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Ten different wheat genotypes were studied for understanding their differential behaviour to different water-deficit stress levels. Hyperspectral data (350–2500 nm) and relative water content (RWC) of plants were measured at different stress level for identifying optimal spectral bands, indices and multivariate models to develop non-invasive phenotyping protocols. Evaluation of water sensitive existing spectral indices, proposed indices and band depth analysis at selected wavelengths was done with respect to RWC and prediction models were developed. The prediction models developed were efficient in predicting RWC with the R 2 values ranging from 0.73 to 0.88 for spectral indices and 0.74–0.85 with continuum depth. Then, the ratio spectral indices (RSI) and normalised difference spectral indices (NDSI) were obtained in all possible combinations within 350–2500 nm and their correlations with RWC were quantified to identify the best indices. The best spectral indices for estimating RWC in wheat were RSI ( R 1391 , R 1830 ) and NDSI ( R 1391 , R 1830 ) with R 2 of 0.86 and 0.81, respectively. Spectral reflectance data were also used to develop partial least squares regression (PLSR) followed by multiple linear regression (MLR), support vector machine regression (SVR), multivariate adaptive regression spline (MARS) and random forest (RF) model to calculate plant RWC. Among these multivariate models, PLSR was the best model for prediction of RWC ( R 2 and RMSE of 0.96 and 3.88%; 0.91 and 6.52% for calibration and validation, respectively). The methodology developed would help for its further use in high-throughput phenomics of different crops for drought stress. [ABSTRACT FROM AUTHOR]

Estimation of aboveground green biomass is essential for evaluating grassland productivity and functioning. This study aimed to explore the potential of band depth indices for estimating aboveground green biomass in grassland with low canopy cover. Field spectral and biomass measurements were conducted during 2009 and 2010 growing seasons in desert steppe of Inner Mongolia. Band depth (BD), band depth ratio (BDR), normalised band depth index (NBDI), band depth normalised to area (BNA), maximum band depth (BD max ), and area of absorption region (BD area ) extracted from red absorption region (650–740 nm) were utilised as band depth indices. Results indicated that: (1) BD at individual bands between 655 and 716 nm showed good accuracy for aboveground green biomass estimation; (2) BD at 698 nm yielded the best accuracy ( R 2 = 0.7, RMSECV = 29.6 g m −2 for calibration; RMSE = 32.4 g m −2 , rRMSE = 26.9% for validation); (3) BDR, NBDI, and BNA at all bands were not reliable estimators of aboveground green biomass ( R 2 < 0.3, RMSECV > 45 g m −2 for calibration; RMSE > 46 g m −2 , rRMSE > 39% for validation); (4) although the performance of BD max ( R 2 = 0.65, RMSECV = 32.1 g m −2 for calibration; RMSE = 34.5 g m −2 , rRMSE = 28.7% for validation) and BD area ( R 2 = 0.69, RMSECV = 30.2 g m −2 for calibration; RMSE = 33.1 g m −2 , rRMSE = 27.6% for validation) was slight lower than that of BD 698nm , the performance was far better than that of BDR, NBDI, and BNA. Our results suggest that BD 698nm has good potential to estimate aboveground green biomass in grassland with low canopy cover. The performance of BD 698nm needs to be further tested using space-borne hyperspectral images. [ABSTRACT FROM AUTHOR]