Integrating High-Resolution Thermal Mapping and Greenhouse Gas Emission Analysis for Climate Resilience in Urban, Peri-Urban and Rural Areas

Monitoring urban, peri-urban, and rural temperatures, along with greenhouse gas (GHG) emissions, is crucial for understanding local climate dynamics, especially in rapidly urbanizing areas. This study leverages advanced remote sensing techniques and environmental analysis to enhance high-resolution Land Surface Temperature (LST) mapping. It further investigates the relationship between LST and methane (CH₄) emissions - a significant driver of climate change - and their combined impact on Urban Heat Island (UHI) effects.

Leveraging multispectral atmospherically corrected imagery from LANDSAT 8-9 and SENTINEL-2 satellites, spectral harmonization techniques and Convolutional Neural Network (CNN)-based super-resolution models were applied to improve the spatial resolution and accuracy of LST calculation. These methods are further refined through the integration of key environmental indices, including soil characteristics, land cover, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI), which capture land use characteristics and their impact on thermal variations. The resultant LST at 1m was statistically validated against meteorological datasets by calculating Root Mean Squared Error and Mean Absolute Error, showing errors consistently below 2°C, with 75% of the values within 1°C. Making use of the accurate LST readings, air temperature (Ta) was derived using polynomial regression models, ultimately resulting in LST-derived air temperature maps with R² values exceeding 0.75.

Building upon this high-resolution thermal mapping, the study examines how agricultural zones are influenced by urban thermal dynamics exacerbated by GHG emissions creating a negative feedback loop where increased temperatures further impact agricultural practices and lead to additional GHG emissions. Seasonal and phenological variations in CH₄ emissions from major crops cultivated in the Loiret region including wheat, were analyzed. Results reveal that land use, crop phenology and soil characteristics significantly modulate LST, influencing both the intensity and distribution of urban heat anomalies. Moreover, the thermal contributions of these areas are analyzed within the context of their dual role. On one hand, these areas can act as potential moderators of UHIs by providing vegetative cover and cooling effects. On the other hand, they contribute to regional methane fluxes due to agricultural practices. This dual role highlights the complexity of peri-urban and rural zones, as they can simultaneously alleviate and exacerbate environmental challenges.

The presented framework can be considered as a contribution to bridging the gap between remote sensing advancements and climate science by providing actionable insights into the interactions between urban and rural thermal dynamics. The methodology not only offers a scalable approach for improving LST and Ta monitoring in data-sparse regions but also highlights the implications of land management practices for mitigating urban heat and reducing GHG emissions. By combining cutting-edge data processing techniques with environmental analysis, the study underscores the importance of integrating thermal mapping with greenhouse gas emission assessments to inform sustainable planning and climate adaptation strategies. In conclusion, this study contributes to the broader understanding of urban-rural thermal interdependencies and their role in shaping regional climate resilience, while also aiming to develop a new approach that leverages remote sensing to GHG emissions across wide areas.