Land use and land cover (LULC) management influences the severity of soil erosion risk. However, crop management (C) is one factor of the Revised Universal Soil Loss Equation (RUSLE) model that should be taken into account in its determination, as it influences soil loss rate estimations. Thus, the present study applied an adapted C-factor estimation approach (CvkA) modified from the former approach (Cvk) to assess the impact of LULC dynamics on soil erosion risk in an agricultural area of Rwanda taking the western province as a case study. The results disclosed that the formerly used Cvk was not suitable, as it tended to overestimate C-factor values compared with the values obtained from t CvkA. An approximated mean soil loss of 15.1 t ha⁻¹ yr⁻¹, 47.4 t ha⁻¹ yr⁻¹, 16.3 t ha⁻¹ yr⁻¹, 66.8 t ha⁻¹ yr⁻¹ and 15.3 t ha⁻¹ yr⁻¹ in 2000, 2005, 2010, 2015 and 2018, respectively, was found. The results also indicated that there was a small increase in mean annual soil loss from 15.1 t ha⁻¹ yr⁻¹ in 2000 to 15.3 t ha⁻¹ yr⁻¹ in 2018 (1.3%). Moreover, the soil erosion risk categories indicated that about 57.5%, 21.8%, 64.9%, 15.5% and 73.8% had a sustainable soil erosion rate tolerance (≤10 t ha⁻¹ yr⁻¹), while about 42.5%, 78.2%, 35.1%, 84.5% and 16.8% had an unsustainable mean soil erosion rate (>10 t ha⁻¹ yr⁻¹) in 2000, 2005, 2010, 2015 and 2018, respectively. A major portion of the area fell under the high and very high probability zones, whereas only a small portion fell under the very low, low, moderate and extremely high probability zones. Therefore, the CvkA approach presents the most suitable alternative to estimate soil loss in the western province of Rwanda with reasonable soil loss prediction results. The study area needs urgent intervention for soil conservation planning, taking into account the implementation of effective conservation practices such as terracing for soil erosion control.

This study presents a comprehensive analysis of the integrated Soil Moisture–Vegetation Health Index (SM-VHI) as a robust tool for drought detection and agricultural monitoring across East Africa using data from 2000 to 2020. A sensitivity analysis within the SM-VHI algorithm identified an optimal parameter weighting (α = 0.5), which improved detection accuracy, achieving a Critical Success Index (CSI) of 0.78. The SM-VHI exhibited strong correlations with independent drought indicators, including the Standardized Soil Moisture Index (SSMI), Vegetation Health Index (VHI), and one-month Standardized Precipitation-Evapotranspiration Index (SPEI-1), confirming its reliability in capturing agricultural drought dynamics and vegetation stress responses across diverse climatic conditions. Through spatial and temporal trend analyses, we identified patterns of drought severity and recovery, which emphasized the importance of tailored management strategies. Furthermore, the analysis incorporated historical maize yield data to evaluate the effectiveness of SM-VHI in representing agricultural drought conditions. A notable positive correlation (R = 0.45–0.72) was identified between SM-VHI anomalies and detrended maize yield throughout East Africa, suggesting that enhanced vegetation and soil moisture conditions are strongly linked to increased crop productivity. This validation demonstrates the capability of SM-VHI to effectively capture drought-induced yield variability. The findings confirm the effectiveness of SM-VHI as a reliable remote-sensing tool for monitoring drought conditions and have strong potential to inform agricultural practices and policy decisions aimed at enhancing food security in a changing climate.