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Delta agro-ecosystems, recognized by their dynamic hydrological conditions and fertile alluvial soils, play a crucial role in global food security. However, these regions face significant environmental and climatic challenges, including soil salinity, waterlogging, and unpredictable weather patterns. Remote sensing and Geographic Information Systems (GIS) have emerged as essential tools for monitoring and managing these ecosystems by providing real-time data on land use, soil health, and crop conditions. Advances in satellite imagery and UAV technology facilitate high-resolution analysis, enhancing precision agriculture practices and resource allocation. According to the Food and Agriculture Organization (FAO), remote sensing applications have improved crop yield estimations by 85% and reduced water usage by 30% in deltaic regions through optimized irrigation strategies. The integration of remote sensing and GIS enables accurate soil moisture assessment, land cover classification, and pest and disease surveillance. Studies indicate that hyperspectral imaging can detect crop stress with 92% accuracy, aiding in early intervention measures. Furthermore, machine learning models trained on remote sensing data can predict climate impacts on delta agriculture with an accuracy exceeding 80%, supporting adaptive strategies against extreme weather events and sea-level rise. Precision agriculture techniques utilizing GIS have demonstrated a 20% increase in productivity and a 15% reduction in input costs in delta farming systems. The use of LiDAR (Light Detection and Ranging) technology for terrain mapping further enhances flood risk assessment and sustainable land management. Despite these technological advancements, challenges persist in data integration, policy implementation, and farmer adoption of remote sensing tools. Effective governance frameworks and international collaboration are essential for bridging the gap between technology development and field applications. Future research should focus on enhancing AI-driven analytics, improving sensor accuracy, and promoting stakeholder engagement for sustainable delta agro-ecosystem management. Remote sensing and GIS not only enhance agricultural productivity but also contribute to environmental conservation and climate resilience.

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Remote Sensing and GIS for Monitoring Delta Agro-ecosystems

  • 章节

  • 发表日期：

    2026年

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  • Harshit Mishra,
  • Fredrick Kayusi,
  • Rashmi Mishra,
  • Ioannis Adamopoulos,
  • Diksha Bohra

摘要

Delta agro-ecosystems, recognized by their dynamic hydrological conditions and fertile alluvial soils, play a crucial role in global food security. However, these regions face significant environmental and climatic challenges, including soil salinity, waterlogging, and unpredictable weather patterns. Remote sensing and Geographic Information Systems (GIS) have emerged as essential tools for monitoring and managing these ecosystems by providing real-time data on land use, soil health, and crop conditions. Advances in satellite imagery and UAV technology facilitate high-resolution analysis, enhancing precision agriculture practices and resource allocation. According to the Food and Agriculture Organization (FAO), remote sensing applications have improved crop yield estimations by 85% and reduced water usage by 30% in deltaic regions through optimized irrigation strategies. The integration of remote sensing and GIS enables accurate soil moisture assessment, land cover classification, and pest and disease surveillance. Studies indicate that hyperspectral imaging can detect crop stress with 92% accuracy, aiding in early intervention measures. Furthermore, machine learning models trained on remote sensing data can predict climate impacts on delta agriculture with an accuracy exceeding 80%, supporting adaptive strategies against extreme weather events and sea-level rise. Precision agriculture techniques utilizing GIS have demonstrated a 20% increase in productivity and a 15% reduction in input costs in delta farming systems. The use of LiDAR (Light Detection and Ranging) technology for terrain mapping further enhances flood risk assessment and sustainable land management. Despite these technological advancements, challenges persist in data integration, policy implementation, and farmer adoption of remote sensing tools. Effective governance frameworks and international collaboration are essential for bridging the gap between technology development and field applications. Future research should focus on enhancing AI-driven analytics, improving sensor accuracy, and promoting stakeholder engagement for sustainable delta agro-ecosystem management. Remote sensing and GIS not only enhance agricultural productivity but also contribute to environmental conservation and climate resilience.