This study explores the potential application of combining ground-based sensing, UAVs, and satellite remote sensing for high-precision estimation of soil properties. Taking three typical agricultural areas in the Huangshui River Basin of Qinghai as the study sites, a total of 838 soil samples were collected over three consecutive years, along with corresponding UAV hyperspectral, in-situ field spectral data, and satellite imagery. By constructing a comprehensive soil spectral data acquisition platform, the study examined the ability of different spectral data to estimate soil texture content in both laboratory and field environments, and analyzed the spatial distribution patterns. The results indicated that the content of sand and clay showed a negative correlation with soil spectra, while the silt content showed a positive correlation, with the main sensitive wavelengths concentrated in the near-infrared range. Model accuracy analysis revealed that the estimation accuracy of different spectral data followed this order: laboratory spectra > in-situ field spectra > UAV spectra > GF1/2/7 spectra > ZY1-02D spectra > Sentinel-2 spectra. All six spectral datasets demonstrated the ability to estimate soil texture content. Based on the XGBoost model and four image mapping methods, it was found that soil texture content changed little in the short term. This study provides an important reference for the application of UAV and satellite remote sensing technologies in soil texture estimation and digital mapping at the field scale.
Song et al. (Mon,) studied this question.