Abstract:Improving the accuracy of salinization monitoring by satellite remote sensing plays a crucial role in salinization. A synthesized model for assessment of regional soil salinity was established based on UAV and GF-1 satellite remote sensing data. Applying the trend surface of the UAV data creation to the GF-1 satellite scale, through the improved TsHARP scale conversion method, after the conversion residual correction, the upscaling results were quantitatively and qualitatively analyzed. The results showed that the blue band B1, the nearinfrared band B5, the salt index SI, the salt index S5, and the improved spectral index NDVI-S1 had a good correlation with the measured soil salinity data in two remote sensing data. Correlation coefficients were more than 03. In the three regression models, the best model for monitoring soil salinization by UAV data was the SRU model, the optimal model of GF-1 data was the MLRS model. After upscale conversion, the inversion accuracy of soil salinity was much higher than that of direct satellite data inversion. The optimal model after ascending scale was obviously improved with the optimal model by directly using GF-1 data inversion, the former R2c was 0.338 higher than that of the latter, R2v was 0.369 higher, but RMSE was 0.057 percentge points lower. The research results can provide a reference for largescale rapid monitoring of salinization in the bare soil period of irrigation districts.
