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Remote Sensing of the Aquatic Environments-Part II

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 25422

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Special Issue Editors

Dr. Giacomo De Carolis

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Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy (CNR), Via Bassini, 15, 20133 Milano, Italy
Interests: SAR; optical imagery; ocean winds; waves; sea ice; internal waters; water quality
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Dr. Virginia Zamparelli

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Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy (CNR), Neaples, Italy
Interests: SAR; SAR processing; sea-surface parameters; sea-surface radial velocity; doppler centroid anomaly
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Dr. Gianfranco Fornaro

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Guest Editor

Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy (CNR), 80124 Neaples, Italy
Interests: SAR; interferometric and tomographic SAR; inverse SAR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote observations of aquatic environments represented by inland surface water, seas, and oceans have traditionally been linked to the need for safe navigation and fishery. Recently, a growing demand has emerged for monitoring capability due to increasing concerns regarding the effect of contaminants produced by anthropogenic activities on the quality of inland and coastal waters.

Remote observations allow the collection of information about ocean bathymetry, ocean waves, sea surface temperature, surface winds, ocean color, coral reefs, sea and lake ice, oil pollutants, suspended solid concentrations, algal blooms, floating plastic waste in marine waters, and other bio–geophysical parameters related to the aquatic environment.

In this context, active and passive remote sensors offer suitable solutions for synoptic monitoring of the water surface, along with all the properties directly involved. A valuable example is illustrated by synthetic aperture radar (SAR) sensors, which demonstrate a unique ability to provide information related to sea-surface mapping. This is also pertinent to the maritime surveillance of coastal areas. Today, there is a wealth of orbiting SAR satellites available as a result of recent initiatives from national and international space agencies—among them: the COSMO-SkyMed Second Generation (CSG) quad-pol, X-band SAR, launched in December 2019, which integrates the already operational (four satellite) X-band SAR COSMO-SkyMed (CSK) constellation, managed by the Italian Space Agency (ASI); the series of Chinese Gaofen satellites—the latest of which, Gaofen-7, was launched in November 2019—which carry multi-polarized C-band SAR instruments; the Canadian Radarsat Constellation Mission (RCM) involving three satellites, each carrying identical C-band quad-pol SAR instruments specifically developed for maritime surveillance; and the SAOCOM program with the polarimetric L-band SAR, managed by CONAE Argentina's Space Agency. Finally, the C-band SAR sensor pair, each onboard the Sentinel-1 satellites, is the European effort within the Copernicus initiative of SAR constellation for environmental monitoring and surveillance of the sea.

The aim is to develop methods and applications to extract detailed environmental information from multi-band observations by taking advantage of the available spaceborne SARs or by exploiting synergy between SAR and optical imagery.

This Special Issue on “Remote Sensing of the Aquatic Environments – Part II” is focused on all the aspects related to the remote measurement of the bio–geophysical properties of water bodies, as well as the methodologies aimed at studying and monitoring the relevant processes. The topics of this Special Issue will include (but are not limited to) the following:

Remote sensing methods for the detection of floating materials and determination of related bio–geophysical properties (type, extent, volume, etc.), with particular focus on sea ice, lake ice, algal blooms, and spilled oil;
PolSAR and InSAR methods for maritime surveillance, ocean waves and sea state measurement;
Remote sensing of the ocean and inland water color;
Maritime surveillance case studies such as oil-spill monitoring, navigation in sea-ice-infested waters, ship detection, ship traffic;
Mapping of the marine/enclosed basins/inland environment: high-resolution wind fields, coastal wave fields, shoreline changes, upwelling phenomena, roll vortices, currents, fronts, gravity waves, internal waves, rain cells, salinity, shallow-water bathymetry;
Innovative SAR/InSAR concepts for optimal sensing of the marine environment;
Remote sensing concepts and advanced sensors for the aquatic environment.

Dr. Giacomo De Carolis
Dr. Virginia Zamparelli
Dr. Gianfranco Fornaro
Guest Editors

Manuscript Submission Information

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Keywords

ocean winds, wave, currents, bathimetry
water quality
oil spill
algal blooms
sea ice
coastline or inland waters
SAR
optical data

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Published Papers (9 papers)

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Research

18 pages, 4088 KiB

Open AccessArticle

SAR Based Sea Surface Complex Wind Fields Estimation: An Analysis over the Northern Adriatic Sea

by Virginia Zamparelli, Francesca De Santi, Giacomo De Carolis and Gianfranco Fornaro

Remote Sens. 2023, 15(8), 2074; https://doi.org/10.3390/rs15082074 - 14 Apr 2023

Cited by 6 | Viewed by 1932

Abstract

Nowadays, sea surface analysis and monitoring increasingly use remote sensing, with particular interest in Synthetic Aperture Radar (SAR). Several SAR techniques exist in literature to understand the marine phenomena affecting the sea surface. In this work, we focus on the Doppler Centroid Anomaly (DCA), which accounts for the Doppler shift induced by sea surface movements. Starting from SAR raw data, we develop a processing chain to elaborate them and output the surface velocity map using DCA. The DCA technique has often been presented in the marine literature for estimating sea surface velocity, but more recently it has also been used to detect near-surface wind fields. This paper deals with estimating the sea surface wind field using Doppler information and SAR backscatter, combined with wind information provided by ECMWF and geophysical wind and Doppler model functions. We investigate the application of the approach in the coastal area of the northern Adriatic Sea (Northeast Italy). The test site is interesting, both for its particular orography, as it is a semi-enclosed basin largely surrounded by mountains, and for its complex meteorological phenomena, such as the Bora wind. Results obtained combining SAR backscatter and DCA information show an improvement in wind field estimation. Full article

(This article belongs to the Special Issue Remote Sensing of the Aquatic Environments-Part II)

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21 pages, 4935 KiB

Open AccessArticle

Assessment of Estimated Phycocyanin and Chlorophyll-a Concentration from PRISMA and OLCI in Brazilian Inland Waters: A Comparison between Semi-Analytical and Machine Learning Algorithms

by Thainara Munhoz Alexandre de Lima, Claudia Giardino, Mariano Bresciani, Claudio Clemente Faria Barbosa, Alice Fabbretto, Andrea Pellegrino and Felipe Nincao Begliomini

Remote Sens. 2023, 15(5), 1299; https://doi.org/10.3390/rs15051299 - 26 Feb 2023

Cited by 14 | Viewed by 2758

Abstract

The aim of this work is to test the state-of-the-art of water constituent retrieval algorithms for phycocyanin (PC) and chlorophyll-a (chl-a) concentrations in Brazilian reservoirs from hyperspectral PRISMA images and concurrent in situ data. One near-coincident Sentinel-3 OLCI dataset has also been considered for PC mapping as its high revisit time is a relevant element for mapping cyanobacterial blooms. The testing was first performed on remote sensing reflectance (

R_{r s}

), as derived by applying two atmospheric correction methods (6SV, ACOLITE) to Level 1 data and as provided in the corresponding Level 2 products (PRISMA L2C and OLCI L2-WFR). Since PRISMA images were affected by sun glint, the testing of three de-glint models was also performed. The applicability of Semi-Analytical (SA) and Mixture Density Network (MDN) algorithms in enabling PC and chl-a concentration retrieval was then tested over three PRISMA scenes; in the case of PC concentration estimation, a Random Forest (RF) algorithm was further applied. Regarding OLCI, the SA algorithm was tested for PC estimation; notably, only SA was calibrated with site-specific data from the reservoir. The algorithms were applied to the

R_{r s}

spectra provided by PRISMA L2C products—and those derived with ACOLITE, in the case of OLCI—as these data showed better agreement with in situ measurements. The SA model provided low median absolute error (MdAE) for PRISMA-derived (MdAE = 3.06 mg.m⁻³) and OLCI-derived (MdAE = 3.93 mg.m⁻³) PC concentrations, while it overestimated PRISMA-derived chl-a (MdAE = 42.11 mg.m⁻³). The RF model for PC applied to PRISMA performed slightly worse than SA (MdAE = 5.21 mg.m⁻³). The MDN showed a rather different performance, with higher errors for PC (MdAE = 40.94 mg.m⁻³) and lower error for chl-a (MdAE = 23.21 mg.m⁻³). The results overall suggest that the model calibrated with site-specific measurements performed better and indicates that SA could be applied to PRISMA and OLCI for remote sensing of PC in Brazilian reservoirs. Full article

(This article belongs to the Special Issue Remote Sensing of the Aquatic Environments-Part II)

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19 pages, 4740 KiB

Open AccessArticle

Assessment of Spatio-Temporal Empirical Forecasting Performance of Future Shoreline Positions

by Md Sariful Islam and Thomas W. Crawford

Remote Sens. 2022, 14(24), 6364; https://doi.org/10.3390/rs14246364 - 16 Dec 2022

Cited by 11 | Viewed by 2282

Abstract

Coasts and coastlines in many parts of the world are highly dynamic in nature, where large changes in the shoreline position can occur due to natural and anthropogenic influences. The prediction of future shoreline positions is of great importance in the better planning and management of coastal areas. With an aim to assess the different methods of prediction, this study investigates the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelines. Multi-temporal Landsat imagery, from 1988 to 2021, was used to quantify the rates of shoreline movement for different time period. Predictions using the simple extrapolation of the end point rate (EPR), linear regression rate (LRR), weighted linear regression rate (WLR), and the Kalman filter method were used to predict future shoreline positions. Root mean square error (RMSE) was used to assess prediction accuracies. For time depth, our results revealed that the higher the number of shorelines used in calculating and predicting shoreline change rates the better predictive performance was yielded. For the time horizon, prediction accuracies were substantially higher for the immediate future years (138 m/year) compared to the more distant future (152 m/year). Our results also demonstrated that the forecast performance varied temporally and spatially by time period and region. Though the study area is located in coastal Bangladesh, this study has the potential for forecasting applications to other deltas and vulnerable shorelines globally. Full article

(This article belongs to the Special Issue Remote Sensing of the Aquatic Environments-Part II)

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27 pages, 4885 KiB

Open AccessArticle

Machine-Learning Classification of SAR Remotely-Sensed Sea-Surface Petroleum Signatures—Part 1: Training and Testing Cross Validation

by Gustavo de Araújo Carvalho, Peter J. Minnett, Nelson F. F. Ebecken and Luiz Landau

Remote Sens. 2022, 14(13), 3027; https://doi.org/10.3390/rs14133027 - 24 Jun 2022

Cited by 10 | Viewed by 2659

Abstract

Sea-surface petroleum pollution is observed as “oil slicks” (i.e., “oil spills” or “oil seeps”) and can be confused with “look-alike slicks” (i.e., environmental phenomena, such as low-wind speed, upwelling conditions, chlorophyll, etc.) in synthetic aperture radar (SAR) measurements, the most proficient satellite sensor to detect mineral oil on the sea surface. Even though machine learning (ML) has become widely used to classify remotely-sensed petroleum signatures, few papers have been published comparing various ML methods to distinguish spills from look-alikes. Our research fills this gap by comparing and evaluating six traditional techniques: simple (naive Bayes (NB), K-nearest neighbor (KNN), decision trees (DT)) and advanced (random forest (RF), support vector machine (SVM), artificial neural network (ANN)) applied to different combinations of satellite-retrieved attributes. 36 ML algorithms were used to discriminate “ocean-slick signatures” (spills versus look-alikes) with ten-times repeated random subsampling cross validation (70-30 train-test partition). Our results found that the best algorithm (ANN: 90%) was >20% more effective than the least accurate one (DT: ~68%). Our empirical ML observations contribute to both scientific ocean remote-sensing research and to oil and gas industry activities, in that: (i) most techniques were superior when morphological information and Meteorological and Oceanographic (MetOc) parameters were included together, and less accurate when these variables were used separately; (ii) the algorithms with the better performance used more variables (without feature selection), while lower accuracy algorithms were those that used fewer variables (with feature selection); (iii) we created algorithms more effective than those of benchmark-past studies that used linear discriminant analysis (LDA: ~85%) on the same dataset; and (iv) accurate algorithms can assist in finding new offshore fossil fuel discoveries (i.e., misclassification reduction). Full article

(This article belongs to the Special Issue Remote Sensing of the Aquatic Environments-Part II)

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20 pages, 48145 KiB

Open AccessArticle

A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS

by Andreas Preußer, Günther Heinemann, Lukas Schefczyk and Sascha Willmes

Remote Sens. 2022, 14(9), 2036; https://doi.org/10.3390/rs14092036 - 23 Apr 2022

Cited by 1 | Viewed by 2200

Abstract

Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020. Full article

(This article belongs to the Special Issue Remote Sensing of the Aquatic Environments-Part II)

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16 pages, 5011 KiB

Open AccessArticle

Influence of Residual Amplitude and Phase Error for GF-3 Quad-Polarization SAR on Wind Vector Retrieval

by Xiaochen Wang, Yuxin Hu, Bing Han, Xinzhe Yuan, Junxin Yang and Jitong Duan

Remote Sens. 2022, 14(6), 1433; https://doi.org/10.3390/rs14061433 - 16 Mar 2022

Viewed by 1832

Abstract

High-resolution wind vector is important to investigate local winds’ variability over the global ocean. Quad-polarization Synthetic Aperture Radar (SAR) can provide wind vector independently without any external wind direction inputs. Although quad-polarization SAR wind retrieval algorithms have been widely studied, improvements are still required. The amplitude and phase imbalance of polarization channel cannot be neglected for improving the wind vector retrieval precision. In this study, rainforest was performed to remove the amplitude and phase imbalance of polarization channel of GF-3 SAR. To explore the applicability of this method for sea surface measurement, the influence of residual amplitude and phase error for GF-3 quad-polarization SAR on wind vector retrieval was assessed. Variation of amplitude and phase imbalance of sea surface for transmit and receive channel were assessed against collocated wind speed and incidence angle. Considering the polarization difference of VV channel relative to HH channel, the residual amplitude and phase error was found to be closely related to wind speed and polarization isolation. Correction of residual amplitude and phase error were employed to improve the retrieval precision of wind vector. It is revealed that the wind speed retrieval precision of VV polarization improved with correction of residual amplitude error. In addition, the influence of residual amplitude and phase error on wind direction retrieval can be neglected. Thus, it is concluded that correction of amplitude and phase error has the potential to improve wind vector retrievals from GF-3 quad-polarization SAR. Full article

(This article belongs to the Special Issue Remote Sensing of the Aquatic Environments-Part II)

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11 pages, 6526 KiB

Open AccessCommunication

Investigation of Turbulent Tidal Flow in a Coral Reef Channel Using Multi-Look WorldView-2 Satellite Imagery

by George Marmorino

Remote Sens. 2022, 14(3), 783; https://doi.org/10.3390/rs14030783 - 8 Feb 2022

Cited by 5 | Viewed by 2075

Abstract

The general topic here is the application of high-resolution satellite imagery to the study of ocean phenomena having horizontal length scales of several meters to a few kilometers. The present study investigates whether multiple images acquired quite closely in time can be used to derive a spatial map of the surface current in situations where the near-surface hydrodynamics are dominated by bed-generated turbulence and associated wave–current interaction. The approach is illustrated using imagery of turbulent tidal flow in a channel through the outer part of the Great Barrier Reef. The main result is that currents derived from the imagery are found to reach speeds of nearly 4 m/s during a flooding tide—three times larger than published values for other parts of the Reef. These new findings may have some impact on our understanding of the transport of tracers and particles over the shelf. Full article

(This article belongs to the Special Issue Remote Sensing of the Aquatic Environments-Part II)

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20 pages, 5124 KiB

Open AccessArticle

Shallow Water Bathymetry Retrieval Using a Band-Optimization Iterative Approach: Application to New Caledonia Coral Reef Lagoons Using Sentinel-2 Data

by Sélim Amrari, Emmanuel Bourassin, Serge Andréfouët, Benoit Soulard, Hugues Lemonnier and Romain Le Gendre

Remote Sens. 2021, 13(20), 4108; https://doi.org/10.3390/rs13204108 - 14 Oct 2021

Cited by 11 | Viewed by 3004

Abstract

To achieve high accuracy bathymetry retrieval using remote sensing images with robust performance in a 0 to 25 m-deep lagoon with sharp bottom depth variations, a new Iterative Multiple Band Ratio (IMBR) algorithm is tested against known Multiple Band Ratio (MBR) and Single Band Ratio (SBR) algorithms. The test was conducted using the five multispectral bands, at 10 to 60 m resolution, of a Sentinel-2 image of the 25 km² Poe lagoon, a UNESCO World Heritage Area. The IMBR approach requires training datasets for the definitions of depth threshold at which optimal band ratios vary. IMBR achieved accuracy, quantified with an original block cross-validation procedure across the entire depth range reached a mean absolute error of 46.0 cm. It compares very favorably against MBR (78.3 cm) and the various SBR results (188–254 cm). The method is suitable for generalization to other sites pending a minimal ground-truth dataset crossing all the depth range being available. We stress that different users may need different precisions and can use MBR or SBR algorithms for their applications. For the hydrodynamic modelling applications that are developing in New Caledonia, the IMBR solutions applied to Sentinel imagery are optimal. Full article

(This article belongs to the Special Issue Remote Sensing of the Aquatic Environments-Part II)

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27 pages, 46566 KiB

Open AccessArticle

Spatiotemporal Changes of Coastline over the Yellow River Delta in the Previous 40 Years with Optical and SAR Remote Sensing

by Quantao Zhu, Peng Li, Zhenhong Li, Sixun Pu, Xiao Wu, Naishuang Bi and Houjie Wang

Remote Sens. 2021, 13(10), 1940; https://doi.org/10.3390/rs13101940 - 16 May 2021

Cited by 37 | Viewed by 5309

Abstract

The integration of multi-source, multi-temporal, multi-band optical, and radar remote sensing images to accurately detect, extract, and monitor the long-term dynamic change of coastline is critical for a better understanding of how the coastal environment responds to climate change and human activities. In this study, we present a combination method to produce the spatiotemporal changes of the coastline in the Yellow River Delta (YRD) in 1980–2020 with both optical and Synthetic Aperture Radar (SAR) satellite remote sensing images. According to the measurement results of GPS RTK, this method can obtain a high accuracy of shoreline extraction, with an observation error of 71.4% within one pixel of the image. Then, the influence of annual water discharge and sediment load on the changes of the coastline is investigated. The results show that there are two significant accretion areas in the Qing 8 and Qingshuigou course. The relative high correlation illustrates that the sediment discharge has a great contribution to the change of estuary area. Human activities, climate change, and sea level rise that affect waves and storm surges are also important drivers of coastal morphology to be investigated in the future, in addition to the sediment transport. Full article

(This article belongs to the Special Issue Remote Sensing of the Aquatic Environments-Part II)

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