Abstract
Inland surface water is highly dynamic, seasonally and inter-annually, limiting the representativity of the water coverage information that is usually obtained at any single date. The long-term dynamic water extent products with high spatial and temporal resolution are particularly important to analyze the surface water change but unavailable up to now. In this paper, we construct a global water Normalized Difference Vegetation Index (NDVI) spatio-temporal parameter set based on the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI. Employing the Google Earth Engine, we construct a new Global Surface Water Extent Dataset (GSWED) with coverage from 2000 to 2018, having an eight-day temporal resolution and a spatial resolution of 250 m. The results show that: (1) the MODIS NDVI-based surface water mapping has better performance compared to other water extraction methods, such as the normalized difference water index, the modified normalized difference water index, and the OTSU (maximal between-cluster variance method). In addition, the water-NDVI spatio-temporal parameter set can be used to update surface water extent datasets after 2018 as soon as the MODIS data are updated. (2) We validated the GSWED using random water samples from the Global Surface Water (GSW) dataset and achieved an overall accuracy of 96% with a kappa coefficient of 0.9. The producer’s accuracy and user’s accuracy were 97% and 90%, respectively. The validated comparisons in four regions (Qinghai Lake, Selin Co Lake, Utah Lake, and Dead Sea) show a good consistency with a correlation value of above 0.9. (3) The maximum global water area reached 2.41 million km2 between 2000 and 2018, and the global water showed a decreasing trend with a significance of P = 0.0898. (4) Analysis of different types of water area change regions (Selin Co Lake, Urmia Lake, Aral Sea, Chiquita Lake, and Dongting Lake) showed that the GSWED can not only identify the seasonal changes of the surface water area and abrupt changes of hydrological events but also reflect the long-term trend of the water changes. In addition, GSWED has better performance in wetland areas and shallow areas. The GSWED can be used for regional studies and global studies of hydrology, biogeochemistry, and climate models.
Highlights
Surface water plays an important role in the terrestrial water cycle and ecosystem balance
Lin et al [30] and Tran et al [31] used algorithms to remove cloud and shadow pixels in MODIS Water Product (MWP). These datasets are committed to providing rapid mapping of water bodies in a specific area and serving more actual social needs, rather than studying the changes of global surface water over the long term and serving climate change and ecosystem researches. Though indexes such as the Normalized Difference Water Index (NDWI), the Modified Normalized Difference Water Index (MNDWI), supervised classification, or even an automation method are useful for extraction of local surface water with low temporal frequency [32,33,34,35,36,37], they are not applicable for large-scale water mapping with high spatial and temporal resolution
After introducing the data sources (Section 2), we propose an effective threshold method (Section 3) for large-scale surface water extraction that could be applied to most optical satellite sensors such as Moderate-resolution Imaging Spectroradiometer (MODIS)
Summary
Surface water plays an important role in the terrestrial water cycle and ecosystem balance. Lin et al [30] and Tran et al [31] used algorithms to remove cloud and shadow pixels in MWP These datasets are committed to providing rapid mapping of water bodies in a specific area and serving more actual social needs, rather than studying the changes of global surface water over the long term and serving climate change and ecosystem researches. Though indexes such as the Normalized Difference Water Index (NDWI), the Modified Normalized Difference Water Index (MNDWI), supervised classification, or even an automation method are useful for extraction of local surface water with low temporal frequency [32,33,34,35,36,37], they are not applicable for large-scale water mapping with high spatial and temporal resolution. The GSWED was constructed and validated by employing the Google Earth Engine (GEE) remote sensing big data cloud platform
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