Abstract
BackgroundThe leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation.MethodsIn this study, two consecutive years of field experiments (different irrigation times and seven wheat varieties) in 2018–2020 were performed to obtain the canopy spectra reflectance and leaf water content (LWC) data. The characteristic bands related to LWC were extracted from correlation coefficient method (CA) and x-Loading weight method (x-Lw). Five modeling methods, spectral index and four other methods (Partial Least-Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Random Trees (ERT), and K-Nearest Neighbor (KNN)) based characteristic bands, were employed to construct LWC estimation models.ResultsThe results showed that the canopy spectral reflectance increased with the increase of irrigation times, especially in the near-infrared band (750–1350 nm). The prediction accuracy of the newly developed differential spectral index DVI (R1185, R1307) was higher than that of the existing spectral index, with R2 of 0.85 and R2 of 0.78 for the calibration and validation, respectively. Due to a large amount of hyperspectral data, the correlation coefficient method (CA) and x-Loading weight (x-Lw) were used to select the water characteristic bands (100 and 28 characteristic bands, respectively) from the full spectrum. We found that the accuracy of the model based on the characteristic bands was not significantly lower than that of the full spectrum-based models. Among these models, the ERT- x-Lw model performed the best (R2 and RMSE of 0.88 and 1.46; 0.84 and 1.62 for the calibration and validation, respectively). In addition, the accuracy of the LWC estimation model constructed by ERT-x-Lw was higher than that of DVI (R1185, R1307).ConclusionThe two models based on ERT-x-Lw and DVI (R1185, R1307) can effectively predict wheat leaf water content. The results provide a technical reference and a basis for crop water monitoring and diagnosis under similar production conditions.
Highlights
The leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation
Effects of the irrigation times on the leaf water content (LWC) and canopy spectral reflectance of wheat To explain the effects of different irrigation times on LWC and canopy spectra, the data of experiment 1 were taken as an example
At the early wheat growth stage, an insignificant difference was observed in LWC among the different irrigation time treatments
Summary
The leaf water content estimation model is established by hyperspectral technology, which is crucial and provides technical reference for precision irrigation. Zhang et al Plant Methods (2021) 17:34 biochemical processes occur; leaf water content is an important indicator that reflects the overall crop water status and indirectly indicates the input and output of soil water [2, 3]. The leaf water content can be used as a reliable reference index for making feasible irrigation decisions [4]. Hyperspectral remote sensing technology has the advantages of being fast, economic, and nondestructive. The development of a diagnostic model of water status by hyperspectral remote sensing technology is of substantial significance for precision irrigation and water-saving irrigation
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