CGMFN: Conditional Generative Model Fusion Network for Land Surface Temperature Generation

Land Surface Temperature (LST) is an important parameter representing surface energy, which is of great significance for monitoring urban heat islands, agricultural drought, and global climate. The high-resolution LST observations will address new applications in hydrology. The spatiotemporal fusion method can generate LST with high temporal and spatial resolution. However, missing data due to cloud cover becomes a main limitation to improving the accuracy of spatiotemporal fusion models. The purpose of the fusion of LST is image prediction and generation, and deep learning generative models provide an effective idea to solve this problem. Therefore, we proposed a conditional generative model fusion network (CGMFN) for LST generation in this paper. Firstly, based on the generated model, we construct an unsupervised generation network that simultaneously learns and iterates, which can generate fine-spatiotemporal-resolution LST data from reference images with missing values. Then, spectral normalization was applied to generators and discriminators to stabilize the training process. The pre-training mechanism was adopted to improve the iteration efficiency of the model. We tested and evaluated the model in the Heihe River Basin using FY-4A LST and MODIS LST datasets. Compared to different methods, CGMFN produces a lower RMSE (average<0.4K), LPIPS (average<0.11), and higher SSIM (average>0.97). In practical applications, CGMFN can reduce the influence of reference image missing values on fusion results and generate land surface temperature products with reliable accuracy.