Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Rapid and accurate acquisition of soil organic matter (SOM) information in cultivated land is important for sustainable agricultural development and carbon balance management. This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale. We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine (GEE) platform, and reflectance bands and vegetation indices were extracted from these composite images. Then the Random Forest (RF), Support Vector Machine (SVM) and Gradient Boosting Regression Tree (GBRT) models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables. Results showed that firstly, all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM (P<0.05) for the months of January, March, April, October, and November. Secondly, in terms of single-monthly composite variables, the prediction accuracy was relatively poor, with the highest R2 value of 0.36 being observed in January. When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year, the first quarter and the fourth quarter showed good performance, and any combination of three quarters was similar estimation accuracy. The overall best performance was observed when all monthly synthetic variables were incorporated into the models. Thirdly, among the three models compared, the RF model was consistently more accurate than the SVM and GBRT models, achieving an R2 value of 0.56. Except for band 12 in December, the importance of the remaining bands did not exhibit significant differences. This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.