Lightweight Neural Network for Spatiotemporal Filling of Data Gaps in Sea Surface Temperature Images

Abstract

Optical remotely sensed data often have data gaps due to cloud coverage, which hinders their full potential in many environmental applications. The question of how to accurately and effectively reconstruct the measurements in the data gaps remains a challenge. In this study, we developed a bidirectional long short-term memory (BiLSTM) model with a novel and adaptable custom temporal penalty layer for spatiotemporal gap-filling. The model was trained and tested in time-series daily night-time sea surface temperature (SST) images acquired by the Himawari-8 satellite. The modelling results showed strong performance, accurately reconstructing spatial and temporal features of cloud affected SST data. Our neural network achieved a per-image MAE of 0.1193°C and per-image RMSE of 0.1293°C. The model was also able to produce realistic SST time-series predictions that were consistent with the expected seasonal variables. Importantly, the BiLSTM model outperformed the previous state-of-the-art Simple Spatial Gapfilling Processor (SSGP) algorithm in terms of error metrics, structural similarity, and computational efficiency. Overall, the results of this study indicates that the BiLSTM neural network model we developed is highly suitable for spatiotemporal gap-filling of SST and other remotely sensed data.