Abstract:Monitoring crop canopy water status is crucial for optimizing irrigation strategies. Low-altitude remote sensing technology was applied via an unmanned aerial vehicle (UAV) to retrieve the canopy equivalent water thickness (CEWT) of winter potato. Field experiments were conducted by using a UAV with multispectral cameras to capture images of winter potato under different irrigation treatments across various growth stages. Simultaneously, three water indicators were determined: leaf water content (LWC), leaf equivalent water thickness (LEWT), and CEWT. Soil backgrounds were removed from the multispectral remote sensing images to obtain average spectral reflectance (ASR), vegetation indices (VIs), and Textures. A dataset was constructed by reducing multicollinearity among independent variables through correlation analysis. Quantitative inversion models were developed by using partial least squares regression (PLSR), random forest (RF), and extreme learning machine (ELM) to obtain spatial distribution information of winter potato canopy water content in the experimental area. The results showed that the canopy water indicators of winter potatoes were increased with the rising of irrigation amounts, while the ASR across growth stages exhibited a pattern of initial decrease followed by an increase with wavelength. Compared with LWC and LEWT, CEWT showed a better correlation with ASR, VIs, and Textures. The RF model based on ASR+VIs+Textures exhibited the best performance, demonstrating strong predictive capability. The determination coefficients of the calibration and prediction datasets were 0.875 and 0.771, respectively, the root mean square errors were 0.062mm and 0.065mm, respectively, and the RPD was 2.055. The research result demonstrated that multivariable fusion can significantly enhance the accuracy of CEWT retrieval for winter potato, providing a reference for assessing the winter potato canopy water status.
