Retrieving Surface Soil Water Content Using a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System Images

Surface soil water content (SWC) is a major determinant of crop production, and accurately retrieving SWC plays crucial role in effective management. Unmanned aerial systems (UAS) can acquire images with high temporal spatial resolutions for monitoring at the field scale. The objective this study was to develop an algorithm retrieve by integrating texture into vegetation index derived from UAS multispectral thermal images. normalized difference (NDVI) surface temperature (Ts) were employed construct dryness (TVDI) using trapezoid model. Soil incorporated model based on relationship between lower upper limits form (TTVDI). For validation, 128 samples, 84 2019 44 2020, collected determine gravimetric SWC. Based linear regression models, TTVDI had better performance estimating compared TVDI, increase R2 (coefficient determination) 14.5% 14.9%, decrease RMSE (root mean square error) 46.1% 10.8%, 2020 respectively. application high-resolution has potential timely