Coupling remote sensing data with in-situ optical measurements to estimate suspended particulate matter under the Evros river influence (North-East Aegean sea, Greece)

2019 ◽  
Vol 41 (6) ◽  
pp. 2062-2080
Author(s):  
Athina Tsapanou ◽  
Emmanouil Oikonomou ◽  
Panagiotis Drakopoulos ◽  
Serafeim Poulos ◽  
Georgios Sylaios
2020 ◽  
Vol 12 (13) ◽  
pp. 2172 ◽  
Author(s):  
Juliana Tavora ◽  
Emmanuel Boss ◽  
David Doxaran ◽  
Paul Hill

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.


2008 ◽  
Vol 57 (8) ◽  
pp. 1729-1738 ◽  
Author(s):  
Theodore D. Kanellopoulos ◽  
Michael O. Angelidis ◽  
Dimitrios Georgopoulos ◽  
Aristomenis P. Karageorgis

Ocean Science ◽  
2011 ◽  
Vol 7 (5) ◽  
pp. 705-732 ◽  
Author(s):  
F. Gohin

Abstract. Sea surface temperature, chlorophyll, and turbidity are three variables of the coastal environment commonly measured by monitoring networks. The observation networks are often based on coastal stations, which do not provide a sufficient coverage to validate the model outputs or to be used in assimilation over the continental shelf. Conversely, the products derived from satellite reflectance generally show a decreasing quality shoreward, and an assessment of the limitation of these data is required. The annual cycle, mean, and percentile 90 of the chlorophyll concentration derived from MERIS/ESA and MODIS/NASA data processed with a dedicated algorithm have been compared to in-situ observations at twenty-six selected stations from the Mediterranean Sea to the North Sea. Keeping in mind the validation, the forcing, or the assimilation in hydrological, sediment-transport, or ecological models, the non-algal Suspended Particulate Matter (SPM) is also a parameter which is expected from the satellite imagery. However, the monitoring networks measure essentially the turbidity and a consistency between chlorophyll, representative of the phytoplankton biomass, non-algal SPM, and turbidity is required. In this study, we derive the satellite turbidity from chlorophyll and non-algal SPM with a common formula applied to in-situ or satellite observations. The distribution of the satellite-derived turbidity exhibits the same main statistical characteristics as those measured in-situ, which satisfies the first condition to monitor the long-term changes or the large-scale spatial variation over the continental shelf and along the shore. For the first time, climatologies of turbidity, so useful for mapping the environment of the benthic habitats, are proposed from space on areas as different as the southern North Sea or the western Mediterranean Sea, with validation at coastal stations.


Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2636
Author(s):  
Dat Dinh Ngoc ◽  
Hubert Loisel ◽  
Vincent Vantrepotte ◽  
Huy Chu Xuan ◽  
Ngoc Nguyen Minh ◽  
...  

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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