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

Water

Mohamed M. Ibrahim¹  Osama Elsherbiny¹  Salah Elsayed²  Mohamed Farouk³  Urs Schmidhalter⁴  Salah El-Hendawy⁵  Yaser Hassan Dewir⁵  Hazem H. Ibrahim⁶ 

[1] Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt;Agricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, Egypt;Agricultural Engineering, Surveying of Natural Resources in Environmental Systems Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, Egypt;Chair of Plant Nutrition, Department of Plant Sciences, Technical University of Munich, Emil-Ramann-Str. 2, D-85350 Freising-Weihenstephan, Germany;Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia;Sustainable Development of Environment and Its Projects Management Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, Egypt;

关键词: 2-D correlogram maps;    3-D correlogram maps;    gravimetric water content;    irrigation management;    PLSR;    RF;   

DOI  :  10.3390/w13192666

来源: DOAJ

【 摘 要 】

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 GWC_F 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.

【 授权许可】

Unknown