Assessing the impact of land surface dynamics and meteorological forcings on land surface temperature characteristics in West Africa

Abstract

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 in West Africa (WA). We employed fourteen machine-learning models, which preserve nonlinear relationships to downscale LST while preserving WA's geographical variability. Our results showed that the simple random model was the best in downscaling predictands. This is important for the sub-region since its access to mainframes, which could power more multiplex machine-learning algorithms, is limited. The yearly vegetation health based on the Normalized Difference Vegetation Index (NDVI) and self-organized maps (SOM) indicates constant healthy vegetation in most southern areas but unhealthy vegetation in the northern area. Locations where we found unhealthy vegetation coincided with the hot LST clusters as categorized by SOM. Also, the southwest winds cause the upwelling of cold waters, lowering LST in southern WA. This emphasizes the cooling influence of water bodies on LST. Identifying high LST locations is vital to prioritizing places for greening. A high heat load and diurnal anisotropic heat might translate to a relatively high LST depending on the topographic slope-facing angle. Therefore, urban planners should consider the joint attribution of LST dynamics to LVCET while planning landscapes.