ANNOUNCEMENTS
In this study, Land Surface Temperature (LST) derived from the INSAT-3DR satellite serves as a critical input for monitoring and predicting extreme heat conditions across India. While direct satellite validation using similar LST payload sensors has not been formally conducted, a comprehensive validation exercise was carried out using MODIS LST data for the years 2023 and 2024, with seasonal assessments performed for both years. Using these validation results, a summer season (March, April, May) LST climatology was created using the INSAT-3DR LST from 2020 to 2024 during the daytime (04–11 UTC) and nighttime (12–00 UTC) hours. To assess heat risk, the INSAT-3DR LST was then joined with Normalized Difference Vegetation Index (NDVI) for March–May 2023 and 2024 into heat risk zones in India to identify extreme temperature risk zones. As a case study, 18 heatwave hotspots were identified based on the LST–NDVI relationship for targeted monitoring. Furthermore, a Pan-India heatwave prediction model was developed using polynomial regression, integrating INSAT- 3DR LST with surface observations from SYNOP stations. This model was operationalized with automation for real-time forecasting. The resulting LST-based geoinformatics product is now made available on the webpage, serving both forecasters and end users in heatwave risk preparedness and response.
Keywords: LST, NDVI, Climatology, GIS Heatwave forecast.
