Estimating the Leaf Water Status and Grain Yield of Wheat under Different Irrigation Regimes Using Optimized Two- and Three-Band Hyperspectral Indices and Multivariate Regression Models

Abstract

Spectral reflectance indices (SRIs) often show inconsistency in estimating plant traits across different growth conditions; thus, it is still necessary to develop further optimized SRIs to guarantee the performance of SRIs as a simple and rapid approach to accurately estimate plant traits. The primary goal of this study was to develop optimized two- and three-band vegetation- and water-SRIs and to apply different multivariate regression models based on these SRIs for accurately estimating the relative water content (RWC), gravimetric water content (GWCF), and grain yield (GY) of two wheat cultivars evaluated under three irrigation regimes (100%, 75%, and 50% of crop evapotranspiration (ETc)) for two seasons. Results showed that the three plant traits and all SRIs showed significant differences (p < 0.05) between the three irrigation treatments for each wheat cultivar. The three-band water-SRIs (NWIs-3b) showed the best performance in estimating the three plant traits for both cultivars (R2 > 0.80), and RWC and GWCF under 75% ETc (R2 ≥ 0.65). Four out of six three-band vegetation-SRIs (NDVIs-3b) performed better than any other SRIs for estimating GY under 100% ETc and 50% ETC, and RWC under 100% ETc (R2 ≥ 0.60). All types of SRIs demonstrated excellent performance in estimating the three plant traits (R2 ≥ 0.70) when the data of all growth conditions were combined and analyzed together. The NWIs-3b coupled with Random Forest models predicted the three plant traits with satisfactory accuracy for the calibration (R2 ≥ 0.96) and validation (R2 ≥ 0.93) datasets. The overall results of this study elucidate that extracting an optimized NWIs-3b from the full spectrum data and combined with an appropriate regression technique could be a practical approach for managing deficit irrigation regimes of crops through accurately, timely, and non-destructively monitoring the water status and final potential yield.

Combining Hyperspectral Reflectance and Multivariate Regression Models to Estimate Plant Biomass of Advanced Spring Wheat Lines in Diverse Phenological Stages under Salinity Conditions

“…The same type of SRIs exhibits high multicollinearity among themselves, which reduces the ability of such indices to detect a variation among genotypes that exhibit similar reflectance and absorption behavior. Therefore, previous studies have reported that using a combination of SRIs and multivariate regression models such as PLSR can overcome these limitations and significantly improve the predication accuracy of relevant plant traits [43,52,54,80,82,[84][85][86][87][88][89]. Most of these studies reported that using several SRIs in tandem with the PLSR model resulted in better performance in the estimation of plant traits, such as plant biomass, grain yield, and plant water content, as compared with a single index.…”

Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt

“…The main advantage of the RF model is the flexibility to variable distribution and it is not at risk to abnormal outputs and/or noise, and also an advanced-dimensional data-sensitive model. The RF model is a reliable model against over-fitting and it has been utilized efficiently in solving regression problems [49]. These two methods involve a great number of spectral or band-ratio indices into a single index to elevate the detection of measured crop characteristics.…”

Indirect Quantitative Analysis of Biochemical Parameters in Banana Using Spectral Reflectance Indices Combined with Machine Learning Modeling

“…The RSI was determined by combining two separate wavelengths in the 302-1148 nm (Figure 1). According to Elsayed et al [34], the contour maps of spectral reflectance of spectrum region were created. The established maps are useful for determining the ideal spectral region with effective (optimized) wavelengths and recognizing the relevance of SRIs.…”