Abstract

Drought remains one of the most complex and destructive natural hazards, exerting significant impacts on agriculture, hydrology, and socio-economic stability. Its assessment requires integrating multi-source datasets and advanced analytical methods to capture spatio-temporal variability. The present study introduces AgroHydro Insight, an automated analytical system that combines Remote Sensing (RS), Geographic Information Systems (GIS), and Machine Learning (ML) for long-term drought assessment over Jalna Tehsil, Maharashtra, spanning the period 2013–2025. The framework utilizes Landsat-8 Surface Reflectance (OLI/TIRS) data to compute the Vegetation Condition Index (VCI) and integrates it with meteorological indicators including Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). The AgroHydro Insight system automates preprocessing, cloud masking, VCI computation, and drought classification while providing an interactive GUI dashboard for visual analytics. Results reveal substantial interannual variability in vegetation health, with 2013 and 2016 identified as extreme drought years where over 80 % of the study area exhibited VCI values below 20. In contrast, the period 2020–2025 shows remarkable vegetation recovery, culminating in 2023 as the wettest and most productive year, with more than 60 % of the area classified under the “Very Good” category (VCI > 80). The integration of VCI with SPI/SPEI enables a comprehensive classification of meteorological, agricultural, and hydrological droughts, enhancing interpretability and reliability. The study demonstrates the potential of the AgroHydro Insight dashboard as a decision-support tool for real-time drought monitoring and mitigation planning. Future extensions include real-time data assimilation, web deployment, and deep learning–based drought forecasting to strengthen climate resilience and sustainable water resource management.

References

• Bhuiyan, C. (2004). Various drought indices for monitoring drought condition in Aravalli terrain of India. Proceedings of the 20th International Conference on Hydrology and Water Resources, New Delhi.
• Hayes, M., Svoboda, M., Wall, N., and Widhalm, M. (2011). The Lincoln declaration on drought indices: Universal meteorological drought index recommended. Bulletin of the American Meteorological Society, 92(4), 485–488.
• Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11), 91–100.
• Mishra, A. K., and Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1–2), 202–216.
• Patel, N. R., Chopra, P., and Dadhwal, V. K. (2012). Analyzing spatial patterns of meteorological drought using standardized precipitation index (SPI) and GIS. International Journal of Remote Sensing, 33(18), 5598–5613.
• Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: The SPEI. Journal of Climate, 23(7), 1696–1718.
• Wilhite, D. A., and Glantz, M. H. (1985). Understanding the drought phenomenon: The role of definitions. Water International, 10(3), 111–120.