Regional soil organic matter mapping models based on the optimal time window, feature selection algorithm and Google Earth Engine

The spatial distribution of soil organic matter (SOM) is highly significant to the assessment of the regional carbon balance, food security and cultivated land quality. Due to climate change and the increasing food demand, the intensity of cultivated land development in the Northeast China black soil region is increasing, and it is urgent to accurately map the SOM content in this region. Remote sensing technology has been widely applied in the field of soil mapping, but large-scale and high-precision soil mapping remains a significant challenge. In this study, the Google Earth Engine (GEE) platform is adopted to generate synthetic soil images based on Landsat-8 and Sentinel-2 images capturing bare soil periods at 20-d intervals. Then, the spectral index and band are adopted as input variables to evaluate the prediction accuracy of these synthetic images depicting different periods using random forest (RF) regression. Finally, two feature selection methods (Boruta and recursive feature elimination (RFE)) are employed to evaluate the performance of these two methods. The results indicate that 1) the optimal time window for SOM prediction is day of year (DOY) 120–140 for the Songnen Plain; 2) the performance of SOM prediction based on Landsat-8 synthetic images is better than that based on Sentinel-2 synthetic images; and 3) both feature selection methods improve the SOM prediction accuracy, but RFE has the highest accuracy(Landsat-8 with Coefficient of Determination (R2) of 0.702, Root Mean Square Error (RMSE) of 0.681%; Sentinel-2 with R2 of 0.5963, RMSE of 0.793%). This study provides a new model for large-scale and high-spatial resolution SOM prediction and verifies the importance of the time window to the SOM prediction accuracy.