Inversion of Winter Wheat Growth Parameters and Yield Under Different Water Treatments Based on UAV Multispectral Remote Sensing - Han, Xin; Wei, Zheng; Chen, He; Zhang, Baozhong; Li, Yinong; Du, Taisheng (Frontiers in Plant Science, 2021)
In recent years, unmanned aerial vehicle (UAV) remote sensing system has been rapidly developed and applied in accurate estimation of crop parameters and yield at farm scale. To develop the major contribution of UAV multispectral images in predicting winter wheat leaf area index (LAI), chlorophyll content (called Soil and Plant Analyzer Development(SPAD)) and yield under different water treatments (low water level, medium water level and high water level), vegetation indices originated from UAV multispectral image were used during key winter wheat growth stages. The estimation performances of the models (linear regression, quadratic polynomial regression, exponential and multiple linear regression models) on basis of vegetation indices (VIs) were compared to get the optimal prediction method of crop parameters and yield. Results showed that LAI and SPAD derived from VIs both had high correlations compared with measured data, with determination coefficient of 0.911 and 0.812 (MLR model, NDVI, SAVI, EVI, DVI), 0.899 and 0.87 (quadratic polynomial regression, NDVI), 0.749 and 0.829 (quadratic polynomial regression, NDVI) under low, medium and high water level, respectively. The LAI and SPAD derived from VIs had better potential in estimating winter wheat yield by using multivariable linear regressions, compared to the estimation yield based on VIs directly derived from UAV multispectral alone by using linear regression model, quadratic polynomial regression, and exponential model. When crop parameters (LAI, SPAD) in flowering period were adopted to estimate yield by using multiple linear regressions, a higher correlation was 0.807, while the accuracy were over 87%. The import of LAI and SPAD obtained from UAV multispectral imagery based on VIs into yield estimation model could significantly enhance the estimation performance. This study indicates that the multivariable linear regression could accurately estimate winter wheat LAI, SPAD and yield under different water treatments, which has a certain reference value for the popularization and application of UAV Remote Sensing in precision agriculture.