High spatiotemporal resolution land surface temperature reveals fine-scale hotspots during heatwave events over India

Abstract The climate change has altered and amplified the spatial distribution, frequency, duration, and severity of heatwaves over the past decade and is projected to increase with severe consequences in the future. Obtaining fine resolution and spatially continuous air temperature data over large regions is challenging, especially in India, due to limited meteorological weather stations, hindering the monitoring of heatwaves at a local level. Satellite retrieved Land Surface Temperature (LST) is an excellent indicator for monitoring the spatiotemporal characteristics of heatwaves. This study explores the utility of high spatiotemporal resolution LST data to better understand the effects of heatwaves on the spatial and diurnal characterization of LST during heatwave events occurred at three regions of India where the vulnerable communities are often affected. A hybrid model, combining spatial disaggregation and Diurnal Temperature Cycle (DTC) modelling was utilized to estimate the high spatial resolution LST and corresponding DTC at 70 m resolution. The estimated LST and DTC at 70 m was compared to medium-resolution LST and DTC at 980 m across different time scales: daily, 8-day, and long-term means. The analysis over different land cover types showed that the DTC of daily data at high spatial resolution was capable of identifying the localised effect of heatwaves which are otherwise not visible at coarser spatial resolution of LST. The study suggests that the high spatiotemporal LST is highly beneficial for identifying hotspots, insights on urban heat island, vegetation health and duration of exposure to heat during heatwave days. Incorporation of LST along with other key variables in heatwave monitoring systems can significantly enhance our capabilities and help reduce the impact of extreme heat events.