Landsat 8 OLI spatial resolution is not adequate to study soil salinity in the ponded coastal zone of the Mekong River Delta
Wetlands are characterized by partly emerged and submerged areas. The coastal portion of the Mekong River Delta is characterized by an almost continuous presence of artificial ponds, mainly used for shrimp farming (Fig. 1). Ponds are surrounded by embankments with little streets, small villages, bare soil as well as vegetated areas. In order to identify emerged vegetation and study its health status, the spatial resolution allowing pixels to be mostly “pure” (i.e., either exclusively composed of fully emerged or fully submerged areas) should first be determined. Too coarse resolution, in fact, gives a large percentage of mixed pixels, i.e., pixels comprising both emerged and flooded areas, with a reflectance that is intermediate between those from both categories. To understand if Landsat data can be used to discriminate ponded vs non-ponded surfaces, we must determine the probability that pixels contain portions of standing waters. We can perform such analysis by generating a grid with cells of 30 m × 30 m, overlaying on L8 pixels, and by counting the number of cells that fully fall on water ponds, land and mixed water/land areas. Since the coastal ponded area within the Tra Vinh Province is very large (more than 275 km2, see Fig. 1), we performed these analyses by generating 20 grids of 10 × 10 cells (of 30 m × 30 m each) distributed across the ponded area. Twelve grids were created around the sampling points described in Nguyen et al. (2020; points with IDs 30–41 listed in Table 4), while other eight grids were randomly generated in QGIS across the remaining ponded area (Fig. 1). The detection of the grid cells falling on water ponds or on emerged land was determined with a visual inspection method in QGIS using Google Earth satellite as base image. Google Earth is the most accurate high-resolution virtual globe currently available and covers almost the entire land surface of the Earth (Yu and Gong 2012; Guo et al. 2020). It integrates aerial photographs with medium to high spatial resolution satellite images mainly collected by WorldView-1/2/3, GeoEye-1, Airbus’ Pleiades, QuickBird, Landsat and SPOT5. Such integration allows the operator to zoom to around 0.3–0.5 m resolution (see Additional file 1: Table S2 for details) depending on the availability of satellite images in different locations of the world. Therefore, the advantage of using Google Earth is that it provides a recent cloud-free base image with an extremely high spatial resolution that allows the operator to clearly detect water ponds and land. The horizontal accuracy of Google Earth varies for different locations of the world and is strongly affected by topography, being lower on mountainous terrain and higher on plains. Several studies have shown that on plains and lowlands the absolute positioning accuracy generally varies between 0.1 and 5.0 m (Benker et al. 2011; Paredes-Hernandez et al. 2013; Goudarzi and Landry Jr 2017; Guo et al. 2020). The disadvantage is that Google Earth is a collage of images collected very recently but in different periods, and some land-use changes may have occurred since 2017, when the L8 image used in this study was acquired. Some differences may also be due to seasonal variations or differences in water levels. Moreover, given the typically high humidity of the Mekong delta, some areas are still covered by clouds. Therefore, to further verify the method, we also used Google Earth Pro to explore the database of available images, including those collected before February 2017, by overlaying the same 20 grids generated in QGIS. Figure 1 shows an example of two generated grids, while Table 1 shows the number of cells that, within each grid of 10 × 10 cells, fully fall within water ponds, land or on mixed surfaces using the recent Google Earth image as well as cloud-free images collected before February 2017. The table includes the dates of the analyzed images. We notice that, on average, more than 86% of the cells contain both water and emerged areas, and almost 4% fall on water ponds, confirming that the large majority of L8 pixels are affected by the presence of water.
Pixels that fully or partially fall on water bodies are affected by the water ability to absorb incident energy in the infrared portion of the electromagnetic spectrum, attenuating the reflected NIR signal (see Dekker et al. 2002) and therefore interfering with any possible signal attenuation linked to vegetation stress. Our results indicate that in the ponded coastal area of the Tra Vinh Province, about 90% of the Landsat pixels are affected by the presence of water and should be masked to avoid the NIR reflectance attenuation exerted by water. Only the remaining 10% can be used to study a possible correlation between soil salinity and NIR reflectance, using salinity field measurements performed exclusively within pixels that fully fall on emerged land. This method would provide a map with more than 90% of masked pixels. Therefore, we conclude that L8 spatial resolution is not suitable for this purpose in the ponded coastal area of the Tra Vinh Province. High spatial resolution satellites, such as WorldView-3 or Planet, would provide a much larger number of pixels falling on emerged land, allowing a more effective analysis.
The presence of ponds influences the NIR signal (and the vegetation indexes containing NIR), masking possible relations with soil EC1:5 in the coastal area of the Tra Vinh Province
We selected twelve L8 pixels corresponding to the field measurement points described in Nguyen et al. (2020) across the coastal area (points with IDs 30–41 listed in Table 4). The location of the twelve points can be seen in Additional file 1: Fig. S1. We notice that the points fall on embankments surrounded by ponds and channels and that eleven out of the twelve L8 OLI pixels corresponding to the sampling points fall on mixed water/land surfaces (Additional file 1: Fig. S1). We notice that two points (point G30 and point G41) fall at the edge between land and water, and point G40 falls on water, even though very close to the embankment. This may be due to a low precision of the Global Navigation Satellite System (GNSS) receiver used by the authors or to a low absolute positioning accuracy of the Google Earth images as discussed in the previous chapter. Nevertheless, we underline that an error of a few meters in the relative positioning of GNSS points is small with respect to the size of the L8 pixels themselves. We notice that in all cases, pixels and their nearest neighbors contain a very high proportion of water. In order to quantify the water percentage cover within each selected pixel, we generated 100 regularly spaced points (see Additional file 1: Fig. S2). As we did for the analysis described at paragraph 2.1, we used recent and historical Google Earth satellite images (see Table 1 for image dates) to visually determine the number of points falling on water ponds (Additional file 1: Fig. S2). This number is the best available proxy of actual water percentage cover. Figure 2a shows that there is a significant linear correlation (P value < 0.01) between the water percentage cover and the L8 NIR band reflectance: Reflectance decreases with increasing water percentage cover, confirming that shrimp farming ponds interfere with the reflectance signal, by absorbing near-infrared electromagnetic energy. This correlation is slightly stronger if we consider the values of water percentage cover determined using historical images in Google Earth. No significant correlation could be found between EC1:5 and the L8 NIR reflectance (Fig. 2). We also performed a multiple regression of L8 NIR reflectance on EC1:5 and water percentage cover as independent variables. We find that L8 NIR reflectance variability is almost totally explained by water percentage cover, while the correlation with EC1:5 is not significant (P value > 0.05). A similar behavior is observed when considering the VSSI as a dependent variable instead of the NIR reflectance.
The presence of ponds reduces NIR reflectance whether they contain saltwater or freshwater
In order to explore the possible influence of salt on the previous analysis, we compared the effect of the water presence on the NIR reflectance in freshwater and saline ponded environments. Therefore, we randomly selected 20 pixels falling on freshwater ponds in the inner Mekong River Delta, at a distance higher than 60 km from the mouth (Fig. 1), so that the ponds are not affected by salinity (Eslami et al. 2019; Wassmann et al. 2019). As for the coastal saline area, in order to match the 20 observations selected in the freshwater area, to the original 12 pixels corresponding to the field observations performed by Nguyen et al. (2020), we added 8 pixels randomly selected within the grids used to determine if the L8 spatial resolution were adequate (Fig. 1). We generated 100 points regularly spaced within all the created pixels. Figure 3 shows that in (a) saltwater ponded areas as well as in (b) freshwater ponded areas, there is a significant linear correlation (P value < 0.01) between water percentage cover and reflectance of the L8 NIR band. Similarly, Fig. 3 shows that there is a significant linear correlation (P value < 0.01) also between water percentage cover and the VSSI for (c) saltwater and (d) freshwater ponded areas. Our results confirm that NIR reflectance is gradually reduced by an increasing water surface, in both salted and freshwater environments. This result confirms that salinity does not influence the absorption of the incident energy exerted by water.
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