Retrieval of suspended sediment concentrations using Landsat-8 OLI satellite images in the Orinoco River (Venezuela)

Yépez, Santiago; Laraque, Alain; Martinez, Jean‐Michel; de, José Roberto Gonçalves; Carrera, Juan Manuel; Castellanos, Bartolo; Gallay, Marjorie; López, José Luís

doi:10.1016/j.crte.2017.08.004

Cited by 55 publications

(44 citation statements)

References 33 publications

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“…The estimated SSC are relatively consistent with the in situ SSC, with a RMSE of 9.08 mg/L, R 2 = 0.75 (Figure 5a), which corresponds to normalized RMSE of <20% given the mean SSC, i.e., 9.08/46.4. The uncertainties in the proposed model resemble the performance of another empirical models using L8 NIR band [28,52] and are lower than those utilizing machine learning methods [52]. Such relatively high uncertainties are expected, due to the lack of representativeness in ρ w -SSC model development associated with the dry season.…”

Section: In Situ Radiometry and Sscmentioning

confidence: 76%

“…The high correlations in in situ ρ w (865) and SSC obtained from the Red River in the two different surveyed times ( Table 2) also confirm the high potential of the L8 NIR band reflectance for estimating SSC in the river water. However, the use of a single band algorithm for a SSC estimate is challenging, due to the effects of the atmosphere on the water-leaving reflectance in visible/NIR region [28], and of the sediment type and particle size on water-leaving reflectance, particularly in highly turbid waters [53]. In Figure 8e, we demonstrate the large uncertainties in the retrieved ρ w (865), which may lead to the overestimation of SSC in the Red River.…”

Section: Discussionmentioning

confidence: 97%

“…The models for estimating SSC can be clustered into two groups: (a) statistical techniques such as machine learning and (b) empirical methods based on the relationship between SSC and water-leaving reflectance [52]. As recognized by previous studies, algorithms for SSC retrievals based upon SSC-reflectance relationships are more robust than those derived from statistical models [28]. Unlike coastal waters and lake waters, which mostly contain mineral particles and organic matters, suspended sediment in river waters is composed of several components, such as mineral particles, organic substances, pollutants, and sewage effluent, which result from erosion, transport, and deposition processes occurring within the catchments.…”

Section: Discussionmentioning

confidence: 99%

“…Preceding studies have indicated that the suitability of the L8's NIR band retrieved reflectance [28,52] or in situ ρ w (860) [53] for estimating SSC in river water. The high correlations in in situ ρ w (865) and SSC obtained from the Red River in the two different surveyed times ( Table 2) also confirm the high potential of the L8 NIR band reflectance for estimating SSC in the river water.…”

Section: Discussionmentioning

confidence: 99%

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

Phạm

Pahlevan

et al. 2018

Remote Sensing

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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.

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Section: In Situ Radiometry and Sscmentioning

confidence: 76%

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Phạm

Pahlevan

et al. 2018

Remote Sensing

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“…The relationship between Landsat data and suspended sediment concentration has been proven by many researchers Olet 2010;Ritchie et al 1990;Wang et al 2009;Zhou et al 2006;Trinh and Tarasov 2016;Wakerman et al 2017;Yepez et al 2018). In the study (Doxaran et al 2006), the authors used the ratio of the near-infrared and green bands of Landsat ETM+ multispectral images to determine concentration of suspended sediment and turbidity in the Gironde estuary (southest France).…”

Section: Introductionmentioning

confidence: 99%

ESTIMATION Of SUSPENDED SEDIMENT CONCENTRATION USING VNREDSAT – 1A MULTISPECTRAL DATA, A CASE STUDY IN RED RIVER, HANOI, VIETNAM

Hung

Zablotskii

et al. 2018

Geogr. environ. sustain.

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The traditional methods for measuring water quality variables are timeconsuming and do not give a synoptic view of a water body or, more significantly, a synoptic view of different water bodies across the landscape. However, remote sensing technology with advantages such as wide area coverage and short revisit interval have been effectively used for environmental pollution applications, such as for monitoring water quality parameters. Many studies around the world show that optical satellite imagery can be used effectively in evaluating suspended sediment concentration. This article presents results of monitoring suspended sediment concentration in Red River, Hanoi, Vietnam through ground truth measurements and VNREDSat-1A multispectral data. The results obtained in the study can be used to serve the management, monitoring and evaluation of surface water quality.kEY wORDS: remote sensing, multispectral image, suspended sediment, surface water quality, VNREDSat-1A CITATION:

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Amazon hydrology from space: scientific advances and future challenges

Fassoni‐Andrade

Fleischmann

Papa³

et al. 2021

Preprint

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As the largest river basin on Earth, the Amazon is of major importance to the world's climate and water resources. Over the past decades, advances in satellite-based remote sensing (RS) have brought our understanding of its terrestrial water cycle and the associated hydrological processes to a new era. Here, we review major studies and the various techniques using satellite RS in the Amazon. We show how RS played a major role in supporting new research and key findings regarding the Amazon water cycle, and how the region became a laboratory for groundbreaking investigations of new satellite retrievals and analyses. At the basin-scale, the understanding of several hydrological processes was only possible with the advent of RS observations, such as the characterization of "rainfall hotspots" in the Andes-Amazon transition, evapotranspiration rates, and variations of surface waters and groundwater storage. These results strongly contribute to the recent advances of hydrological models and to our new understanding of the Amazon water budget and aquatic environments. In the context of upcoming hydrology-oriented satellite missions, which will offer the opportunity for new synergies and new observations with finer space-time resolution, this review aims to guide future research agenda toward integrated monitoring and understanding of the Amazon water from space. Integrated multidisciplinary studies, fostered by international collaborations, set up future directions to tackle the great challenges the Amazon is currently facing, from climate change to increased anthropogenic pressure. Plain Language SummaryThe Amazon basin is the largest river basin in the world, characterized by complex hydrological processes that connect high rates of precipitation, extensive floodplains, dense tropical forests, complex topography, and large variations in freshwater storage and discharge. It plays a key role in the water, energy, and carbon cycles and interacts with the global climate system. Earth observations have played a major role in supporting research in Amazon hydrology, and the characterization of several hydrological processes was only possible with the help of remote sensing data. The basin is now facing great risk under current climate change and increased anthropogenic pressure and the resulting environmental alterations require a better understanding of the overall basin's water cycle across scales. We review the strengths and limitations of observations from satellites in the context of the current and upcoming hydrology-oriented satellite missions, and we make recommendations for improving satellite observations of the Amazon basin water cycle, along with an interdisciplinary and stepwise approach to guide research for the next decades.

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Retrieval of suspended sediment concentrations using Landsat-8 OLI satellite images in the Orinoco River (Venezuela)

Cited by 55 publications

References 33 publications

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

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

ESTIMATION Of SUSPENDED SEDIMENT CONCENTRATION USING VNREDSAT – 1A MULTISPECTRAL DATA, A CASE STUDY IN RED RIVER, HANOI, VIETNAM

Amazon hydrology from space: scientific advances and future challenges

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