Soil erosion undermines the sustainable development of land—a vital resource for human survival. Research into the spatiotemporal dynamics of soil erosion is therefore crucial for formulating effective soil and water conservation strategies and advancing ecological protection efforts. In the domain of soil erosion research, the Universal Soil Loss Equation and Revised Universal Soil Loss Equation (USLE/RUSLE) model represent the dominant approach for quantifying soil erosion volumes. While this methodology yields reliable outcomes, it fails to incorporate an assessment of the relative significance of the factors embedded within the model. This study selected the Henan section of the Yellow River Basin as the research area, using monthly remote sensing data from 2010 to 2025 as the main data source. Taking into account factors such as rainfall, slope, elevation, vegetation coverage, and hydrological conservation measures, the RUSLE model was used to calculate and combine Geographic Information System (GIS) geographic detectors for quantitative analysis of soil erosion factors. The results showed the following: (1) The average soil erosion modulus in the study area from 2010 to 2025 was mainly micro and mild erosion. (2) Soil erosion exhibits a certain periodicity, with a year of significant soil erosion occurring every 3–4 years. The overall trend of soil erosion is a decrease. (3) Geographic detector analysis shows that slope has the greatest impact on soil erosion, with larger slopes leading to more severe soil erosion. The influence of each factor ranges from large to small as slope > water conservation measures > rainfall > vegetation coverage > elevation. (4) The interaction between factors can enhance the influence on soil erosion, and the interaction between vegetation cover factors and other factors significantly increases the influence; after interacting with various factors, the slope factor will significantly increase the influence of soil erosion. The research results can provide technical support and decision-making basis for ecological protection in the Yellow River Basin, such as through soil and water conservation, returning farmland to forests, and slope greening; The dominant factors and obvious interaction factors in the research area can provide a scientific basis for subsequent scholars to optimize the parameters of regional models.

The sustainable management of land resources and the formulation of land policies are closely linked to the stability and health of terrestrial ecological systems, which in turn underpin sustainable regional economic, social, and environmental development. However, land use change has a time effect on the environment and requires long-term observation to discover its impact on landscape patterns. The Yellow River Basin functions as a critical ecological barrier in northern China, where land use changes are particularly intense in the transitional zone between its middle and lower reaches. Using Landsat imagery as the data source, this study adopts the Random Forest (RF) algorithm to classify eight sets of sequential data covering a 35-year period from 1990 to 2025 in the study area. Landscape pattern metrics and transfer matrices are employed to conduct qualitative and quantitative analyses of the spatiotemporal dynamics of land use changes. Additionally, land expansion analysis strategies and the RF algorithm are applied to identify the relative importance of different driving factors. The results show that: (1) The classification accuracy based on the Google Earth Engine (GEE) cloud platform remains consistently high, exceeding 90% across all phases. (2) Patch density decreases significantly, while the largest patch index continues to decline; the Shannon diversity index shows a fluctuating upward trend, and the aggregation index exhibits a slight increase. (3) Mutual conversions among farmland, forest, and grassland are the dominant processes driving land use changes in the region. (4) The Digital Elevation Model (DEM), construction land area distribution, and distance to primary roads are the key factors influencing land use patterns, with human activities acting as the primary driver of land use type transformations in the area.