Drought is one of the most elaborate and destructive natural calamities to affect agriculture, water resource management, and socio-economic stability. Being a rainfed country, India is highly prone to drought-related hazards in the agricultural sector, placing soil moisture and crop productivity at a rapid fall, reporting a loss of approximately 33 per cent or more from the 35 million hectares of cropped land between 2016-17 and 2021-22. This review provides a comprehensive analysis of the meteorological and remote sensing indices used for drought monitoring, with a specific focus on their application in India. Meteorological indices derived from historical climate data, along with remote sensing technologies, enhance drought assessment by identifying severity and spatial extent through satellite imagery and vegetation indices. The limitation with each procedure, however lies in its requirement for data availability as well as resolution concerns. This review also alludes to the possibility of integrating machine learning and artificial intelligence that might monitor and enhance drought management. However, this is not yet free from regional disparities in data gathering and access to technology in under-resourced areas. For instance, the more developed states like Punjab, Tamil Nadu, Karnataka, benefit from dense meteorological networks, observation stations and detailed agricultural records, enabling a more robust drought monitoring and assessment. In contrast, majority of the northeastern states often lacks sufficient ground-based stations and consistent historical data, making reliable drought monitoring more difficult. This disparity affects the effectiveness of advanced techniques, such as machine learning and artificial intelligence, which rely on robust datasets for accurate drought prediction and management. This could be addressed by interdisciplinary collaboration and international best practices-India can improve its preparedness and drought resilience.
Lalmuanzuala et al. (Fri,) studied this question.