A Remote Sensing‐Based Early Warning System for Desertification: Monitoring Spatiotemporal Dynamics and Identifying Risk Zones

ABSTRACT Desertification, as a complex phenomenon in natural resource management, leads to land degradation, biodiversity loss, and soil quality decline, necessitating rigorous investigations. This study aimed to analyze the potential of satellite data in spatiotemporal monitoring of desertification risk and assess its impacts on ecosystems in Central Asian countries. In this study, remote sensing indices including Albedo, NDVI, NDWI, NDSI, and TGSI were utilized to assess desertification trends. These indices were extracted from the Google Earth Engine platform at a monthly temporal resolution between 2005 and 2023. For early warning signal detection, three statistical indicators—standard deviation, skewness, and autocorrelation coefficient—were evaluated using Kendall's tau values. Analyses and image processing were conducted using the R statistical software, Google Earth Engine, and GIS. Based on the MEDALUS model results, approximately 90% of the entire region (364,133 km 2 ) falls into the very severe desertification class, 3.5% into the severe class, 4% into the moderate class, and only 2.5% into the low desertification class. The VQI (1.69), CQI (1.61), MQI (1.34), and SQI (1.24) indices exhibited the most decisive influence on desertification in Central Asia. Temporally, the most significant breakpoint in albedo, NDSI, and NDVI indices occurred in 2018, while NDWI and TGSI indices showed no discernible breakpoints. The findings indicate that remote sensing indices (albedo, NDSI, NDVI), combined with statistical metrics like autocorrelation coefficient and standard deviation, can effectively signal desertification risks in Central Asia. These indices signaled desertification risks in southwestern Central Asia, particularly in Turkmenistan, Uzbekistan, and Kazakhstan. Given the critical impact of desertification on agricultural, environmental, and economic domains, comprehensive studies and operational solutions are essential to preserve and enhance soil quality, water resources, and biodiversity. As this phenomenon is influenced by complex, multi‐factorial drivers, evidence‐based analyses such as the present study are vital for predicting and mitigating this existential challenge.