Developing a land-cover-assisted spatiotemporal fusion model for producing pre-2000 MODIS-like data over the continental United States

Long-term satellite-based imagery provides fundamental data support for identifying and analyzing land surface dynamics. Although moderate-spatial-resolution data, like the Moderate Resolution Imaging Spectroradiometer (MODIS), were widely used for large-scale regional studies, their limited availability before 2000 restricts their usage in long-term investigations. To reconstruct retrospective MODIS-like data, this study proposes a novel deep learning-based model, named the Land-Cover-assisted SpatioTemporal Fusion model (LCSTF). LCSTF leverages medium-grained spatial class features from Landcover300m and temporal seasonal fluctuations from the Global Inventory Modelling and Mapping Studies (GIMMS) NDVI3g time series data to generate 500-meter MODIS-like data from 1992 to 2010 over the continental United States. The model also implements the Long Short-Term Memory (LSTM) sensor-bias correction method to mitigate systematic differences between sensors. Validation against actual MODIS images confirms the model’s ability to produce accurate MODIS-like data. Additionally, when assessed with Landsat data prior to 2000, the model demonstrates excellent performance in reconstructing retrospective data. The developed model and the reconstructed biweekly MODIS-like dataset offer significant potential for extending the temporal coverage of moderate-spatial-resolution data, enabling comprehensive long-term and large-scale studies of land surface dynamics.