Dynamic evaluation of cropland degradation risk by combining multi-temporal remote sensing and geographical data in the Black Soil Region of Jilin Province, China

Cropland degradation has attracted increasing attention because the important production functions of cropland and its sensitivity to climate change. Based on multi-temporal remote sensing and geographical data, this study developed a comprehensive index of cropland degradation risk (CDRI) using the improved k-means method and applied it to the Black Soil Region of Jilin Province, China, to analyze the changes in cropland degradation risk from 2005 to 2020. The average cropland degradation risk in 2020 was generally higher than that in 2005. Croplands with changes in degradation risk types accounted for 14.27% of the total cropland area, of which croplands showing an increase and decrease in degradation risk accounted for 8.47% and 5.80% of total, respectively. From 2005 to 2020, areas of croplands with a low degradation risk decreased, but those of croplands with a medium degradation risk increased significantly. The synthetic image of bare soil, temperature, precipitation, and soil nutrients contributed greatly to the CDRI. Degradation risk of croplands distributed in black soil, chernozem, and meadow soil fluctuated significantly. The proposed CDRI method comprehensively considers the degree and spatial pattern of cropland degradation risk and has good prospects for the rapid detection of cropland degradation on a large scale.