Remote sensing of suspended particulate matter: case studies of the Sulak (Caspian Sea) and the Mzymta (Black Sea) mouth areas

The performance of various standard algorithms for the retrieval of suspended particulate matter (SPM) from Sentinel-2 MSI and Landsat-8 OLI satellite data obtained in 2019 and 2021 is discussed. The study was conducted for the estuaries of 2 mountainous rivers originating in the Caucasus Mountains: the Sulak River flowing to the Caspian Sea and the Mzymta River flowing to the Black Sea. The rivers differ in the degree of flow control and the composition of terrigenous suspended matter carried to the sea. The main objective of the study was to compare SPM retrieval results of the C2RCC (Case 2 Regional Coast Color) processor and the ACOLITE (Atmospheric Correction for OLI ‘lite’) algorithms Nechad 2009, Nechad 2015 and Dogliotti. The satellite data were verified against in situ measurements of turbidity and SPM performed synchronously with the satellite survey. Field measurements from a small boat were performed in April and May 2019, 2021 in the northeast Black Sea, in the mouth area of the Mzymta, and in May 2021 in the Sulak mouth area. The measuring instruments and methods included a turbidity sensor mounted on a CTD (conductivity / temperature / depth) probe, a portable turbidimeter, and water sampling for further laboratory analysis. It was established that for low SPM, 20-30 g/m3, performances of C2RCC and Nechad 2015 practically coincided and correlated well with the in situ data. For large SPM, over 300 g/m3, the best performance was demonstrated by Dogliotti, an algorithm designed especially for extreme SPM values.

[1]  B. Nechad,et al.  Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters , 2010 .

[2]  O. Lavrova,et al.  Current capabilities and experience of using the See the Sea information system for studying and monitoring phenomena and processes on the sea surface , 2019, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa.

[3]  Carsten Brockmann,et al.  Evolution of the C2RCC Neural Network for Sentinel 2 and 3 for the Retrieval of Ocean Colour Products in Normal and Extreme Optically Complex Waters , 2016 .

[4]  Submesoscale variability of the current and wind fields in the coastal region of Sochi , 2011 .

[5]  David Doxaran,et al.  Estimating turbidity and total suspended matter in the Adour River plume (South Bay of Biscay) using MODIS 250-m imagery , 2010 .

[6]  O. Kopelevich,et al.  Regional algorithms for the estimation of chlorophyll and suspended matter concentration in the Gulf of Finland from MODIS-Aqua satellite data , 2014 .

[7]  K. Nazirova,et al.  Features of monitoring near the mouth zones by contact and contactless methods , 2019, Remote Sensing.

[8]  Lino Augusto Sander de Carvalho,et al.  Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes , 2017, Remote. Sens..

[9]  R. Doerffer,et al.  The MERIS Case 2 water algorithm , 2007 .

[10]  P. Zavialov,et al.  Hydrophysical and hydrochemical characteristics of the sea areas adjacent to the estuaries of small rivers of the Russian coast of the Black Sea , 2014, Oceanology.

[11]  B. Nechad,et al.  CoastColour Round Robin data sets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters , 2015 .

[12]  Els Knaeps,et al.  A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters , 2015 .

[13]  David Doxaran,et al.  A reflectance band ratio used to estimate suspended matter concentrations in sediment-dominated coastal waters , 2002 .

[14]  I. Caballero,et al.  Atmospheric correction for satellite-derived bathymetry in the Caribbean waters: from a single image to multi-temporal approaches using Sentinel-2A/B. , 2020, Optics express.

[15]  The rivers of the Black Sea , 2022 .

[16]  Francis Gohin,et al.  Annual cycles of chlorophyll- a , non-algal suspended particulate matter, and turbidity observed from space and in-situ in coastal waters , 2011 .

[17]  A. Osadchiev,et al.  Estimation of river discharge based on remote sensing of a river plume , 2015, SPIE Remote Sensing.

[18]  Olga Lavrova,et al.  Comparison of In Situ and Remote-Sensing Methods to Determine Turbidity and Concentration of Suspended Matter in the Estuary Zone of the Mzymta River, Black Sea , 2021, Remote. Sens..

[19]  A. Osadchiev,et al.  Spreading dynamics of small river plumes off the northeastern coast of the Black Sea observed by Landsat 8 and Sentinel-2 , 2018, Remote Sensing of Environment.

[20]  Kevin Ruddick,et al.  Calibration and validation of a generic multisensor algorithm for mapping of turbidity in coastal waters , 2009, Remote Sensing.

[21]  C. Giardino,et al.  Assessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland waters , 2019, Remote Sensing of Environment.