Using Landsat-8 Images for Quantifying Suspended Sediment Concentration in Red River (Northern Vietnam)
AbstractAnalyzing 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 (ρw), 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. View Full-Text
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Pham, Q.V.; Ha, N.T.T.; Pahlevan, N.; Oanh, L.T.; Nguyen, T.B.; Nguyen, N.T. Using Landsat-8 Images for Quantifying Suspended Sediment Concentration in Red River (Northern Vietnam). Remote Sens. 2018, 10, 1841.
Pham QV, Ha NTT, Pahlevan N, Oanh LT, Nguyen TB, Nguyen NT. Using Landsat-8 Images for Quantifying Suspended Sediment Concentration in Red River (Northern Vietnam). Remote Sensing. 2018; 10(11):1841.Chicago/Turabian Style
Pham, Quang V.; Ha, Nguyen T.T.; Pahlevan, Nima; Oanh, La T.; Nguyen, Thanh B.; Nguyen, Ngoc T. 2018. "Using Landsat-8 Images for Quantifying Suspended Sediment Concentration in Red River (Northern Vietnam)." Remote Sens. 10, no. 11: 1841.
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