Corrigendum to “Climate- and human-induced changes in suspended particulate matter over Lake Hongze on short and long timescales” [Remote Sensing of Environment 192 (2017) 98–113]

VNREDSat-1 is the first Vietnamese satellite enabling the survey of environmental parameters, such as vegetation and water coverages or surface water quality at medium spatial resolution (from 2.5 to 10 m depending on the considered channel). The New AstroSat Optical Modular Instrument (NAOMI) sensor on board VNREDSat-1 has the required spectral bands to assess the suspended particulate matter (SPM) concentration. Because recent studies have shown that the remote sensing reflectance, Rrs(λ), at the blue (450–520 nm), green (530–600 nm), and red (620–690 nm) spectral bands can be assessed using NAOMI with good accuracy, the present study is dedicated to the development and validation of an algorithm (hereafter referred to as V1SPM) to assess SPM from Rrs(λ) over inland and coastal waters of Vietnam. For that purpose, an in-situ data set of hyper-spectral Rrs(λ) and SPM (from 0.47 to 240.14 g·m−3) measurements collected at 205 coastal and inland stations has been gathered. Among the different approaches, including four historical algorithms, the polynomial algorithms involving the red-to-green reflectance ratio presents the best performance on the validation data set (mean absolute percent difference (MAPD) of 18.7%). Compared to the use of a single spectral band, the band ratio reduces the scatter around the polynomial fit, as well as the impact of imperfect atmospheric corrections. Due to the lack of matchup data points with VNREDSat-1, the full VNREDSat-1 processing chain (atmospheric correction (RED-NIR) and V1SPM), aiming at estimating SPM from the top-of-atmosphere signal, was applied to the Landsat-8/OLI match-up data points with relatively low to moderate SPM concentration (3.33–15.25 g·m−3), yielding a MAPD of 15.8%. An illustration of the use of this VNREDSat-1 processing chain during a flooding event occurring in Vietnam is provided.

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

Remote Sensing ◽

10.3390/rs12132172 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2172 ◽

Cited By ~ 1

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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Harbour dredging and fish mortality in an aquaculture zone: assessment of changes in suspended particulate matter using multi-sensor remote-sensing data

International Journal of Remote Sensing ◽

10.1080/01431161.2014.916446 ◽

2014 ◽

Vol 35 (11-12) ◽

pp. 4383-4398 ◽

Cited By ~ 4

Author(s):

Ming-Xia He ◽

Lianbo Hu ◽

Chuanmin Hu

Keyword(s):

Remote Sensing ◽

Particulate Matter ◽

Suspended Particulate Matter ◽

Remote Sensing Data ◽

Fish Mortality ◽

Sensing Data ◽

Suspended Particulate

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Comparison of remote sensing algorithms for retrieval of suspended particulate matter concentration from reflectance in coastal waters

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.11.046028 ◽

2017 ◽

Vol 11 (04) ◽

pp. 1 ◽

Cited By ~ 3

Author(s):

Lauren A. Freeman ◽

Steven G. Ackleson ◽

William Joseph Rhea

Keyword(s):

Remote Sensing ◽

Particulate Matter ◽

Suspended Particulate Matter ◽

Coastal Waters ◽

Suspended Particulate Matter Concentration ◽

Particulate Matter Concentration ◽

Suspended Particulate ◽

Matter Concentration

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Suspended Particulate Matter Concentrations Retrieved from Self-Calibrated Multispectral Satellite Imagery

Journal of Hydrology and Hydromechanics ◽

10.2478/v10098-011-0021-9 ◽

2011 ◽

Vol 59 (4) ◽

pp. 251-261 ◽

Cited By ~ 2

Author(s):

Milan Onderka ◽

Marek Rodný ◽

Yvetta Velísková

Keyword(s):

Remote Sensing ◽

Particulate Matter ◽

Satellite Imagery ◽

Suspended Particulate Matter ◽

Optical Model ◽

Near Infrared ◽

Remotely Sensed Data ◽

Suspended Particulate ◽

Multispectral Satellite Imagery ◽

Visual Domain

Suspended Particulate Matter Concentrations Retrieved from Self-Calibrated Multispectral Satellite ImageryInland waters are known to be laden with high levels of suspended particulate matter (SPM). Remotely sensed data have been shown to provide a true synoptic view of SPM over vast areas. However, as to date, there is no universal technique that would be capable of retrieving SPM concentrations without a complete reliance on time-consuming and costly ground measurements ora prioriknowledge of inherent optical properties of water-borne constituents. The goal of this paper is to present a novel approach making use of the synergy found between the reflectance in the visual domain (~ 400-700 nm) with the near-infrared portion of the spectrum (~ 700-900 nm). The paper begins with a brief discourse of how the shape and spectral dependence of reflectance is determined by high concentrations of SPM. A modeled example is presented to mimic real-world conditions in fluvial systems, with specific absorption and scattering coefficients of the virtual optically active constituents taken from the literature. Using an optical model, we show that in the visual spectral domain (~ 400-700 nm) the water-leaving radiance responds to increasing SPM (0-100 g m-3) in a non-linear manner. Contrarily to the visual spectra, reflectance in the near infrared domain (~ 700-900 nm) appears to be almost linearly related to a broad range of SPM concentrations. To reduce the number of parameters, the reflectance function (optical model) was approximated with a previously experimentally verified exponential equation (Schiebeet al., 1992: Remote sensing of suspended sediments: the Lake Chicot, Arkansas project, Int. J. Remote Sensing,13, 8, 1487-1509). The SPM term in Schiebe's equation was expressed as a linear function of top-of-atmosphere reflectance. This made it possible to calibrate the reflectance in the visual domain by reflectance values from the near-IR portion of the spectrum. The possibility to retrieve SPM concentrations from only remote sensing data without any auxiliary ground mea-surements is tested on a Landsat ETM + scene acquired over a reservoir with moderately turbid water with SPM concentrations between 15-70 g m-3. The retrieved concentrations (on average) differ from in-situ measurement by ~ 10.5 g m-3.

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