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Methodological Advancements in Remote Sensing of Biophysical Parameters in Inland and Coastal Waters

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 11527

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Special Issue Editors

Dr. Milad Niroumand-Jadidi

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Guest Editor

Fondazione Bruno Kessler (FBK), Trento, Italy
Interests: remote sensing; inland waters; coastal waters
Special Issues, Collections and Topics in MDPI journals

Dr. Peter Gege

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Guest Editor

German Aerospace Center (DLR), Remote Sensing Technology Institute, Münchner Str. 20, Oberpfaffenhofen, D-82234 Weßling, Germany
Interests: radiative transfer modeling in water and the atmosphere; algorithm development for optically complex waters; inverse modeling; calibration of field spectrometers and hyperspectral sensors

Dr. Andreas Jechow

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Guest Editor

1. Department of Ecohydrology, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany
2. Remote Sensing and Geoinformatics, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Interests: inland water remote sensing; lake color; water quality; proximity sensing; optical sensors; light pollution; night-time lights; environmental monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The biophysical attributes of inland and coastal waters, including the concentration of constituents (e.g., chlorophyll-a and suspended matters), bathymetry, and benthic habitats are closely linked to a variety of aquatic ecosystem services. Timely and accurate information on the aquatic biophysical parameters is crucial to enable sustainable management of natural waters, urban and agricultural water supply, navigation, fisheries, tourism, recreational activities, and so on. Furthermore, reliable quantification of the aforementioned parameters both in space and time can enhance our understanding of processes such as eutrophication and harmful algal blooms, carbon cycle, as well as climate change impacts. In this context, optical remote sensing provides an efficient means of characterizing these parameters across large spatial and temporal scales.

This Special Issue aims to disseminate the latest research findings concerned with the development of novel methodological approaches, for example, advanced machine learning and physics-based methods for the remote sensing of biophysical parameters such as water quality, bathymetry, and substrate properties. We welcome the submission of original manuscripts concerned with all aspects of developing and assessing methods for estimation of the parameters from multispectral and hyperspectral data. Welcome topics include but are not limited to the following:

Novel machine/deep learning methods for the estimation of biophysical parameters;
Advanced physics-based inversion methods;
Method comparison and review studies;
Synergic use and fusion of machine learning and physics-based approaches;
Multitemporal analysis;
Cross-sensor fusion of spectral or other data;
Spectrally based in situ measurement approaches.

Dr. Milad Niroumand-Jadidi
Dr. Peter Gege
Dr. Andreas Jechow
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

remote sensing methods
physics-based inversion
machine learning
inland and coastal waters
biophysical parameters
water quality
bathymetry
substrate types and compositions

Published Papers (3 papers)

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Research

34 pages, 1949 KiB

Open AccessArticle

Depths Inferred from Velocities Estimated by Remote Sensing: A Flow Resistance Equation-Based Approach to Mapping Multiple River Attributes at the Reach Scale

by Carl Legleiter and Paul Kinzel

Remote Sens. 2021, 13(22), 4566; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224566 - 13 Nov 2021

Cited by 4 | Viewed by 1881

Abstract

Remote sensing of flow conditions in stream channels could facilitate hydrologic data collection, particularly in large, inaccessible rivers. Previous research has demonstrated the potential to estimate flow velocities in sediment-laden rivers via particle image velocimetry (PIV). In this study, we introduce a new framework for also obtaining bathymetric information: Depths Inferred from Velocities Estimated by Remote Sensing (DIVERS). This approach is based on a flow resistance equation and involves several assumptions: steady, uniform, one-dimensional flow and a direct proportionality between the velocity estimated at a given location and the local water depth, with no lateral transfer of mass or momentum. As an initial case study, we performed PIV and inferred depths from videos acquired from a helicopter hovering at multiple waypoints along a large river in central Alaska. The accuracy of PIV-derived velocities was assessed via comparison to field measurements and the performance of an optimization-based approach to DIVERS was quantified by comparing calculated depths to those observed in the field. We also examined the ability of two variants of DIVERS to reproduce the discharge recorded at a gaging station. This analysis indicated that the accuracy of PIV-based velocity estimates varied considerably from hover to hover along the reach, with observed vs. predicted R² values ranging from 0.22 to 0.97 and a median of 0.57. Calculated depths were also reasonably accurate, with median normalized biases from −4% to 9.9% for the two versions of DIVERS, but tended to be under-predicted in meander bends. Discharges were reproduced to within 1% and 4% when applying the optimization-based technique to individual hovers or reach-aggregated data, respectively. The results of this investigation suggest that, in addition to the velocity field derived via PIV, DIVERS could provide a plausible, first-order approximation to the reach-scale bathymetry. This framework could be refined by incorporating hydraulic processes that were not represented in the initial iteration of the approach described herein. Full article

(This article belongs to the Special Issue Methodological Advancements in Remote Sensing of Biophysical Parameters in Inland and Coastal Waters)

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32 pages, 11065 KiB

Open AccessFeature PaperArticle

Coastal Remote Sensing: Merging Physical, Chemical, and Biological Data as Tailings Drift onto Buffalo Reef, Lake Superior

by W. Charles Kerfoot, Martin M. Hobmeier, Gary Swain, Robert Regis, Varsha K. Raman, Colin N. Brooks, Amanda Grimm, Chris Cook, Robert Shuchman and Molly Reif

Remote Sens. 2021, 13(13), 2434; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132434 - 22 Jun 2021

Cited by 4 | Viewed by 2680

Abstract

On the Keweenaw Peninsula of Lake Superior, two stamp mills (Mohawk and Wolverine) discharged 22.7 million metric tonnes (MMT) of tailings (1901–1932) into the coastal zone off the town of Gay. Migrating along the shoreline, ca. 10 MMT of the tailings dammed stream and river outlets, encroached upon wetlands, and contaminated recreational beaches. A nearly equal amount of tailings moved across bay benthic environments into critical commercial fish spawning and rearing grounds. In the middle of the bay, Buffalo Reef is important for commercial and recreational lake trout and lake whitefish production (ca. 32% of the commercial catch in Keweenaw Bay, 22% along southern Lake Superior). Aerial photographs (1938–2016) and five LiDAR and multispectral over-flights (2008–2016) emphasize: (1) the enormous amounts of tailings moving along the beach; and (2) the bathymetric complexities of an equal amount migrating underwater across the shelf. However, remote sensing studies encounter numerous specific challenges in coastal environments. Here, we utilize a combination of elevation data (LiDAR digital elevation/bathymetry models) and in situ studies to generate a series of physical, chemical, and biological geospatial maps. The maps are designed to help assess the impacts of historical mining on Buffalo Reef. Underwater, sand mixtures have complicated multispectral bottom reflectance substrate classifications. An alternative approach, in situ simple particle classification, keying off distinct sand end members: (1) allows calculation of tailings (stamp sand) percentages; (2) aids indirect and direct assays of copper concentrations; and (3) permits determinations of density effects on benthic macro-invertebrates. The geospatial mapping shows how tailings are moving onto Buffalo Reef, the copper concentrations associated with the tailings, and how both strongly influence the density of benthic communities, providing an excellent example for the International Maritime Organization on how mining may influence coastal reefs. We demonstrate that when large amounts of mine tailings are discharged into coastal environments, temporal and spatial impacts are progressive, and strongly influence resident organisms. Next steps are to utilize a combination of hi-resolution LiDAR and sonar surveys, a fish-monitoring array, and neural network analysis to characterize the geometry of cobble fields where fish are successful or unsuccessful at producing young. Full article

(This article belongs to the Special Issue Methodological Advancements in Remote Sensing of Biophysical Parameters in Inland and Coastal Waters)

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25 pages, 11056 KiB

Open AccessArticle

Inter-Comparison of Methods for Chlorophyll-a Retrieval: Sentinel-2 Time-Series Analysis in Italian Lakes

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

Remote Sens. 2021, 13(12), 2381; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122381 - 18 Jun 2021

Cited by 27 | Viewed by 5741

Abstract

Different methods are available for retrieving chlorophyll-a (Chl-a) in inland waters from optical imagery, but there is still a need for an inter-comparison among the products. Such analysis can provide insights into the method selection, integration of products, and algorithm development. This work aims at inter-comparison and consistency analyses among the Chl-a products derived from publicly available methods consisting of Case-2 Regional/Coast Colour (C2RCC), Water Color Simulator (WASI), and OC3 (3-band Ocean Color algorithm). C2RCC and WASI are physics-based processors enabling the retrieval of not only Chl-a but also total suspended matter (TSM) and colored dissolved organic matter (CDOM), whereas OC3 is a broadly used semi-empirical approach for Chl-a estimation. To pursue the inter-comparison analysis, we demonstrate the application of Sentinel-2 imagery in the context of multitemporal retrieval of constituents in some Italian lakes. The analysis is performed for different bio-optical conditions including subalpine lakes in Northern Italy (Garda, Idro, and Ledro) and a turbid lake in Central Italy (Lake Trasimeno). The Chl-a retrievals are assessed versus in situ matchups that indicate the better performance of WASI. Moreover, relative consistency analyses are performed among the products (Chl-a, TSM, and CDOM) derived from different methods. In the subalpine lakes, the results indicate a high consistency between C2RCC and WASI when

a_{C D O M} (440)

< 0.5 m⁻¹, whereas the retrieval of constituents, particularly Chl-a, is problematic based on C2RCC for high-CDOM cases. In the turbid Lake Trasimeno, the extreme neural network of C2RCC provided more consistent products with WASI than the normal network. OC3 overestimates the Chl-a concentration. The flexibility of WASI in the parametrization of inversion allows for the adaptation of the method for different optical conditions. The implementation of WASI requires more experience, and processing is time demanding for large lakes. This study elaborates on the pros and cons of each method, providing guidelines and criteria on their use. Full article

(This article belongs to the Special Issue Methodological Advancements in Remote Sensing of Biophysical Parameters in Inland and Coastal Waters)

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