Winter Wheat Canopy Research Articles

Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

Establishing NDRE dynamic models of winter wheat under multi-nitrogen rates based on a field spectral sensor.

Field spectral sensors provide real-time, reliable, quantitative monitoring of crop growth. Fitting the continuous growth in the entire growing period from the measurements of limited frequency is helpful to the comparative analysis of interannual growth and fertilizer management in the field. To exploit this capacity, our work presents a model that uses the normalized difference red edge (NDRE) index derived from the field spectral sensor for real-time monitoring of the canopy growth of winter wheat in the whole growing period. We developed this model from experiments in three counties in Hebei province, China, where we obtained the near-infrared and red edge reflectance, grain yield, and canopy parameters for eight growth stages and for various nitrogen (N) rates. Given the correlation between effective accumulated temperature and crop growth, we used the growing degree-days as an adjustment parameter to develop models for dynamic monitoring of the NDRE of the winter wheat canopy during the entire growing period. The results show that high determination coefficients (R2=0.89 to 0.96) are obtained from all models based on relative NDRE and effective accumulative temperature (independent of N fertilization rates). The model based on the rational function is the best of all models tested, with the accuracy for normal and high N fertilization rates being slightly greater than that for low N fertilization rates. Therefore, a relative-NDRE model with the accumulative growing degree-days since sowing could allow monitoring canopy NDRE of winter wheat at any time, which could be helpful for overcoming the shortage of incomparable growth derived from the differences of sensing date, sowing date, and fertilizer, etc.

Evaluating Hyperspectral Vegetation Indices for Estimating Nitrogen Concentration of Winter Wheat in Different Growth Stages

Plant health and plant density can be monitored through indices values calculated from reflectance charateristics of plants. This study aims to investigate the effects of different rates of nitrogen fertilizer on spectral reflectance characteristics of dryland winter wheat and to determine and select the most reliable vegetation indices for Nitrogen (N) status assesment (N contents of leaves) of winter wheat canopy in early, late and whole development stages. The field experiment was carried out in randomized block design with three replications and 0, 40, 80, 120, 160 kg/ha N doses applied between 2012-2013 years in İkizce Farm experiment field. In each plot, spectroradiometer readings were taken during growing period from planting to harvest various plant vegetation indices (NDVI, RDVI, SAVI, MTVI, MCARI-1, MCARI-2, TCARI, TVI, SIPI, NPCI, Red Edge (750-700), Red Edge (740-720)) were calculated from measured spectroradiometric values. A total of 90 different indices were calculated to obtain the relationship between nitrogen accumulation and hyperspectral indices (structural, chlorophyll pigment, red edge indices). The simple linear determination coefficients (R²) between those indices and leaf nitrogen contents in different development stages of early, late, and whole season were calculated. During the early period of tillering to bolting, (Feekes 4-7) NDI-1, NDI-2, SR-7, Red Edge indices (708-850 nm.) have the highest determination coefficients (R²) of 0.643, 0.641, 0.620 with RMSE values of 5.996, 6.039, 6.129 and relative percentage error values (%RE) of 23.07, 23.23, 23.58 % respectively. During the period of heading to ripening (Feekes 8-10), PhRI, NDV-3, NDVI-4 visible (Green Zone), red, red edge and near infrared (NIR) indices (531-800 nm.) showed the highest determination coefficients of 0.734, 0.708, 0.699 with RMS values of 3.089, 3.205, 3.149 and relative percentage error values of 40.37, 41.89, 41.16 % respectively. Considering all growing period in 2013 of tillering to ripening, ( Feekes 4-10); SR-14, SRPI, TVI visible area (blue + green) and Red Edge indices (415-750 nm.) have determination coefficients of 0.742, 0.699, 0.646 and RMS values 1.203, 0.902, 0.697 and relative percentage error values of 7.15, 5.36, 4.14 %, respectively. High determination coefficient (R²) between plant nitrogen uptake and reflectance charecteristics were attained as HVI (r= 0.806 p<0.0.1 **), OSAVI (r= 0.794, p<0.0.1**), NDVI (r= 0.794, p<0.0.1**) and HNDVI (r= 0.793, p<0.0.1**).

Winter Wheat Nitrogen Status Estimation Using UAV-Based RGB Imagery and Gaussian Processes Regression

Predicting the crop nitrogen (N) nutrition status is critical for optimizing nitrogen fertilizer application. The present study examined the ability of multiple image features derived from unmanned aerial vehicle (UAV) RGB images for winter wheat N status estimation across multiple critical growth stages. The image features consisted of RGB-based vegetation indices (VIs), color parameters, and textures, which represented image features of different aspects and different types. To determine which N status indicators could be well-estimated, we considered two mass-based N status indicators (i.e., the leaf N concentration (LNC) and plant N concentration (PNC)) and two area-based N status indicators (i.e., the leaf N density (LND) and plant N density (PND)). Sixteen RGB-based VIs associated with crop growth were selected. Five color space models, including RGB, HSV, L*a*b*, L*c*h*, and L*u*v*, were used to quantify the winter wheat canopy color. The combination of Gaussian processes regression (GPR) and Gabor-based textures with four orientations and five scales was proposed to estimate the winter wheat N status. The gray level co-occurrence matrix (GLCM)-based textures with four orientations were extracted for comparison. The heterogeneity in the textures of different orientations was evaluated using the measures of mean and coefficient of variation (CV). The variable importance in projection (VIP) derived from partial least square regression (PLSR) and a band analysis tool based on Gaussian processes regression (GPR-BAT) were used to identify the best performing image features for the N status estimation. The results indicated that (1) the combination of RGB-based VIs or color parameters only could produce reliable estimates of PND and the GPR model based on the combination of color parameters yielded a higher accuracy for the estimation of PND (R2val = 0.571, RMSEval = 2.846 g/m2, and RPDval = 1.532), compared to that based on the combination of RGB-based VIs; (2) there was no significant heterogeneity in the textures of different orientations and the textures of 45 degrees were recommended in the winter wheat N status estimation; (3) compared with the RGB-based VIs and color parameters, the GPR model based on the Gabor-based textures produced a higher accuracy for the estimation of PND (R2val = 0.675, RMSEval = 2.493 g/m2, and RPDval = 1.748) and the PLSR model based on the GLCM-based textures produced a higher accuracy for the estimation of PNC (R2val = 0.612, RMSEval = 0.380%, and RPDval = 1.601); and (4) the combined use of RGB-based VIs, color parameters, and textures produced comparable estimation results to using textures alone. Both VIP-PLSR and GPR-BAT analyses confirmed that image textures contributed most to the estimation of winter wheat N status. The experimental results reveal the potential of image textures derived from high-definition UAV-based RGB images for the estimation of the winter wheat N status. They also suggest that a conventional low-cost digital camera mounted on a UAV could be well-suited for winter wheat N status monitoring in a fast and non-destructive way.

Estimation of winter-wheat above-ground biomass using the wavelet analysis of unmanned aerial vehicle-based digital images and hyperspectral crop canopy images

ABSTRACT The crop above-ground biomass (AGB) is critically important for monitoring crop growth, and its accurate estimation can be used by agricultural managers to improve farmland management and to predict crop grain yield. Many studies have shown that models for the estimation of AGB for multiple crop growth stages based on optical remote sensing spectral indices (SIs) often underestimate the crop AGB in later growth stages due to saturation problems. The purpose of this study was to estimate winter-wheat AGB using (i) high-frequency information obtained from the image wavelet decomposition (IWD) of unmanned aerial vehicle (UAV)-based digital images of winter-wheat canopy (i.e. high-frequency IWD information), and (ii) variables obtained from the continuous wavelet transform (CWT) of hyperspectral images of winter-wheat canopy (i.e. hyperspectral CWT variables). Digital and hyperspectral images were acquired using a digital camera and an imaging spectrometer, both mounted on a UAV. Unlike optical SIs, high-frequency IWD information and hyperspectral CWT variables may not be limited by saturation problems for high winter-wheat canopy cover. Our results indicate that high-frequency IWD information and hyperspectral CWT variables both increase (decrease) with increasing winter-wheat AGB. A multiple linear stepwise regression technique was used to analyse the performance of (1) SIs, (2) CWT, (3) IWD, (4) SIs + CWT, (5) SIs + IWD, (6) CWT + IWD, and (7) SIs + CWT + IWD, for estimating AGB. Our results indicate that: (i) the method based only on optical SIs may not support the estimation of winter-wheat AGB for multiple growth stages (SIs: coefficient of determination (R 2) = 0.62, mean absolute error (MAE) = 1.30 t ha–1, root-mean-square error (RMSE) = 1.63 t ha–1); (ii) the combined use of the IWD of digital images of winter-wheat canopy and the CWT of hyperspectral images of crop canopy can support the estimation of winter-wheat AGB for multiple growth stages (CWT + IWD: R 2 = 0.85, MAE = 0.79 t ha–1, RMSE = 1.01 t ha–1), including later growth stages; and (iii) the combined use of SIs and the IWD of digital images of winter-wheat canopy can improve the estimation accuracy of winter-wheat AGB (SIs + IWD: R 2 = 0.80, MAE = 0.93 t ha–1, RMSE = 1.22 t ha–1), which may indicate that imaging spectrometers and cheap digital cameras have distinct advantages and can both be used to obtain AGB estimates using different methods. This work provides a new perspective on the use of high-frequency IWD information, hyperspectral CWT variables, and their combination to estimate AGB for multiple crop growth stages.

Using UAV Collected RGB and Multispectral Images to Evaluate Winter Wheat Performance across a Site Characterized by Century-Old Biochar Patches in Belgium

Remote sensing data play a crucial role in monitoring crop dynamics in the context of precision agriculture by characterizing the spatial and temporal variability of crop traits. At present there is special interest in assessing the long-term impacts of biochar in agro-ecosystems. Despite the growing body of literature on monitoring the potential biochar effects on harvested crop yield and aboveground productivity, studies focusing on the detailed crop performance as a consequence of long-term biochar enrichment are still lacking. The primary objective of this research was to evaluate crop performance based on high-resolution unmanned aerial vehicle (UAV) imagery considering both crop growth and health through RGB and multispectral analysis, respectively. More specifically, this approach allowed monitoring of century-old biochar impacts on winter wheat crop performance. Seven Red-Green-Blue (RGB) and six multispectral flights were executed over 11 century-old biochar patches of a cultivated field. UAV-based RGB imagery exhibited a significant positive impact of century-old biochar on the evolution of winter wheat canopy cover (p-value = 0.00007). Multispectral optimized soil adjusted vegetation index indicated a better crop development over the century-old biochar plots at the beginning of the season (p-values < 0.01), while there was no impact towards the end of the season. Plant height, derived from the RGB imagery, was slightly higher for century-old biochar plots. Crop health maps were computed based on principal component analysis and k-means clustering. To our knowledge, this is the first attempt to quantify century-old biochar effects on crop performance during the entire growing period using remotely sensed data. Ground-based measurements illustrated a significant positive impact of century-old biochar on crop growth stages (p-value of 0.01265), whereas the harvested crop yield was not affected. Multispectral simplified canopy chlorophyll content index and normalized difference red edge index were found to be good linear estimators of harvested crop yield (p-value(Kendall) of 0.001 and 0.0008, respectively). The present research highlights that other factors (e.g., inherent pedological variations) are of higher importance than the presence of century-old biochar in determining crop health and yield variability.

Winter Wheat Canopy Water Content Monitoring Based on Spectral Transforms and “Three-edge” Parameters

Suitable spectral monitoring models of canopy water content provide a scientific basis for real-time dynamic, accurate, non-destructive diagnosis over large acreage. This work investigates winter wheat under different water treatments to examine the relationship between canopy water content and spectral reflectance. Principal component regression spectral monitoring models are developed based on the combination of growth stages. The growth stage constraints are divided, and the influence of other background noises is removed to achieve accurate and stable spectral monitoring results of canopy water content at all growth stages. The following main conclusions are derived. (1) At the stem elongation–booting, booting–milking, and milking–ripening stages and during the entire growth period, the spectral transforms with the highest correlation with winter wheat canopy water content are the first-order derivative, division by R930, division by R450-750, and division by R930, respectively; the corresponding sensitivity bands are 758, 759, 690, and 759 nm, respectively. At the stem elongation–booting, booting–milking, and milking–ripening stages and during the entire growth period, the “three-edge” parameters with the highest correlation with winter wheat canopy water content are Rg/Rr, SDr/Sdy, (Rg − Rr)/(Rg + Rr), and (SDr-SDb), respectively. (2) In accordance with the rationale that the spectral parameters should have the highest correlation coefficients with canopy water content at each growth stage, combinational models of canopy water content that are specific to individual growth stages are developed based on spectral transforms or “three-edge” parameters. Compared with the optimal single-parameter regression model, the combinational models significantly improve the estimation accuracy of canopy water content at each growth stage. (3) Monitoring models based on principal component analysis are constructed with comprehensive spectral information. These models can improve the monitoring accuracy at other growth stages, especially at the stem elongation–booting stage, compared with combinational models developed based on spectral transforms or “three-edge” parameters.

Radiation use efficiency of winter wheat canopy during pre-anthesis growth

Radiation use efficiency of winter wheat canopy during pre-anthesis growth

A novel wavelength selection strategy for chlorophyll prediction by MWPLS and GA

The research proposed a novel wavelength selection strategy by the combination of moving window partial least squares (MWPLS) and genetic algorithm (GA) for the chlorophyll content detection of winter wheat canopy using spectroscopy technology. Firstly, the original spectral dataset was pre-processed by wavelet denosing, multiple scatter correction. Then, abnormal data samples were removed by Pauta Criterion and the dataset was divided into modeling set and validation set by SPXY. Finally, the sensitive wavebands were selected using MWPLS method and MWPLS+GA respectively and partial least squares (PLS) models were established for chlorophyll content prediction. For the model established by using all the wavebands in the region of 400-900 nm, its Rc2 and Rv2 were 0.4468 and 0.3821 respectively; its modeling root mean square error (RMSEM) and verification root mean square error (RMSEV) were 2.9057 and 1.7589 respectively. For the model established by using 151 wavebands selected by MWPLS, its Rc2 and Rv2 were 0.6210 and 0.5901 respectively; its RMSEM and RMSEV were 2.4007 and 1.6408 respectively. For the model established by using 36 wavebands selected by MWPLS+GA, its Rc2 and Rv2 were 0.7805 and 0.7497 respectively; its RMSEM and RMSEV were 1.8504 and 1.1315 respectively. The results show that wavelength selection can remove redundant information and improve model performance. The strategy of combining MWPLS with GA has also been proved to work well in selecting sensitive wavebands for chlorophyll content prediction. Keywords: MWPLS, GA, canopy spectral reflectance, Chlorophyll content prediction DOI: 10.25165/j.ijabe.20191205.4033 Citation: Liu H J, Li M Z, Zhang J Y , Gao D H , Sun H , Zhang M, et al. A novel wavelength selection strategy for chlorophyll prediction by MWPLS and GA. Int J Agric & Biol Eng, 2019; 12(5): 149–155.

Winter Wheat Canopy Height Extraction from UAV-Based Point Cloud Data with a Moving Cuboid Filter

Plant height can be used as an indicator to estimate crop phenology and biomass. The Unmanned Aerial Vehicle (UAV)-based point cloud data derived from photogrammetry methods contains the structural information of crops which could be used to retrieve crop height. However, removing noise and outliers from the UAV-based crop point cloud data for height extraction is challenging. The objective of this paper is to develop an alternative method for canopy height determination from UAV-based 3D point cloud datasets using a statistical analysis method and a moving cuboid filter to remove outliers. In this method, first, the point cloud data is divided into many 3D columns. Secondly, a moving cuboid filter is applied in each column and moved downward to eliminate noise points. The threshold of point numbers in the filter is calculated based on the distribution of points in the column. After applying the moving cuboid filter, the crop height is calculated from the highest and lowest points in each 3D column. The proposed method achieved high accuracy for height extraction with low Root Mean Square Error (RMSE) of 6.37 cm and Mean Absolute Error (MAE) of 5.07 cm. The canopy height monitoring window for winter wheat using this method starts from the beginning of the stem extension stage to the end of the heading stage (BBCH 31 to 65). Since the height of wheat has limited change after the heading stage, this method could be used to retrieve the crop height of winter wheat. In addition, this method only requires one operation of UAV in the field. It could be an effective method that can be widely used to help end-user to monitor their crops and support real-time decision making for farm management.

A New Integrated Vegetation Index for the Estimation of Winter Wheat Leaf Chlorophyll Content

Leaf chlorophyll content (LCC) provides valuable information about the nutrition and photosynthesis statuses of crops. Vegetation index-based methods have been widely used in crop management studies for the non-destructive estimation of LCC using remote sensing technology. However, many published vegetation indices are sensitive to crop canopy structure, especially the leaf area index (LAI), when crop canopy spectra are used. Herein, to address this issue, we propose four new spectral indices (The red-edge-chlorophyll absorption index (RECAI), the red-edge-chlorophyll absorption index/optimized soil-adjusted vegetation index (RECAI/OSAVI), the red-edge-chlorophyll absorption index/ the triangular vegetation index (RECAI/TVI), and the red-edge-chlorophyll absorption index/the modified triangular vegetation index(RECAI/MTVI2)) and evaluate their performance for LCC retrieval by comparing their results with those of eight published spectral indices that are commonly used to estimate LCC. A total of 456 winter wheat canopy spectral data corresponding to physiological parameters in a wide range of species, growth stages, stress treatments, and growing seasons were collected. Five regression models (linear, power, exponential, polynomial, and logarithmic) were built to estimate LCC in this study. The results indicated that the newly proposed integrated RECAI/TVI exhibited the highest LCC predictive accuracy among all indices, where R2 values increased by more than 13.09% and RMSE values reduced by more than 6.22%. While this index exhibited the best association with LCC (0.708** ≤ r ≤ 0.819**) among all indices, RECAI/TVI exhibited no significant relationship with LAI (0.029 ≤ r ≤ 0.167), making it largely insensitive to LAI changes. In terms of the effects of different field management measures, the LCC predictive accuracy by RECAI/TVI can be influenced by erective winter wheat varieties, low N fertilizer application density, no water application, and early sowing dates. In general, the newly developed integrated RECAI/TVI was sensitive to winter wheat LCC with a reduction in the influence of LAI. This index has strong potential for monitoring winter wheat nitrogen status and precision nitrogen management. However, further studies are required to test this index with more diverse datasets and different crops.

Nitrogen Vertical Distribution Differed in Foliar and Nonfoliar Organs of Dryland Wheat during Grain Filling

Core Ideas There was a positive correlation between nitrogen concentration in grain and between Layer 1 and Layer 4 (Gtotal). The nonfoliar organs were involved in to determine the nitrogen vertical distribution in winter wheat canopy. The nitrogen contributions of various organs to grain nitrogen were determined. ABSTRACTThe role of N remobilization in nonfoliar and foliar organs influences the N vertical distribution and accumulation of N in grain. We hypothesized that the N concentrations in foliar and nonfoliar organs affect the remobilization, vertical distribution, and accumulation on N in grain. A 2‐yr field experiment was conducted to evaluate the effects of N input and mulching practice on N remobilization and N vertical distribution in the canopy of winter wheat (Triticum aestivum L.). The results showed that foliar organs had higher N concentration than nonfoliar organs in all treatments. Among the nonfoliar organs, the flag leaf sheath had the highest N concentration, followed by glumes and rachillae combination, while the stem had the lowest N concentration at anthesis and maturity. In the different internode segments of the stem, N concentrations decreased with grain filling, except for the exposed part of the peduncle. There was a top‐to‐down decline in N concentration within the canopy. Foliar organs contributed the most N to grain followed by the stem and the glumes and rachillae combination. The N286 treatment significantly improved N accumulation, remobilization, and vertical distribution in the canopy and N content in grain. Nitrogen concentration in the grain was positively correlated with the difference in N concentrations between apical and basal vegetative modules.

Optimal Hyperspectral Characteristics Determination for Winter Wheat Yield Prediction

Crop growth in different periods influences the final yield. This study started from the agronomic mechanism of yield formation and aimed to extract useful spectral characteristics in different phenological phases, which could directly describe the final yield and dynamic contributions of different phases to the yield formation. Hyperspectral information of the winter wheat canopy was acquired during three important phases (jointing stage, heading stage, and grain-filling stage). An enhanced 2D correlation spectral analysis method modified by mutual information was proposed to identify the sensitive wavebands. The selected wavebands performed well with good mechanism interpretation and close correlation with important crop growth parameters and main physiological activities related to yield formation. The quantitative contribution proportions of plant growth in three phases to the final yield were estimated by determining the coefficients of partial least square models based on full spectral information. They were then used as single-phase weight factors to merge the selected wavebands. The support vector machine model based on the weighted spectral dataset performed well in yield prediction with satisfactory accuracy and robustness. This result would provide rapid and accurate guidance for agricultural production and would be valuable for the processing of hyperspectral remote sensing data.

Estimation of heavy-metal concentrations in winter wheat leaves from typical sewage irrigation area based on canopy reflectance spectra

Heavy-metal pollution of crops seriously affects food safety and human health in China. The purpose of this study is to estimate the concentrations of heavy metals economically and accurately using spectroscopy. The spectral data of winter wheat canopy at 61 sampling points were collected in Longkou City, and the concentration of Pb, Zn, Cd, Cr, Ni, and Hg was measured. Eight estimation models were established based on the back-propagation neural network (BPNN), the partial least squares regression, and the stepwise multiple linear regression methods combined with reflectance spectra, first-order derivative of spectral reflectance (FDR), second-order derivative of spectral reflectance, and spectral parameters (SPs). Spatial interpolation was used to map the heavy-metal concentration. The results showed that the best model for Pb, Zn, and Cd was the BPNN model with SPs, for Cr, Ni, and Hg was the BPNN model with FDR. The concentration of Hg was lower in the northwest, Pb and Zn were higher in the middle and south, Cd was lower in the middle and west, and Cr and Ni were higher in the southeast. This study will provide effective technical support for the economical and accurate monitoring of heavy metals.

UAV-based hyperspectral analysis and spectral indices constructing for quantitatively monitoring leaf nitrogen content of winter wheat.

In flag leaf and flowering stages of winter wheat, unmanned aerial vehicle (UAV)-based and ground-measured hyperspectral data were collected simultaneously, and leaf nitrogen content (LNC) data were then measured in a laboratory. First, the accuracy of UAV-based hyperspectral data was analyzed using ground-measured hyperspectral data, and the analysis showed that the effectiveness and spectrum sampling precision of the UAV-based hyperspectral data are reliable. Hyperspectral characteristic analysis of winter wheat canopies of different LNCs was also conducted. Second, representative spectrum bands that are sensitive to the LNC of winter wheat were extracted through first-order differential spectral, continuum-removed reflectance, and band correlation prediction threshold methods. The optimal band combination that is sensitive to the LNC of winter wheat was obtained by comparing and analyzing the representative spectrum band results. Thus, several LNC spectral indices (LNCSI) were established through ratio, difference, and normalization methods, and linear regression statistical models for quantitatively simulating LNCs were established using the LNCSIs. Finally, comprehensive and comparative analyses of the LNCSIs and the inversion values of the LNC using the LNCSIs confirmed that the LNCSIs are effective in quantitatively inversing the LNC of winter wheat.

PCA based model on chlorophyll content diagnosis of winter wheat

The spectroscopy is one of the efficient methods used in crop monitor. In order to break through the limit of sensitive waveband instability at different growth stages, a full spectrum analysis method was applied in this study. Firstly, the winter wheat canopy reflectance were collected in the range of 325nm ~1075nm by ASD at the setting stage, jointing stage, booting stage and flowering stage respectively. And the corresponding chlorophyll contents were measured by spectrophotometric method. Secondly, the spectral data were pre-processed by wavelet de-noising and multiple scattering correction (MSC). Then, the principal component analysis (PCA) algorithm was used to reduce the dimension of spectra and the correlation coefficient analysis method was used to select the best principal component combinations at different growth stages. Finally, multiple regression models of chlorophyll content were established based on the selected principal component combinations at each growth stage. As a result, the decision coefficients (Rc2) for all the models were above 0.6, of which the maximum reached 0.8385 at the booting stage. The minimum of validation decision coefficient (Rv2) was only 0.4887 at the setting stage, while the maximum reached 0.9063 at the jointing stage. It could provide a technical support for the diagnosis of chlorophyll content during the winter wheat growth process.

Retrieving Leaf and Canopy Water Content of Winter Wheat Using Vegetation Water Indices

This study investigates the capability of spectral indices for estimating winter wheat leaf and canopy water content using radiative transfer modeling and field measurements. An irrigation treatment experiment was conducted to investigate response of crop growth to water supply in 2014 and 2015. Plant sampling and canopy spectral reflectance were measured in the two growing seasons. The main goal was to evaluate the potential of selected spectral indices formulated with the Sentinel-2 bands for winter wheat water status assessment. A global sensitivity analysis using reflectance simulated by the PROSPECT-5 and SAILH models showed that leaf water contributed the most to the variation of spectral water indices derived from leaf reflectance but had reduced contribution when the indices were derived from canopy reflectance. Correlation between canopy water content ( Cw, C ) and canopy spectral water indices was significant, although it was impacted by canopy structural descriptors such as the leaf inclination angle and the leaf area index. Satisfactory estimation of w, C could be achieved using the normalized difference water index (NDWI) ( R 2 = 0.68, RMSEcv = 0.148 kg⋅m−2, and n = 463). The estimated C w, C at the jointing stage was significantly correlated with grain yield. A map of Cw, C was generated from Sentinel-2 image acquired on March 30, 2016, showing spatial variation of winter wheat canopy water status comparable with the drought indicators reported by the Meteorological Bureau at the regional scale. It also showed variations at the field scale. Hence, there is a great potential to use the NDWI derived from Sentinel-2 data for detecting crop response to water stress and provide support to irrigation decision.

Off-Nadir Hyperspectral Sensing for Estimation of Vertical Profile of Leaf Chlorophyll Content within Wheat Canopies.

Monitoring the vertical profile of leaf chlorophyll (Chl) content within winter wheat canopies is of significant importance for revealing the real nutritional status of the crop. Information on the vertical profile of Chl content is not accessible to nadir-viewing remote or proximal sensing. Off-nadir or multi-angle sensing would provide effective means to detect leaf Chl content in different vertical layers. However, adequate information on the selection of sensitive spectral bands and spectral index formulas for vertical leaf Chl content estimation is not yet available. In this study, all possible two-band and three-band combinations over spectral bands in normalized difference vegetation index (NDVI)-, simple ratio (SR)- and chlorophyll index (CI)-like types of indices at different viewing angles were calculated and assessed for their capability of estimating leaf Chl for three vertical layers of wheat canopies. The vertical profiles of Chl showed top-down declining trends and the patterns of band combinations sensitive to leaf Chl content varied among different vertical layers. Results indicated that the combinations of green band (520 nm) with NIR bands were efficient in estimating upper leaf Chl content, whereas the red edge (695 nm) paired with NIR bands were dominant in quantifying leaf Chl in the lower layers. Correlations between published spectral indices and all NDVI-, SR- and CI-like types of indices and vertical distribution of Chl content showed that reflectance measured from 50°, 30° and 20° backscattering viewing angles were the most promising to obtain information on leaf Chl in the upper-, middle-, and bottom-layer, respectively. Three types of optimized spectral indices improved the accuracy for vertical leaf Chl content estimation. The optimized three-band CI-like index performed the best in the estimation of vertical distribution of leaf Chl content, with R2 of 0.84–0.69, and RMSE of 5.37–5.56 µg/cm2 from the top to the bottom layers, while the optimized SR-like index was recommended for the bottom Chl estimation due to its simple and universal form. We suggest that it is necessary to take into account the penetration characteristic of the light inside the canopy for different Chl absorption regions of the spectrum and the formula used to derive spectral index when estimating the vertical profile of leaf Chl content using off-nadir hyperspectral data.

Estimation of canopy carotenoid content of winter wheat using multi-angle hyperspectral data

Precise estimation of carotenoid (Car) content in crops, using remote sensing data, could be helpful for agricultural resources management. Conventional methods for Car content estimation were mostly based on reflectance data acquired from nadir direction. However, reflectance acquired at this direction is highly influenced by canopy structure and soil background reflectance. Off-nadir observation is less impacted, and multi-angle viewing data are proven to contain additional information rarely exploited for crop Car content estimation. The objective of this study was to explore the potential of multi-angle observation data for winter wheat canopy Car content estimation. Canopy spectral reflectance was measured from nadir as well as from a series of off-nadir directions during different growing stages of winter wheat, with concurrent canopy Car content measurements. Correlation analyses were performed between Car content and the original and continuum removed spectral reflectance. Spectral features and previously published indices were derived from data obtained at different viewing angles and were tested for Car content estimation. Results showed that spectral features and indices obtained from backscattering directions between 20° and 40° view zenith angle had a stronger correlation with Car content than that from the nadir direction, and the strongest correlation was observed from about 30° backscattering direction. Spectral absorption depth at 500nm derived from spectral data obtained from 30° backscattering direction was found to reduce the difference induced by plant cultivars greatly. It was the most suitable for winter wheat canopy Car estimation, with a coefficient of determination 0.79 and a root mean square error of 19.03mg/m2. This work indicates the importance of taking viewing geometry effect into account when using spectral features/indices and provides new insight in the application of multi-angle remote sensing for the estimation of crop physiology.

Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy

The future German Environmental Mapping and Analysis Program (EnMAP) mission, due to launch in late 2019, will deliver high resolution hyperspectral data from space and will thus contribute to a better monitoring of the dynamic surface of the earth. Exploiting the satellite’s ±30° across-track pointing capabilities will allow for the collection of hyperspectral time-series of homogeneous quality. Various studies have shown the possibility to retrieve geo-biophysical plant variables, like leaf area index (LAI) or leaf chlorophyll content (LCC), from narrowband observations with fixed viewing geometry by inversion of radiative transfer models (RTM). In this study we assess the capability of the well-known PROSPECT 5B + 4SAIL (Scattering by Arbitrarily Inclined Leaves) RTM to estimate these variables from off-nadir observations obtained during a field campaign with respect to EnMAP-like sun–target–sensor-geometries. A novel approach for multiple inquiries of a large look-up-table (LUT) in hierarchical steps is introduced that accounts for the varying instances of all variables of interest. Results show that anisotropic effects are strongest for early growth stages of the winter wheat canopy which influences also the retrieval of the variables. RTM inversions from off-nadir spectra lead to a decreased accuracy for the retrieval of LAI with a relative root mean squared error (rRMSE) of 18% at nadir vs. 25% (backscatter) and 24% (forward scatter) at off-nadir. For LCC estimations, however, off-nadir observations yield improvements, i.e., rRMSE (nadir) = 24% vs. rRMSE (forward scatter) = 20%. It follows that for a variable retrieval through RTM inversion, the final user will benefit from EnMAP time-series for biophysical studies regardless of the acquisition angle and will thus be able to exploit the maximum revisit capability of the mission.