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

Hui Sun,Xueqin Jia,Guangwei Ding,Meichen Feng,Song Zhang,Gaihong Wu,Lujie Xiao,Wude Yang,Chao Wang

doi:10.3389/fpls.2021.631573

Abstract

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, R2C = 0.96; root mean square error in calibration, RMSEC = 20.49%; ratio of performance to interquartile distance in calibration, RPIQC = 9.19; and coefficient of determination in validation, R2V = 0.86; root mean square error in validation, RMSEV = 46.27%; ratio of performance to interquartile distance in validation, RPIQV = 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 (R2C = 0.99, RMSEC = 11.53%, and RPIQC = 16.34; and R2V = 0.84, RMSEV = 44.40%, and RPIQV = 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.

Highlights

Climate change has increased the frequency and intensity of drought events
Winter wheat plant water content (PWC) tended to decrease throughout the entire growing seasons of two consecutive years, and the minimum PWC was obtained on May 22, 2015, and May 21, 2016, respectively
The exception was that Multiplicative scatter correction (MSC) The correlation coefficients for the treatment of continuum removal (CR) significantly was positive in the visible region

Summary

Introduction

Climate change has increased the frequency and intensity of drought events. In most part of Asia, drought has been recorded to intensify during the last decades (Miyan, 2015), which raises numerous challenges in agriculture. Plant water condition directly influences cell turgor and internal space of tissue resulting in the changes of leaf structure. It causes the absorption, transmission, and reflection of light in the leaves changing the value of canopy reflection eventually. The near-infrared and short-infrared spectral regions are sensitive to the water content of plant leaf and canopy (Ihuoma and Madramootoo, 2017). Plant water content affects chlorophyll content and canopy size. For this reason, previous findings indicated that bands in the visible region can be used indirectly to assess plant water status (Das et al, 2017; El-Hendawy et al, 2019a)

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Results

Discussion

Conclusion