A Comprehensive Flexible Spatiotemporal DAta Fusion Method (CFSDAF) for Generating High Spatiotemporal Resolution Land Surface Temperature in Urban Area
Chenlie Shi, Ninglian Wang, Quan Zhang, Zhuang Liu, Xinming Zhu
Abstract
Spatiotemporal fusion of land surface temperature (LST) has a vital significance in studying the temporal and spatial variation of urban heat islands. But most existing LST fusion methods don't consider the high heterogenous urban surface and complexity of spatial layout. In this study, a Comprehensive Flexible Spatiotemporal DAta Fusion (CFSDAF) method was proposed to generate high spatiotemporal resolution urban LST image which was improvements of the Flexible Spatiotemporal DAta Fusion (FSDAF). The CFSDAF first adjusted the differences between coarse resolution LST and fine resolution LST. Then, the visible and near-infrared image of fine resolution was introduced to execute spectral unmixing and to conduct soft classification, which considered the mixed pixel of fine resolution LST. The inverse distance weighting (IDW) interpolation was used in improving the computational efficiency, and the constrained least squares (CLS) was selected to better distribute the residual. The performance of CFSDAF was compared with the temporal adaptive reflectance fusion model (STARFM) and FSDAF. The results indicate that the predicted images by CFSDAF are better than STARFM and FSDAF from both visual comparison and quantitative assessment in two experiments, and CFSDAF can reserve more spatial details and accurately restore the spatial continuity of urban LST than others. Moreover, CFSDAF has high computational efficiency, and can monitor land cover changes same as FSDAF. Due to the above advantages, the CFSDAF has great potential for studying spatiotemporal changes of LST and UHI in urban area.