[Estimation of Soil Organic Carbon Content in the Bosten Lake Basin Based on Synthetic Images and Multi-variables]

The selection of appropriate multi-temporal remote sensing image synthesis methods and modeling variables plays an important role in the estimation of soil organic carbon content and its spatial distribution inversion. Taking the soil organic carbon content in Xinjiang Bosten lake basin as the research object， spectral variables were generated according to the minimum， median， and mean Sentinel-2 multi-temporal satellite image synthesis method， and environmental variables such as climate and topography were added as modeling variables. The effects of different image compositing methods and variable sets on the estimation of soil organic carbon content in tillage were analyzed using the Boruta variable screening algorithm and random forest （RF） model. The results showed that： ① The combination of environmental and spectral variables significantly improved SOC content estimation， with climate variables among the environmental factors playing a crucial role in the Bosten lake basin. ② Models using variables selected by the Boruta algorithm outperformed those using the full variable set， demonstrating that the Boruta algorithm effectively enhanced model accuracy. ③ Among all models， the one using mean-synthesized spectral variables combined with environmental variables yielded the best results， with the optimal model achieving an estimation accuracy in which R² was 0.970， RMSE was 2.919 g·kg^-1， and RPD was 5.319. The integration of multi-temporal mean-synthesized spectral variables， environmental variables， Boruta algorithm， and random forest modeling provides a reliable approach for estimating the spatial distribution of topsoil SOC content in the Bosten Lake Basin， offering a technical reference for accurately assessing soil organic carbon storage in arid regions.