Canopy Spectral Indices Research Articles

Canopy reflectance spectra’s variability, physical traits’ uniqueness and the prediction of rubber clones (Hevea brasiliensis)

Before implementing any further precision agriculture applications, the crucial task is to determine the rubber (Hevea brasiliensis) clones. This paper addresses the potential of using canopy spectral indices (CSIs) to delineate mature rubber clones. Each rubber clone responds differently to its local environment. Unfortunately, checking a sizeable mature rubber plantation and determining each rubber tree’s clone is tedious and time-consuming. It is, therefore, of interest and objective to classify and predict rubber clones via CSIs. This study highlighted statistically significant differences in the physical and spectral functional traits’ variability among nine mature rubber clones in a micro-agroclimatic environment with uniform management practices. We analysed the trunk girth, leaf physical properties and multispectral remote sensing data collected by a low-cost unmanned aerial vehicle (UAV) to explore the mature rubber canopy’s spectral indices, which are a crucial unique trait of rubber clones. We found that rubber leaf area increases with leaf mass. There is a positive relationship between trunk girth and leaf mass per area (LMA). Furthermore, leaf density and NDVI have a strong relationship that potentially links both traits. The additional assessment indicates that none of the 89 CSIs independently could discriminate between all nine rubber clones. However, several CSIs could distinguish several individual rubber clones or two or more groups of rubber clones. The classification results selected B1, NDVI, NDRE, RB3B2B4, GARI and BWDRVI as the most important CSIs. The generated models estimated that individual rubber clone prediction by CSIs provided low to moderate overall accuracy between 41.8 ± 5.3% and 82.3 ± 3.9%. Despite the variability, our findings highlighted a promising approach using CSI traits as a practical way to achieve accurate rubber clone predictions because of their uniqueness for specific clones or groups of clones.

Assessment of plant water status in winter wheat (Triticum aestivum L.) based on canopy spectral indices.

Rapid and non-destructive estimation of plant water status is essential for adjusting field practices and irrigation schemes of winter wheat. The objective of this study was to find new combination spectral indices based on canopy reflectance for the estimation of plant water status. Two experiments with different irrigation regimes were conducted in 2015–2016 and 2016–2017. The canopy spectra were collected at different growth stages of winter wheat. The raw and derivative reflectance of canopy spectra showed obvious responses to the change of plant water status. Except for equivalent water thickness (EWT), other water metrics had good relationships with new combination spectral indices (R2>0.7). An acceptable model of canopy water content (CWC) was established with the best spectral index (RVI (1605, 1712)). Models of leaf water content (LWC) and plant water content (PWC) had better performances. Optimal spectral index of LWC was FDRVI (687, 531), having R2, RMSE and RPD of 0.77, 2.181 and 2.09; R2, RMSE and RPD of 0.87, 2.652 and 2.34 for calibration and validation, respectively. And PWC could be well estimated with FDDVI (688, 532) (R2, RMSE and RPD of 0.79, 3.136 and 2.21; R2, RMSE and RPD of 0.83, 3.702 and 2.18 for calibration and validation, respectively). Comparing the performances of estimation models, the new combination spectral indices FDRVI (687, 531) based on canopy reflectance improved the accuracy of estimation of plant water status. Besides, based on FDRVI (687, 531), LWC was the optimal water metrics for plant water status estimation.

Vegetation Index Weighted Canopy Volume Model (CVMVI) for soybean biomass estimation from Unmanned Aerial System-based RGB imagery

Crop biomass estimation with high accuracy at low-cost is valuable for precision agriculture and high-throughput phenotyping. Recent technological advances in Unmanned Aerial Systems (UAS) significantly facilitate data acquisition at low-cost along with high spatial, spectral, and temporal resolution. The objective of this study was to explore the potential of UAS RGB imagery-derived spectral, structural, and volumetric information, as well as a proposed vegetation index weighted canopy volume model (CVMVI) for soybean [Glycine max (L.) Merr.] aboveground biomass (AGB) estimation. RGB images were collected from low-cost UAS throughout the growing season at a field site near Columbia, Missouri, USA. High-density point clouds were produced using the structure from motion (SfM) technique through a photogrammetric workflow based on UAS stereo images. Two-dimensional (2D) canopy structure metrics such as canopy height (CH) and canopy projected basal area (BA), as well as three-dimensional (3D) volumetric metrics such as canopy volume model (CVM) were derived from photogrammetric point clouds. A variety of vegetation indices (VIs) were also extracted from RGB orthomosaics. Then, CVMVI, which combines canopy spectral and volumetric information, was proposed. Commonly used regression models were established based on the UAS-derived information and field- measured AGB with a leave-one-out cross-validation. The results show that: (1) In general, canopy 2D structural metrics CH and BA yielded higher correlation with AGB than VIs. (2) Three-dimensional metrics, such as CVM, that encompass both horizontal and vertical properties of canopy provided better estimates for AGB compared to 2D structural metrics (R2 = 0.849; RRMSE = 18.7%; MPSE = 20.8%). (3) Optimized CVMVI, which incorporates both canopy spectral and 3D volumetric information outperformed the other indices and metrics, and was a better predictor for AGB estimation (R2 = 0.893; RRMSE = 16.3%; MPSE = 19.5%). In addition, CVMVI showed equal prediction power for different genotypes, which indicates its potential for high-throughput soybean biomass estimation. Moreover, a CVMVI based univariate regression model yielded AGB predicting capability comparable to multivariate complex regression models such as stepwise multilinear regression (SMR) and partial least squares regression (PLSR) that incorporate multiple canopy spectral indices and structural metrics. Overall, this study reveals the potential of canopy spectral, structural and volumetric information, and their combination (i.e., CVMVI) for estimations of soybean AGB. CVMVI was shown to be simple but effective in estimating AGB, and could be applied for high-throughput phenotyping and precision agro-ecological applications and management.

Monitoring of Aphis gossypii Using Greenseeker and SPAD Meter

Cotton aphid (Aphis gossypii) is considered as one of the most important agriculture pest for the cotton production. However, it is generally labor-intensive and time-consuming to obtain some information of Cotton aphid with conventional methods through direct measurement by sampling in the field. This study explores the potential of using a new method to obtain information of the Cotton aphid rapidly. In our study, the cotton canopy spectral indices (NDVI, VI_2, REDrefc, NIRrefc) and chlorophyll concentration, obtained from hand-held high spectrometer GreenSeeker and chlorophyll meter SPAD-502 and Cotton aphid amount derived from the artificial field-based survey were used to uncover the relationship between Cotton aphid amount and canopy spectral index and SPAD value of the cotton in city of Shihezi, China. The results showed that NDVI and NIRrefc were negatively related to Cotton aphid amount. VI_2 content had a significant and positive relationship with its amount. The non-linear three cubic models with alate Aphid amount as independent variables have been established between VI_2 value and alatae Aphid amount, which could explain 92.37 % of VI_2 value variance. SPAD values were also significantly and negatively correlated to the Aphid amount. The non-linear logarithm model with wingless Aphid amount as independent variables was the best for uncovering the relationship between SPAD value and wingless Aphid amount, which could explain 85.48 % of SPAD value variance. The results demonstrate the establishment of the function model provides a theoretical basis and techniques for indirect and rapid monitoring and management of Cotton aphid.

Estimation of wheat nitrogen status under drip irrigation with canopy spectral indices

SUMMARYCrop nitrogen (N) status is an important indicator of crop health and predictor of subsequent crop yield. The present study was conducted to analyse the relationships between nitrogen nutrition index (NNI), nitrogen biomass difference (ΔNB) and spectral indices in wheat, and then attempt to improve field N management. Spectral indices and concurrent sample N and biomass parameters were obtained from the Shihezi University experimental site in Xinjiang, China during 2009 and 2010. The results showed that all spectral indices were significantly correlated with NNI. Regression functions with the highest determination coefficient (R2) and the lowest root mean square error (RMSE) were used to improve prediction of NNI, and then the selected spectral index was used to estimate NNI and ΔNB. The strongest relationships were observed for the products of modified normalized difference 705 × biomass dry weight (BND705) and the enhanced vegetation index 2 (EVI2) for estimating NNI. There were also strong relationships between the NNI and the normalized NNI (ΔNNI) as well as between ΔNNI and ΔNB, with a linear relationship between ΔNB and the spectral index BND705 and a linear relationship between ΔNB and the spectral index EVI2. These results indicated that BND705 and EVI2 can be used to improve the accuracy of NNI estimation, and the correlations of ΔNB and NNI with BND705 and EVI2 can be used to further improve field N management in wheat.