Analysis of the adjacency effect on retrieval of land surface temperatures based on multimodal images from unmanned aerial vehicles

Land surface temperatures (LSTs) critically govern urban thermal environments. While algorithms have been proposed to retrieve LSTs from high-spatial-resolution images derived from unmanned aerial vehicles, efforts to explore urban geometry and adjacency effects on such retrieved LSTs are faced with challenges. In this study, a new urban radiative transfer model was proposed, which incorporated the emitted radiance from a target pixel and the atmosphere, the scattered radiance of the target pixel itself and its neighboring pixels, and the reflected atmospheric downwelling radiance. Based on the model and UAV-derived orthophotos, urban LSTs that considered the contributions of the surroundings were accurately retrieved. Depending on the emissivity and the surroundings of the target pixel, the magnitude of the adjacency effect on retrieved LSTs almost varied from 0.0 K to 3.0 K. Validation based on in-situ LSTs demonstrated that the absolute biases between the retrieved and measured temperatures were below 1.0 K. These findings have collectively underlined the necessity to consider the adjacency effect for the retrieval of urban LSTs from high-spatial-resolution images.