Potential of Multivariate Statistical Technique Based on the Effective Spectra Bands to Estimate the Plant Water Content of Wheat Under Different Irrigation Regimes.

Real-time, nondestructive, and accurate estimation of plant water status is important to the precision irrigation of winter wheat. The objective of this study was to develop a method to estimate plant water content (PWC) by using canopy spectral proximal sensing data. Two experiments under different water stresses were conducted in 2014-2015 and 2015-2016. The PWC and canopy reflectance of winter wheat were collected at different growth stages (the jointing, booting, heading, flowering, and filling stages in 2015 and the jointing, booting, flowering, and filling stages in 2016). The performance of different spectral transformation approaches was further compared. Based on the optimal pretreatment, partial least squares regression (PLSR) and four combination methods [i.e., PLSR-stepwise regression (SR), PLSR-successive projections algorithm (SPA), PLSR-random frog (RF), and PLSR-uninformative variables elimination (UVE)] were used to extract the sensitive bands of PWC. The results showed that all transformed spectra were closely correlated to PWC. The PLSR models based on the first derivative transformation method exhibited the best performance (coefficient of determination in calibration, = 0.96; root mean square error in calibration, RMSE = 20.49%; ratio of performance to interquartile distance in calibration, RPIQ = 9.19; and coefficient of determination in validation, = 0.86; root mean square error in validation, RMSE = 46.27%; ratio of performance to interquartile distance in validation, RPIQ = 4.34). Among the combination models, the PLSR model established with the sensitive bands from PLSR-RF demonstrated a good performance for calibration and validation ( = 0.99, RMSE = 11.53%, and RPIQ = 16.34; and = 0.84, RMSE = 44.40%, and RPIQ = 4.52, respectively). This study provides a theoretical basis and a reference for estimating PWC of winter wheat by using canopy spectral proximal sensing data.