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Comparisons with MODIS/Terra-derived inundation maps 72

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3.2 Sentinel-1 SAR data and the ancillary datasets

3.4.3 Comparisons with MODIS/Terra-derived inundation maps 72

In this section, the 30 m SAR surface water maps are compared to the 500 m MODIS/Terra-derived surface water maps, for a region in the Mekong Delta. One year (2015) of SAR Sentinel-1 and MODIS/Terra data are extracted, over the same region (latitude [9.8°N–11.3°N]; longitude [104.75°E–107°E]). The MODIS surface water maps are derived from the method described bySakamoto et al.[2007]. The methodology is re-produced to calculate surface water maps with three different states of non-water, water, and mixed pixels, respectively (as already described in Chapter 2) . The total MODIS surface water is the sum of the water pixels (100% area is inundated) and mixed pixels (part of these pixels is inundated). For a mixed pixel, two hypothesis are tested: 25% or 50% of the pixel is inundated.

Feb2015 Mar2015 May2015 Jul2015 Aug2015 Oct2015 Dec2015 0

Feb2015 Mar2015 May2015 Jul2015 Aug2015 Oct2015 Dec2015 0

FIGURE 3.12: Time series of the surface water detected by SAR (red) and MODIS data (black), over the Mekong Delta (Latitude [9.8°N–11.3°N]; Longitude [104.75°E–107°E]), for 2015. Two hypotheses are tested for the MODIS mixed

pix-els: 50% inundated (top Panel), and 25% inundated (bottom Panel).

Twenty SAR Sentinel-1 observations are available over the selected region for the year 2015 (less than two images per month—see Table3.3). The surface wa-ter extent calculated from the SAR and MODIS data are presented in Figure3.12.

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% (Figure3.12-bottom). For the second assumption (the sur-face water extent of a mixed pixel increases to 50%), the difference in sursur-face wa-ter areas increases, but without significant changes in the seasonal cycle and still

with high correlation to the SAR surface water time series. For both hypotheses, the SAR and MODIS surface water extents reach their maximum at the same time (around 20 October 2015). Total inundated areas derived from SAR and MODIS are very close during the dry season (January to July). The cloud contamination of the MODIS estimate is low during that season. During the rainy season, more cloud contamination is expected in the MODIS estimates, and the discrepancies between the two surface water extents increase. The SAR-derived surface water estimate is expected to be more reliable due to its insensitivity to the cloud cover, but at this stage there is no convincing dataset at this spatial resolution to confirm it, as mentioned before.

water pixels mixed pixels

N

100 km

(d) (c)

(b) (a)

FIGURE3.13: (a,c) SAR and (b,d) MODIS surface water maps at 500 m resolution over the Mekong Delta in May (a,b) and October (c,d) 2015.

To 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 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 3.13a,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. For the dry season (Figure 3.13a,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. For the rainy season (October 2015), the spatial cor-relation of the two maps increases to 78%, with 8% inundated area with the SAR and 11% with MODIS. For these calculations, the hypothesis of 25% inundation of the MODIS mixed pixels is used. Although SAR-derived and MODIS-derived water maps have a very similar seasonal cycle and similar spatial distribution of the water bodies, confirming the wetland seasonal cycle over this region, there are differences in the total surface of inundated areas. It comes mainly from the difference of spatial resolution between the two satellites. First, MODIS sensors cannot detect very small surface water fractions due to their spatial resolution.

Second, the MODIS mixed pixels include water surfaces, vegetation surface and bare soil, and the percentage of each surface type within the pixel is not quanti-fied.

3.5 Improvement of the Neural Network

One of the limitations of the proposed NN is its inability to classify perfectly be-tween water surfaces and very dry, flat surfaces because of their similar backscat-ter signatures [Prigent et al., 2001, 2007, 2015]. Both surface types reflect almost all incoming signals to other directions, making their backscatter coefficient very low, and they all appear very dark in the images for both VH and VV polariza-tions. In this section, temporal information from a set of several SAR Sentinel-1

(a) (b)

FIGURE3.14: (a) Minimum backscatter and (b) Temporal variability of the VH polarization over the Vietnam Mekong Delta and Cambodia, calculated from 12

other SAR Sentinel-1 observations in 2016.

observations are added to the NN to examine if they can improve the perfor-mance of the NN over these problematic areas.

Two new parameters derived from the VH polarization are added to the in-put of the NN: the temporal variability (TV_VH) and the minimum backscatter (MB_VH). The temporal variability is defined as the standard deviation of the multi-temporal of the backscatter dataset, and the minimum backscatter is de-fined as the 5th percentile of the backscatter dataset [Santoro et al.,2015b] . These two new parameters are calculated from 12 other Sentinel-1 observations. The TV and MB from the VV polarization are not studied because of its high linear cor-relations with that derived from the VH polarization (more than 85% as seen in Table3.7between BS_VH and BS_VV, or between spatial STD_VH and STD_VV).

Examples of TV_VH and MB_VH maps, calculated from 12 other Sentinel-1 ob-servations between January and December 2016, are shown in Figure3.14. As a consequence, a new NN with 7 input parameters is trained. Performance compar-isons between the 5-input NN (NN5) and the 7-input NN (NN7) are discussed in the following section.

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