Sustainable Agriculture, Climate Change, and Remote Sensing: A Review

Sustainable agriculture has become a critical framework for addressing the escalating challenges posed by climate change, food security, and environmental degradation. This review article provides a comprehensive synthesis of recent advances linking sustainable agricultural practices and their adaptation to and mitigation of climate change impacts, with a particular focus on the role of remote sensing technologies. We review the integration of remote sensing and artificial intelligence (AI) tools in monitoring land use and land cover changes, crop growth dynamics, soil organic carbon status, and greenhouse gas emissions in agricultural landscapes. The findings reveal that remote sensing, combined with advanced machine learning models, significantly enhances the spatial and temporal precision of agricultural monitoring, enabling improved decision-making for sustainable management. However, persistent challenges remain, including data gaps for smallholder farms, regional disparities in technology adoption, and the need for multi-factorial, integrative models that incorporate socioeconomic variables alongside climatic drivers. This article underscores the imperative of deploying scalable, cost-effective remote sensing data coupled with AI to foster climate-smart agriculture, emphasizing policy implications and future research directions to bridge existing knowledge gaps and improve resilience against climate variability.