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Remote Sensing of the Aquatic Environments

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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 (1 May 2021) | Viewed by 58041

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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
Special Issues, Collections and Topics in MDPI journals

Dr. Francesca De Santi

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Guest Editor

Institute for Electromagnetic Sensing of the Environment (IREA) - National Research Council of Italy (CNR) - via Bassini, 15 - 20133 Milano, Italy
Interests: ocean waves; winds; sea ice; wave propagation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Observation of the aquatic environments represented by inland surface water, seas and oceans has been traditionally linked to the need of safe navigation and fishery. More recently, there is a growing demand on monitoring capability due to the increasing concerns of contaminants produced by anthropogenic activities on the quality of inland and coastal waters.
Remote observations allow to gather plenty 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 a synoptic monitoring of the water surface along with all the properties directly involved. The perspective is to develop methods and applications to extract detailed environmental information from multi-sensor observations.
This Special Issue on “Remote Sensing of the Aquatic Environments” is focused on all the aspects related to the remote measurement of the bio-geophysical properties of the water bodies and the methodologies aimed at studying the relevant processes. The topics of this Special Issue will include, without being limited to, the following subjects:

Remote sensing of wind, waves, salinity, precipitation.
Remote sensing methods for the detection of floating materials and determination of related bio-geophysical properties (such as type, extent, volume) with special focus on sea ice, lake ice, algal blooms, spilled oil.
Remote sensing of shorelines, bathymetry, upwelling phenomena.
PolSAR and InSAR methods for ocean waves and sea state measurement.
Remote sensing of the ocean and inland waters color.
Remote sensing concepts and advanced sensors for the aquatic environment.

Dr. Giacomo De Carolis
Dr. Francesca De Santi
Guest Editor

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Keywords

Ocean winds, wave, currents, bathimetry
Water quality
Oil spill, algal blooms
Sea ice
Coastline, inland waters
Sar, optical data

Published Papers (13 papers)

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21 pages, 18888 KiB

Open AccessArticle

Monitoring Lakes Surface Water Velocity with SAR: A Feasibility Study on Lake Garda, Italy

by Marina Amadori, Virginia Zamparelli, Giacomo De Carolis, Gianfranco Fornaro, Marco Toffolon, Mariano Bresciani, Claudia Giardino and Francesca De Santi

Remote Sens. 2021, 13(12), 2293; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122293 - 11 Jun 2021

Cited by 10 | Viewed by 3358

Abstract

The SAR Doppler frequencies are directly related to the motion of the scatterers in the illuminated area and have already been used in marine applications to monitor moving water surfaces. Here we investigate the possibility of retrieving surface water velocity from SAR Doppler analysis in medium-size lakes. ENVISAT images of the test site (Lake Garda) are processed and the Doppler Centroid Anomaly technique is adopted. The resulting surface velocity maps are compared with the outputs of a hydrodynamic model specifically validated for the case study. Thermal images from MODIS Terra are used in support of the modeling results. The surface velocity retrieved from SAR is found to overestimate the numerical results and the existence of a bias is investigated. In marine applications, such bias is traditionally removed through Geophysical Model Functions (GMFs) by ascribing it to a fully developed wind waves spectrum. We found that such an assumption is not supported in our case study, due to the small-scale variations of topography and wind. The role of wind intensity and duration on the results from SAR is evaluated, and the inclusion of lake bathymetry and the SAR backscatter gradient is recommended for the future development of GMFs suitable for lake environments. Full article

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

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15 pages, 2591 KiB

Open AccessArticle

Empirical Estimation of Nutrient, Organic Matter and Algal Chlorophyll in a Drinking Water Reservoir Using Landsat 5 TM Data

by Md Mamun, Jannatul Ferdous and Kwang-Guk An

Remote Sens. 2021, 13(12), 2256; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122256 - 09 Jun 2021

Cited by 10 | Viewed by 3235

Abstract

The main objective of this study was to develop empirical models from Landsat 5 TM data to monitor nutrient (total phosphorus: TP), organic matter (biological oxygen demand: BOD), and algal chlorophyll (chlorophyll-a: CHL-a). Instead of traditional monitoring techniques, such models could be substituted for water quality assessment in aquatic systems. A set of models were generated relating surface reflectance values of four bands of Landsat 5 TM and in-situ data by multiple linear regression analysis. Radiometric and atmospheric corrections improved the satellite image quality. A total of 32 compositions of different bands of Landsat 5 TM images were considered to find the correlation coefficient (r) with in-situ measurement of TP, BOD, and CHL-a levels collected from five sampling sites in 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a correlate well with Landsat 5 TM band reflectance values. TP (r = −0.79) and CHL-a (r = −0.79) showed the strongest relations with B1 (Blue). In contrast, BOD showed the highest correlation with B1 (Blue) (r = −0.75) and B1*B3/B4 (Blue*Red/Near-infrared) (r = −0.76). Considering the r values, significant bands and their compositions were identified and used to generate linear equations. Such equations for Landsat 5 TM could detect TP, BOD, and CHL-a with accuracies of 67%, 65%, and 72%, respectively. The developed empirical models were then applied to all study sites on the Paldang Reservoir to monitor spatio-temporal distributions of TP, BOD, and CHL-a for the month of September using Landsat 5 TM images of the year 2001, 2006, and 2010. The results showed that TP, BOD, and CHL-a decreased from 2001 to 2006 and 2010. However, S3 and S4 still have water quality issues and are influenced by climatic and anthropogenic factors, which could significantly affect reservoir drinking water quality. Overall, the present study suggested that the Landsat 5 TM may be appropriate for estimating and monitoring water quality parameters in the reservoir. Full article

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

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19 pages, 8297 KiB

Open AccessArticle

A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration

by Daeyong Jin, Eojin Lee, Kyonghwan Kwon and Taeyun Kim

Remote Sens. 2021, 13(10), 2003; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13102003 - 20 May 2021

Cited by 22 | Viewed by 3408

Abstract

In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R²) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a. Full article

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

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21 pages, 7142 KiB

Open AccessArticle

CDOM Optical Properties and DOC Content in the Largest Mixing Zones of the Siberian Shelf Seas

by Anastasia N. Drozdova, Andrey A. Nedospasov, Nikolay V. Lobus, Svetlana V. Patsaeva and Sergey A. Shchuka

Remote Sens. 2021, 13(6), 1145; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061145 - 17 Mar 2021

Cited by 13 | Viewed by 3594

Abstract

Notable changes in the Arctic ecosystem driven by increased atmospheric temperature and ice cover reduction were observed in the last decades. Ongoing environmental shifts affect freshwater discharge to the Arctic Ocean, and alter Arctic land-ocean fluxes. The monitoring of DOC distribution and CDOM optical properties is of great interest both from the point of view of validation of remote sensing models, and for studying organic carbon transformation and dynamics. In this study we report the DOC concentrations and CDOM optical characteristics in the mixing zones of the Ob, Yenisei, Khatanga, Lena, Kolyma, and Indigirka rivers. Water sampling was performed in August–October 2015 and 2017. The DOC was determined by high-temperature combustion, and absorption coefficients and spectroscopic indices were calculated using the seawater absorbance obtained with spectrophotometric measurements. Kara and Laptev mixing zones were characterized by conservative DOC behavior, while the East Siberian sea waters showed nonconservative DOC distribution. Dominant DOM sources are discussed. The absorption coefficient a_CDOM (350) in the East Siberian Sea was two-fold lower compared to Kara and Laptev seawaters. For the first time we report the DOC content in the Khatanga River of 802.6 µM based on the DOC in the Khatanga estuary. Full article

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

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23 pages, 3972 KiB

Open AccessArticle

Synergy between Satellite Altimetry and Optical Water Quality Data towards Improved Estimation of Lakes Ecological Status

by Ave Ansper-Toomsalu, Krista Alikas, Karina Nielsen, Lea Tuvikene and Kersti Kangro

Remote Sens. 2021, 13(4), 770; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040770 - 19 Feb 2021

Cited by 6 | Viewed by 2499

Abstract

European countries are obligated to monitor and estimate ecological status of lakes under European Union Water Framework Directive (2000/60/EC) for sustainable lakes’ ecosystems in the future. In large and shallow lakes, physical, chemical, and biological water quality parameters are influenced by the high natural variability of water level, exceeding anthropogenic variability, and causing large uncertainty to the assessment of ecological status. Correction of metric values used for the assessment of ecological status for the effect of natural water level fluctuation reduces the signal-to-noise ratio in data and decreases the uncertainty of the status estimate. Here we have explored the potential to create synergy between optical and altimetry data for more accurate estimation of ecological status class of lakes. We have combined data from Sentinel-3 Synthetic Aperture Radar Altimeter and Cryosat-2 SAR Interferometric Radar Altimeter to derive water level estimations in order to apply corrections for chlorophyll a, phytoplankton biomass, and Secchi disc depth estimations from Sentinel-3 Ocean and Land Color Instrument data. Long-term in situ data was used to develop the methodology for the correction of water quality data for the effects of water level applicable on the satellite data. The study shows suitability and potential to combine optical and altimetry data to support in situ measurements and thereby support lake monitoring and management. Combination of two different types of satellite data from the continuous Copernicus program will advance the monitoring of lakes and improves the estimation of ecological status under European Union Water Framework Directive. Full article

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

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23 pages, 7526 KiB

Open AccessArticle

Spatio-Temporal Variations and Driving Forces of Harmful Algal Blooms in Chaohu Lake: A Multi-Source Remote Sensing Approach

by Jieying Ma, Shuanggen Jin, Jian Li, Yang He and Wei Shang

Remote Sens. 2021, 13(3), 427; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030427 - 26 Jan 2021

Cited by 37 | Viewed by 4183

Abstract

Harmful algal blooms (hereafter HABs) pose significant threats to aquatic health and environmental safety. Although satellite remote sensing can monitor HABs at a large-scale, it is always a challenge to achieve both high spatial and high temporal resolution simultaneously with a single earth observation system (EOS) sensor, which is much needed for aquatic environment monitoring of inland lakes. This study proposes a multi-source remote sensing-based approach for HAB monitoring in Chaohu Lake, China, which integrates Terra/Aqua MODIS, Landsat 8 OLI, and Sentinel-2A/B MSI to attain high temporal and spatial resolution observations. According to the absorption characteristics and fluorescence peaks of HABs on remote sensing reflectance, the normalized difference vegetation index (NDVI) algorithm for MODIS, the floating algae index (FAI) and NDVI combined algorithm for Landsat 8, and the NDVI and chlorophyll reflection peak intensity index (ρ_chl) algorithm for Sentinel-2A/B MSI are used to extract HAB. The accuracies of the normalized difference vegetation index (NDVI), floating algae index (FAI), and chlorophyll reflection peak intensity index (ρ_chl) are 96.1%, 95.6%, and 93.8% with the RMSE values of 4.52, 2.43, 2.58 km², respectively. The combination of NDVI and ρ_chl can effectively avoid misidentification of water and algae mixed pixels. Results revealed that the HAB in Chaohu Lake breaks out from May to November; peaks in June, July, and August; and more frequently occurs in the western region. Analysis of the HAB’s potential driving forces, including environmental and meteorological factors of temperature, rainfall, sunshine hours, and wind, indicated that higher temperatures and light rain favored this HAB. Wind is the primary factor in boosting the HAB’s growth, and the variation of a HAB’s surface in two days can reach up to 24.61%. Multi-source remote sensing provides higher observation frequency and more detailed spatial information on a HAB, particularly the HAB’s long-short term changes in their area. Full article

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

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19 pages, 5342 KiB

Open AccessArticle

Hindcast and Near Real-Time Monitoring of Green Macroalgae Blooms in Shallow Coral Reef Lagoons Using Sentinel-2: A New-Caledonia Case Study

by Maële Brisset, Simon Van Wynsberge, Serge Andréfouët, Claude Payri, Benoît Soulard, Emmanuel Bourassin, Romain Le Gendre and Emmanuel Coutures

Remote Sens. 2021, 13(2), 211; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020211 - 09 Jan 2021

Cited by 15 | Viewed by 3801

Abstract

Despite the necessary trade-offs between spatial and temporal resolution, remote sensing is an effective approach to monitor macroalgae blooms, understand their origins and anticipate their developments. Monitoring of small tropical lagoons is challenging because they require high resolutions. Since 2017, the Sentinel-2 satellites has provided new perspectives, and the feasibility of monitoring green algae blooms was investigated in this study. In the Poé-Gouaro-Déva lagoon, New Caledonia, recent Ulva blooms are the cause of significant nuisances when beaching. Spectral indices using the blue and green spectral bands were confronted with field observations of algal abundances using images concurrent with fieldwork. Depending on seabed compositions and types of correction applied to reflectance data, the spectral indices explained between 1 and 64.9% of variance. The models providing the best statistical fit were used to revisit the algal dynamics using Sentinel-2 data from January 2017 to December 2019, through two image segmentation approaches: unsupervised and supervised. The latter accurately reproduced the two algal blooms that occurred in the area in 2018. This paper demonstrates that Sentinel-2 data can be an effective source to hindcast and monitor the dynamics of green algae in shallow lagoons. Full article

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

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21 pages, 7239 KiB

Open AccessArticle

RCSANet: A Full Convolutional Network for Extracting Inland Aquaculture Ponds from High-Spatial-Resolution Images

by Zhe Zeng, Di Wang, Wenxia Tan, Gongliang Yu, Jiacheng You, Botao Lv and Zhongheng Wu

Remote Sens. 2021, 13(1), 92; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010092 - 30 Dec 2020

Cited by 15 | Viewed by 2976

Abstract

Numerous aquaculture ponds are intensively distributed around inland natural lakes and mixed with cropland, especially in areas with high population density in Asia. Information about the distribution of aquaculture ponds is essential for monitoring the impact of human activities on inland lakes. Accurate and efficient mapping of inland aquaculture ponds using high-spatial-resolution remote-sensing images is a challenging task because aquaculture ponds are mingled with other land cover types. Considering that aquaculture ponds have intertwining regular embankments and that these salient features are prominent at different scales, a Row-wise and Column-wise Self-Attention (RCSA) mechanism that adaptively exploits the identical directional dependency among pixels is proposed. Then a fully convolutional network (FCN) combined with the RCSA mechanism (RCSANet) is proposed for large-scale extraction of aquaculture ponds from high-spatial-resolution remote-sensing imagery. In addition, a fusion strategy is implemented using a water index and the RCSANet prediction to further improve extraction quality. Experiments on high-spatial-resolution images using pansharpened multispectral and 2 m panchromatic images show that the proposed methods gain at least 2–4% overall accuracy over other state-of-the-art methods regardless of regions and achieve an overall accuracy of 85% at Lake Hong region and 83% at Lake Liangzi region in aquaculture pond extraction. Full article

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

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15 pages, 6973 KiB

Open AccessArticle

Improving Water Leaving Reflectance Retrievals from ABI and AHI Data Acquired Over Case 2 Waters from Present Geostationary Weather Satellite Platforms

by Bo-Cai Gao and Rong-Rong Li

Remote Sens. 2020, 12(19), 3257; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12193257 - 07 Oct 2020

Cited by 4 | Viewed by 2422

Abstract

The current generation of geostationary weather satellite instruments, such as the Advanced Baseline Imagers (ABIs) on board the US NOAA GOES 16 and 17 satellites and the Advanced Himawari Imagers (AHIs) on board the Japanese Himawari-8/9 satellites, have six channels located in the visible to shortwave IR (SWIR) spectral range. These instruments can acquire images over both land and water surfaces at spatial resolutions between 0.5 and 2 km and with a repeating cycle between 5 and 30 min depending on the mode of operation. The imaging data from these instruments have clearly demonstrated the capability in detecting sediment movements over coastal waters and major chlorophyll blooms over deeper oceans. At present, no operational ocean color data products have been produced from ABI data. Ocean color data products have been operationally generated from AHI data at the Japan Space Agency, but the spatial coverage of the products over very turbid coastal waters are sometimes lacking. In this article, we describe atmospheric correction algorithms for retrieving water leaving reflectances from ABI and AHI data using spectrum-matching techniques. In order to estimate aerosol models and optical depths, we match simultaneously the satellite-measured top of atmosphere (TOA) reflectances on the pixel by pixel basis for three channels centered near 0.86, 1.61, and 2.25 μm (or any combinations of two channels among the three channels) with theoretically simulated TOA reflectances. We demonstrate that water leaving reflectance retrievals can be made from ABI and AHI data with our algorithms over turbid case two waters. Our spectrum-matching algorithms, if implemented onto operational computing facilities, can be complimentary to present operational ocean versions of atmospheric correction algorithms that are mostly developed based on the SeaWiFS type of two-band ratio algorithm. Full article

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

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21 pages, 6247 KiB

Open AccessArticle

Spatiotemporal Dynamics and Environmental Controlling Factors of the Lake Tana Water Hyacinth in Ethiopia

by Abeyou W. Worqlul, Essayas K. Ayana, Yihun T. Dile, Mamaru A. Moges, Minychl G. Dersseh, Getachew Tegegne and Solomon Kibret

Remote Sens. 2020, 12(17), 2706; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172706 - 21 Aug 2020

Cited by 19 | Viewed by 4836

Abstract

The largest freshwater lake in Ethiopia, Lake Tana, has faced ecological disaster due to water hyacinth (Eichhornia crassipes) infestation. The water hyacinth is a threat not only to the ecology but also to the socioeconomic development of the region and cultural value of the lake, which is registered as a UNESCO reserve. This study aims to map the spatiotemporal dynamics of the water hyacinth using high-resolution PlanetScope satellite images and assesses the major environmental variables that relate to the weed spatial coverage dynamics for the period August 2017 to July 2018. The plausible environmental factors studied affecting the weed dynamics include lake level, water and air temperature, and turbidity. Water temperature and turbidity were estimated from the moderate resolution imaging spectroradiometer (MODIS) satellite image and the water level was estimated using Jason-1 altimetry data while the air temperature was obtained from the nearby meteorological station at Bahir Dar station. The results indicated that water hyacinth coverage was increasing at a rate of 14 ha/day from August to November of 2017. On the other hand, the coverage reduced at a rate of 6 ha/day from December 2017 to June 2018. However, the length of shoreline infestation increased significantly from 4.3 km in August 2017 to 23.4 km in April 2018. Lake level and night-time water temperatures were strongly correlated with water hyacinth spatial coverage (p < 0.05). A drop in the lake water level resulted in a considerable reduction of the infested area, which is also related to decreasing nutrient levels in the water. The water hyacinth expansion dynamics could be altered by treating the nutrient-rich runoff with best management practices along the wetland and in the lake watershed landscape. Full article

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

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18 pages, 5791 KiB

Open AccessArticle

Monitoring the Seasonal Hydrology of Alpine Wetlands in Response to Snow Cover Dynamics and Summer Climate: A Novel Approach with Sentinel-2

by Bradley Z. Carlson, Marie Hébert, Colin Van Reeth, Marjorie Bison, Idaline Laigle and Anne Delestrade

Remote Sens. 2020, 12(12), 1959; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12121959 - 17 Jun 2020

Cited by 11 | Viewed by 3757

Abstract

Climate change in the European Alps during recent years has led to decreased snow cover duration as well as increases in the frequency and intensity of summer heat waves. The risk of drought for alpine wetlands and temporary pools, which rely on water from snowmelt and provide habitat for specialist plant and amphibian biodiversity, is largely unknown and understudied in this context. Here, we test and validate a novel application of Sentinel-2 imagery aimed at quantifying seasonal variation in water surface area in the context of 95 small (median surface area <100 m²) and shallow (median depth of 20 cm) alpine wetlands in the French Alps, using a linear spectral unmixing approach. For three study years (2016–2018), we used path-analysis to correlate mid-summer water surface area to annual metrics of snowpack (depth and duration) and spring and summer climate (temperature and precipitation). We further sought to evaluate potential biotic responses to drought for study years by monitoring the survival of common frog (Rana temporaria) tadpoles and wetland plant biomass production quantified using peak Normalized Difference Vegetation Index (NDVI). We found strong agreement between citizen science-based observations of water surface area and Sentinel-2 based estimates (R² = 0.8–0.9). Mid-summer watershed snow cover duration and summer temperatures emerged as the most important factors regulating alpine wetland hydrology, while the effects of summer precipitation, and local and watershed snow melt-out timing were not significant. We found that a lack of summer snowfields in 2017 combined with a summer heat wave resulted in a significant decrease in mid-summer water surface area, and led to the drying up of certain wetlands as well as the observed mortality of tadpoles. We did not observe a negative effect of the 2017 summer on the biomass production of wetland vegetation, suggesting that wetlands that maintain soil moisture may act as favorable microhabitats for above treeline vegetation during dry years. Our work introduces a remote sensing-based protocol for monitoring the surface hydrology of alpine wetland habitats at the regional scale. Given that climate models predict continued reduction of snow cover in the Alps during the coming years, as well as particularly intense warming during the summer months, our conclusions underscore the vulnerability of alpine wetlands in the face of ongoing climate change. Full article

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

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42 pages, 4383 KiB

Open AccessEditor’s ChoiceReview

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

by Mercedes Vélez-Nicolás, Santiago García-López, Luis Barbero, Verónica Ruiz-Ortiz and Ángel Sánchez-Bellón

Remote Sens. 2021, 13(7), 1359; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071359 - 01 Apr 2021

Cited by 57 | Viewed by 14117

Abstract

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs. Full article

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

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Other

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12 pages, 2882 KiB

Open AccessFeature PaperLetter

Low-cost Fiberoptic Probe for Ammonia Early Detection in Fish Farms

by Arnaldo G. Leal-Junior, Anselmo Frizera and Carlos Marques

Remote Sens. 2020, 12(9), 1439; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091439 - 01 May 2020

Cited by 27 | Viewed by 3588

Abstract

Recirculating aquaculture systems (RAS) are complex systems in which there is an interaction between the fish biomass and water chemistry, where small variations in the environment can lead to major effects in the production. Ammonia is one of the key limiting factors in RAS and its early detection in small concentrations prevents fish mortality and improves the production quality. Aiming at this background, this paper presents a low-cost fiberoptic probe for the early detection of ammonia. The sensor was based on the chemical interaction between the Oxazine 170 perchlorate layer, deposited in an uncladed polymer optical fiber (POF), and the ammonia dissolved in water. In addition, a thin metallic layer (composed by gold and palladium) was deposited in the fiber end facet and acted as a reflector for the optical signals, enabling the use of the proposed sensor in reflection mode. Different configurations of the sensor were tested, where the effects of polydimethylsiloxane (PDMS) protective layer, thermal treatments, and the use on reflection or transmission modes were compared in the assessment of ammonia concentrations in the range of 100 ppb to 900 ppb. Results showed a better performance (as a function of the sensor sensitivity and linearity) of the sensor with the annealing thermal treatment and without the PDMS layer. Then, the proposed fiberoptic probe was applied on the ammonia detection in high-salinity water, where ammonia concentrations as low as 100 ppb were detected. Full article

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

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