A novel approach for downscaling land surface temperature from 30 m to 10 m using land features multi-interaction - Mfondoum, Alfred Homère Ngandam; Hakdaoui, Sofia; Mihi, Ali; Diba, Ibrahima; Tchindjang, Mesmin; Moutila, Luc Beni; Tchatchouang, Frederic Chamberlain Lounang (Annals of GIS, 2025)
This paper explores the potential of downscaling Land Surface Temperature, LST, based on land features multi-interaction with a spatial regression multi-modelling. The Radiative Transfer Equation first helped to create an LST 15 m layer over Landsat-OLI/TIRS. Next, a bilinear assessment of LST is conducted over elevation and hillshade, so to adjust shadow/brightness. Then, interactions are modelled on a feature-to-feature linear basis between spectral indices, SI's, representing vegetation, built-up, soil, water and shadow. A multilinear regression model is further built between combined pairs of interactions and LST 15 m . The first principal Component, PC1, of all subtractions of each pair of interactions from others is stacked with individual SI's, to build another multi-regression model around LST 15 m . Each of the three models is individually subtracted from LST 15 m , normalized, [0-1], and their sum serves as the residuals layer. The downscaling step uses coefficients of the interactions model with PC1 over the corresponding Sentinel2-MSI 10 m SI's, and adds back the gaussian-kernel of residuals. The Normalized Urban-High Spatial Resolution-Land Surface Temperature, NU-HSR-LST 10 m , is the final product, that sharpens hot/cold spots, with a highly spread of values among land features. As supporting results, directions of relations with vegetation and built-up were improved, while unexpected relations were alternatively revealed (water) or reversed (soil, shadow); determination coefficients, R 2 , shows a strong correlation of NU-HSR-LST 10 m to LST 30 m (R 2 :[0.7304-0.9844]), even stronger with a closest model (R 2 :[0.85-0.99]); a variance analysis between NU-HSR-LST 10 m and LST 30 m is quasi-insignificant between [0.0002-0.00297]; and a root mean square error computed in a war-disturbed urban context, was lower for NU-HSR-LST 10 m , [0.057-0.096], than for LST 30 m ,[0.106-0.151], as stability in dynamics depiction. Finally, the machine learning algorithm of random forest based on different seeds achieved overall accuracy between [0.92-1]. From these results, the downscaling process is efficient in better distinguishing contributions per land feature in diverse urban environments, while more cross-validation based on meteorological stations is still needed.