Heat Wave Study using Satellite LST and Air Temperature Data over Gujarat Region

Parmar, P.; Pandya, Mehul R.; Dave, Jalpesh A.; Varchand, Hasmukh K.; Trivedi, Himanshu J.

doi:10.23919/ursi-rcrs56822.2022.10118491

Cited by 1 publication

(3 citation statements)

References 14 publications

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“…Our study highlights that LST demonstrates lower amplification for HWM while being higher for HWA compared to air temperature measurements, particularly in urban settings during daytime heatwaves. Our findings, in line with [37], reveal that comparing air temperatures and satellite-derived LST data between normal and heatwave years shows a significant increase in daytime air temperature during heatwaves. For example, the Don Muang region in Bangkok exhibited the highest annual temperature extremes, with observed HWM fluctuating between 33 [38,139], demonstrating the strong correlations between T air and MODIS-LST data.…”

Section: Satellite-based Land Surface Temperature Assessmentsupporting

confidence: 90%

“…Leveraging LST as a vital indicator for heatwaves, we propose an innovative, integrated RF model that combines satellite-derived LST data with air temperatures and spatial and temporal features. Unlike previous methods [16,37,[40][41][42][43][44][45][46] reliant solely on remote sensing data or ground-based observations, our model bridges the gap between the two sources, enhancing the accuracy and reliability of heatwave predictions.…”

Section: Our Contributionsmentioning

confidence: 99%

“…Satellite remote sensing offers significant promise for the precise study and detection of heatwaves from a broad perspective, aiding in the comprehension of heatwave patterns [37]. Defined by its high spatial resolution and temporal frequency, this approach allows for extensive analyses, yielding regular and long-term data crucial for grasping the behavior and effects of heatwaves.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integrating Remote Sensing and Ground-Based Data for Enhanced Spatial–Temporal Analysis of Heatwaves: A Machine Learning Approach

Chongtaku,

Taparugssanagorn,

Miyazaki

et al. 2024

Applied Sciences

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In response to the urgent global threat posed by human-induced extreme climate hazards, heatwaves are still systematically under-reported and under-researched in Thailand. This region is confronting a significant rise in heat-related mortality, which has resulted in hundreds of deaths, underscoring a pressing issue that needs to be addressed. This research article is one of the first to present a solution for assessing heatwave dynamics, using machine learning (ML) algorithms and geospatial technologies in this country. It analyzes heatwave metrics like heatwave number (HWN), heatwave frequency (HWF), heatwave duration (HWD), heatwave magnitude (HWM), and heatwave amplitude (HWA), combining satellite-derived land surface temperature (LST) data with ground-based air temperature (Tair) observations from 1981 to 2019. The result reveals significant marked increases in both the frequency and intensity of daytime heatwaves in peri-urban areas, with the most pronounced changes being a 0.45-day/year in HWN, a 2.00-day/year in HWF, and a 0.27-day/year in HWD. This trend is notably less pronounced in urban areas. Conversely, rural regions are experiencing a significant escalation in nighttime heatwaves, with increases of 0.39 days/year in HWN, 1.44 days/year in HWF, and 0.14 days/year in HWD. Correlation analysis (p<0.05) reveals spatial heterogeneity in heatwave dynamics, with robust daytime correlations between Tair and LST in rural (HWN, HWF, HWD, r>0.90) and peri-urban (HWM, HWA, r>0.65) regions. This study emphasizes the importance of considering microclimatic variations in heatwave analysis, offering insights for targeted intervention strategies. It demonstrates how enhancing remote sensing with ML can facilitate the spatial–temporal analysis of heatwaves across diverse environments. This approach identifies critical risk areas in Thailand, guiding resilience efforts and serving as a model for managing similar microclimates, extending the applicability of this study. Overall, the study provides policymakers and stakeholders with potent tools for climate action and effective heatwave management. Furthermore, this research contributes to mitigating the impacts of extreme climate events, promoting resilience, and fostering environmental sustainability.

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Section: Satellite-based Land Surface Temperature Assessmentsupporting

confidence: 90%