Abstract
The trade-off between spatial and temporal resolution limits the acquisition of dense time series of Landsat images, and limits the ability to properly monitor land surface dynamics in time. Spatiotemporal image fusion methods provide a cost-efficient alternative to generate dense time series of Landsat-like images for applications that require both high spatial and temporal resolution images. The Spatial and Temporal Reflectance Unmixing Model (STRUM) is a kind of spatial-unmixing-based spatiotemporal image fusion method. The temporal change image derived by STRUM lacks spectral variability and spatial details. This study proposed an improved STRUM (ISTRUM) architecture to tackle the problem by taking spatial heterogeneity of land surface into consideration and integrating the spectral mixture analysis of Landsat images. Sensor difference and applicability with multiple Landsat and coarse-resolution image pairs (L-C pairs) are also considered in ISTRUM. Experimental results indicate the image derived by ISTRUM contains more spectral variability and spatial details when compared with the one derived by STRUM, and the accuracy of fused Landsat-like image is improved. Endmember variability and sliding-window size are factors that influence the accuracy of ISTRUM. The factors were assessed by setting them to different values. Results indicate ISTRUM is robust to endmember variability and the publicly published endmembers (Global SVD) for Landsat images could be applied. Only sliding-window size has strong influence on the accuracy of ISTRUM. In addition, ISTRUM was compared with the Spatial Temporal Data Fusion Approach (STDFA), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the Hybrid Color Mapping (HCM) and the Flexible Spatiotemporal DAta Fusion (FSDAF) methods. ISTRUM is superior to STDFA, slightly superior to HCM in cases when the temporal change is significant, comparable with ESTARFM and a little inferior to FSDAF. However, the computational efficiency of ISTRUM is much higher than ESTARFM and FSDAF. ISTRUM can to synthesize Landsat-like images on a global scale.
Highlights
Landsat images have the longest continuous record of the Earth surface with fine resolution of 30 m
improved STRUM (ISTRUM) aims to synthesize time series of Landsat-like images based on at least one L-C pair observed on TB, and a coarse-resolution image observed on the prediction date (TP)
Deriving accurate ∆F is the key step for both ISTRUM and Spatial and Temporal Reflectance Unmixing Model (STRUM)
Summary
Landsat images have the longest continuous record of the Earth surface with fine resolution of 30 m (denoted by fine-resolution image ). For regions with frequent cloud contamination, the time interval may be longer to achieve an image that can be used to extract information on the instantaneous field of view [4] To overcome this constraint, spatiotemporal image fusion methods have been developed to synthesize time series of Landsat-like images by blending low spatial resolution but high temporal resolution images ( denoted by coarse-resolution image) such as the MODerate resolution Imaging Spectroradiometer (MODIS) and the Medium Resolution Imaging Spectrometer (MERIS) [5,6,7,8,9,10,11,12]. Step (2) calculates coarse-resolution abundance by counting the number of fine pixels, and Step (4) directly assign spectra of endmembers to fine pixels It results in the lack of intra-class spectral variability on image ∆F because pixels of same class type have same values. Sensor difference should be adjusted before adding ∆F to FB
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