Temporal Shape-Based Fusion Method to Generate Continuous Vegetation Index at Fine Spatial Resolution

In this study, a temporal shape-based fusion method (TSFM) using a spatially and temporally moving window is proposed to incorporate the time lag of fine- and coarse-resolution observations and to fully utilize target fine-resolution pixels and similar coarse-resolution pixels in the process. This method provides high-accuracy fused images with Pearson’s r of ~0.95, a root-mean-square error (RMSE) of ~0.04, and a bias of ~0.01 for commonly used fine spatial resolution satellites, including Landsat 7 and 8, Sentinel 2, and Gaofen 1, over different heterogeneous regions, such as urban, mountain, forest, and savanna regions. The fused fine-resolution enhanced vegetation index (EVI) time series using different fine spatial resolution satellite data as input are all highly correlated with the PhenoCam-monitored green chromatic coordinate (GCC), with no temporal lag. Compared with the commonly used data fusion method, this method provides equivalent and slightly higher accuracy because both neighboring similar pixels and the annual temporal variation are fully considered. This TSFM does not require each input fine-resolution image to be cloud-free; therefore, it can be used at a large spatial scale without further preprocessing and generates continuous datasets over a long-time range with only one input preparation process. The factors that could affect the method accuracy are the cloud detection accuracy of fine-resolution data and the temporal continuity of the coarse-resolution data. The method may also be used to produce spatially and temporally continuous surface reflectance and other surface reflectance-derived indices.