Land surface temperature (LST) is a critical physical parameter affecting energy and water exchange that has attracted much attention in various fields, such as environmental protection, agriculture, and climate change. Studies on spatially continuous and high-resolution LST retrieval methods, which can be efficiently acquired using thermal infrared (TIR) remote sensing technology, have been developed for many years, resulting in various LST remote sensing products. The typical mechanism thermal radiative transfer model is based on the assumption that the land surface is flat, with the TIR remote sensing image of the spatial resolution of the enhancement of the ability to observe the land surface of the 3-D geometric structure of the fine observation, due to the terrain caused by the topographic effect caused by the topography of the undulation becomes nonnegligible, the assumption of flat surface may cause apparent errors. Some LST retrieval algorithms considering topographic effects have also been proposed recently. However, they are still inaccessible due to dependence on emissivity or atmospheric parameters, which limit the accuracy and timeliness of the retrieval algorithms. In addition, various machine learning algorithms for end-to-end LST retrieval have been proposed, which utilizes their ability to handle complex nonlinear relationships to retrieve LST without external parameters. However, such models currently do not fully consider the topographic effect due to a lack of account of the radiative transfer process in undulating terrain conditions. In this study, utilizing the ability of convolutional neural networks (CNNs) to extract spatial features from adjacent pixels, a radiative transfer model-driven CNN model is proposed to realize the end-to-end retrieval of LST, considering the topographic effect. During training, a computational method based on ambient radiance scattered from the surrounding adjacent pixels in the improved radiative transfer model i...