Soil erosion represents a major challenge to natural resource conservation, causing land degradation, biodiversity loss, and diminished soil quality. This study explored the use of satellite imagery to evaluate the spatiotemporal risk of soil erosion in northeastern Iran. The ICONA model was applied to identify areas at severe erosion risk, while remote sensing indices (NDVI, NDSI, and TGSI) were employed to analyze erosion trends. NDVI is used to monitor vegetation health, NDSI detects soil salinity levels, and TGSI assesses topsoil grain size distribution, collectively providing critical insights into soil erosion risk in the study area. These indices, derived from the Google Earth Engine with a 30-meter spatial resolution and monthly temporal intervals (2003–2022), were assessed at 100 points, equally divided between eroded and non-eroded regions. Field data, including vegetation plots and soil profiles, were used to validate the remote sensing outputs. Early warning signals were analyzed through three statistical indices—autocorrelation coefficient, skewness, and standard deviation—using Kendall’s tau. Results revealed that 39.7% of the area falls under low erosion risk, 58.4% under medium risk, and 1.9% under severe risk. Significant breakpoints in NDSI and NDVI were identified in 2013, while TGSI showed no detectable change. Major shifts occurred near the Alagol, Almagol, and Ajigol wetlands and northern drylands. This study underscores the importance of integrating satellite data with field validation to improve soil management, protect biodiversity, and guide sustainable erosion mitigation strategies.

This study explores the spatio-temporal dynamics of the Normalized Difference Vegetation Index (NDVI) to detect early signs of land degradation. Utilizing high-resolution NDVI data from the Google Earth Engine, spanning from 2004 to 2023 with a 30-meter resolution, this research analyzes monthly variations. To illustrate these dynamics, the study focuses on Sabzevar County, located in northeastern Iran, which extends over 7,217 km^2and is approximately 220 kilometers distant from Mashhad. Validation of the NDVI data was performed using field observations from strategically located vegetation plots. One square meter plots were systematically established along 100-meter transects (10 transects in total), where the vegetation coverage in each plot was quantitatively assessed by experts. Comprehensive statistical analysis incorporated Kendall's tie test, alongside measurements of autocorrelation, coefficient of variation, and standard deviation, using R software to assess the trends and intensities of NDVI changes. The findings revealed a critical breakpoint in 2020, with increases in all three statistical indices—autocorrelation 0.82, coefficient of variation 0.65, and standard deviation 0.58—indicative of accelerating degradation prior to this year. Furthermore, the intensity of NDVI changes varied significantly across the study area, ranging from 0.05 in central and northern regions to 0.76 in the western parts. This research underscores the value of integrating field data with remote sensing technology to provide a robust analytical tool for early detection of land degradation. This method enables precise, timely assessment and proactive management of vulnerable ecosystems, particularly in arid regions.

This study assessed the soil erosion dynamics in Iran's Maharloo watershed using remote sensing indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Salinity Index (NDSI), and Topsoil Grain Size Index (TGSI)) and machine learning models (RF, SVM, and BRT). Landsat 8 satellite images (2005–2024) were processed via the Google Earth Engine, with field validation ensuring accuracy. Among the indices, TGSI (R² = 0.86), NDSI (R² = 0.89), and NDVI (R² = 0.87) showed the strongest correlations with ground data (Rain, Soil and Vegetation). The RF outperformed the other models (AUC = 0.89), identifying the central and western regions as warning erosion zones. Breakpoint analysis revealed abrupt changes in NDVI and NDSI (2013), while early warning signals (autocorrelation, variance, and skewness) indicated an escalating erosion warning, particularly near wetlands and rainfed fields. Spatial trends highlighted significant NDVI declines (Kendall's τ = 0.69) in wetland peripheries and NDSI increased (τ = 0.52) in northern farmlands. These findings underscore the efficacy of integrating machine learning and remote sensing for erosion monitoring, providing actionable insights for land management and conservation strategies.