The thermal band of a satellite platform enables the measurement of land surface temperature (LST), which captures the spatial-temporal distribution of energy exchange between the Earth and the atmosphere. LST plays a critical role in simulation models, enhancing our understanding of physical and biochemical processes in nature. However, the limitations in swath width and orbit altitude prevent a single sensor from providing LST data with both high spatial and high temporal resolution. To tackle this challenge, the unmixing-based spatiotemporal fusion model (STFM) offers a promising solution by integrating data from multiple sensors. In these models, the surface reflectance is decomposed from coarse pixels to fine pixels using the linear unmixing function combined with fractional coverage. However, when downsizing LST through STFM, the linear mixing hypothesis fails to adequately represent the nonlinear energy mixing process of LST. Additionally, the original weighting function is sensitive to noise, leading to unreliable predictions of the final LST due to small errors in the unmixing function. To overcome these issues, we selected the U-STFM as the baseline model and introduced an updated version called the nonlinear U-STFM. This new model incorporates two deep learning components: the Dynamic Net (DyNet) and the Chang Ratio Net (RatioNet). The utilization of these components enables easy training with a small dataset while maintaining a high generalization capability over time. The MODIS Terra daytime LST products were employed to downscale from 1000 m to 30 m, in comparison with the Landsat7 LST products. Our results demonstrate that the new model surpasses STARFM, ESTARFM, and the original U-STFM in terms of prediction accuracy and anti-noise capability. To further enhance other STFMs, these two deep-learning components can replace the linear unmixing and weighting functions with minor modifications. As a deep learning-based model, it can be pretrained and deployed for online prediction.

The unmixing-based spatiotemporal fusion model is one of the effective ways to solve limitations in temporal and spatial resolution tradeoffs in a single satellite sensor. By using fusion data from different satellite platforms, high resolution in both temporal and spatial domains can be produced. However, due to the ill-posed characteristic of the unmixing function, the model performance may vary due to the different model setups. The key factors affecting the model stability most and how to set up the unmixing strategy for data downscaling remain unknown. In this study, we use the multisource land surface temperature as the case and focus on the three major factors to analyze the stability of the unmixing-based fusion model: (1) the definition of the homogeneous change regions (HCRs), (2) the unmixing levels, and (3) the number of HCRs. The spatiotemporal data fusion model U-STFM was used as the baseline model. The results show: (1) The clustering-based algorithm is more suitable for detecting HCRs for unmixing. Compared with the multi-resolution segmentation algorithm and k-means algorithm, the ISODATA clustering algorithm can more accurately describe LST’s temporal and spatial changes on HCRs. (2) For the U-STFM model, applying the unmixing processing at the change ratio level can significantly reduce the additive and multiplicative noise of the prediction. (3) There is a tradeoff effect between the number of HCRs and the solvability of the linear unmixing function. The larger the number of HCRs (less than the available MODIS pixels), the more stable the model is. (4) For the fusion of the daily 30 m scale LST product, compared with STARFM and ESTARFM, the modified U-STFM (iso_USTFM) achieved higher prediction accuracy and a lower error ($R{ }^2$: 0.87 and RMSE:1.09 k). With the findings of this study, daily fine-scale LST products can be predicted based on the unmixing-based spatial–temporal model with lower uncertainty and stable prediction.