Reconstruction of Optical Image Time Series With Unequal Lengths SAR Based on Improved Sequence–Sequence Model

Optical remote sensing time series are optimal for understanding and monitoring biochemical changes of key phenological parameters, which is essential for the assessment of vegetation health. However, due to cloud contamination, optical images often lack several days’ to months’ worth of information. Therefore, reconstructing optical time series based on the synthetic aperture radar (SAR) is necessary, which has the advantage of the production of continuous images under all weather conditions. In this study, an improved sequence-to-sequence (Seq2Seq) model was proposed, which integrated teacher forcing (TF) with the attention mechanism to handle input and output time series with unequal lengths. The proposed model could be used to reconstruct optical full-band time series using SAR time series data and reduce sample requirements based on the modification of the loss calculation method. To explore the spatiotemporal scalability of the proposed model, the test samples were divided into three categories: same time but different spatial (STDS) domain, different time but same spatial (DTSS) domain, and both different temporal and spatial (DTDS) domains. The major findings can be summarized as follows: 1) 72.7% of the generated Landsat 8 sequence values had an absolute error (AE) of less than 0.05; 2) the mean AE (MAE) of all bands was less than 0.0812; 3) the mean squared errors (MSEs) of all samples were lower than 0.015 regardless of the type of the test sample; and 4) TF was introduced to the model to improve the accuracy of the generated Landsat 8 sequence, yielding an increase of more than 5.97%. We concluded that the proposed model had good robustness both temporally and spatially. It performed well in the reconstruction of optical image time series, provided a basis for the use of time series pairs with unequal lengths, and can be applied to cloud removal and vegetation index reconstruction.