Using Landsat-8 Images for Quantifying Suspended Sediment Concentration in Red River (Northern Vietnam)

Abstract

Analyzing the trends in the spatial distribution of suspended sediment concentration (SSC) in riverine surface water enables better understanding of the hydromorphological properties of its watersheds and the associated processes. Thus, it is critical to identify an appropriate method to quantify spatio-temporal variability in SSC. This study aims to estimate SSC in a highly turbid river, i.e., the Red River in Northern Vietnam, using Landsat 8 (L8) images. To do so, in situ radiometric data together with SSC at 60 sites along the river were measured on two different dates during the dry and wet seasons. Analyses of the in situ data indicated strong correlations between SSC and the band-ratio of green and red channels, i.e., r-squared = 0.75 and a root mean square error of~0.3 mg/L. Using a subsample of in situ radiometric data (n = 30) collected near-concurrently with one L8 image, four different atmospheric correction methods were evaluated. Although none of the methods provided reasonable water-leaving reflectance spectra (ρ w ), it was found that the band-ratio of the green-red ratio is less sensitive to uncertainties in the atmospheric correction for mapping SSC compared to individual bands. Therefore, due to its ease of access, standard L8 land surface reflectance products available via U.S. Geological Survey web portals were utilized. With the empirical relationship derived, we produced Landsat-derived SSC distribution maps for a few images collected in wet and dry seasons within the 2013-2017 period. Analyses of image products suggest that (a) the Thao River is the most significant source amongst the three major tributaries (Lo, Da and Thao rivers) providing suspended load to the Red River, and (b) the suspended load in the rainy season is nearly twice larger than that in the dry season, and it correlates highly with the runoff (correlation coefficient = 0.85). Although it is demonstrated that the atmospheric correction in tropical areas over these sediment-rich waters present major challenges in the retrievals of water-leaving reflectance spectra, the study signifies the utility of band-ratio techniques for quantifying SSC in highly turbid river waters. With Sentinel-2A/B data products combined with those of Landsat-8, it would be possible to capture temporal variability in major river systems in the near future.

The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters

“…The measured R rs within the range of 400-900 nm, associated with 74 sites over three lakes (Figure 1), are shown in Figure 2a (Lake Hongze), 2b (Lake Chaohu), and 2c (Lake Taihu). The spectral shape of Lake Hongze on 24 October 2014 ( Figure 2a) was monotonous, with a flat spectrum at 550-650 nm, and the R rs are characteristic of highly turbid waters and are similar in shape to previously reported spectra [65,66]. The Figure 2b,c showed that the spectral characteristic of Lake Chaohu and Lake Taihu are complex.…”

Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters

“…The data originated from Lake Erie, Green Bay (WI), Lake Michigan (MI) from U.S. inland waters (N = 150), Lake Taihu (N = 164) in China (Shi et al, 2018), and the SeaWiFS Bio-optical Archive and Storage System (SeaBASS) (N = 293). In sediment-rich waters (where TSS is commonly > 60 [g/ m 3 ]), we applied all available R rs and TSS data (N = 112), acquired in European waters (Knaeps et al, 2018) (N = 78) and in the Red River (N = 34), Vietnam (Pham et al, 2018).…”

Effective Band Ratio of Landsat 8 Images Based on VNIR-SWIR Reflectance Spectra of Topsoils for Soil Moisture Mapping in a Tropical Region

“…The effects of atmosphere, land-cover, surface roughness, and soil texture on these signals are still challenges for estimating and mapping SMC from satellite data [36]. The use of the spectral band ratio, as in this work, has been proven to reduce the effects of not only the atmosphere [37], but also the roughness of the topographic surface [38] on the satellite-derived reflectance. Figure 7 presents an improved accuracy of the spectral band ratio compared to the single spectral bands after processing by two atmospheric correction methods: DOS and LaSRC (Figure 7c), using two datasets of in situ R rs (λ) measured at seven soil sampling points in the Nam Can commune, and the retrieved R rs (λ) of seven corresponding free-cloud pixels of the L8 image, acquired concurrently over the commune area on 11 April 2018.…”