4.7 Article

Land surface dynamics and meteorological forcings modulate land surface temperature characteristics

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SUSTAINABLE CITIES AND SOCIETY
卷 101, 期 -, 页码 -

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DOI: 10.1016/j.scs.2023.105072

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Machine learning; Self-organized maps; Vegetation; Terrain; Remote sensing; Meteorological variables; Trends; Land surface temperature; Downscaling; Sustainable society

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This study examines the impact of land cover, vegetation health, climatic forcings, elevation heat loads, and terrain characteristics on land surface temperature distribution over West Africa. The random forest model performs the best in downscaling predictands. The southern regions consistently exhibit healthy vegetation, while areas with unhealthy vegetation coincide with hot land surface temperature clusters. Positive Normalized Difference Vegetation Index trends in the Sahel highlight rainfall recovery and subsequent greening. Southwest winds cause the upwelling of cold waters, resulting in low land surface temperatures in southern West Africa. Considering LVCET factors is crucial for prioritizing greening initiatives and urban planning.
This study examines the effect of land cover, vegetation health, climatic forcings, elevation heat loads, and terrain characteristics (LVCET) on land surface temperature (LST) distribution over West Africa (WA). We employ fourteen machine-learning models, which preserve nonlinear relationships, to downscale LST and other predictands while preserving the geographical variability of WA. Our results showed that the random forest model performs best in downscaling predictands. This is important for the sub-region since it has limited access to mainframes to power multiplex machine-learning algorithms. In contrast to the northern regions, the southern regions consistently exhibit healthy vegetation. Also, areas with unhealthy vegetation coincide with hot LST clusters. The positive Normalized Difference Vegetation Index (NDVI) trends in the Sahel underscore rainfall recovery and subsequent Sahelian greening. The southwesterly winds cause the upwelling of cold waters, lowering LST in southern WA and highlighting the cooling influence of water bodies on LST. Identifying regions with elevated LST is paramount for prioritizing greening initiatives, and our study underscores the importance of considering LVCET factors in urban planning. Topographic slope-facing angles, heat loads, and diurnal anisotropic heat all contribute to variations in LST, emphasizing the need for a holistic approach when designing resilient and sustainable landscapes.

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