Retrieval technology of soil moisture content in crops cotton field based on UAV hyperspectral data

Abstract

With the rapid development of modern agriculture, the traditional agricultural farming model has been unable to meet the requirements of the rapid development of contemporary society for productivity. The monitoring of crop soil moisture content by satellite hyperspectral is limited by the problems of spatial resolution and data source. The rapid development of UAV has made up for the defects of satellite hyperspectral. In this manuscript, the hyperspectral data of UAV is used as the main data source, the measured spectrum of vegetation in cotton field and the measured ground moisture content data are used as auxiliary data. Different vegetation indexes are calculated by using the measured spectral curve on the ground. Meanwhile, the correlation with the measured soil moisture content and vegetation indexes is also analyzed. The quantitative relationship between vegetation canopy spectral information and ground measured soil moisture content is established and the soil moisture content is retrieved through the vegetation canopy spectral information indirectly. In order to optimize the hyperspectral data of UAV, the regression relationship between the same vegetation index of two data sources is established, and the soil moisture content model constructed by the measured spectral curve vegetation index is applied to the UAV hyperspectral image in order to complete the large-scale spatial inversion mapping of soil water content. The results showed that there was a positive correlation between soil water content and vegetation index as a whole. The correlation between soil moisture content and normalized vegetation index (NDVI), green wave vegetation index (GNDVI), soil regulated vegetation index (OSAVI) and soil ratio vegetation index (SR) reached 0.79, 0.72, 0.73 and 0.84. NDVI and SR are selected to construct the soil moisture content inversion model, and the model determination coefficients are 0.63 and 0.77, respectively. Due to the difference between the vegetation index of ground measured spectrum and hyperspectral data of UAV, the hyperspectral data are optimized through the vegetation index established by ground measured spectrum to realize the inversion mapping of soil moisture content of UAV hyperspectral data.