Fine Land Cover Classification Using Daily Synthetic Landsat-Like Images at 15-m Resolution

Abstract

There is currently no unified remote sensing system available that can simultaneously produce images with fine spatial, temporal, and spectral resolutions. This letter proposes a unified spatiotemporal spectral blending model using Landsat Enhanced Thematic Mapper Plus and Moderate Resolution Imaging Spectroradiometer images to predict synthetic daily Landsat-like data with a 15-m resolution. The results of tests using both simulated and actual data over the Poyang Lake Nature Reserve show that the model can accurately capture the general trend of changes for the predicted period and can enhance the spatial resolution of the data, while at the same time preserving the original spectral information. The proposed model is also applied to improve land cover classification accuracy. The application in Wuhan, Hubei Province shows that the overall classification accuracy is markedly improved. With the integration of dense temporal characteristics, the user and producer accuracies for land cover types are also improved.