Spatiotemporal CNN-Transformer Networks for Reconstructing Historical Landsat Time Series

Reconstructing dense and temporally continuous Landsat time series remains challenging because clear-sky observations are often sparse and irregular due to cloud and shadow contamination, limited revisit frequency, and sensor-specific data gaps such as the Landsat 7 Scan Line Corrector (SLC) failure. Although recent deep learning approaches have improved Landsat time series modeling, their performance degrades under very low observation densities, particularly when models rely primarily on temporal information. To address this limitation, this study developed a spatiotemporal Convolutional Neural Network (CNN)–Transformer framework that extends a temporal-only 1D Transformer by incorporating local spatial and spectral context from neighboring pixels. Multispectral image patches are first encoded using a ResNet-based convolutional encoder, and the resulting spatial–spectral features are combined with the central pixel sequence before temporal dependencies are modeled using a Transformer. This design allows the model to leverage neighboring observations when information at the central pixel is limited or missing. Two complementary training strategies were employed: (1) dense reference time series generated from combined Landsat–MODIS observations using Gaussian Process Regression (GPR) and (2) 30 m Harmonized Landsat–Sentinel-2 (HLS) observations. The models were evaluated using 1500 independent test time series and 319 hold-out Landsat images from three test areas in southeastern Alberta, Canada, under different seasons, land cover types, observation densities, and SLC conditions. The spatiotemporal model reduced RMSE by approximately 10% across spectral bands in the GPR-based evaluation. In the independent Landsat-image evaluation, average RMSE improvements across spectral bands were 29%, 4%, 20%, and 6% in spring, summer, fall, and winter, respectively. The spatiotemporal model also showed improved reconstruction under Landsat 7 SLC-off conditions, reducing spatial errors across heterogeneous landscapes, particularly in cropland areas. Overall, the results demonstrate that incorporating local spatial–spectral context into Transformer-based temporal modeling can improve Landsat time-series reconstruction, with the greatest benefits occurring during periods of rapid temporal change and limited clear-sky observations.