Abstract
Surface water is the most important resource and environmental factor in maintaining human survival and ecosystem stability; therefore, timely accurate information on dynamic surface water is urgently needed. However, the existing water datasets fall short of the current needs of the various organizations and disciplines due to the limitations of optical sensors in dynamic water mapping. The advancement of the cloud-based Google Earth Engine (GEE) platform and free-sharing Sentinel-1 imagery makes it possible to map the dynamics of a surface water body with high spatial-temporal resolution on a large scale. This study first establishes a water extraction method oriented towards Sentinel-1 Synthetic Aperture Radar (SAR) data based on the statistics of a large number of samples of land-cover types. An unprecedented high spatial-temporal water body dataset in China (HSWDC) with monthly temporal and 10-m spatial resolution using the Sentinel-1 data from 2016 to 2018 is developed in this study. The HSWDC is validated by 14,070 random samples across China. A high classification accuracy (overall accuracy = 0.93, kappa coefficient = 0.86) is achieved. The HSWDC is highly consistent with the Global Surface Water Explorer dataset and water levels from satellite altimetry. In addition to the good performance of detecting frozen water and small water bodies, the HSWDC can also classify various water cover/uses, which are obtained from its high spatial-temporal resolution. The HSWDC dataset can provide more detailed information on surface water bodies in China and has good application potential for developing high-resolution wetland maps.
Highlights
Surface water, as the most important terrestrial resource, is undergoing spatial and temporal changes caused by many factors, such as land-use/cover changes, climate changes, seasonal changes, and environmental changes, throughout the world [1]
In order to explore the potentiality of Sentinel-1 in large-scale water body mapping and obtain an unprecedented high spatial-temporal water body dataset, in this study, our work includes: (1) establishing a large-scale water classification method based on time series Sentinel-1 data, (2) extracting China surface water body on monthly temporal and 10-m spatial resolution from 2016 to 2018 based on the Google Earth Engine (GEE) platform and evaluating of its accuracy, and (3) comparing our dataset with existing water products to assess their spatial and temporal differences
Based on Global Surface Water Explorer (GSWE) monthly water products in 2018, we divide China into two layers of water and land, and use the stratified random sampling method to generate a set of points on each month of GSWE products
Summary
As the most important terrestrial resource, is undergoing spatial and temporal changes caused by many factors, such as land-use/cover changes, climate changes, seasonal changes, and environmental changes, throughout the world [1]. Many authors have already attempted to map dynamic changes of water bodies [7,8,9,10,11,12,13] These datasets provide information on the extent of the water bodies at daily to monthly intervals and cover a limited geographic area. Pekel et al [11] produced an excellent Global Surface Water Explorer (GSWE) dataset with 30-m spatial resolution and a monthly time interval. It comes from the entire multi-temporal orthorectified Landsat 5, 7 and 8 archive spanning the past 32 years and shows the spatial and temporal variability of global surface water and its long-term changes. In order to explore the potentiality of Sentinel-1 in large-scale water body mapping and obtain an unprecedented high spatial-temporal water body dataset, in this study, our work includes: (1) establishing a large-scale water classification method based on time series Sentinel-1 data, (2) extracting China surface water body on monthly temporal and 10-m spatial resolution from 2016 to 2018 based on the GEE platform and evaluating of its accuracy, and (3) comparing our dataset with existing water products to assess their spatial and temporal differences
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