Detección de cambios en suelos utilizando imágenes de radar de apertura sintética SAR

Abstract

Soil degradation is an increasingly pressing global concern, with direct impacts on agriculture, climate stability, and ecosystem health. In regions such as the Ecuadorian Amazon, this issue is intensified by persistent cloud cover, which hinders consistent land monitoring using optical imagery. This context presents a significant technical challenge, as conventional methods based on optical images depend on favorable atmospheric conditions, limiting their ability to deliver reliable and frequent information in cloud-covered areas. The lack of updated data restricts the early detection of land-use changes and delays decision-making aimed at environmental conservation and sustainable land management. To address this limitation, this study proposes an alternative approach for change detection in land use by employing Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 satellite, which enables surface observation regardless of cloud cover or time of day. A comprehensive process was developed, including image preprocessing, calculation of the RVI index, and the application of deep learning models—highlighting the Bi-temporal Adapter Network (BAN) for its adaptability. The model was retrained using local data and validated through fieldwork. As a final outcome, an interactive visual interface was developed to explore detected changes intuitively, supporting its application in environmental monitoring and land-use planning.