Abstract
Surface water bodies exhibit high dynamic variability on seasonal and interannual scales, and high spatiotemporal resolution water bodies extent data are crucial for studying surface water bodies’ evolution. Existing surface water bodies datasets are mainly based on optical data acquisition, which has the advantages of long temporal coverage and convenience but is susceptible to cloud contamination, leading to low spatiotemporal continuity. Although microwave remote sensing data are not affected by clouds, early SAR acquisition and short temporal coverage limit its use. Therefore, existing surface water bodies datasets face the problem of insufficient spatiotemporal resolution or low continuity. This research integrates Sentinel-2 optical data and Sentinel-1 Synthetic Aperture Radar (SAR) observations to reconstruct the surface water bodies dataset with a 6-day and 10-meter spatiotemporal resolution. Then, the proposed method introduces a spatiotemporal correlation model and predicts the land cover (water or land) of Sentinel-2 cloudy pixels, which improves the spatiotemporal continuity of the reconstructed surface water bodies dataset further. Based on the proposed method, we construct the Haihe River Water Dataset (HRWD) from 2016 to 2020 with a 6-day and 10-meter spatiotemporal resolution. Compared with the European Commission’s Joint Research Centre’s (JRC’s) Global Surface Water Explorer and Global Surface Water Extent Dataset (GSWED), the HRWD shows a rational accuracy (e.g., the overall accuracy of the HRWD is more than 93%) and a better spatiotemporal continuity, which provide an improved performance in identifying and monitoring surface water bodies in the Haihe River Basin. This indicates that the proposed method can improve the spatiotemporal continuity of surface water body mapping and meet the needs of accurate and long-term quantitative observation of the distribution of large-scale and high spatiotemporal continuity surface water bodies.
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