Spatiotemporal Reconstruction of Land Surface Temperature Derived From FengYun Geostationary Satellite Data

The FengYun-2F (FY-2F) geostationary satellite land surface temperature (LST) and its diurnal variation are important when evaluating climate change, the land-atmosphere energy budget, and the hydrological cycle. However, the presence of clouds generates numerous meaningless pixels that constrain the potential application of the available satellite LST products. These pixels covered by cloud are assigned –2, and otherwise are the LST values, based on the result of a double-channel threshold cloud detection algorithm. This paper proposes a combined temporal and spatial information reconstruction method for the missing FY-2F LST data reconstruction with a good spatial continuity, where cloud detection has already been undertaken. Compared with the methods used in the past, the main characteristics of the proposed method are: 1) the consideration of a free parameter $\delta T$ when modeling the diurnal temperature cycle curve; 2) the introduction of the genetic algorithm for solving the parameters; 3) the adoption of the spectral multimanifold clustering algorithm for clustering the multitemporal geostationary satellite LST data; and 4) the accurate and efficient combined temporal and spatial reconstruction method. The proposed combined temporal and spatial reconstruction method was tested and quantitatively assessed with both simulated and real data experiments, using the FY-2F LST products. The results indicate that the combined reconstruction method is accurate to within about 2 °C, which can significantly improve the practical value of FY-2F LST datasets.