Satellite-guided decision support systems (DSS) have emerged as critical tools for sustainable land management amid global challenges such as land degradation, climate change, and the increasing demand for agricultural productivity. This study presents a cross-regional approach that integrates satellite remote sensing data, agricultural growth models, and multi-criteria analysis to optimize crop monitoring and resource use across diverse agro-ecological zones. Utilizing advanced geospatial technologies and real-time climate datasets, the developed DSS provides precise, adaptive recommendations to farmers and policymakers, enhancing decision-making processes for sustainable agriculture. Field validations across multiple regions demonstrated the system’s capability to accurately monitor crop conditions and optimize resource allocation, resulting in improved productivity while maintaining environmental sustainability. The findings highlight the importance of incorporating dynamic satellite data and region-specific models to address variability in land use and socio-economic contexts. Despite challenges related to data uncertainty and user engagement, this research advances the integration of satellite technologies in land management frameworks and underscores the potential of cross-regional DSS in supporting adaptive, efficient, and sustainable agricultural practices.
Sunarko Sunarko (Fri,) studied this question.