop density). However, one important limitation to their widespread use is the availability of algorithms that would be reliable in a variety of soil and weather conditions.
For high water usage cropping systems such as irrigated rice, the positive outcomes of producing a staple food source and sustaining the economy often come at the cost of high resource use and environmental degradation. Advances in geospatial technology will play an increasingly important role in raising productivity and resource use efficiency and reducing environmental degradation, both worldwide and within Australia. This paper reviews the current use of one of these technologies, remote sensing, with the rice-growing region in Australia as a case study. Specifically, we review applications of remote sensing in crop identification, area measurement, regional yield forecasting, and on-farm productivity monitoring and management. Within this context, consideration is given to classification algorithms and accuracy assessment, hyperspectral remote sensing, positional and areal accuracy, linear mixture modelling, methane (CH4) emissions, yield forecasting techniques, and precision agriculture. We also discuss the potential for using remote sensing to assess crop water use, which has received little attention in rice-based irrigation systems, even though it is becoming increasingly important in land and water management planning for irrigation areas. Accordingly, special attention is given to the role of remote sensing with respect to the surface energy balance, the relationship between surface temperature and remotely sensed vegetation indices, and water use efficiency. A general discussion of other geospatial issues, namely geographic information systems and spatial interpolation, is provided because earth-science analysis using remote sensing is often intrinsically integrated with other spatially based technologies and aspects of geographical science.
The influence of morphophysiological variation at different growth stages on the performance of vegetation indices for estimating plant N status has been confirmed. However, the underlying mechanisms explaining how this variation impacts hyperspectral measures and canopy N status are poorly understood. In this study, four field experiments involving different N rates were conducted to optimize the selection of sensitive bands and evaluate their performance for modeling canopy N status of rice at various growth stages in 2007 and 2008. The results indicate that growth stages negatively affect hyperspectral indices in different ways in modeling leaf N concentration (LNC), plant N concentration (PNC) and plant N uptake (PNU). Published hyperspectral indices showed serious limitations in estimating LNC, PNC and PNU. The newly proposed best 2-band indices significantly improved the accuracy for modeling PNU (R2 = 0.75–0.85) by using the lambda by lambda band-optimized algorithm. However, the newly proposed 2-band indices still have limitations in modeling LNC and PNC because the use of only 2-band indices is not fully adequate to provide the maximum N-related information. The optimum multiple narrow band reflectance (OMNBR) models significantly increase the accuracy for estimating the LNC (R2 = 0.67–0.71) and PNC (R2 = 0.57–0.78) with six bands. Results suggest the combinations of center of red-edge (735 nm) with longer red-edge bands (730–760 nm) are very efficient for estimating PNC after heading, whereas the combinations of blue with green bands are more efficient for modeling PNC across all stages. The center of red-edge (730–735 nm) paired with early NIR bands (775–808 nm) are predominant in estimating PNU before heading, whereas the longer red-edge (750 nm) paired with the center of “NIR shoulder” (840–850 nm) are dominant in estimating PNU after heading and across all stages. The OMNBR models have the advantage of modeling canopy N status for the entire growth period. However, the best 2-band indices are much easier to use. Alternatively, it is also possible to use the best 2-band indices to monitor PNU before heading and PNC after heading. This study systematically explains the influences of N dilution effect on hyperspectral band combinations in relating to the different N variables and further recommends the best band combinations which may provide an insight for developing new hyperspectral vegetation indices.
Plant biomass and nitrogen status are important factors to consider when making in-season crop management decisions. Traditional sampling and analysis are time-consuming, labor-intensive and costly. It is desirable to estimate these parameters nondestructively using remote sensing technology. The objective of this study is to evaluate the potential of using an active crop canopy sensor, GreenSeeker, for estimating winter wheat biomass, nitrogen concentration and uptake in North China Plain. A total of 13 field experiments involving different N rates, varieties and sites were conducted from 2004 to 2007 in Shandong Province, China. In addition, data from 69 farmer's fields were also collected to further evaluate the sensor's potential application under on-farm conditions. The results indicated that across sites, years, experiments and growth stages, normalized difference vegetation index became saturated when biomass reached 3736 kg ha-1 , or when plant nitrogen uptake reached 131 kg ha-1. Ratio vegetation index was linearly related with winter wheat biomass and plant nitrogen uptake and did not show obvious saturation effect. However, none of the two vegetation indices performed well for nitrogen concentration estimation. We conclude that RVI should be selected when using the GreenSeeker crop sensor to estimate winter wheat biomass or N uptake across sites, years and growth stages. The NDVI index can also be used before plant biomass and N uptake reach threshold values. More research is needed to further evaluate the results under more diverse conditions, and develop strategies of using the GreenSeeker 1220 Intelligent Automation and Soft Computing active sensor for diagnosing crop growth and N status and making in-season management decisions, especially under high-yielding conditions.
The chlorophyll meter (CM) has been commonly used for in-season nitrogen(N) management of corn (Zea mays L.). Nevertheless, it has limited potential for site specific N management in large fields due to difficulties in using it to generate N status maps. The objective of this study was to determine how well CM readings can be estimated using aerial hyperspectral and simulated multispectral remote sensing images at different corn growth stages. Two field experiments were conducted in Minnesota, USA during 2005 involving different N application rates and timings on a corn-soybean [Glycine max(L.) Merr.] rotation field and a corn-corn rotation field. Four flights were made during the growing season using the AISA Eagle Hyper-spectral Imager and CM readings were collected at four or five different growth stages. The results
indicated that single multispectral and hyperspectral band or vegetation index could explain 64–86% and 73–88% of the variability in CM readings, respectively, except at growth stage V9 in the corn-soybean rotation field where no band or vegetation index could explain more than 37% of the variability in CM readings. Multiple regression analysis demonstrated that the combination of 2–4 broad-bands or 3–8 narrow-bands could explain 41–92% or 61–94% of the variability in CM readings across the two fields and different corn growth stages investigated. It was concluded that the combination of CM readings with high spatial resolution hyperspectral or multispectral remote sensing images can overcome the limitations of using them individually, thus offering a practical solution to N deficiency detection and possibly in-season site-specific N management in large continuous corn fields or at later stages in corn-soybean rotation fields.
The main objectives of this research were to: (a) determine the best hyperspectral wavebands in the study of vegetation and agricultural crops over the spectral range of 400 – 2500 nm; and (b) assess the vegetation and agricultural crop classification accuracies achievable using the various combinations of the best hyperspectral narrow wavebands. The hyperspectral data were gathered for shrubs, grasses, weeds, and agricultural crop species from the four ecoregions of African savannas using a 1-nm-wide hand-held spectroradiometer but was aggregated to 10-nm-wide bandwidths to match the first spaceborne hyperspectral sensor, Hyperion. After accounting for atmospheric widows and/or areas of significant noise, a total of 168 narrowbands in 400 – 2500 nm was used in the analysis. Rigorous data mining techniques consisting of principal component analysis (PCA), lambda – lambda R 2 models (LL R 2 M), stepwise discriminant analysis (SDA), and derivative greenness vegetation indices (DGVI) established 22 optimal bands (in 400 – 2500 nm spectral range) that best characterize and classify vegetation and agricultural crops. Overall accuracies of over 90% were attained when the 13 – 22 best narrowbands were used in classifying vegetation and agricultural crop species. Beyond 22 bands, accuracies only increase marginally up to 30 bands. Accuracies become asymptotic or near zero beyond 30 bands, rendering 138 of the 168 narrowbands redundant in extracting vegetation and agricultural crop information. Relative to Landsat Enhanced Thematic Mapper plus (ETM +) broadbands, the best hyperspectral narrowbands provided an increased accuracy of 9 – 43% when classifying shrubs, weeds, grasses, and agricultural crop species.
Many hyperspectral vegetation indices (VIs) have been developed to estimate crop nitrogen (N) status at leaf and canopy levels. However, most of these indices have not been evaluated for estimating plant N concentration (PNC) of winter wheat (Triticum aestivum L.) at different growth stages using a common on-farm dataset. The objective of this study was to evaluate published VIs for estimating PNC of winter wheat in the North China Plain for different growth stages and years using data from both N experiments and farmers’ fields, and to identify alternative promising hyperspectral VIs through a thorough evaluation of all possible two band combinations in the range of 350–1075 nm. Three field experiments involving different winter wheat cultivars and 4–6 N rates were conducted with cooperative farmers from 2005 to 2007 in Shandong Province, China. Data from 69 farmers’ fields were also collected to evaluate further the published and newly identified hyperspectral VIs. The results indicated that best performing published and newly identified VIs could explain 51% (R700/R670) and 57% (R418/R405), respectively, of the variation in PNC at later growth stages (Feekes 8–10), but only 22% (modified chlorophyll absorption ratio index, MCARI) and 43% (R763/R761), respectively, at the early stages (Feekes 4–7). Red edge and near infrared (NIR) bands were more effective for PNC estimation at Feekes 4–7, but visible bands, especially ultraviolet, violet and blue bands, were more sensitive at Feekes 8–10. Across site-years, cultivars and growth stages, the combination of R370 and R400 as either simple ratio or a normalized difference index performed most consistently in both experimental (R
2 = 0.58) and farmers’ fields (R
2 = 0.51). We conclude that growth stage has a significant influence on the performance of different vegetation indices and the selection of sensitive wavelengths for PNC estimation, and new approaches need to be developed for monitoring N status at early growth stages.
There is currently a great deal of interest in the quantitative characterization of temporal and spatial vegetation patterns with remotely sensed data for the study of earth system science and global change. Spectral models and indices are being developed to improve vegetation sensitivity by accounting for atmosphere and soil effects. The soil-adjusted vegetation index (SAVI) was developed to minimize soil influences on canopy spectra by incorporating a soil adjustment factor L into the denominator of the normalized difference vegetation index (NDVI) equation. For optimal adjustment of the soil effect, however, the L factor should vary inversely with the amount of vegetation present. A modified SAVI (MSAVI) that replaces the constant L in the SAVI equation with a variable L function is presented in this article. The L function may be derived by induction or by using the product of the NDVI and weighted difference vegetation index (WDVI). Results based on ground and aircraft-measured cotton canopies are presented. The MSAVI is shown to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a “vegetation signal” to “soil noise” ratio.
This discussion concerns the reflectance of near-infrared light from plant leaves and plant cellular constituents in the 750-to 1350-nm wavelength interval.
Spectral reflectance of several rice varieties with different morphological characteristics was measured at Thematic Mapper wavebands using a portable radiometer. The visible and near infrared response of rice growing in flooded soils was similar to that of other crops growing in dryland conditions. At middle infrared wavelengths, however, reflectance either increased with leaf area index or remained relatively unaffected in contrast to what has been found for other crops. Results of the research suggest that LAI of conventional rice research plots may be monitored using field spectral measurements.-from Authors Evapotranspiration Lab, Dept of Agrnomy, Kansas State Univ, Manhattan, KS 66506, USA.
Plant biomass and nitrogen status are important factors to consider when making in-season crop management decisions. Traditional sampling and analysis are time-consuming, labor-intensive and costly. It is desirable to estimate these parameters nondestructively using remote sensing technology. The objective of this study is to evaluate the potential of using an active crop canopy sensor, GreenSeeker, for estimating winter wheat biomass, nitrogen concentration and uptake in North China Plain. A total of 13 field experiments involving different N rates, varieties and sites were conducted from 2004 to 2007 in Shandong Province, China. In addition, data from 69 farmer's fields were also collected to further evaluate the sensor's potential application under on-farm conditions. The results indicated that across sites, years, experiments and growth stages, normalized difference vegetation index became saturated when biomass reached 3736 kg ha-1, or when plant nitrogen uptake reached 131 kg ha-1. Ratio vegetation index was linearly related with winter wheat biomass and plant nitrogen uptake and did not show obvious saturation effect. However, none of the two vegetation indices performed well for nitrogen concentration estimation. We conclude that RVI should be selected when using the Green Seeker crop sensor to estimate winter wheat biomass or N uptake across sites, years and growth stages. The NDVI index can also be used before plant biomass and N uptake reach threshold values. More research is needed to further evaluate the results under more diverse conditions, and develop strategies of using the Green Seeker active sensor for diagnosing crop growth and N status and making in-season management decisions, especially under high-yielding conditions. Copyright
Crop Circle is an active multispectral canopy sensor developed to support precision crop management. The Crop Circle ACS-470 model is user configurable, with a choice of six wavebands covering blue, green, red, red edge and near infrared spectral regions. The objectives of this study were to determine how well nitrogen (N) status of rice (Oryza sativa L.) can be estimated with the Crop Circle ACS-470 active sensor using green, red edge and near infrared (NIR) bands at key growth stages and identify important vegetation indices for estimating rice N status indicators. Six field experiments involving different N rates and two varieties were conducted in Sanjiang Plain in Heilongjiang Province, China during 2011 and 2012. Crop sensor data and plant samples were also collected from five farmers’ fields to further evaluate the sensor and selected vegetation indices. The results of the study indicated that among 43 different vegetation indices evaluated, modified chlorophyll absorption reflectance index 1 (MCARI1) had consistent correlations with rice aboveground biomass (R2 = 0.79) and plant N uptake (R2 = 0.83) across growth stages. Four red edge-based indices, red edge soil adjusted vegetation index (RESAVI), modified RESAVI (MRESAVI), red edge difference vegetation index (REDVI) and red edge re-normalized difference vegetation index (RERDVI), performed equally well for estimating N nutrition index (NNI) across growth stages (R2 = 0.76). For rice plant N concentration, the highest R2 was 0.33, and none of the indices performed satisfactorily with validation using farmers’ field data. We conclude that the Crop Circle ACS-470 active canopy sensor allows users the flexibility to select suitable bands and calculate different vegetation indices and has a great potential for in-season non-destructive estimation of rice biomass, plant N uptake and NNI.
Normalized difference vegetation index (NDVI) measurements have the potential to improve mid-season N crop management decisions in rice (Oryza sativa L.). The objectives of this study were to determine the optimum sensing timing and establish a yield prediction model using NDVI measurements acquired with the GreenSeeker sensor. Weekly sensor readings were collected over a 5-wk period from multi-rate N fertilization trials established at six different locations from 2008 to 2010. Categorizing sensing timing by growth stage demonstrated that late sensing timings beyond panicle differentiation (PD) were impractical and reduced yield potential estimation as opposed to panicle initiation (PI) and PD timings. Regression analysis produced two viable yield potential prediction equations at PI (r(2) = 0.36) and PD (r(2) = 0.42). When sensor timings were categorized by cumulative growing degree days (GDD), 1301 to 1500 and > 2100 GDD groupings (r(2) = 0.28 and 0.37, respectively) were found to be inferior yield predictors as compared with 1501 to 1700 and 1701 to 1900 GDD groupings (r(2) = 0.41 for both). In almost all instances, normalization of NDVI data using days from seeding (DFS; NDVI/DFS) or GDD (NDVI/GDD) did not improve yield potential prediction as compared with NDVI alone. Yield potential, response index, and N response to fertilization are the three major components needed to produce a working algorithm capable of predicting mid-season N fertilization needs in rice. The four yield prediction models gleaned from this study provide the yield potential component for this algorithm. Multiple yield prediction models give crop managers freedom to select a model based on either physical growth stage or by accumulated GDD units.
In attempting to analyze, on digital computers, data from basically continuous physical experiments, numerical methods of performing familiar operations must be developed. The operations of differentiation and filtering are especially important both as an end in themselves, and as a prelude to further treatment of the data. Numerical counterparts of analog devices that perform these operations, such as RC filters, are often considered. However, the method of least squares may be used without additional computational complexity and with considerable improvement in the information obtained. The least squares calculations may be carried out in the computer by convolution of the data points with properly chosen sets of integers. These sets of integers and their normalizing factors are described and their use is illustrated in spectroscopic applications. The computer programs required are relatively simple. Two examples are presented as subroutines in the FORTRAN language.
Timely and accurate quantification of aerial nitrogen (N) uptake in crops is important for the calculation of regional N balances and the study of the N budget in agro-ecosystems. Experiments in the present study were conducted from 2007 to 2011 to remotely estimate the aerial N uptake of diverse winter wheat cultivars grown in contrasting climatic and geographic zones in China and Germany. Potentials and limitations of hyperspectral indices obtained from (i) optimized algorithms and (ii) 15 representative indices reported in the literature were tested for stability in estimating the aerial N uptake of winter wheat across different growth stages, cultivars, sites and years. Growth stage, cultivar, N application rates, site and year greatly influenced the relationship between hyperspectral indices and aerial N uptake. The optimized hyperspectral indices generally had more robust aerial N uptake prediction abilities than the published indices. Compared with the algorithms of all possible two-band combinations and red-edge position-based algorithms, area-based algorithms for a three-band optimized combination were more stable in deriving the aerial N uptake of winter wheat. Optimized algorithms can potentially be implemented in future aerial N uptake monitoring by hyperspectral sensing.
Changing sensor view angles can alter the proportion of water background in rice (Oryza sativa L.) canopy reflectance spectra. However, its impact on the reported interference of water background in rice fields and performance of spectral vegetation indices (SVI) derived from canopy reflectance as predictors of rice biomass and grain yield is unknown. The objective of this study was to evaluate the relationships of normalized difference vegetation index (NDVI) and simple ratio (SR), measured at different view angles using an active sensor, with rice biomass and grain yield. Sensor readings and biomass at panicle differentiation (PD) and 50% heading, and grain yield were collected from multiple variety x N rate trials established at different rice-producing areas of the mid-southern United States in 2009 and 2010. Three sensor view angles were evaluated: nadir 0 degrees, and two off-nadirs. The relationships of NDVI and SR with biomass and grain yield were exponential and linear (P < 0.05), respectively. At PD, the pattern and values of NDVI and SR with biomass were similar across sensing view angles. At 50% heading, the off-nadir angles viewed more green vegetation scene from rice stems than the nadir which caused NDVI to approach saturation at a lower biomass level. In addition, there were a higher number of sites where the nadir-acquired NDVI obtained better exponential relationships with biomass than the off-nadir view angles. There were larger differences in coefficient of determination (r(2)) values across site years than view angles implying the larger influence of spatiotemporal variability on NDVI and SR.
Crop Circle is an active multispectral canopy sensor developed to support precision crop management. The Crop Circle ACS-470 model is user configurable, with a choice of six wavebands covering blue, green, red, red edge and near infrared spectral regions. The objectives of this study were to determine how well nitrogen (N) status of rice (Oryza sativa L.) can be estimated with the Crop Circle ACS-470 active sensor using green, red edge and near infrared (NIR) bands at key growth stages and identify important vegetation indices for estimating rice N status indicators. Six field experiments involving different N rates and two varieties were conducted in Sanjiang Plain in Heilongjiang Province, China during 2011 and 2012. Crop sensor data and plant samples were also collected from five farmers' fields to further evaluate the sensor and selected vegetation indices. The results of the study indicated that among 43 different vegetation indices evaluated, modified chlorophyll absorption reflectance index 1 (MCARI1) had consistent correlations with rice aboveground biomass (R 2 = 0.79) and plant N uptake (R 2 = 0.83) across growth stages. Four red edge-based indices, red edge soil adjusted vegetation index (RESAVI), modified RESAVI (MRESAVI), red edge difference vegetation index (REDVI) and red edge re-normalized difference vegetation index (RERDVI), performed equally well for estimating N nutrition index (NNI) across growth stages (R 2 = 0.76). For rice plant N concentration, the highest R 2 was 0.33, and none of the indices performed satisfactorily with validation using farmers' field data. We conclude that the Crop Circle ACS-470 active canopy sensor allows users the flexibility to select suitable bands and calculate different vegetation indices and has a great potential for in-season non-destructive estimation of rice biomass, plant N uptake and NNI.
Conventional farming has led to extensive use of chemicals and, in turn, to negative environmental impacts such as soil erosion, groundwater pollution and atmosphere contamination. Farming systems should be more sustainable to reach economical and social profitability as well as environmental preservation. A possible solution is to adopt precision agriculture, a win–win option for sustaining food production without degrading the environment. Precision technologies are used for gathering information about spatial and temporal differences within the field in order to match inputs to site-specific field conditions. Here we review reports on the precision N management of wheat crop. The aims are to perform an investigation both on approaches and results of site-specific N management of wheat and to analyse performance and sustainability of this agricultural practice. In this context, we analysed literature of the last 10–15 years. The major conclusions are: (a) before making N management decisions, both the measurement and understanding of soil spatial variability and the wheat N status are needed. Complementary use of different sensors has improved soil properties assessment at relatively low cost; (b) results show the usefulness of airborne images, remote and proximal sensing for predicting crop N status by responsive in-season management approaches; (c) red edge and near-infrared bands can penetrate into higher vegetation fraction of the canopy. These narrowbands better estimated grain yield, crop N and water status, with R
2 higher than 0.70. In addition, different hyperspectral vegetation indices accounted for a high variability of 40–75 % of wheat N status; (d) various diagnostic tools and procedures have been developed in order to help wheat farmers for planning variable N rates. In-season adjustments in N fertilizer management can account for the specific climatic conditions and yield potential since less than 30 % of spatial variance could show temporal stability; (e) field studies in which sensor-based N management systems were compared with common farmer practices showed high increases in the N use efficiency of up to 368 %. These systems saved N fertilizers, from 10 % to about 80 % less N, and reduced residual N in the soil by 30–50 %, without either reducing yields or influencing grain quality; (f) precision N management based on real-time sensing and fertilization had the highest profitability of about $5–60 ha−1 compared to undifferentiated applications.
Excessive nitrogen (N) fertilizer application is very common in the North China Plain. Diagnosis of in-season N status in crops is critical for precision N management in this area. Remote sensing, as a timely and nondestructive tool, could be an alternative to traditional plant testing for diagnosing crop N status. The objectives of this study were to determine which vegetation indices could be used to estimate N status in winter wheat (Triticum aestivum L.) under high N input conditions, develop models to predict winter wheat N uptake using spectral vegetation indices and validate the models with data from farmers’ fields. An N rate experiment and a variety-N experiment were conducted in Huimin, Shandong Province from 2005/2006 to 2006/2007 to develop the models. Positive linear relationships between simple ratio vegetation indices (red vegetation index, RVI and green vegetation index, GVI) and N uptake were observed independent of growth stages and varieties (R2, 0.48–0.74). In contrast, the relationships between normalized difference vegetation indices (NDVI and GNDVI), red and green normalized difference vegetation index (RGNDI), and red and green ratio vegetation index (RGVI) were exponentially related to N uptake (R2, 0.43–0.79). Subsequently, 69 farmers’ fields in four different villages were selected as datasets to validate the developed models. The results indicated that the prediction using RVI had the highest coefficient of determination (R2, 0.60), the lowest root mean square error (RMSE, 39.7 kg N ha−1) and relative error (RE, 30.5%) across different years, varieties and growth stages. We conclude that RVI can be used to estimate nitrogen status for winter wheat in over-fertilized farmers’ fields before heading.
ABSTRACT,Gilabert et al., 1996; Huete, 1988; Price, 1992; Price and Bausch, 1995; Wiegand et al., 1991). Field experiments were conducted to study the seasonal changes These ratio-based spectral indices such as NDVI (Gi- of rice reflectance spectra and approaches to model rice (Oryza sativa labert et al., 1996) and soil adjusted vegetation index L.) growth with high-resolution reflectance data. Ground-based re- (SAVI) (Huete, 1988) were designed to minimize inter- motely sensed canopy spectra and growth parameters of rice plants, including fresh weights of leaves and shoots, dry weights of leaves ference by internal geometry noise caused mostly by and shoots, plant height, and leaf area index, wereet al., 1974; Tucker et al., 1979). for estimating rice growth. These MLR models combining spectral In addition to spectral indices, combining spectral reflectance from more than two wavebands provided flexibility in reflectance from two or more characteristic wavebands choosing the individual narrow bands, exhibited a greater sensitivity into single numbers may improve sensitivity to plant to phenological variation, and improved the models’ ability to estimate vegetation relative to using individual wavebands,(Wan- plant growth. jura and Hatfield, 1987). Many studies have analyzed the relationships between,spectral characteristics and vegetation attributes through regression analysis, show- O ne of the advantagesof remote sensing is that the,ing that multiple regression models,provide flexibility technique can provide timely information,with fair,in choosing discrete narrow,bands and give better infor- accuracy and precision on the current status of inter- mation from spectral data (Lawrence and Ripple, 1998; ested targets (Barrett and Curtis, 1982). However, the
This paper studied the cumulative effects of different cultivating patterns on the properties of albic soils in the Sanjiang
Plain using correlation analysis. The results showed that the physical and chemical properties of the albic soil changed greatly
when it was cultivated as farmland. As for physical properties of the soil, bulk density and specific gravity increased gradually,
the porosity and field capacity decreased gradually year by year, but they increased after being abandoned. As for chemical
properties, pH increased, organic matter and other nutrients decreased with increasing of the cultivating years. For the albic
soil cultivated with forage, the cumulative effects were apparently strengthened with the increase of cultivating years, especially
for the bulk density, total porosity, capillary porosity and capillary moisture capacity. Moreover, fertilization also had
great effects on the albic soil. Applying magnetism fertilizer improved the physical properties such as bulk density, soil
moisture and porosity, raised the utilization rate of nitrogen and phosphorus fertilizer. Compared with nutrient fertilizer,
utilization of the magnetism fertilizer made production increase by 5.9%–13.9%. At the same time, using organic material and
loosing the albic layer could improve not only the physical, chemical and biological properties of the cultivating layer,
but also the ill properties of the albic layer, thus making organic carbon and heavy fraction carbon contents increase, and
biological activity increase obviously.
Leaf-area index of a forest can be measured by determining the ratio of light at 800 mμ to that at 675 mμ on the forest floor. It is based on the principle that leaves absorb relatively more red than infrared light, and therefore, the more leaves that are present in the canopy, the greater will be the ratio.
Red light based broadband vegetation indices are widely applied to derive aerial nitrogen (N) status parameters. With the advance of growth stages, however, crop canopy structure and aerial biomass will vary greatly, which negatively influences the relationships between spectral indices and the crop canopy N status. The current research aimed to assess the performance of red edge based vegetation indices, derived from simulated broadband WorldView-2 data, to remotely sense aerial N concentration and uptake in winter wheat (Triticum aestivum L.). Six experiments with different N rates for five German cultivars and four Chinese cultivars of winter wheat were conducted in southeast Germany and in the North China Plain from 2007 to 2010. The results showed that aerial biomass strongly affected the relationships between broadband vegetation indices and aerial N concentration before the heading stage. Normalising by using the planar domain index approach significantly improved the prediction power of red edge dependent broadband vegetation indices in estimating aerial N status. The two-dimensional broadband canopy chlorophyll content index (CCCI) and a newly proposed nitrogen planar domain index (NPDI) involving the WorldView-2 satellite red edge region were found to be more stable and better predictors than traditional red light based broadband vegetation indices in estimating aerial N concentration after the heading stage and in assessing aerial N uptake before the heading stage. The findings from this study may be useful for managing the application of N fertiliser for winter wheat in Zadoks growth stages 30–55 and in indirectly monitoring aerial N content in Zadoks growth stages 59–75 at landscape scales.
The objective of this paper is to determine spectral bands that are best suited for characterizing agricultural crop biophysical variables. The data for this study comes from ground-level hyperspectral reflectance measurements of cotton, potato, soybeans, corn, and sunflower. Reflectance was measured in 490 discrete narrow bands between 350 and 1,050 nm. Observed crop characteristics included wet biomass, leaf area index, plant height, and (for cotton only) yield. Three types of hyperspectral predictors were tested: optimum multiple narrow band reflectance (OMNBR), narrow band normalized difference vegetation index (NDVI) involving all possible two-band combinations of 490 channels, and the soil-adjusted vegetation indices. A critical problem with OMNBR models was that of “over fitting” (i.e., using more spectral channels than experimental samples to obtain a highly maximum R2 value). This problem was addressed by comparing the R2 values of crop variables with the R2 values computed for random data of a large sample size. The combinations of two to four narrow bands in OMNBR models explained most (64% to 92%) of the variability in crop biophysical variables. The second part of the paper describes a rigorous search procedure to identify the best narrow band NDVI predictors of crop biophysical variables. Special narrow band lambda (λ1) versus lambda (λ2) plots of R2 values illustrate the most effective wavelength combinations (λ1 and λ2) and bandwidths (Δλ1 and Δλ2) for predicting the biophysical quantities of each crop. The best of these two-band indices were further tested to see if soil adjustment or nonlinear fitting could improve their predictive accuracy. The best of the narrow band NDVI models explained 64% to 88% variability in different crop biophysical variables. A strong relationship with crop characteristics is located in specific narrow bands in the longer wavelength portion of the red (650 nm to 700 nm), with secondary clusters in the shorter wavelength portion of green (500 nm to 550 nm), in one particular section of the near-infrared (900 nm to 940 nm), and in the moisture sensitive near-infrared (centered at 982 nm). This study recommends a 12 narrow band sensor, in the 350 nm to 1,050 nm range of the spectrum, for optimum estimation of agricultural crop biophysical information.
It is very important to know the spectral characteristics for the sake of understanding the remote sensing data. The reflectance characteristics of paddy field canopies vary with time or observational conditions (solar zenith angle, solar azimuth angle, and view zenith angle). A number of field studies have clarified the effects of these conditions on grain canopy reflectance. Most of the field data used in these study, however, were conducted only through the growing season in one year or by grains planted in pots. A series of authors’ experiments were initiated in 1982 and continued from the spring to the autumn every year to 1987. In this study we describe that the remotely sensed spectral data measured on the ground are influenced not only by the grain type, observational conditions, and growing season but also by the solar zenith angle, solar azimuth angle and view zenith angle in relation to scene. In this paper we report the results from the investigation of these various fundamental properties.
Ground-based radiometric measurements were conducted on six varieties of rice crop during an entire growth cycle using a hand-held seven-band radiometer. Concomitant measurements of some of the yield attributes were also made. Spectral data were also collected on a single variety grown under 12 different fertilizer treatments. Spectral data have been correlated with leaf area index, total wet biomass, total dry biomass, plant water content and final grain, straw and total yield. The results show similar temporal spectral responses of all six varieties and a strong correlation of agronomic parameters with spectral parameters derived from the near-infrared and red radiances. Red and near-infrared radiance ratio and normalized differences were found sensitive to the N fertilizer application but not to the P and K fertilizers. Linear correlations were observed between spectral parameters and final grain, straw and total yield
Crop gross primary productivity (GPP) is an important characteristic for evaluating crop nitrogen content and yield, as well as the carbon exchange. Based on the close relationship observed between GPP and total chlorophyll content in crops, we applied a model that relies on a product of chlorophyll-related vegetation index and incoming photosynthetically active radiation for remote estimation of GPP in maize. In this study, we tested the performance of this model for maize GPP estimation based on spectral reflectance collected at a close range, 6 m above the top of the canopy, over a period of eight years from 2001 through 2008. Fifteen widely used chlorophyll-related vegetation indices were employed for GPP estimation in irrigated and rainfed maize, and accuracy and uncertainties of the models were compared. We also explored the possibility of using a unified algorithm in estimating maize GPP in fields that are different in irrigation, field history and climatic conditions. The results showed that vegetation indices that closely relate to total canopy chlorophyll content and/or green leaf area index were accurate in GPP estimation. Both green and red edge Chlorophyll Indices, MERIS Terrestrial Chlorophyll Index as well as Simple Ratio were the best approximations of the widely variable GPP in maize under different crop managements and climatic conditions. They were able to predict daily GPP reaching 30 gC/m2/d with RMSE below 2.75 gC/m2/d.Highlights► Crop gross primary production (GPP) is closely related to crop chlorophyll content. ► Chlorophyll-related vegetation indices (VI) can be used as a proxy for GPP. ► The models were able to accurately predict widely variable GPP in maize.
Polyethersulfone (PES) based ultrafiltration membranes were fabricated via phase inversion by adding silver-loaded sodium zirconium phosphate nanoparticles (nanoAgZ) in PES casting solutions. The effect of nanoAgZ concentration on the membrane performance, i.e., morphology, hydrophilicity, thermal stability, permeation and antifouling properties was investigated. The results of thermal gravitational analysis (TGA) showed that the thermal stability of the hybrid membrane had been improved by the addition of nanoAgZ particles. Con-tact angle results indicated that the hydrophilicity of the modified membranes was enhanced. The contact angle of the membrane decreased from 71.5° to 52.6° with the increase of the nanoparticle content in the casting solution. Permeation experiment results showed that the modified PES membranes demonstrate bet-ter separation performance over the pure PES membrane. The pure water flux of PES membrane increased from 82.1 L/m 2 h to 100.6 L/m 2 h with the addition of the nanoparticles. Most importantly, the incorporation of the nanoAgZ particles enhanced the BSA fouling resistance and also the anti-biofouling performance of the membrane.
Signals for site-specific nitrogen-top-dressing can be obtained by a sensor mounted on a tractor. The plant appearance can
serve as a criterion. The question is which plant criteria provide pertinent information and how this can be indicated. Increasing
the nitrogen-supply changes leaf colour from yellow-green to blue-green via the chlorophyll-concentration in the leaves and
leads to growth of plants. Present sensing systems measure either chlorophyll concentration in the leaves, total area of the
leaves or crop resistance against bending. The aim and purpose of this study is to outline prospects for application. Therefore,
the emphasis is on results and not on experimental methods, to which references are given. Mainly optical sensing systems
relying on reflectance or fluorescence are dealt with. Good signals of the nitrogen-supply can be obtained from the red edge
plus the near infrared range of the reflectance. The results with some new spectral indices were better than those with standard
spectral indices. Fluorescence sensing instead of reflectance sensing eliminates erroneous signals from bare soil. However,
only low supply rates were clearly indicated. The biomass of the crop or the total area of its leaves is a very important
criterion. Reflectance indices can take this into account. Fluorescence signals are barely influenced by this parameter.
The Sanjiang Plain is the largest and most concentrated wetland region in China, the total area is about 1.088 × 107ha with rich marsh resources and biodiversity. Before 1949, the Sanjiang Plain was a large untravelled wild plant and waterfowl
habitat, and there were some rare swans, red-crowned cranes and thousands of hydrophytes. From 1950, the local government
began to reclaim the marsh in the Sanjiang Plain, built the commodity grain base of Northeast China, and developed the industry
of grain processing, animal husbandry, etc. Up to now, there are 54 farms which control 3. 5087 × 106ha agriculture field. The marsh areas are reduced by 1/2; many rare animals and plants are near extinction. The human activities
and agriculture reclamation made a great change on the environment, especially made water balance change and regional climate
change. So to study and protect the wetland ecosystem and marsh resource are extremely urgent. This paper focus on the hydrology
change and climate change before and after marsh reclamation, including evapotranspiration, run off, soil character, micro-climate
on both marsh and agriculture field, and the reason that cause seasonal drought, waterlogging and degeneration of marsh.
Detecting plant health condition is an important step in controlling disease and insect stress in agricultural crops. In this study, we applied neural network and principal components analysis techniques for discriminating and classifying different fungal infection levels in rice (Oryza sativa L.) panicles. Four infection levels in rice panicles were used in the study: no infection condition, light and moderate infection caused by rice glume blight disease, and serious infection caused by rice false smut disease. Hyperspectral reflectance of rice panicles was measured through the wavelength range from 350 to 2500 nm with a portable spectroradiometer in the laboratory. The spectral response characteristics of rice panicles were analyzed, and principal component analysis (PCA) was performed to obtain the principal components (PCs) derived from different spectra processing methods, namely raw, inverse logarithmic, first, and second derivative reflectance. A learning vector quantization (LVQ) neural network classifier was employed to classify healthy, light, moderate, and serious infection levels. Classification accuracy was evaluated using overall accuracy and Kappa coefficient. The overall accuracies of LVQ with PCA derived from the raw, inverse logarithmic, first, and second derivative reflectance spectra for the validation dataset were 91.6%, 86.4%, 95.5%, and 100% respectively, and the corresponding Kappa coefficients were 0.887, 0.818, 0.939 and 1. Our results indicated that it is possible to discriminate different fungal infection levels of rice panicles under laboratory conditions using hyperspectral remote sensing data.
Derivative spectrum analysis has the advantage of reducing additive constants and minimizing soil background effects and is a potential method for exploiting hyperspectral data in vegetated areas. The shift of red-edge position (REP) of vegetation reflectance spectra, which is a focus point of derivative spectrum analysis, has been studied by many researchers as an indicator of environmental stresses and LAI. However, it is less satisfactory at canopy level than at leaf level.A field experiment was conducted, using 3-year-old potted Quercus glauca and Q. serrata, to examine the utility of derivative spectrum analysis for detecting drought status and LAI at canopy level and to find the optimal bands that can independently detect those variables. Five levels of drought status (including a control) and three levels of LAI were set. Measurements were made of canopy reflectance spectra at approximately 3 nm intervals, xylem water potentials, leaf water contents and LAI. Two measures for representing high or low drought status were chosen. One was water-cessation duration in hours (WD) and the other was leaf water content (LWC). They corresponded to a gradual change and an abrupt change, respectively, during drought development.The results showed that REP and REP-relevant indices were not very successful for independent detection of WD, LWC or LAI at canopy level. The best single bands for detecting WD, LWC and LAI were 611.4 nm in the first derivative (r=0.807), 519.6 nm in the first derivative (r=0.916) and 676.0 nm in the second derivative (r=0.828), respectively. The initial part of the red-edge peak was a better indicator than the top (i.e. REP or the first derivative at REP) for the independent detection of LAI. A simulation to test lower spectral resolution proved that the wavelengths at 10 nm intervals that approximated the desirable bands at 3 nm intervals retained similar correlation coefficients for the three variables.
Spectral reflectance factors were measured for paddy rice canopies in the visible (VIS), reflective near-infrared (NIR), and mid-infrared (MIR) wavelength regions. Radiometric and agronomic measurements were performed every other week from 45 days before heading to 64 days after heading in early- and late-planted rice plots consisting of five cultivars. The relation between radiometric data an and the leaf area index (LAI) and above-ground dry phytomass (GDM) was investigated during 11 development periods of 10-day intervals. Reflectance values in the VIS bands were lowest when the panicles emerged, whereas the values at 840 and 1100 nm increased until the time when the grains reached maturity. Reflectances at 1200 and 1650 nm were similar in pattern but less pronounced than at 840 and 1100 nm. Ratios using VIS and NIR reflectances (R840 / R560, R1100 / R840) for the whole season were associated with 71of the variation in LAI, while the difference between NIR and MIR reflectances (R1100 - R1650, R1100 - R1200) combined with ratios using VIS and NIR reflectances (R840 / R560, R1100 / R840) was associated with 93% of the variation in GDM of the paddy rice canopies. Thus, the use of mid-infrared wavelengths may result in improved estimates of seasonal GDM.
The use of derivative spectra is an established technique in analytical chemistry for the elimination of background signals and for resolving overlapping spectral features. Application of this technique for tackling analogous problems such as interference from soil background reflectance in the remote sensing of vegetation or for resolving complex spectra of several target species within individual pixels in remote sensing is proposed. Methods for generating derivatives of high spectral resolution data are reviewed. Results of experiments to test the use of derivatives for monitoring chlorosis in vegetation show that derivative spectral indices are superior to conventional broad-band spectral indices such as the near-infrared / red reflectance ratio. Conventional broad-band indices are sensitive to both leaf cover as well as leaf color. New derivative spectral indices which were able to monitor chlorosis unambiguously were identified. Potential areas for the application of this technique in remote sensing are considered.
Hyperspectral reflectance (438 to 884 nm) data were recorded at five different growth stages of winter wheat in a field experiment including two cultivars, three plant densities, and four levels of N application. All two-band combinations in the normalized difference vegetation index (λ1−λ2)/(λ1+λ2) were subsequently used in a linear regression analysis against green biomass (GBM, g fresh weight m−2 soil), leaf area index (LAI, m2 green leaf m−2 soil), leaf chlorophyll concentration (Chlconc, mg chlorophyll g−1 leaf fresh weight), leaf chlorophyll density (Chldensity, mg chlorophyll m−2 soil), leaf nitrogen concentration (Nconc, mg nitrogen g−1 leaf dry weight), and leaf nitrogen density (Ndensity, g nitrogen m−2 soil). A number of grouped wavebands with high correlation (R2>95%) were revealed. For the crop variables based on quantity per unit surface area, i.e. GBM, LAI, Chldensity, and Ndensity, these wavebands had in the majority (87%) of the cases a center wavelength in the red edge spectral region from 680 to 750 nm and the band combinations were often paired so that both bands were closely spaced in the steep linear shift between Rred and Rnir. The red edge region was almost absent for bands related to Chlconc and Nconc, where the visible spectral range, mainly in the blue region, proved to be better. The selected narrow-band indices improved the description of the influence of all six-crop variables compared to the traditional broad- and short-band indices normally applied on data from satellite, aerial photos, and field spectroradiometers. For variables expressed on the basis of soil or canopy surface area, the relationship was further improved when exponential curve fitting was used instead of linear regression. The best of the selected narrow-band indices was compared to the results of a partial least square regression (PLS). This comparison showed that the narrow-band indices related to LAI and Chlconc, and to some extent also Chldensity and Ndensity, were optimal and could not be significantly improved by PLS using the information from all wavelengths in the hyperspectral region. However, PLS improved the prediction of GBM and Nconc by lowering the RMSE with 22% and 24%, respectively, compared to the best narrow-band indices. It is concluded that PLS regression analysis may provide a useful exploratory and predictive tool when applied on hyperspectral reflectance data.