Fusion level of satellite and UAV image data for soil salinity inversion in the coastal area of the Yellow River Delta

Rapid and accurate determination of soil salt content (SSC) and its spatial distribution are of great significance for the prevention and improvement of soil salinization. Satellite and unmanned aerial vehicle (UAV) remote sensing data have complementary advantages. The fusion of satellite and UAV multisource remote sensing data to improve the accuracy of SSC based on inversion methods has become a hot topic, and the appropriate fusion level of multisource remote sensing data needs to be explored and determined. The objective of this study was to determine the appropriate fusion level of Sentinel-2A Multispectral Instrument (Sentinel-MSI) and UAV image data for SSC inversion by comparing the fusion effect of three levels (spectral data, spectral index, and spectral model). A numerical regression method was employed to analyse the relationship between Sentinel-MSI and UAV image data (MSI-UAV), and MSI-UAV data were fused at different levels. Then, the appropriate fusion level and best inversion model were optimized to realize regional SSC inversion. The results indicate that spectral data fusion was better than spectral index fusion for enhancing the SSC spectral response, with the correlation between spectral indices and SSC increasing by 0.139–0.167 after fusion. After spectral data fusion, the model improved the SSC inversion accuracy most obviously, with a calibration R2 of 0.623, validation R2 of 0.571, and ratio of performance to deviation (RPD) of 1.821. Therefore, spectral data fusion was found to be superior in enhancing the spectral response of soil salinity and in improving the accuracy of the estimation model. This research optimized spectral data fusion as the appropriate fusion level of MSI-UAV for SSC inversion and formed a set of high-precision MSI-UAV multisource remote sensing fusion inversion approaches for SSC.