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Soil erosion is one of the most serious ecological and environmental problems facing southern China. The Changhua River Basin on Hainan Island is affected by soil erosion, which is causing the soil environment to become more fragile. Compared with the Revised Soil Erosion Equation (RUSLE), the Chinese Soil Loss Equation (CSLE) is based on a large amount of Chinese local data and research results, which more accurately reflect the actual situation of soil erosion in China and therefore have better accuracy and applicability in the Chinese region. By combining GIS and RS technologies, this study establishes the CSLE model of the Changhua River Basin, quantifies the soil erosion data via image elements from 2020 to 2022 using the spatial interpolation method, classifies the erosion intensity, and analyzes the spatial and temporal change characteristics of soil erosion. The statistical results show that, during the period from 2020 to 2022, the area of slight erosion in the Changhua River Basin increased by 553.25 km2, with a rate of change of 15.83 %, and the areas of mild erosion, moderate erosion, intense erosion, very intense erosion, and severe erosion decreased by 446.42 km2, 64.4 km2, 25.73 km2, 11.25 km2, and 5.45 km2, respectively, with rates of change of −31.05 %, −30.08 %, −36.58 %, −18.02 %, and −13.85 %, respectively. Slight erosion is defined as soil erosion less than the permissible soil loss and is not regarded as soil erosion, and the other erosion intensities showed a yearly decreasing trend, indicating that the soil erosion control was effective during this three-year period. In the work of soil and water conservation, it is especially necessary to determine the main factors influencing soil erosion and predict the areas that may be prone to such erosion. Therefore, on the basis of establishing a characteristic model using land use type, slope and soil type, and through superposition analysis, we obtained the spatial and temporal change characteristics of soil erosion. The research results are as follows: (1) slight erosion is mainly concentrated in forested areas, and forested land has a better capacity for soil and water conservation; (2) mild, moderate, and strong erosion mainly occur in cultivated areas and areas with a slope of 0–5°; (3) areas of built land and areas with a slope of 8°–15° are more prone to intense erosion, although they cover a smaller area; (4) when the slope is greater than 15°, the overlap range with the forest area is larger and the slope is no longer the main factor leading to soil erosion. Thus, it can be seen that forest land significantly reduces the impact of soil erosion. (5) among the different soil types, Technosol, Ferralsol and Fluvisol all have less than 55 per cent uneroded area and are generally less erosion-resistant, while Lixisol and Acrisol are relatively more susceptible to a high degree of erosion hazard (Extremely strong erosion, severe erosion).

Ordos drought impacts are complex; the Geodetector model is able to explore the interaction between impact factors. Based on the drought severity index (DSI), this study explored the spatio-temporal dynamics and changing trends of drought, and analyzed the driving factors of DSI spatial differentiation by using the Geodetector model. The results show that: the evapotranspiration (ET) and normalized difference vegetation index (NDVI) in Ordos showed a significant increasing trend (p < 0.05). The increasing rates were ET (4.291 mm yr−1) and NDVI (0.004 yr−1). In addition, the interannual variation of the DSI also showed a significant increase, with a trend change rate of 0.089. The spatial pattern of ET and the NDVI was low in the southwest and high in the northeast, and the spatial pattern of potential evapotranspiration (PET) was high in the southwest and low in the northeast, while the distribution of the DSI was dry in the west and wet in the east. The spatial differentiation of the DSI was mainly affected by five factors: air temperature, precipitation, land use type, soil type, and the digital elevation model (DEM), with q exceeding 0.15, which were the main driving factors of drought in the Loess Plateau. Under the interaction of multiple factors, the four combinations of temperature and the DEM, precipitation and the DEM, sunshine duration and the DEM, and relative humidity and the DEM jointly drive drought, in which precipitation (0.156) ∩ DEM (0.248) has the strongest influence on drought occurrence, and q reaches 0.389. This study directly informs specific drought management strategies or ecological conservation efforts in the region.