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.
Highlights
Wheat is the most important cereal crop worldwide, with a total production of more than 700 million metric tons of grain per year [1]
The reduction in the final grain yield (GY) was accompanied by parallel reductions in relative water content (RWC) and GWCF, with the three plant traits showing significant variations (p < 0.05) among the three drip irrigation regimes (Table 3)
The main finding was that all spectral reflectance indices (SRIs) belonging to NWIs-3b, which were constructed in this study using 3-D correlogram maps, had the best performance in the estimation of three plant traits for both cultivars (R2 > 0.80) as well as RWC and GWCF under 75% ETc (R2 ≥ 0.65)
Summary
Wheat is the most important cereal crop worldwide, with a total production of more than 700 million metric tons of grain per year [1]. Water stress, which is caused by insufficient freshwater supply, is a major destructive environmental stress factor that threatens the sustainability of the world’s wheat production, in arid and semiarid regions. It leads to unprecedented competition for freshwater between various water-consuming sectors This competition has led to a decrease in the amount of freshwater allocated to the agriculture sector due to its high share of water consumption, which consumes. Shifting the crop irrigation strategies from the paradigm of full irrigation to appropriate deficit irrigation regimes that tend to decrease water loss to minimum levels is one reasonable approach to achieve MAWP. Deficit irrigation practice is the most applicable approach when irrigation water supplies are limited or irrigation costs are high [7,8,9]
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