High Spatial Resolution Topsoil Organic Matter Content Mapping Across Desertified Land in Northern China

Soil organic matter (SOM) content is an effective indicator of desertification; thus, monitoring its spatial‒temporal changes on a large scale is important for combating desertification. However, mapping SOM content in desertified land is challenging owing to the heterogeneous landscape, relatively low SOM content and vegetation coverage. Here, we modeled the SOM content in topsoil (0–20 cm) of desertified land in northern China by employing a high spatial resolution dataset and machine learning methods, with an emphasis on quarterly green and non-photosynthetic vegetation information, based on the Google Earth Engine (GEE). The results show: 1) the machine learning model performed better than the traditional multiple linear regression model (MLR) for SOM content estimation, and the Random Forest (RF) model was more accurate than the Support Vector Machine (SVM) model; 2) the quarterly information regarding green vegetation and non-photosynthetic were identified as key covariates for estimating the SOM content in desertified land, and an obvious improvement could be observed after simultaneously combining the Dead Fuel Index (DFI) and Normalized Difference Vegetation Index (NDVI) of the four quarters (R 2 increased by 0.06, the root mean square error decreased by 0.05, the ratio of prediction deviation increased by 0.2, and the ratio of performance to interquartile distance increased by 0.5). In particular, the effects of the DFI in Q1 (the first quarter) and Q2 (the second quarter) on estimating low SOM content (<1%) were identified; finally, a timely (2019) and high spatial resolution (30 m) SOM content map for the desertified land in northern China was drawn which shows obvious advantages over existing SOM products, thus providing key data support for monitoring and combating desertification.