ALBERT

Description: High-quality and long-term soil moisture products are significant for hydrologic monitoring and agricultural management. However, the acquired daily Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture products are incomplete in global land (just about 30 %–80 % coverage ratio), due to the satellite orbit coverage and the limitations of soil moisture retrieval algorithms. To solve this inevitable problem, we develop a novel spatio-temporal partial convolutional neural network (CNN) for AMSR2 soil moisture product gap-filling. Through the proposed framework, we generate the seamless daily global (SGD) AMSR2 long-term soil moisture products from 2013 to 2019. To further validate the effectiveness of these products, three verification methods are used as follows: (1) in situ validation, (2) time-series validation, and (3) simulated missing-region validation. Results show that the seamless global daily soil moisture products have reliable cooperativity with the selected in situ values. The evaluation indexes of the reconstructed (original) dataset are a correlation coefficient (R) of 0.685 (0.689), root-mean-squared error (RMSE) of 0.097 (0.093), and mean absolute error (MAE) of 0.079 (0.077). The temporal consistency of the reconstructed daily soil moisture products is ensured with the original time-series distribution of valid values. The spatial continuity of the reconstructed regions is in accordance with the spatial information (R: 0.963–0.974, RMSE: 0.065–0.073, and MAE: 0.044–0.052). This dataset can be downloaded at https://doi.org/10.5281/zenodo.4417458 (Zhang et al., 2021).