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Download or read book Satellite Remote Sensing of the Variability of the Continental Hydrology Cycle in the Lower Mekong Basin Over the Last Two Decades written by Binh Pham-Duc and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Surface water is essential for all forms of life since it is involved in almost all processes of life on Earth. Quantifying and monitoring surface water and its variations are important because of the strong connections between surface water, other hydrological components (groundwater and soil moisture, for example), and the changing climate system. Satellite remote sensing of land surface hydrology has shown great potential in studying hydrology from space at regional and global scales. In this thesis, different techniques using several types of satellite estimates have been made to study the variation of surface water, as well as other hydrological components in the lower Mekong basin (located in Vietnam and Cambodia) over the last two decades. This thesis focuses on four aspects. First, the use of visible/infrared MODIS/Terra satellite observations to monitor surface water in the lower Mekong basin is investigated. Four different classification methods are applied, and their results of surface water maps show similar seasonality and dynamics. The most suitable classification method, that is specially designed for tropical regions, is chosen to produce regular surface water maps of the region at 500 m spatial resolution, from January 2001 to present time. Compared to reference data, the MODIS-derived surface water time series show the same amplitude, and very high temporal correlation for the 2001-2007 period (> 95%). Second, the use of SAR Sentinel-1 satellite observations for the same objective is studied. Optical satellite data are replaced by SAR satellite data to benefit the ability of their microwave wavelengths to pass through clouds. Free-cloud Landsat-8 satellite imagery are set as targets to train and optimize a Neural Network (NN). Predicted surface water maps (30 m spatial resolution) are built for the studied region from January 2015 to present time, by applying a threshold (0.85) to the output of the NN. Compared to reference free-cloud Landsat-8 surface water maps, results derived from the NN show high spatial correlation (_90%), as well as true positive detection of water pixels (_90%). Predicted SAR surface water maps are also compared to floodability maps derived from topography data, and results show high consistency between the two independent maps with 98% of SAR-derived water pixels located in areas with a high probability of inundation (>60%). Third, the surface water volume variation is calculated as the product of the surface water extent and the surface water height. The two components are validated with other hydrological products, and results show good consistencies. The surface water height are linearly interpolated over inundated areas to build monthly maps at 500 m spatial resolution, then are used to calculate changes in the surface water volume. Results show high correlations when compared to variation of the total land surface water volume derived from GRACE data (95%), and variation of the in situ discharge estimates (96%). Fourth, two monthly global multi-satellite surface water products (GIEMS & SWAMPS) are compared together over the 1993-2007 period at regional and global scales. Ancillary data are used to support the analyses when available. Similar temporal dynamics of global surface water are observed when compared GIEMS and SWAMPS, but _50% of the SWAMPS inundated surfaces are located along the coast line. Over the Amazon and Orinoco basins, GIEMS and SWAMPS have very high water surface time series correlations (95% and 99%, respectively), but SWAMPS maximum water extent is just a half of what observed from GIEMS and SAR estimates. SWAMPS fails to capture surface water dynamics over the Niger basin since its surface water seasonality is out of phase with both GIEMS- and MODIS-derived water extent estimates, as well as with in situ river discharge data.