Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production. Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelf-sea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river-sea systems such as the Danube-Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river-sea continuum including the Danube-Black Sea is considerable

Hyperspectral sensing for turbid water quality monitoring in freshwater rivers: Empirical relationship between reflectance and turbidity and total solids

Total suspended solid (TSS) is an important water quality parameter. This study was conducted to test the feasibility of the band combination of hyperspectral sensing for inland turbid water monitoring in Taiwan. The field spectral reflectance in the Wu river basin of Taiwan was measured with a spectroradiometer; the water samples were collected from the different sites of the Wu river basin and some water quality parameters were analyzed on the sites (in situ) as well as brought to the laboratory for further analysis. To obtain the data set for this study, 160 in situ sample observations were carried out during campaigns from August to December, 2005. The water quality results were correlated with the reflectivity to determine the spectral characteristics and their relationship with turbidity and TSS. Furthermore, multiple-regression (MR) and artificial neural network (ANN) were used to model the transformation function between TSS concentration and turbidity levels of stream water, and the radiance measured by the spectroradiometer. The value of the turbidity and TSS correlation coefficient was 0.766, which implies that turbidity is significantly related to TSS in the Wu river basin. The results indicated that TSS and turbidity are positively correlated in a significant way across the entire spectrum, when TSS concentration and turbidity levels were under 800 mg·L(-1) and 600 NTU, respectively. Optimal wavelengths for the measurements of TSS and turbidity are found in the 700 and 900 nm range, respectively. Based on the results, better accuracy was obtained only when the ranges of turbidity and TSS concentration were less than 800 mg·L(-1) and less than 600 NTU, respectively and used rather than using whole dataset (R(2) = 0.93 versus 0.88 for turbidity and R(2) = 0.83 versus 0.58 for TSS). On the other hand, the ANN approach can improve the TSS retrieval using MR. The accuracy of TSS estimation applying ANN (R(2) = 0.66) was better than with the MR approach (R(2) = 0.58), as expected due to the nonlinear nature of the transformation model

Remote sensing for water quality studies: test of Suspended Particulate Matter and turbidity algorithms for Portuguese transitional and inland waters

Tese de mestrado em Ciências do Mar, Universidade de Lisboa, Faculdade de Ciências, 2020As partículas em Suspensão (SPM) é um dos principais constituintes da água nos estuários e, juntamente com a turbidez (T), é um parâmetro chave para a avaliação da qualidade da água. Através da absorção e difusão da luz, a concentração de SPM reduz a penetração da irradiância solar na coluna de água e limita a radiação fotossinteticamente disponível (PAR) para os produtores primários. Uma vez que a turbidez é altamente correlacionada com a concentração de SPM, para fins de monitorização da qualidade da água, a turbidez é listada como parâmetro obrigatório a ser medido pelos estados membros da União Europeia na Diretiva-Quadro Estratégia Marinha. Portanto, a quantificação destes dois parâmetros, a sua distribuição geográfica e o modo como se relacionam são de interesse crucial para o estudo dos ecossistemas, assim como para a investigação de diferentes processos, como transporte de sedimentos, produção primária e funcionamento de comunidades bentónicas. A monitorização dos parâmetros da qualidade da água, é geralmente alcançado através de programas de amostragem in situ. No entanto, a realização regular de amostragens exige trabalho intensivo e é dispendioso. Além disso, é necessário assumir que as amostras analisadas, que estão limitadas em termos espaciais e temporais, são representativas da área total de interesse. Neste âmbito, a deteção remota da cor do oceano é uma ferramenta eficiente para monitorizar os parâmetros da qualidade da água. O crescente interesse em entender o potencial desta técnica é impulsionado pelos custos reduzidos e pela alta resolução espacial que permite obter resultados para grandes áreas, mas também pela grande frequência temporal dos dados. No entanto, a complexidade das águas costeiras, transitórias e interiores dificulta a deteção das variáveis de interesse devido à proximidade da terra e aos elevados níveis de reflectância causados pela alta concentração de SPM nas regiões espectrais do visível e infravermelho próximo. Não obstante, algoritmos têm vindo a ser desenvolvidos para estimar a concentração de SPM e turbidez, que são geralmente calibrados regionalmente para as características óticas dos diferentes locais. Neste contexto, a presente dissertação teve como foco o teste de diferentes algoritmos com aplicabilidade global para estimar o SPM e a turbidez, bem como a avaliação de diferentes modelos de correção atmosférica. O principal objetivo deste trabalho foi determinar o esquema de processamento mais apropriado para quantificar o SPM e a turbidez em águas de transição e interiores em Portugal, determinando as incertezas associadas aos algoritmos de aplicabilidade global (Nechad et. al. (2009) para o SPM e Dogliotti et. al. (2015) para a turbidez) quando aplicados fora da sua região de calibração. Para este fim, o estuário do Tejo e do Sado e cinco albufeiras na região do Alentejo em Portugal, foram utilizados como casos de estudo para testar o uso de imagens de satélite na monitorização da turbidez e SPM. A base de dados in situ foi adquirida no contexto de diferentes projetos (PLATAGUS, NIPOGES, Valor Sul, AQUASado, GAMEFISH) entre julho de 2017 e julho de 2019, dependendo do projeto. Os dados de satélite testados foram obtidos pelos Sentinel-2 MultiSpectral Instrument (S2-MSI) e o Sentinel-3 Ocean and Land Colour Instrument (S3-OLCI), missões do programa de Observação da Terra da Comissão Europeia - Copernicus. No estuário do Tejo, as medições radiométricas in situ realizadas no contexto do projeto PLATAGUS permitiram também testar diretamente diferentes processadores para a correção atmosférica, nomeadamente o Acolite (S2-MSI), C2RCC (S2-MSI e S3-OLCI), L2 padrão MSI (Sen2Cor), L2 padrão OLCI (BAC / BPAC) e Polymer (S2-MSI e S3-OLCI). Tendo-se obtido melhores resultados com o Polymer e o C2RCC utilizando dados do S2-MSI, e resultados inconclusivos na avaliação dos dados com o S3-OLCI devido ao reduzido número de dados disponíveis. Na avaliação dos algoritmos de SPM e turbidez, os resultados obtidos sugerem que o produto de turbidez é mais fácil de estimar com menores incertezas associadas. Em relação à estimativa do SPM através dos dados S2-MSI e S3-OLCI, as correlações e erros associados indicam que ainda há uma forte necessidade de desenvolvimento de novos algoritmos, com uma calibração regional específica para as características óticas das áreas de estudo ou para encontrar uma relação local entre SPM e turbidez, como já sugerido anteriormente na literatura. Além disso, o sensor S3-OLCI, que apresentou resultados satisfatórios para o estuário do Tejo, mostrou resultados discordantes para o estuário do Sado, sugerindo uma menor adequação da resolução espacial do OLCI (300 m) para estuários de menor dimensão. No território português, as técnicas de deteção remota para monitorização da qualidade da água já estão em uso, mas têm sido testadas e aplicadas principalmente em águas costeiras. Este trabalho é um primeiro esforço para validar produtos de qualidade da água em águas de transição e interiores em Portugal. A importância destes ecossistemas, assim como o papel crucial da validação de produtos de deteção remota para monitorização ambiental e a principal motivação deste trabalho, e determinantes na definição das principais questões abordadas.Suspended particulate matter (SPM) is one of the main water constituents in estuaries and along with turbidity (T), which is highly correlated with SPM concentration, are key parameters to evaluate water quality. Through light absorption and scattering, the SPM concentration reduces the penetration of solar irradiance within the water column and limits the photosynthetically available radiation (PAR) for primary producers making it a relevant indicator for water quality monitoring. In fact, regarding water quality monitoring, turbidity is listed as a mandatory parameter to be measured by EU member states in the Marine Strategy Framework Directive. Therefore, the quantification of these two parameters, their geographical distribution and their relationship are of crucial interest for ecosystems studies and to understand different processes such as sediment transport, primary production and the functioning of benthic communities. Monitoring water quality parameters is usually achieved through field sampling programs. However, conducting regular field sampling is labor intensive and expensive and it is often necessary to assume that field samples, which are limited both spatially and temporally, are representative of the total area of interest. Satellite Ocean Colour Remote Sensing is an efficient tool to monitor these two parameters and the incrementing interest on understanding the potential of this technique is driven by the reduced costs and the high spatial and temporal resolution that allows obtaining results for large areas. However, remote sensing of coastal, transitional and inland waters is a complicated issue due to the proximity of the land and the high levels of reflectance caused by high SPM concentration in the visible and near infrared spectral regions. Many algorithms to retrieve SPM and T already exist and are often calibrated regionally for the optical characteristics of the different sites. In this context, this thesis focuses on the test of different algorithms with global applicability for SPM and turbidity retrieval, as well as different atmospheric corrections. The main aim of the present work is to determine the most appropriate processing scheme to retrieve SPM and turbidity for Portuguese transitional and inland waters and to determine the accuracy of retrieval algorithms with global applicability (Nechad et. al, 2009 for SPM retrieval and Dogliotti et. al., 2015 for turbidity) outside their calibration region. For this purpose, Tagus and Sado estuary, and five small water reservoirs in the Alentejo region in Portugal have been used as case-studies to test satellite imagery capability to monitor SPM and turbidity products. The in situ data for reference has been collected within the context of different projects (PLATAGUS, NIPOGES, Valor Sul, AQUASado, GAMEFISH) from July 2017 to July 2019 depending on the project. The satellite data used were obtained from the Sentinel-2 MultiSpectral Instrument (S2- MSI) and the Sentinel-3 Ocean and Land Colour Instrument. (S3-OLCI), missions from the European Commission Earth Observation program, Copernicus. In the Tagus estuary, in situ radiometric measurements conducted within the context of the PLATAGUS project allowed also to directly test different atmospheric corrections processors, namely (S2-MSI), C2RCC (S2-MSI and S3-OLCI), L2 standard MSI (Sen2Cor), L2 standard OLCI (BAC/BPAC) and Polymer (S2-MSI and S3-OLCI). Being Polymer and C2RCC the best performing algorithms for S2- MSI, while no definite results was found for S3-OLCI given the low number available data. Results suggested that turbidity is easier to retrieve with smaller uncertainties associated. Regarding the SPM retrieval from S2-MSI and S3-OLCI data, the associated correlations and errors indicate that there is still a strong need of algorithms development perhaps with a regional calibration specific for the optical characteristics of the study areas or finding a local relationship between SPM and turbidity as has been previously suggested. Moreover, the S3-OLCI sensor, which gave satisfactory results for the Tagus estuary, showed discordant results for the Sado estuary suggesting a poor suitability of the OLCI spatial resolution (300m) for smaller estuaries. In the Portuguese territory, remote sensing techniques have been tested and are in place for water quality monitoring mostly for coastal application. This work is a first effort to validate satellite-derived water quality products for monitoring transitional and inland waters in Portugal. The well-known importance of such ecosystems and the crucial role of satellite-data validation for reliable monitoring activities through remote sensing techniques drove the motivations and helped defining the main questions addressed in the present work

Application of machine learning techniques to derive sea water turbidity from Sentinel-2 imagery

Earth Observation (EO) from satellites has the potential to provide comprehensive, rapid and inexpensive information about water bodies, integrating in situ measurements. Traditional methods to retrieve optically active water quality parameters from satellite data are based on semiempirical models relying on few bands, which often revealed to be site and season specific. The use of machine learning (ML) for remotely sensed water quality estimation has spread in recent years thanks to the advances in algorithm development and computing power. These models allow to exploit the wealth of spectral information through more flexible relationships and are less affected by atmospheric and other background factors. The present study explores the use of Sentinel-2 MultiSpectral Instrument (MSI) Level-1C Top of Atmosphere spectral radiance to derive water turbidity, through application of machine learning techniques. A dataset of 222 combination of turbidity measurements, collected in the North Tyrrhenian Sea – Italy from 2015 to 2021, and values of the 13 spectral bands in the pixel corresponding to the sample location was used. Two regression techniques were tested and compared: a Stepwise Linear Regression (SLR) and a Polynomial Kernel Regression. The two models show accurate and similar performance (R2 = 0.736, RMSE = 2.03 NTU, MAE = 1.39 NTU for the SLR and R2 = 0.725, RMSE = 2.07 NTU, MAE = 1.40 NTU for the Kernel). A band importance analysis revealed the contribution of the different spectral bands and the main role of the red-edge range. The work shows that it is possible to reach a good accuracy in turbidity estimation from MSI TOA reflectance using ML models, fed by the whole spectrum of available bands, although the possible generation of errors related to atmospheric effect in turbidity estimates was not evaluated. Comparison between turbidity estimates obtained from the models with turbidity data from Copernicus CMEMS dataset named ‘Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation’ produced consistent results. Finally, turbidity maps from satellite imagery were produced for the study area, showing the ability of the models to catch extreme events

Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries, and coastal waters. In this study, using a large in situ database (N \u3e 3500), the SOLID model is devised using a three-step procedure: (a) water-type classification of the input remote sensing reflectance (Rrs), (b) retrieval of particulate backscattering (bbp) in the red or near-infrared (NIR) regions using semi-analytical, machine-learning, and empirical models, and (c) estimation of TSS from bbp via water-type-specific empirical models. Using an independent subset of our in situ data (N = 2729) with TSS ranging from 0.1 to 2626.8 [g/m3], the SOLID model is thoroughly examined and compared against several state-of-the-art algorithms (Miller and McKee, 2004; Nechad et al., 2010; Novoa et al., 2017; Ondrusek et al., 2012; Petus et al., 2010). We show that SOLID outperforms all the other models to varying degrees, i.e.,from 10 to \u3e100%, depending on the statistical attributes (e.g., global versus water-type-specific metrics). For demonstration purposes, the model is implemented for images acquired by the MultiSpectral Imager aboard Sentinel-2A/B over the Chesapeake Bay, San-Francisco-Bay-Delta Estuary, Lake Okeechobee, and Lake Taihu. To enable generating consistent, multimission TSS products, its performance is further extended to, and evaluated for, other missions, such as the Ocean and Land Color Instrument (OLCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and Operational Land Imager (OLI). Sensitivity analyses on uncertainties induced by the atmospheric correction indicate that 10% uncertainty in Rrs leads to \u3c20% uncertainty in TSS retrievals from SOLID. While this study suggests that SOLID has a potential for producing TSS products in global coastal and inland waters, our statistical analysis certainly verifies that there is still a need for improving retrievals across a wide spectrum of particle loads

Multi-platform assessment of turbidity plumes during dredging operations in a major estuarine system

Abstract Dredging activities in estuaries frequently cause deleterious environmental effects on the water quality which can impact flora, fauna, and hydrodynamics, among others. A medium- and high-resolution satellite-based procedure is used in this study to monitor turbidity plumes generated during the dredging operations in the Guadalquivir estuary, a major estuarine system providing important ecosystem services in southwest Europe. A multi-sensor scheme is evaluated using a combination of five public and commercial medium- and high-resolution satellites, including Landsat-8, Sentinel-2A, WorldView-2, WorldView-3, and GeoEye-1, with pixel sizes ranging from 30 m to 0.3 m. Applying a multi-conditional algorithm after the atmospheric correction of the optical imagery with ACOLITE, Sen2Cor and QUAC processors, it is demonstrated the feasibility to monitoring suspended solids during dredging operations at a spatial resolution unachievable with traditional satellite-based ocean color sensors (>300 m). The frame work can be used to map on-going, post and pre-dredging activities and asses Total Suspended Solids (TSS) anomalies caused by natural and anthropogenic processes in coastal and inland waters. These promising results are suitable to effectively improve the assessment of features relevant to environmental policies for the challenging coastal management and might serve as a notable contribution to the Earth Observation Program

A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques

Remotely sensed data can reinforce the abilities of water resources researchers and decision makers to monitor waterbodies more effectively. Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e., suspended sediments, colored dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as airplanes, are currently used to measure the amount of radiation at different wavelengths reflected from the water’s surface. In this review paper, various properties (spectral, spatial and temporal, etc.) of the more commonly employed spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. Furthermore, this paper investigates the commonly used approaches and sensors employed in evaluating and quantifying the eleven water quality parameters. The parameters include: chlorophyll-a (chl-a), colored dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD)

Estimating specific inherent optical properties of tropical coastal waters using bio-optical model inversion and in situ measurements: case of the Berau estuary, East Kalimantan, Indonesia

Specific inherent optical properties (SIOP) of the Berau coastal waters were derived from in situ measurements and inversion of an ocean color model. Field measurements of water-leaving reflectance, total suspended matter (TSM), and chlorophyll a (Chl a) concentrations were carried out during the 2007 dry season. The highest values for SIOP were found in the turbid waters, decreasing in value when moving toward offshore waters. The specific backscattering coefficient of TSM varied by an order of magnitude and ranged from 0.003 m2 g-1, for clear open ocean waters, to 0.020 m2 g-1, for turbid waters. On the other hand, the specific absorption coefficient of Chl a was relatively constant over the whole study area and ranged from 0.022 m2 mg-1, for the turbid shallow estuary waters, to 0.027 m2 mg-1, for deeper shelf edge ocean waters. The spectral slope of colored dissolved organic matter light absorption was also derived with values ranging from 0.015 to 0.011 nm-1. These original derived values of SIOP in the Berau estuary form a corner stone for future estimation of TSM and Chl a concentration from remote sensing data in tropical equatorial water

Mar Menor lagoon (SE Spain) chlorophyll-a and turbidity estimation with Sentinel-2

Mar Menor is a Mediterranean Coastal lagoon with high environmental and social value, but has suffered important episodes of contamination in recent years due to heavy rains, sediment dragging and polluting substances mainly from agriculture as well as the entry of mining waste, causing an increase in eutrophication. Water quality variables such as chlorophyll-a concentration [Chl-a] and turbidity, can be studied through its optical properties by remote sensing techniques. In this work, a methodology is proposed for monitoring [Chl-a] and the turbidity of the Mar Menor using Sentinel-2 images. For this purpose, an extensive database of both variables was used consisting of data taken on different dates between 2016 and 2019 at 12 points of Mar Menor. The images were atmospherically corrected using Case 2 Regional Coast Color Processor (C2RCC) version for turbid waters (C2X) to obtain the water surface reflectance. Then several arithmetic relations between database and reflectance bands used in the bibliography for [Chl-a] and turbidity were analyzed. Comparing the results of each one of these relations with the in situ data, decided that the best index for [Chl-a] estimation is the relation (R560 + R705)/ (R560 + R665) with an RMSE = 2.6 mg/m3 and a NRMSE = 9.1 % and for turbidity R705*R705/R490 with an RMSE = 1.5 NTU and a NRMSE= 10.9 %. Finally, by applying these relationships on different dates, thematic maps of [Chl-a] and turbidity of Mar Menor were obtained. One of these images was some days after September 2019 torrential rains, in which a considerable [Chl-a] and turbidity increase was observed