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Understanding the Complexity of Coastal and Inland Waters using Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 58639

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Dr. Susanne Kratzer

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Department of Ecology, Environment and Plant Sciences, Stockholm University, 10961 Stockholm, Sweden
Interests: marine ecology; bio-optical oceanography; marine remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Samantha Lavender

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Guest Editor

Pixalytics Ltd., Plymouth Science Park, 1 Davy Road, Plymouth, UK
Interests: atmospheric correction; downstream applications; multi-sensor synergies

Dr. Susanne Craig

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Guest Editor

NASA Goddard Space Flight Center, Universities Space Research Association, Code 616, Greenbelt, MD, USA
Interests: bio-optics; ocean color; phytoplankton ecology; radiative transfer modeling; algorithm development

Special Issue Information

Dear Colleagues,

The Copernicus missions, commissioned by ESA up until 2030, and several of the instruments relevant for aquatic remote sensing are already in space. Amongst this suite of instruments are those with the spatial resolution and dynamic range necessary for imaging coastal and inland waters. These waters are particularly vulnerable to anthropogenic influence, play host to numerous, dynamic biogeochemical processes, and support economically vital activities such as the fisheries, tourism and recreation. However, these waters tend to be optically complex, meaning that, for a variety of reasons, retrieval of quantitative metrics of water quality and productivity from remote sensing is often challenging. In this Special Issue, we consider the optical properties of a variety of different types of coastal and inland waters—some dominated by CDOM absorption and others dominated by scatter from inorganic particles.

We encourage submissions focusing on ocean color applications in coastal and inland waters, including, but not limited to:

The measurement of inherent optical properties (IOPs)
Ocean color algorithm development, validation and calibration in optically-complex waters
Remote sensing of algal blooms, and their driving factors
Ocean colour remote sensing for management applications
The use of ocean color data to observe phytoplankton phenology and possible regime shifts in coastal and freshwater ecosystems

Assoc. Prof. Dr. Susanne Kratzer
Dr. Samantha Lavender
Dr. Susanne Craig
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

Inherent optical properties
Copernicus missions (S1, S2 & S3)
Management of coastal and inland waters
Time series analysis
Phytoplankton phenology and ecology
Climate change

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Published Papers (10 papers)

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Research

16 pages, 3486 KiB

Open AccessArticle

Comparison of Lake Optical Water Types Derived from Sentinel-2 and Sentinel-3

by Tuuli Soomets, Kristi Uudeberg, Dainis Jakovels, Matiss Zagars, Anu Reinart, Agris Brauns and Tiit Kutser

Remote Sens. 2019, 11(23), 2883; https://doi.org/10.3390/rs11232883 - 3 Dec 2019

Cited by 20 | Viewed by 5231

Abstract

Inland waters play a critical role in our drinking water supply. Additionally, they are important providers of food and recreation possibilities. Inland waters are known to be optically complex and more diverse than marine or ocean waters. The optical properties of natural waters are influenced by three different and independent sources: phytoplankton, suspended matter, and colored dissolved organic matter. Thus, the remote sensing of these waters is more challenging. Different types of waters need different approaches to obtain correct water quality products; therefore, the first step in remote sensing of lakes should be the classification of the water types. The classification of optical water types (OWTs) is based on the differences in the reflectance spectra of the lake water. This classification groups lake and coastal waters into five optical classes: Clear, Moderate, Turbid, Very Turbid, and Brown. We studied the OWTs in three different Latvian lakes: Burtnieks, Lubans, and Razna, and in a large Estonian lake, Lake Võrtsjärv. The primary goal of this study was a comparison of two different Copernicus optical instrument data for optical classification in lakes: Ocean and Land Color Instrument (OLCI) on Sentinel-3 and Multispectral Instrument (MSI) on Sentinel-2. We found that both satellite OWT classifications in lakes were comparable (R² = 0.74). We were also able to study the spatial and temporal changes in the OWTs of the study lakes during 2017. The comparison between two satellites was carried out to understand if the classification of the OWTs with both satellites is compatible. Our results could give us not only a better overview of the changes in the lake water by studying the temporal and spatial variability of the OWTs, but also possibly better retrieval of Level 2 satellite products when using OWT guided approach. Full article

(This article belongs to the Special Issue Understanding the Complexity of Coastal and Inland Waters using Remote Sensing)

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29 pages, 13763 KiB

Open AccessArticle

Deriving Particulate Organic Carbon in Coastal Waters from Remote Sensing: Inter-Comparison Exercise and Development of a Maximum Band-Ratio Approach

by Trung Kien Tran, Lucile Duforêt-Gaurier, Vincent Vantrepotte, Daniel Schaffer Ferreira Jorge, Xavier Mériaux, Arnaud Cauvin, Odile Fanton d’Andon and Hubert Loisel

Remote Sens. 2019, 11(23), 2849; https://doi.org/10.3390/rs11232849 - 30 Nov 2019

Cited by 21 | Viewed by 4666

Abstract

Recently, different algorithms have been developed to assess near-surface particulate organic matter (POC) concentration over coastal waters. In this study, we gathered an extensive in situ dataset representing various contrasted bio-optical coastal environments at low, medium, and high latitudes, with various bulk particulate matter chemical compositions (mineral-dominated, 50% of the data set, mixed, 40%, or organic-dominated, 10%). The dataset includes 606 coincident measurements of POC concentration and remote-sensing reflectance, R_rs, with POC concentrations covering three orders of magnitude. Twelve existing algorithms have then been tested on this data set, and a new one was proposed. The results show that the performance of historical algorithms depends on the type of water, with an overall low performance observed for mineral-dominated waters. Furthermore, none of the tested algorithms provided satisfactory results over the whole POC range. A novel approach was thus developed based on a maximum band ratio of R_rs (red/blue, red/yellow or red/green ratio). Based on the standard statistical metric for the evaluation of inverse models, the new algorithm presents the best performance. The root-mean square deviation for log-transformed data (RMSD_log) is 0.25. The mean absolute percentage difference (MAPD) is 37.48%. The mean bias (MB) and median ratio (MR) values are 0.54 μg L⁻¹ and 1.02, respectively. This algorithm replicates quite well the distribution of in situ data. The new algorithm was also tested on a matchup dataset gathering 154 coincident MERIS (MEdium Resolution Imaging Spectrometer) R_rs and in situ POC concentration sampled along the French coast. The matchup analysis showed that the performance of the new algorithm is satisfactory (RMSD_log = 0.24, MAPD = 34.16%, MR = 0.92). A regional illustration of the model performance for the Louisiana continental shelf shows that monthly mean POC concentrations derived from MERIS with the new algorithm are consistent with those derived from the 2016 algorithm of Le et al. which was specifically developed for this region. Full article

(This article belongs to the Special Issue Understanding the Complexity of Coastal and Inland Waters using Remote Sensing)

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18 pages, 5458 KiB

Open AccessEditor’s ChoiceArticle

The Effect of Mineral Sediments on Satellite Chlorophyll-a Retrievals from Line-Height Algorithms Using Red and Near-Infrared Bands

by Chuiqing Zeng and Caren Binding

Remote Sens. 2019, 11(19), 2306; https://doi.org/10.3390/rs11192306 - 3 Oct 2019

Cited by 20 | Viewed by 4230

Abstract

Red and near-infrared line-height algorithms such as the maximum chlorophyll index (MCI) are often considered optimal for remote sensing of chlorophyll-a (Chl-a) in turbid eutrophic waters, under the assumption of minimal influence from mineral sediments. This study investigated the impact of mineral turbidity on line-height algorithms using MCI as a primary example. Inherent optical properties from two turbid eutrophic lakes were used to simulate reflectance spectra. The simulated results: (1) confirmed a non-linear relationship between Chl-a and MCI; (2) suggested optimal use of the MCI at Chl-a < ~100 mg/m³ and saturation of the index at Chl-a ~300 mg/m³; (3) suggested significant variability in the MCI:Chl-a relationship due to mineral scattering, resulting in an RMSE in predicted Chl-a of ~23 mg/m³; and (4) revealed elevated Chl a retrievals and potential false positive algal bloom reports for sediment concentrations > 20 g/m³. A novel approach combining both MCI and its baseline slope, MCIslope reduced the RMSE to ~5 mg/m³. A quality flag based on MCIslope was proposed to mask erroneously high Chl-a retrievals and reduce the risk of false positive bloom reports in highly turbid waters. Observations suggest the approach may be valuable for all line-height-based Chl-a algorithms. Full article

(This article belongs to the Special Issue Understanding the Complexity of Coastal and Inland Waters using Remote Sensing)

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20 pages, 6017 KiB

Open AccessArticle

Using Optical Water Types to Monitor Changes in Optically Complex Inland and Coastal Waters

by Kristi Uudeberg, Ilmar Ansko, Getter Põru, Ave Ansper and Anu Reinart

Remote Sens. 2019, 11(19), 2297; https://doi.org/10.3390/rs11192297 - 2 Oct 2019

Cited by 41 | Viewed by 6244

Abstract

The European Space Agency’s Copernicus satellites Sentinel-2 and Sentinel-3 provide observations with high spectral, spatial, and temporal resolution which can be used to monitor inland and coastal waters. Such waters are optically complex, and the water color may vary from completely clear to dark brown. The main factors influencing water color are colored dissolved organic matter, phytoplankton, and suspended sediments. Recently, there has been a growing interest in the use of the optical water type (OWT) classification in the remote sensing of ocean color. Such classification helps to clarify relationships between different properties inside a certain class and quantify variation between classes. In this study, we present a new OWT classification based on the in situ measurements of reflectance spectra for boreal region lakes and coastal areas without extreme optical conditions. This classification divides waters into five OWT (Clear, Moderate, Turbid, Very Turbid, and Brown) and shows that different OWTs have different remote sensing reflectance spectra and that each OWT is associated with a specific bio-optical condition. Developed OWTs are distinguishable by both the MultiSpectral Instrument (MSI) and the Ocean and Land Color Instrument (OLCI) sensors, and the accuracy of the OWT assignment was 95% for both the MSI and OLCI bands. To determine OWT from MSI images, we tested different atmospheric correction (AC) processors, namely ACOLITE, C2RCC, POLYMER, and Sen2Cor and for OLCI images, we tested AC processors ALTNNA, C2RCC, and L2. The C2RCC AC processor was the most accurate and reliable for use with MSI and OLCI images to estimate OWTs. Full article

(This article belongs to the Special Issue Understanding the Complexity of Coastal and Inland Waters using Remote Sensing)

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30 pages, 5817 KiB

Open AccessFeature PaperArticle

Synergy of Satellite, In Situ and Modelled Data for Addressing the Scarcity of Water Quality Information for Eutrophication Assessment and Monitoring of Swedish Coastal Waters

by Susanne Kratzer, Dmytro Kyryliuk, Moa Edman, Petra Philipson and Steve W. Lyon

Remote Sens. 2019, 11(17), 2051; https://doi.org/10.3390/rs11172051 - 31 Aug 2019

Cited by 15 | Viewed by 3557

Abstract

Monthly CHL-a and Secchi Depth (SD) data derived from the full mission data of the Medium Resolution Imaging Spectrometer (MERIS; 2002–2012) were analysed along a horizontal transect from the inner Bråviken bay and out into the open sea. The CHL-a values were calibrated using an algorithm derived from Swedish lakes. Then, calibrated Chl-a and Secchi Depth (SD) estimates were extracted from MERIS data along the transect and compared to conventional monitoring data as well as to data from the Swedish Coastal zone Model (SCM), providing physico-biogeochemical parameters such as temperature, nutrients, Chlorophyll-a (CHL-a) and Secchi depth (SD). A high negative correlation was observed between satellite-derived CHL-a and SD (ρ = −0.91), similar to the in situ relationship established for several coastal gradients in the Baltic proper. We also demonstrate that the validated MERIS-based estimates and data from the SCM showed strong correlations for the variables CHL-a, SD and total nitrogen (TOTN), which improved significantly when analysed on a monthly basis across basins. The relationship between satellite-derived CHL-a and modelled TOTN was also evaluated on a monthly basis using least-square linear regression models. The predictive power of the models was strong for the period May-November (R²: 0.58–0.87), and the regression algorithm for summer was almost identical to the algorithm generated from in situ data in Himmerfjärden bay. The strong correlation between SD and modelled TOTN confirms that SD is a robust and reliable indicator to evaluate changes in eutrophication in the Baltic proper which can be assessed using remote sensing data. Amongst all three assessed methods, only MERIS CHL-a was able to correctly depict the pattern of phytoplankton phenology that is typical for the Baltic proper. The approach of combining satellite data and physio-biogeochemical models could serve as a powerful tool and value-adding complement to the scarcely available in situ data from national monitoring programs. In particular, satellite data will help to reduce uncertainties in long-term monitoring data due to its improved measurement frequency. Full article

(This article belongs to the Special Issue Understanding the Complexity of Coastal and Inland Waters using Remote Sensing)

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50 pages, 7469 KiB

Open AccessArticle

Optimal Cyanobacterial Pigment Retrieval from Ocean Colour Sensors in a Highly Turbid, Optically Complex Lake

by Caitlin A.L. Riddick, Peter D. Hunter, José Antonio Domínguez Gómez, Victor Martinez-Vicente, Mátyás Présing, Hajnalka Horváth, Attila W. Kovács, Lajos Vörös, Eszter Zsigmond and Andrew N. Tyler

Remote Sens. 2019, 11(13), 1613; https://doi.org/10.3390/rs11131613 - 7 Jul 2019

Cited by 18 | Viewed by 5760

Abstract

To date, several algorithms for the retrieval of cyanobacterial phycocyanin (PC) from ocean colour sensors have been presented for inland waters, all of which claim to be robust models. To address this, we conducted a comprehensive comparison to identify the optimal algorithm for retrieval of PC concentrations in the highly optically complex waters of Lake Balaton (Hungary). MEdium Resolution Imaging Spectrometer (MERIS) top-of-atmosphere radiances were first atmospherically corrected using the Self-Contained Atmospheric Parameters Estimation for MERIS data v.B2 (SCAPE-M_B2). Overall, the Simis05 semi-analytical algorithm outperformed more complex inversion algorithms, providing accurate estimates of PC up to ±7 days from the time of satellite overpass during summer cyanobacteria blooms (RMSE_log < 0.33). Same-day retrieval of PC also showed good agreement with cyanobacteria biomass (R² > 0.66, p < 0.001). In-depth analysis of the Simis05 algorithm using in situ measurements of inherent optical properties (IOPs) revealed that the Simis05 model overestimated the phytoplankton absorption coefficient [a_ph(λ)] by a factor of ~2. However, these errors were compensated for by underestimation of the mass-specific chlorophyll absorption coefficient [a*_chla(λ)]. This study reinforces the need for further validation of algorithms over a range of optical water types in the context of the recently launched Ocean Land Colour Instrument (OLCI) onboard Sentinel-3. Full article

(This article belongs to the Special Issue Understanding the Complexity of Coastal and Inland Waters using Remote Sensing)

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23 pages, 3936 KiB

Open AccessArticle

Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data

by Marcela Pereira-Sandoval, Ana Ruescas, Patricia Urrego, Antonio Ruiz-Verdú, Jesús Delegido, Carolina Tenjo, Xavier Soria-Perpinyà, Eduardo Vicente, Juan Soria and José Moreno

Remote Sens. 2019, 11(12), 1469; https://doi.org/10.3390/rs11121469 - 21 Jun 2019

Cited by 99 | Viewed by 7803

Abstract

The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology. Full article

(This article belongs to the Special Issue Understanding the Complexity of Coastal and Inland Waters using Remote Sensing)

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29 pages, 8118 KiB

Open AccessArticle

Regional Models for High-Resolution Retrieval of Chlorophyll a and TSM Concentrations in the Gorky Reservoir by Sentinel-2 Imagery

by Alexander A. Molkov, Sergei V. Fedorov, Vadim V. Pelevin and Elena N. Korchemkina

Remote Sens. 2019, 11(10), 1215; https://doi.org/10.3390/rs11101215 - 22 May 2019

Cited by 53 | Viewed by 5609

Abstract

The possibilities of chlorophyll a (Chl a) and total suspended matter (TSM) retrieval using Sentinel-2/MSI imagery and in situ measurements in the Gorky Reservoir are investigated. This water body is an inland freshwater ecosystem within the territory of the Russian Federation. During the algal bloom period, the optical properties of water are extremely heterogeneous and vary on scales of tens of meters. Additionally, they vary in time under the influence of currents and wind forcing. In this case, the usage of the traditional station-based sampling to describe the state of the reservoir may be uninformative and not rational. Therefore, we proposed an original approach based on simultaneous in situ measurements of the remote sensing reflectance by a single radiometer and the concentration of water constituents by an ultraviolet fluorescence LiDAR from a high-speed gliding motorboat. This approach provided fast data collection including 4087 synchronized LiDAR and radiometric measurements with high spatial resolutions of 8 m for two hours. A part of the dataset was coincided with Sentinel-2 overpass and used for the development of regional algorithms for the retrieval of Chl a and TSM concentrations. For inland waters of the Russian Federation, such research was performed for the first time. The proposed algorithms can be used for regular environmental monitoring of the Gorky Reservoir using ship measurements or Sentinel-2 images. Additionally, they can be adapted for neighboring reservoirs, for example, for other seven reservoirs on the Volga River. Moreover, the proposed ship measurement approach can be useful in the practice of limnological monitoring of inland freshwater ecosystems with high spatiotemporal variability of the optical properties. Full article

(This article belongs to the Special Issue Understanding the Complexity of Coastal and Inland Waters using Remote Sensing)

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15 pages, 4548 KiB

Open AccessArticle

Simulation of Sedimentation in Lake Taihu with Geostationary Satellite Ocean Color Data

by Anpeng He, Xianqiang He, Yan Bai, Qiankun Zhu, Fang Gong, Haiqing Huang and Delu Pan

Remote Sens. 2019, 11(4), 379; https://doi.org/10.3390/rs11040379 - 13 Feb 2019

Cited by 6 | Viewed by 3108

Abstract

In this study, the goal is to estimate the sedimentation on the bottom bed of Lake Taihu using numerical simulation combined with geostationary satellite ocean color data. A two-dimensional (2D) model that couples the dynamics of shallow water and sediment transport is presented. The shallow water equations are solved using a semi-implicit finite difference method with an Alternating Direction Implicit (ADI) method. Suspended sediment transport is simulated by solving the general convection-diffusion equation with resuspension and deposition terms using a second-order explicit central difference method in space and two-step Adams–Bashforth method in time. Moreover, the total suspended particulate matter (TSM) is retrieved by the world’s first geostationary satellite ocean color sensor Geostationary Ocean Color Imager (GOCI) using atmospheric correction algorithm for turbid waters using ultraviolet wavelengths (UV-AC) and regional empirical TSM algorithm. The 2D model and GOCI-retrieved TSM are applied to study the sediment transport and sedimentation in Lake Taihu. Validation results show rationale TSM concentration retrieved by GOCI, and the simulated TSM concentrations are consistent with GOCI observations. In addition, simulated sedimentation results reveal the dangerous locations that must be observed and desilted. Full article

(This article belongs to the Special Issue Understanding the Complexity of Coastal and Inland Waters using Remote Sensing)

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26 pages, 11010 KiB

Open AccessArticle

Retrieval of Chlorophyll a from Sentinel-2 MSI Data for the European Union Water Framework Directive Reporting Purposes

by Ave Ansper and Krista Alikas

Remote Sens. 2019, 11(1), 64; https://doi.org/10.3390/rs11010064 - 31 Dec 2018

Cited by 176 | Viewed by 11430

Abstract

The European Parliament and The Council of the European Union have established the Water Framework Directive (2000/60/EC) for all European Union member states to achieve, at least, “good” ecological status of all water bodies larger than 50 hectares in Europe. The MultiSpectral Instrument onboard European Space Agency satellite Sentinel-2 has suitable 10, 20, 60 m spatial resolution to monitor most of the Estonian lakes as required by the Water Framework Directive. The study aims to analyze the suitability of Sentinel-2 MultiSpectral Instrument data to monitor water quality in inland waters. This consists of testing various atmospheric correction processors to remove the influence of atmosphere and comparing and developing chlorophyll a algorithms to estimate the ecological status of water in Estonian lakes. This study shows that the Sentinel-2 MultiSpectral Instrument is suitable for estimating chlorophyll a in water bodies and tracking the spatial and temporal dynamics in the lakes. However, atmospheric corrections are sensitive to surrounding land and often fail in narrow and small lakes. Due to that, deriving satellite-based chlorophyll a is not possible in every case, but initial results show the Sentinel-2 MultiSpectral Instrument could still provide complementary information to in situ data to support Water Framework Directive monitoring requirements. Full article

(This article belongs to the Special Issue Understanding the Complexity of Coastal and Inland Waters using Remote Sensing)

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