Remote Sensing (RS) technology, which harnesses electromagnetic radiation, has changed agricultural disaster management. By identifying spectral signatures generated various objects on the Earth's surface, it enables effective monitoring of crop health, soil conditions and environmental changes. RS linked with Geographic Information Systems (GIS), prioritizes response actions, anticipates damaged areas and simulates disaster scenarios, revolutionizing disaster management approaches. Integration with modern analytics, such as machine learning, enhances catastrophe impact assessments, agricultural production estimations and land cover classification, thereby boosting disaster preparedness and response. Recent innovations, such as Unmanned Aerial Vehicles (UAVs) and high-resolution satellite imaging systems, offer rapid mapping of flooded areas and accurate post-disaster damage assessment. The merging of remote sensing with artificial intelligence (AI) and big data analytics further enhances disaster response and management, lowering the dangers associated with socio-ecological vulnerability. However, obstacles remain, including the need to boost sensor capabilities, improve data delivery and address regulatory issues with UAVs. Future directions include combining hazard and disaster process models, developing user-centric solutions and utilizing IoT and big data for more accurate disaster prediction and mitigation. Overall, RS and GIS offer vital tools for mitigating agricultural disasters, delivering early information for decision-making and decreasing the impact on food security and agricultural output.
Shanmugam et al. (Wed,) studied this question.
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