1: The atmospheric transmission windows from radio to X-ray wavelengths. Figure created by NASA (https://earthobservatory.nasa. gov/).

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... wavelengths easily pass through the atmosphere, other wavelengths are blocked or absorbed partly to totally by the atmosphere. Figure 1.1 shows details of the at- mospheric transmission windows from radio to X-ray wavelengths. High energy wavelengths (Ultraviolet, X-rays, and Gamma-rays) are absorbed by the ozone in the Earth's upper atmosphere. ...

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... observations are very useful to study land surface hydrological cycle over large areas from space ( Figure 1.2 provides an overview of the hydrologi- cal cycle). ...

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... is several times higher than that at their minimum states during the dry sea- sons when water throws out from the lake to the Mekong Delta (1-2 m for water height, and ∼ 3,000 km 2 for flooded surface areas). An overview of the Mekong River and its catchment is shown in Figure 1.3. ...

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... series and anomaly correlations between GIEMS/SWAMPS and other ancillary datasets are shown in Table 5.4. Time series of GIEMS and SWAMPS are in opposite phase ( Figure 5.14), making the time series correlation negative (-40%, see Table 5.4). ...

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... the Mekong Delta, there are two polarizations available: the VH and VV polarizations. Some pre-processing steps have to be carried out using the free Sentinel Application Platform (SNAP) software developed by ESA, before moving to the analysis steps (see Figure 1). These pre-processing steps are described in the "SAR Basics with the Sentinel-1 Toolbox in SNAP tutorial" [22]. ...

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... floodability index provides a static map of an estimate of the probability for a pixel to be inundated (between 0% and 100%) at the spatial resolution of 90 m, based only on topography information (such as slope in the pixel, distance to the closest river, difference of elevation with the closest river). Figure 10a presents this floodability index map over the whole Mekong Delta. As expected, all rivers and lakes in this area have a very high probability of being inundated (over 80%). ...

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... comparing these two products, we can see where and how Sentinel-1 water pixels are located with respect to the floodability index, and test the consistency between two independent products. Figure 10b-e show floodability maps at 30 m spatial resolution and predicted Sentinel-1 water maps, over four different areas in the Mekong Delta. SAR surface water areas are generally located in areas with high predicted inundation probabilities, as expected (see Table 8). ...

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... Sentinel-1 SAR observations are available over the selected region for the year 2015 (less than two images per month-see Table 2). The surface water extent calculated from the SAR and MODIS data are presented in Figure 11. With the first assumption (25% of a mixed MODIS pixel is covered by water), the two surface water extents have very similar seasonal cycles and amplitudes, with a correlation of ∼99% (Figure 11-bottom). ...

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... surface water extent calculated from the SAR and MODIS data are presented in Figure 11. With the first assumption (25% of a mixed MODIS pixel is covered by water), the two surface water extents have very similar seasonal cycles and amplitudes, with a correlation of ∼99% (Figure 11-bottom). For the second assumption (the surface water extent of a mixed pixel is increased to 50%), the difference in surface water areas increases (without significant changes in the seasonal cycle with still high correlation with the SAR surface water time series). ...

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... evaluate the consistency of the spatial structure between the SAR-derived and the MODIS-derived surface water maps, 10 SAR Sentinel-1 images were downloaded to cover the whole Mekong Delta and the Tonle Sap Lake (five images in May and five images in October 2015). For comparison purposes and to calculate the spatial correlation, the SAR surface water maps are aggregated from the 30 m resolution to the 500 m resolution of the MODIS-derived inundation maps (see Figure 12a,c). As a consequence, Sentinel-1-derived inundation maps are not binary (0 for non-water pixels or 1 for water pixels), but they are converted into a percentage of surface water at 500 m spatial resolution. ...

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... a consequence, Sentinel-1-derived inundation maps are not binary (0 for non-water pixels or 1 for water pixels), but they are converted into a percentage of surface water at 500 m spatial resolution. For the dry season (Figure 12a,b-May 2015), the spatial correlation between the two surface water maps is 68%. A total of 4% of the area is inundated for the SAR estimation, while it is 5% for the MODIS estimates. ...

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... comparison is also systematically performed with the two static datasets previously described: GLWD and the dataset. Figure 1a shows the GIEMS long-term monthly-mean maximum inundation for each pixel over 1993-2007, along with the SWAMPS equivalent information (Fig. 1b), for comparison with GLWD (Fig. 1c). Even at this scale, large differences are evident between the three datasets. ...

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... tropics, the Niger basin in a semiarid area, and the Ob basin in the boreal region. The comparison is also systematically performed with the two static datasets previously described: GLWD and the dataset. Figure 1a shows the GIEMS long-term monthly-mean maximum inundation for each pixel over 1993-2007, along with the SWAMPS equivalent information (Fig. 1b), for comparison with GLWD (Fig. 1c). Even at this scale, large differences are evident between the three datasets. GIEMS and GLWD show much larger inland water fractions than SWAMPS. GLWD has particularly large inundation extent in Canada, where many small lakes are located. The major large river floodings (e.g., Amazon, Orinoco, and ...

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... area, and the Ob basin in the boreal region. The comparison is also systematically performed with the two static datasets previously described: GLWD and the dataset. Figure 1a shows the GIEMS long-term monthly-mean maximum inundation for each pixel over 1993-2007, along with the SWAMPS equivalent information (Fig. 1b), for comparison with GLWD (Fig. 1c). Even at this scale, large differences are evident between the three datasets. GIEMS and GLWD show much larger inland water fractions than SWAMPS. GLWD has particularly large inundation extent in Canada, where many small lakes are located. The major large river floodings (e.g., Amazon, Orinoco, and Ganges- Brahmaputra) appear clearly ...

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... we propose to filter the SWAMPS data to eliminate the ocean contamination close to the coast. Figure 1d rep- resents the SWAMPS data where the contaminated coastal pixels are masked. SWAMPS also detects water almost everywhere on the globe, even in the North African desert. ...

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... data to eliminate the ocean contamination close to the coast. Figure 1d rep- resents the SWAMPS data where the contaminated coastal pixels are masked. SWAMPS also detects water almost everywhere on the globe, even in the North African desert. Histograms of the maximum frac- tional water surface are presented in Fig. 2 for the four datasets in Fig. 1. GLWD shows a large number of highly inundated pixels (.90%), mostly located in Canada (see Fig. 1c). SWAMPS has a very large number of fractional water surfaces below 0.2, much more than the two other datasets. However, it has much less large water fractions, especially after fil- tering of the ...

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... data where the contaminated coastal pixels are masked. SWAMPS also detects water almost everywhere on the globe, even in the North African desert. Histograms of the maximum frac- tional water surface are presented in Fig. 2 for the four datasets in Fig. 1. GLWD shows a large number of highly inundated pixels (.90%), mostly located in Canada (see Fig. 1c). SWAMPS has a very large number of fractional water surfaces below 0.2, much more than the two other datasets. However, it has much less large water fractions, especially after fil- tering of the ...

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... Niger basin is characterized by a large inner delta that results in a region of braided streams and has marked seasonal floods. Long-term maximum in- undation maps are shown over the Niger basin in Fig. 8, while their minimum and maximum are presented in Fig. 9, and Fig. 10 shows their time series and anomalies, as well as that derived from the river discharge data. Time series and anomaly correlations between GIEMS/ SWAMPS and other ancillary datasets are shown in Table 3. Time series of GIEMS and SWAMPS are in opposite phase (Fig. 10), making the time series corre- lation negative (240%). Again, GIEMS ...

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... basin in Fig. 8, while their minimum and maximum are presented in Fig. 9, and Fig. 10 shows their time series and anomalies, as well as that derived from the river discharge data. Time series and anomaly correlations between GIEMS/ SWAMPS and other ancillary datasets are shown in Table 3. Time series of GIEMS and SWAMPS are in opposite phase (Fig. 10), making the time series corre- lation negative (240%). Again, GIEMS shows a much stronger seasonal cycle than SWAMPS over this basin. GIEMS and the river discharge (brown) show similar behavior with a time series correlation of nearly 81% (for the common period [1998][1999][2000][2001][2002][2003][2004][2005]. In contrast, SWAMPS does ...

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... at high spatial resolution (500 m) based on the existing low-spatial-resolution results of the GIEMS dataset and observations from MODIS. Time series of the surface water derived from MODIS visible images over the Niger basin for the period 2000-07 (Bergé-Nguyen and were also compared to the behavior of GIEMS and SWAMPS over this re- gion. From Fig. 10 (top) and Fig. 11, it is clear that GIEMS and MODIS surface water time series have similar seasonal dynamics over the common period . However, GIEMS has a higher maximum value than MODIS, which could suggest an over- estimation from GIEMS over this region. In addition, the interannual variability is not totally similar between GIEMS ...

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... resolution (500 m) based on the existing low-spatial-resolution results of the GIEMS dataset and observations from MODIS. Time series of the surface water derived from MODIS visible images over the Niger basin for the period 2000-07 (Bergé-Nguyen and were also compared to the behavior of GIEMS and SWAMPS over this re- gion. From Fig. 10 (top) and Fig. 11, it is clear that GIEMS and MODIS surface water time series have similar seasonal dynamics over the common period . However, GIEMS has a higher maximum value than MODIS, which could suggest an over- estimation from GIEMS over this region. In addition, the interannual variability is not totally similar between GIEMS and MODIS. Similar ...

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... Ob basin in western Siberia is selected to repre- sent the boreal environments. SWAMPS surface waters are again much less extended than the other estimates (see Figs. 12 and 13). SWAMPS surface water peaks generally in May, one month earlier than GIEMS. Time series correlation between GIEMS (SWAMPS) and the river discharge for the studied period is 91% (62%). When calculated with 1-month lag, time series correla- tion decreases for GIEMS to 80%, while it increases for SWAMPS to 91% (Table 4). The same ...

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... already observed in Fig. 1, the maximum surface water extent estimated by SWAMPS for the major ba- sins is limited compared to the other estimates. The annual maximum SWAMPS surface extent (including TABLE 3. Time series and anomaly correlations between GIEMS, SWAMPS, and river discharge Q over the Niger basin for the period 1993-2007. Numbers in parentheses are ...

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... GIEMS, the systematic use of visible and near-infrared obser- vations helps suppress these ambiguities. Figure 14 (top) shows the time correlation between the two datasets and Fig. 14 (bottom) shows the time corre- lation between their anomalies, for the major 23 river basins in the world. The correlation is important for most basins, for the time series as well as for their anomalies. ...

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... surface waters from passive microwave observations. This is typically what happens over deserts with SWAMPS, with anomalous detection of surface water over arid regions. In GIEMS, the systematic use of visible and near-infrared obser- vations helps suppress these ambiguities. Figure 14 (top) shows the time correlation between the two datasets and Fig. 14 (bottom) shows the time corre- lation between their anomalies, for the major 23 river basins in the world. The correlation is important for most basins, for the time series as well as for their anomalies. It is even very high for some tropical basins (Orinoco and Mekong). This tends to confirm the seasonal variations of the surface water ...