基于无人机遥感的宁夏引黄灌区夏玉米土壤含水率反演OA

Soil moisture dynamic monitoring is crucial for agricultural water resource management in arid regions.This study integrated multispectral and thermal infrared remote sensing data from unmanned aerial vehicles with synchronous ground measurements to construct a temperature vegetation dryness index(TVDI)model,systematically evaluating the inversion accuracy of soil moisture at 0-30 cm depths in summer maize fields within the Ningxia Yellow River Irrigation District.The results demonstrated that the TVDI model achieved higher accuracy for shallow soil moisture(0-10 cm)with R2=0.677 and RMSE=0.020,significantly outperforming the deeper layer(10-30 cm)with R2=0.475 and RMSE=0.026.Shallow soil moisture dynamics exhibited strong correlations with thermal infrared signals,primarily driven by evaporation-irrigation coupling effects.In contrast,deeper soil moisture was influenced by root uptake and soil heterogeneity,leading to a 42.5%decline in inversion accuracy as the growing season progressed.Post-tasseling canopy closure induced thermal signal attenuation,increasing the RMSE of deep-layer inversion to 0.028.Additionally,the model exhibited systematic underestimation when soil moisture content exceeded 22%,with a maximum deviation of 18.7%.This study revealed the applicability and limitations of remote sensing inversion for soil moisture in vegetation-covered areas,providing technical support for precision irrigation in arid regions.Future research could enhance model stability through multi-source data fusion.