Large-scale remote sensing model enables an integrated monitoring approach for high-resolution tracking pest vole populations

Grassland voles pose significant challenges to agriculture and public health due to their population outbreaks. Traditional monitoring methods are labor-intensive and costly, particularly in heterogeneous landscapes. This study integrates Sentinel-2 satellite imagery with field data to develop a predictive framework for monitoring fossorial water vole (Arvicola scherman) populations in northwestern Spain. We present a high-resolution habitat suitability model (97% accuracy) and an Optimized Damage Index that accounts for climatic variability to reliably infer fossorial water vole abundance based on vegetation damage of grasslands and meadows. April and August were identified as optimal monitoring periods, as they coincide with opposing grass conditions and vole activity. Our approach enables early detection of outbreak zones, even in the absence of continuous field surveys, and supports scalable, cost-effective vole management. The framework improves decision-making for vole population control, optimizes resource allocation, and can be adapted to other species or regions. These findings highlight the value of remote sensing for proactive, real-time vole management, enhancing sustainable crop protection strategies.