Tracking Changing Evidence of Water Erosion in Ordos Plateau, China Using the Google Earth Engine

Abstract

Ordos Plateau is one of the primary sources of sediment in the Yellow River, and changes in regional soil erosion directly affect the ecological status of the lower reaches of the Yellow River. Many recent studies have been published using remote sensing (RS) and geographic information systems (GIS) to evaluate soil erosion. In this study, much satellite remote sensing data in the Google Earth Engine (GEE) can better track soil erosion protection, which is significant in guiding the ecological protection and restoration of the Ordos Plateau and the Yellow River basin. In this study, we used GEE to observe the changes in soil erosion in the Ordos area from 2013 to 2021. The Theil–Sen procedure and Mann–Kendall significance test methods were used to evaluate the trend of land erosion in the Ordos area from 2013 to 2021. Based on GEE, the RUSLE is applied to evaluate soil erosion and analyze the changing trend. As a result, (1) we found that the annual change of soil and water loss in the Ordos Plateau showed three stages: 2013–2015, 2016–2018, and 2018–2021. After 2018, soil loss decreased from 14 × 1017 Mg in 2018 to 4 × 1017 Mg in 2021, which indicates that the environmental restoration project started in 2018 has achieved encouraging results. (2) The results showed that 40.9% of the regional soil erosion trend showed a significant decline, and 45.7% of the regional soil erosion trend showed a slight decline. Only 13.3% of the regional soil erosion is on the rise. (3) The test results of different land use types show that 87.3% of soil erosion occurs in bare and cultivated land. Because the terrain of Ordos is relatively flat, 95.39–96.17% of soil erosion occurs in areas with a slope of 0 to 5. (4) The reliability of the RUSLE model based on the GEE platform is proved by regression model verification of observation data and model prediction results. (5) GEE’s cloud-based features can provide data and scripts to users in developing countries which lack sufficient observation data or the necessary computing resources to develop these data. The results show that GEE has robust analysis and processing ability, can analyze a large amount of data, and can provide efficient digital products for soil erosion research.