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Remote Sensing of Coastal and Inland Waters

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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 December 2020) | Viewed by 27692

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Dr. Joana Fernandes

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Department of Geosciences, Environment and Spatial Planning, University of Porto, Rua do Campo Alegre, 687, 4169-007 Porto, Portugal
Interests: remote sensing; satellite altimetry; coastal and inland water altimetry; range and geophysical corrections; wet tropospheric correction; sea state bias
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Dr. Clara Lázaro

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Department of Geosciences, Environment and Spatial Planning, University of Porto, Rua do Campo Alegre, 687, 4169-007 Porto, Portugal
Interests: remote sensing; satellite altimetry; coastal and inland water altimetry; range and geophysical corrections—wet tropospheric correction, climate variability, ocean circulation, and ecosystems from remote sensing

Dr.-Ing habil. Luciana Fenoglio

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Guest Editor

Institute of Geodesy and Geoinformation, University of Bonn, Bonn, Germany
Interests: sea level change; climate change; ocean dynamics; coastal and inland water altimetry; improved altimeter processing and waveform retracking

Prof. Dr. Stelios Mertikas

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Geodesy and Geomatics Engineering Lab, University Campus, Technical University of Crete, GR-73100 Chania, Crete, Greece
Interests: geodesy; satellite positioning, navigation; remote sensing; altimetry; calibration/validation; data analysis; sea level change; metrology; statistical process control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing (RS) has revolutionized our understanding of sensitive regions such as coastal zones and inland waters which play a crucial role in human life as most world’s population concentrates on these regions. Amidst the various RS techniques, due to its all weather, day and night capability, satellite altimetry gained increasing importance over the last 26 years. While over the ocean satellite altimetry has long gained a stage of maturity, over regions of coastal zones and inland waters the success of satellite altimetry is even challenging as these measurements require tuned waveform retracking and corrections to the measured range. In spite of this, there is an increasing number of applications over coastal zones and inland waters using satellite altimetry alone or in combination with other remote sensing data (e.g., space-borne gravimetry, sea surface temperature and ocean colour), in situ data (e.g., tide gauges) and ocean, climate and hydrologic models.

Papers on all aspects related with coastal and inland waters studies that make use of remote sensing techniques, in particular satellite altimetry, in combination with in situ observations and models are welcome in this Special Issue.

Paper topics may include but are not limited to the following:

Regional and coastal sea level change and monitoring;
Coastal dynamics;
Data processing techniques for improving satellite altimetry over coastal zones and inland waters, both for low resolution mode (LRM) and synthetic aperture radar (SAR) altimeters: waveform retracking, range and geophysical corrections
Regional tide models;
Remote Sensing products for applications over coastal zones and inland waters;
Satellite altimetry calibration and validation with fiducial reference measurements;
Coastal upwelling ecosystems change monitoring using Remote Sensing data synergy (e.g. space-borne gravimetry, sea surface temperature, ocean colour, imaging SAR), in particular from the Sentinel constellation
River and lake water level monitoring;
Regional studies over closed and semi-enclosed seas;
Assimilation of Remote Sensing data into coastal dynamics, storm surge and hydrologic models;
Innovative applications over coastal zones and inland waters.

Dr. Joana Fernandes
Dr. Clara Lázaro
Dr.-Ing habil. Luciana Fenoglio
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Satellite altimetry
Coastal sea level change
Continental water storage
Coastal zone management
Shelf sea ecosystems
Natural hazard management
Data quality enhancement
Data synergy
Data assimilation

Published Papers (6 papers)

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Research

21 pages, 11781 KiB

Open AccessArticle

LaeNet: A Novel Lightweight Multitask CNN for Automatically Extracting Lake Area and Shoreline from Remote Sensing Images

by Wei Liu, Xingyu Chen, Jiangjun Ran, Lin Liu, Qiang Wang, Linyang Xin and Gang Li

Remote Sens. 2021, 13(1), 56; https://doi.org/10.3390/rs13010056 - 25 Dec 2020

Cited by 20 | Viewed by 3317

Abstract

Variations of lake area and shoreline can indicate hydrological and climatic changes effectively. Accordingly, how to automatically and simultaneously extract lake area and shoreline from remote sensing images attracts our attention. In this paper, we formulate lake area and shoreline extraction as a multitask learning problem. Different from existing models that take the deep and complex network architecture as the backbone to extract feature maps, we present LaeNet—a novel end-to-end lightweight multitask fully CNN with no-downsampling to automatically extract lake area and shoreline from remote sensing images. Landsat-8 images over Selenco and the vicinity in the Tibetan Plateau are utilized to train and evaluate our model. Experimental results over the testing image patches achieve an Accuracy of 0.9962, Precision of 0.9912, Recall of 0.9982, F1-score of 0.9941, and mIoU of 0.9879, which align with the mainstream semantic segmentation models (UNet, DeepLabV3+, etc.) or even better. Especially, the running time of each epoch and the size of our model are only 6 s and 0.047 megabytes, which achieve a significant reduction compared to the other models. Finally, we conducted fieldwork to collect the in-situ shoreline position for one typical part of lake Selenco, in order to further evaluate the performance of our model. The validation indicates high accuracy in our results (DRMSE: 30.84 m, DMAE: 22.49 m, DSTD: 21.11 m), only about one pixel deviation for Landsat-8 images. LaeNet can be expanded potentially to the tasks of area segmentation and edge extraction in other application fields. Full article

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

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35 pages, 9184 KiB

Open AccessFeature PaperArticle

Evaluation of HF Radar Wave Measurements in Iberian Peninsula by Comparison with Satellite Altimetry and in Situ Wave Buoy Observations

by Isabel Bué, Álvaro Semedo and João Catalão

Remote Sens. 2020, 12(21), 3623; https://doi.org/10.3390/rs12213623 - 04 Nov 2020

Cited by 10 | Viewed by 2283

Abstract

The skills of CODAR SeaSonde coastal high-frequency radars (HFR) located in the West Iberian Peninsula on measuring wave parameters are compared to in situ (buoy) and satellite altimeters (SA) wave observations. Significant wave heights (SWH), wave periods, and wave directions are compared over a time window of 36-months, from January 2017 to December 2019. The ability of HFR systems to capture extreme wave events is also assessed by comparing SWH measurements during the Emma storm, which hit the Iberian Peninsula in March 2018. The analysis presented in this study shows a slight overestimation of the SWH by the HFR systems. Comparisons with in situ observations revealed correlation coefficients (R) of the order of 0.69–0.87, biases below 0.60 m, root-mean-squared errors (RMSE) between 0.89 m to 1.18 m, and a slope regression between 1.01 and 1.26. Using buoy observations as reference ground truth, the comparisons with SA revealed Rs higher than 0.94, biases under 0.19 m, and RMSEs between 0.17 m and 0.42 m. Since in situ observations do not overlap all the HFR range cells (RC), and its correlation coefficients with SA have shown good agreement (R > 0.94), Sentinel-3 SA (SRAL) SWH measurements are further used for the validation of the HFR systems SWH observations. The comparison between the HFR and the SA collocated SWH observations allowed the evaluation of the ability of the radars to retrieve wave data as a function of the distance to the coast, particularly during extreme wave events. The comparison of the lower frequency (4.86 MHz) HFR coastal radars with the SA measurements showed an R of 0.94–0.99, a negative but reduced bias (−0.37), and an RMSE of 0.53 m. The higher frequency HFR systems (12–13.5 MHz) showed R between 0.53 and 0.82, and a clear overestimation of the SWH by the HFR sites. Full article

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

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17 pages, 7666 KiB

Open AccessArticle

Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon

by Milad Niroumand-Jadidi, Francesca Bovolo, Lorenzo Bruzzone and Peter Gege

Remote Sens. 2020, 12(15), 2381; https://doi.org/10.3390/rs12152381 - 24 Jul 2020

Cited by 58 | Viewed by 8851

Abstract

The recent PlanetScope constellation (130+ satellites currently in orbit) has shifted the high spatial resolution imaging into a new era by capturing the Earth’s landmass including inland waters on a daily basis. However, studies on the aquatic-oriented applications of PlanetScope imagery are very sparse, and extensive research is still required to unlock the potentials of this new source of data. As a first fully physics-based investigation, we aim to assess the feasibility of retrieving bathymetric and water quality information from the PlanetScope imagery. The analyses are performed based on Water Color Simulator (WASI) processor in the context of a multitemporal analysis. The WASI-based radiative transfer inversion is adapted to process the PlanetScope imagery dealing with the low spectral resolution and atmospheric artifacts. The bathymetry and total suspended matter (TSM) are mapped in the relatively complex environment of Venice lagoon during two benchmark events: The coronavirus disease 2019 (COVID-19) lockdown and an extreme flood occurred in November 2019. The retrievals of TSM imply a remarkable reduction of the turbidity during the lockdown, due to the COVID-19 pandemic and capture the high values of TSM during the flood condition. The results suggest that sizable atmospheric and sun-glint artifacts should be mitigated through the physics-based inversion using the surface reflectance products of PlanetScope imagery. The physics-based inversion demonstrated high potentials in retrieving both bathymetry and TSM using the PlanetScope imagery. Full article

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

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18 pages, 6548 KiB

Open AccessArticle

Multiplatform Earth Observation Systems for Monitoring Water Quality in Vulnerable Inland Ecosystems: Maspalomas Water Lagoon

by Francisco Eugenio, Javier Marcello and Javier Martín

Remote Sens. 2020, 12(2), 284; https://doi.org/10.3390/rs12020284 - 15 Jan 2020

Cited by 7 | Viewed by 3429

Abstract

The accurate monitoring of water quality indicators, bathymetry and distribution of benthic habitats in vulnerable ecosystems is key to assessing the effects of climate change, the quality of natural areas and to guide appropriate biodiversity, tourism or fisheries policies. Coastal and inland water ecosystems are very complex but crucial due to their richness and primary production. In this context, remote sensing can be a reliable way to monitor these areas, mainly thanks to satellite sensors’ improved spatial and spectral capabilities and airborne or drone instruments. In general, mapping bodies of water is challenging due to low signal-to-noise (SNR) at sensor level, due to the very low reflectance of water surfaces as well as atmospheric effects. Therefore, the main objective of this work is to provide a robust processing framework to estimate water quality parameters in inland shallow waters using multiplatform data. More specifically, we measured chlorophyll concentrations (Chl-a) from multispectral and hyperspectral sensors on board satellites, aircrafts and drones. The Natural Reserve of Maspalomas, Canary Island (Spain), was chosen for the study because of its complexity as well as being an inner lagoon with considerable organic and inorganic matter and chlorophyll concentration. This area can also be considered a well-known coastal-dune ecosystem attracting a large amount of tourists. The water quality parameter estimated by the remote sensing platforms has been validated using co-temporal in situ measurements collected during field campaigns, and quite satisfactory results have been achieved for this complex ecosystem. In particular, for the drone hyperspectral instrument, the root mean square error, computed to quantify the differences between the estimated and in situ chlorophyll-a concentrations, was 3.45 with a bias of 2.96. Full article

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

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30 pages, 8968 KiB

Open AccessArticle

Hybrid Chlorophyll-a Algorithm for Assessing Trophic States of a Tropical Brazilian Reservoir Based on MSI/Sentinel-2 Data

by Carolline Cairo, Claudio Barbosa, Felipe Lobo, Evlyn Novo, Felipe Carlos, Daniel Maciel, Rogério Flores Júnior, Edson Silva and Victor Curtarelli

Remote Sens. 2020, 12(1), 40; https://doi.org/10.3390/rs12010040 - 20 Dec 2019

Cited by 35 | Viewed by 5296

Abstract

Using remote sensing for monitoring trophic states of inland waters relies on the calibration of chlorophyll-a (chl-a) bio-optical algorithms. One of the main limiting factors of calibrating those algorithms is that they cannot accurately cope with the wide chl-a concentration ranges in optically complex waters subject to different trophic states. Thus, this study proposes an optical hybrid chl-a algorithm (OHA), which is a combined framework of algorithms for specific chl-a concentration ranges. The study area is Ibitinga Reservoir characterized by high spatiotemporal variability of chl-a concentrations (3–1000 mg/m³). We took the following steps to address this issue: (1) we defined optical classes of specific chl-a concentration ranges using Spectral Angle Mapper (SAM); (2) we calibrated/validated chl-a bio-optical algorithms for each trophic class using simulated Sentinel-2 MSI (Multispectral Instrument) bands; (3) and we applied a decision tree classifier in MSI/Sentinel-2 image to detect the optical classes and to switch to the suitable algorithm for the given class. The results showed that three optical classes represent different ranges of chl-a concentration: class 1 varies 2.89–22.83 mg/m³, class 2 varies 19.51–87.63 mg/m³, and class 3 varies 75.89–938.97 mg/m³. The best algorithms for trophic classes 1, 2, and 3 are the 3-band (R² = 0.78; MAPE - Mean Absolute Percentage Error = 34.36%), slope (R² = 0.93; MAPE = 23.35%), and 2-band (R² = 0.98; MAPE = 20.12%), respectively. The decision tree classifier showed an accuracy of 95% for detecting SAM’s optical trophic classes. The overall performance of OHA was satisfactory (R² = 0.98; MAPE = 26.33%) using in situ data but reduced in the Sentinel-2 image (R² = 0.42; MAPE = 28.32%) due to the temporal gap between matchups and the variability in reservoir hydrodynamics. In summary, OHA proved to be a viable method for estimating chl-a concentration in Ibitinga Reservoir and the extension of this framework allowed a more precise chl-a estimate in eutrophic inland waters. Full article

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

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21 pages, 4145 KiB

Open AccessArticle

Modelling the Altitude Dependence of the Wet Path Delay for Coastal Altimetry Using 3-D Fields from ERA5

by Telmo Vieira, M. Joana Fernandes and Clara Lázaro

Remote Sens. 2019, 11(24), 2973; https://doi.org/10.3390/rs11242973 - 11 Dec 2019

Cited by 11 | Viewed by 3504

Abstract

Wet path delay (WPD) for satellite altimetry has been provided from external sources, raising the need of converting this value between different altitudes. The only expression available for this purpose considers the same altitude reduction, irrespective of geographic location and time. The focus of this study is the modelling of the WPD altitude dependence, aiming at developing improved expressions. Using ERA5 pressure level fields (2010–2013), WPD vertical profiles were computed globally. At each location and for each vertical profile, an exponential function was fitted using least squares, determining the corresponding decay coefficient. The time evolution of these coefficients reveals regions where they are highly variable, making this modelling more difficult, and regions where an annual signal exists. The output of this modelling consists of a set of so-called University of Porto (UP) coefficients, dependent on geographic location and time. An assessment with ERA5 data (2014) shows that for the location where the Kouba coefficient results in a maximum Root Mean Square (RMS) error of 3.2 cm, using UP coefficients this value is 1.2 cm. Independent comparisons with WPD derived from Global Navigation Satellite Systems and radiosondes show that the use of UP coefficients instead of Kouba’s leads to a decrease in the RMS error larger than 1 cm. Full article

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

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