scholarly journals Evaluation of Remote-Sensing Reflectance Products from Multiple Ocean Color Missions in Highly Turbid Water (Hangzhou Bay)

2021 ◽  
Vol 13 (21) ◽  
pp. 4267
Author(s):  
Yuzhuang Xu ◽  
Xianqiang He ◽  
Yan Bai ◽  
Difeng Wang ◽  
Qiankun Zhu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Infrared Imaging ◽  
Ocean Color ◽  
Hangzhou Bay ◽  
Turbid Water ◽  
A Value ◽  
Remote Sensing Reflectance ◽  
Percentage Difference ◽  
Absolute Percentage Difference

Validation of remote-sensing reflectance (Rrs) products is necessary for the quantitative application of ocean color satellite data. While validation of Rrs products has been performed in low to moderate turbidity waters, their performance in highly turbid water remains poorly known. Here, we used in situ Rrs data from Hangzhou Bay (HZB), one of the world’s most turbid estuaries, to evaluate agency-distributed Rrs products for multiple ocean color sensors, including the Geostationary Ocean Color Imager (GOCI), Chinese Ocean Color and Temperature Scanner aboard HaiYang-1C (COCTS/HY1C), Ocean and Land Color Instrument aboard Sentinel-3A and Sentinel-3B, respectively (OLCI/S3A and OLCI/S3B), Second-Generation Global Imager aboard Global Change Observation Mission-Climate (SGLI/GCOM-C), and Visible Infrared Imaging Radiometer Suite aboard the Suomi National Polar-orbiting Partnership satellite (VIIRS/SNPP). Results showed that GOCI and SGLI/GCOM-C had almost no effective Rrs products in the HZB. Among the others four sensors (COCTS/HY1C, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP), VIIRS/SNPP obtained the largest correlation coefficient (R) with a value of 0.7, while OLCI/S3A obtained the best mean percentage differences (PD) with a value of −13.30%. The average absolute percentage difference (APD) values of the four remote sensors are close, all around 45%. In situ Rrs data from the AERONET-OC ARIAKE site were also used to evaluate the satellite-derived Rrs products in moderately turbid coastal water for comparison. Compared with the validation results at HZB, the performances of Rrs from GOCI, OLCI/S3A, OLCI/S3B, and VIIRS/SNPP were much better at the ARIAKE site with the smallest R (0.77) and largest APD (35.38%) for GOCI, and the worst PD for these four sensors was only −13.15%, indicating that the satellite-retrieved Rrs exhibited better performance. In contrast, Rrs from COCTS/HY1C and SGLI/GCOM-C at ARIAKE site was still significantly underestimated, and the R values of the two satellites were not greater than 0.7, and the APD values were greater than 50%. Therefore, the performance of satellite Rrs products degrades significantly in highly turbid waters and needs to be improved for further retrieval of ocean color components.

scholarly journals Validation of Ocean Color Remote Sensing Reflectance Using Autonomous Floats

2016 ◽  
Vol 33 (11) ◽  
pp. 2331-2352 ◽  
Author(s):  
Gregory P. Gerbi ◽  
Emmanuel Boss ◽  
P. Jeremy Werdell ◽  
Christopher W. Proctor ◽  
Nils Haëntjens ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Infrared Imaging ◽  
Ocean Color ◽  
Validation Dataset ◽  
High Quality ◽  
Ocean Color Remote Sensing ◽  
Remote Sensing Reflectance

AbstractThe use of autonomous profiling floats for observational estimates of radiometric quantities in the ocean is explored, and the use of this platform for validation of satellite-based estimates of remote sensing reflectance in the ocean is examined. This effort includes comparing quantities estimated from float and satellite data at nominal wavelengths of 412, 443, 488, and 555 nm, and examining sources and magnitudes of uncertainty in the float estimates. This study had 65 occurrences of coincident high-quality observations from floats and MODIS Aqua and 15 occurrences of coincident high-quality observations floats and Visible Infrared Imaging Radiometer Suite (VIIRS). The float estimates of remote sensing reflectance are similar to the satellite estimates, with disagreement of a few percent in most wavelengths. The variability of the float–satellite comparisons is similar to the variability of in situ–satellite comparisons using a validation dataset from the Marine Optical Buoy (MOBY). This, combined with the agreement of float-based and satellite-based quantities, suggests that floats are likely a good platform for validation of satellite-based estimates of remote sensing reflectance.

scholarly journals Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea

2019 ◽  
Vol 11 (14) ◽  
pp. 1631 ◽  
Author(s):  
Xiaocan Huang ◽  
Jianhua Zhu ◽  
Bing Han ◽  
Cédric Jamet ◽  
Zhen Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Near Infrared ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
The Yellow Sea ◽  
Optical Parameters ◽  
Band Ratio ◽  
Percentage Difference

Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color over the same area. The first geostationary ocean color sensor, i.e., the Geostationary Ocean Color Imager (GOCI), was launched in 2010. Using GOCI data acquired over the Yellow Sea in summer 2017 at three principal overpass times (02:16, 03:16, 04:16 UTC) with ±1 and ±3 h match-up times, this study compared four GOCI AC algorithms: (1) the standard near infrared (NIR) algorithm of NASA (NASA-STD), (2) the Korea Ocean Satellite Center (KOSC) standard algorithm for GOCI (KOSC-STD), (3) the diffuse attenuation coefficient at 490 nm Kd (490)-based NIR correction algorithm (Kd-based), and (4) the Management Unit of the North Sea Mathematical Models (MUMM). The GOCI-estimated remote sensing reflectance (Rrs), aerosol parameters [aerosol optical thickness (AOT), Angström Exponent (AE)], and chlorophyll-a (Chla) were validated using in situ data. For Rrs, AOT, AE, and Chla, GOCI-retrieved results performed well within the ±1 h temporal window, but the number of match-ups was extended within the ±3 h match-up window. For ±3 h GOCI-derived Rrs, all algorithms had an absolute percentage difference (APD) at 490 and 555 nm of <40%, while other bands showed larger differences (APD > 60%). Compared with in situ values, the APD of the Rrs(490)/Rrs(555) band ratio was <20% for all ACs. For AOT and AE, the APD was >40% and >200%, respectively. Of the four algorithms, the KOSC-STD algorithm demonstrated satisfactory performance in deriving Rrs for the region of interest (Rrs APD: 22.23%–73.95%) in the visible bands. The Kd-based algorithm worked well obtaining Ocean Color 3 GOCI Chla because Rrs(443) is more accurate than the KOSC-STD. The poorest Rrs retrievals were achieved using the NASA-STD and the MUMM algorithms. Statistical analysis indicated that all methods had optimal performance at 04:16 UTC.

scholarly journals Impact of Monsoon-Transported Anthropogenic Aerosols and Sun-Glint on the Satellite-Derived Spectral Remote Sensing Reflectance in the Indian Ocean

2021 ◽  
Vol 13 (2) ◽  
pp. 184
Author(s):  
Rongjie Liu ◽  
Jie Zhang ◽  
Tingwei Cui ◽  
Haocheng Yu
Keyword(s):  
Remote Sensing ◽  
Indian Ocean ◽  
Infrared Imaging ◽  
Atmospheric Correction ◽  
Spatiotemporal Pattern ◽  
Anthropogenic Aerosols ◽  
Remote Sensing Reflectance ◽  
The Indian Ocean ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Modis

Spectral remote sensing reflectance (Rrs(λ), sr−1) is one of the most important products of ocean color satellite missions, where accuracy is essential for retrieval of in-water, bio-optical, and biogeochemical properties. For the Indian Ocean (IO), where Rrs(λ) accuracy has not been well documented, the quality of Rrs(λ) products from Moderate Resolution Imaging Spectroradiometer onboard both Terra (MODIS-Terra) and Aqua (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership spacecraft (VIIRS-NPP), is evaluated and inter-compared based on a quality assurance (QA) system, which can objectively grade each individual Rrs(λ) spectrum, with 1 for a perfect spectrum and 0 for an unusable spectrum. Taking the whole year of 2016 as an example, spatiotemporal pattern of Rrs(λ) quality in the Indian Ocean is characterized for the first time, and the underlying factors are elucidated. Specifically, QA analysis of the monthly Rrs(λ) over the IO indicates good quality with the average scores of 0.93 ± 0.02, 0.92 ± 0.02 and 0.92 ± 0.02 for VIIRS-NPP, MODIS-Aqua, and MODIS-Terra, respectively. Low-quality (~0.7) data are mainly found in the Bengal Bay (BB) from January to March, which can be attributed to the imperfect atmospheric correction due to anthropogenic absorptive aerosols transported by the northeasterly winter monsoon. Moreover, low-quality (~0.74) data are also found in the clear oligotrophic gyre zone (OZ) of the south IO in the second half of the year, possibly due to residual sun-glint contributions. These findings highlight the effects of monsoon-transported anthropogenic aerosols, and imperfect sun-glint removal on the Rrs(λ) quality. Further studies are advocated to improve the sun-glint correction in the oligotrophic gyre zone and aerosol correction in the complex ocean–atmosphere environment.

Spectral interdependence of remote-sensing reflectance and its implications on the design of ocean color satellite sensors

2014 ◽  
Vol 53 (15) ◽  
pp. 3301 ◽  
Author(s):  
Zhongping Lee ◽  
Shaoling Shang ◽  
Chuanmin Hu ◽  
Giuseppe Zibordi
Keyword(s):  
Remote Sensing ◽  
Ocean Color ◽  
Remote Sensing Reflectance ◽  
Satellite Sensors

scholarly journals Analyzing Satellite Ocean Color Match-Up Protocols Using the Satellite Validation Navy Tool (SAVANT) at MOBY and Two AERONET-OC Sites

2021 ◽  
Vol 13 (14) ◽  
pp. 2673
Author(s):  
Adam Lawson ◽  
Jennifer Bowers ◽  
Sherwin Ladner ◽  
Richard Crout ◽  
Christopher Wood ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Ocean Color ◽  
Visible Spectrum ◽  
Ground Truth ◽  
Naval Research ◽  
Satellite Validation ◽  
The Difference ◽  
Stability And Accuracy ◽  
Satellite Ocean Color

The satellite validation navy tool (SAVANT) was developed by the Naval Research Laboratory to help facilitate the assessment of the stability and accuracy of ocean color satellites, using numerous ground truth (in situ) platforms around the globe and support methods for match-up protocols. The effects of varying spatial constraints with permissive and strict protocols on match-up uncertainty are evaluated, in an attempt to establish an optimal satellite ocean color calibration and validation (cal/val) match-up protocol. This allows users to evaluate the accuracy of ocean color sensors compared to specific ground truth sites that provide continuous data. Various match-up constraints may be adjusted, allowing for varied evaluations of their effects on match-up data. The results include the following: (a) the difference between aerosol robotic network ocean color (AERONET-OC) and marine optical Buoy (MOBY) evaluations; (b) the differences across the visible spectrum for various water types; (c) spatial differences and the size of satellite area chosen for comparison; and (d) temporal differences in optically complex water. The match-up uncertainty analysis was performed using Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) SNPP data at the AERONET-OC sites and the MOBY site. It was found that the more permissive constraint sets allow for a higher number of match-ups and a more comprehensive representation of the conditions, while the restrictive constraints provide better statistical match-ups between in situ and satellite sensors.

scholarly journals Spatio-Temporal Variability in Bio-Optical Properties of the Southern Caspian Sea: A Historic Analysis of Ocean Color Data

2020 ◽  
Vol 12 (23) ◽  
pp. 3975
Author(s):  
Bonyad Ahmadi ◽  
Mehdi Gholamalifard ◽  
Tiit Kutser ◽  
Stefano Vignudelli ◽  
Andrey Kostianoy
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Attenuation Coefficient ◽  
Temporal Variability ◽  
Caspian Sea ◽  
Ocean Color ◽  
Time Lags ◽  
Chl A ◽  
Remote Sensing Reflectance ◽  
Southern Caspian Sea

Currently, satellite ocean color imageries play an important role in monitoring of water properties in various oceanic, coastal, and inland ecosystems. Although there is a long-time and global archive of such valuable data, no study has comprehensively used these data to assess the changes in the Caspian Sea. Hence, this study assessed the variability of bio-optical properties of the upper-water column in the Southern Caspian Sea (SCS) using the archive of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The images acquired from SeaWiFS (January 1998 to December 2002) and MODIS Aqua (January 2003 to December 2015) satellites were used to investigate the spatial–temporal variability of bio-optical properties including Chlorophyll-a (Chl-a), attenuation coefficient, and remote sensing reflectance, and environmental parameters such as sea surface temperature (SST), wind stress and the El Nino-southern oscillation (ENSO) phenomena at different time lags in the study area. The trend analysis demonstrated an overall increase of 0.3358 mg m−3 in the Chl-a concentration during 1998–2015 (annual increase rate of 0.018 mg m−3 year−1) and four algal blooms and in turn an abnormal increase in Chl-a concentration were occurred in August 2001, September 2005, 2009, and August 2010. The linear model revealed that Chl-a in the northern and middle part of the study area had been influenced by the attenuation coefficient after a one-month lag time. The analysis revealed a sharp decline in Chl-a concentration during 2011–2015 and showed a high correlation with the turbidity and attenuation coefficient in the southern region, while Kd_490nm and remote sensing reflectance did a low one. Generally, Chl-a concentration exhibited a positive correlation with the attenuation coefficient (r = 0.63) and with remote sensing reflectance at the 555 nm (r = 0.111). This study can be used as the basis for predictive modeling to evaluate the changes of water quality and bio-optical indices in the Southern Caspian Sea (SCS).

scholarly journals Comparison of Above-Water Seabird and TriOS Radiometers along an Atlantic Meridional Transect

2020 ◽  
Vol 12 (10) ◽  
pp. 1669
Author(s):  
Krista Alikas ◽  
Viktor Vabson ◽  
Ilmar Ansko ◽  
Gavin H. Tilstone ◽  
Giorgio Dall’Olmo ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ocean Color ◽  
Percentage Difference ◽  
Wide Range ◽  
Before And After ◽  
Reference Measurements ◽  
Satellite Ocean Color ◽  
Good Agreement

The Fiducial Reference Measurements for Satellite Ocean Color (FRM4SOC) project has carried out a range of activities to evaluate and improve the state-of-the-art in ocean color radiometry. This paper described the results from a ship-based intercomparison conducted on the Atlantic Meridional Transect 27 from 23rd September to 5th November 2017. Two different radiometric systems, TriOS-Radiation Measurement Sensor with Enhanced Spectral resolution (RAMSES) and Seabird-Hyperspectral Surface Acquisition System (HyperSAS), were compared and operated side-by-side over a wide range of Atlantic provinces and environmental conditions. Both systems were calibrated for traceability to SI (Système international) units at the same optical laboratory under uniform conditions before and after the field campaign. The in situ results and their accompanying uncertainties were evaluated using the same data handling protocols. The field data revealed variability in the responsivity between TRiOS and Seabird sensors, which is dependent on the ambient environmental and illumination conditions. The straylight effects for individual sensors were mostly within ±3%. A near infra-red (NIR) similarity correction changed the water-leaving reflectance (ρw) and water-leaving radiance (Lw) spectra significantly, bringing also a convergence in outliers. For improving the estimates of in situ uncertainty, it is recommended that additional characterization of radiometers and environmental ancillary measurements are undertaken. In general, the comparison of radiometric systems showed agreement within the evaluated uncertainty limits. Consistency of in situ results with the available Sentinel-3A Ocean and Land Color Instrument (OLCI) data in the range from (400…560) nm was also satisfactory (−8% < Mean Percentage Difference (MPD) < 15%) and showed good agreement in terms of the shape of the spectra and absolute values.

scholarly journals An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4285 ◽  
Author(s):  
Shubha Sathyendranath ◽  
Robert Brewin ◽  
Carsten Brockmann ◽  
Vanda Brotas ◽  
Ben Calton ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Marine Ecosystem ◽  
Ocean Colour ◽  
Remote Sensing Reflectance ◽  
Resolution Imaging ◽  
In Situ Observations ◽  
Climate Studies ◽  
Reflectance Data

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

scholarly journals Empirical Relationships between Remote-Sensing Reflectance and Selected Inherent Optical Properties in Nordic Sea Surface Waters for the MODIS and OLCI Ocean Colour Sensors

2020 ◽  
Vol 12 (17) ◽  
pp. 2774
Author(s):  
Marta Konik ◽  
Piotr Kowalczuk ◽  
Monika Zabłocka ◽  
Anna Makarewicz ◽  
Justyna Meler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Field Work ◽  
The Arctic ◽  
Significant Shift ◽  
Form Complex ◽  
Water Properties ◽  
Remote Sensing Reflectance ◽  
Phytoplankton Communities

The Nordic Seas and the Fram Strait regions are a melting pot of a number of water masses characterized by distinct optical water properties. The warm Atlantic Waters transported from the south and the Arctic Waters from the north, combined with the melt waters contributing to the Polar Waters, mediate the dynamic changes of the year-to-year large-scale circulation patterns in the area, which often form complex frontal zones. In the last decade, moreover, a significant shift in phytoplankton phenology in the area has been observed, with a certain northward expansion of temperate phytoplankton communities into the Arctic Ocean which could lead to a deterioration in the performance of remote sensing algorithms. In this research, we exploited the capability of the satellite sensors to monitor those inter-annual changes at basin scales. We propose locally adjusted algorithms for retrieving chlorophyll a concentrations Chla, absorption by particles ap at 443 and 670 nm, and total absorption atot at 443 and 670 nm developed on the basis of intensive field work conducted in 2013–2015. Measured in situ hyper spectral remote sensing reflectance has been used to reconstruct the MODIS and OLCI spectral channels for which the proposed algorithms have been adapted. We obtained MNB ≤ 0.5% for ap(670) and ≤3% for atot(670) and Chla. RMS was ≤30% for most of the retrieved optical water properties except ap(443) and Chla. The mean monthly mosaics of ap(443) computed on the basis of the proposed algorithm were used for reconstructing the spatial and temporal changes of the phytoplankton biomass in 2013–2015. The results corresponded very well with in situ measurements.

scholarly journals An Algorithm to Estimate Suspended Particulate Matter Concentrations and Associated Uncertainties from Remote Sensing Reflectance in Coastal Environments

2020 ◽  
Vol 12 (13) ◽  
pp. 2172 ◽  
Author(s):  
Juliana Tavora ◽  
Emmanuel Boss ◽  
David Doxaran ◽  
Paul Hill
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Particulate Matter ◽  
Suspended Particulate Matter ◽  
Ocean Color ◽  
Major Constituent ◽  
Coastal Environments ◽  
Suspended Particulate ◽  
Spm Concentration

Suspended Particulate Matter (SPM) is a major constituent in coastal waters, involved in processes such as light attenuation, pollutant propagation, and waterways blockage. The spatial distribution of SPM is an indicator of deposition and erosion patterns in estuaries and coastal zones and a necessary input to estimate the material fluxes from the land through rivers to the sea. In-situ methods to estimate SPM provide limited spatial data in comparison to the coverage that can be obtained remotely. Ocean color remote sensing complements field measurements by providing estimates of the spatial distributions of surface SPM concentration in natural waters, with high spatial and temporal resolution. Existing methods to obtain SPM from remote sensing vary between purely empirical ones to those that are based on radiative transfer theory together with empirical inputs regarding the optical properties of SPM. Most algorithms use a single satellite band that is switched to other bands for different ranges of turbidity. The necessity to switch bands is due to the saturation of reflectance as SPM concentration increases. Here we propose a multi-band approach for SPM retrievals that also provides an estimate of uncertainty, where the latter is based on both uncertainties in reflectance and in the assumed optical properties of SPM. The approach proposed is general and can be applied to any ocean color sensor or in-situ radiometer system with red and near-infra-red bands. We apply it to six globally distributed in-situ datasets of spectral water reflectance and SPM measurements over a wide range of SPM concentrations collected in estuaries and coastal environments (the focus regions of our study). Results show good performance for SPM retrieval at all ranges of concentration. As with all algorithms, better performance may be achieved by constraining empirical assumptions to specific environments. To demonstrate the flexibility of the algorithm we apply it to a remote sensing scene from an environment with highly variable sediment concentrations.

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