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Remote Sensing of Water Resources Vulnerability

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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 (10 January 2024) | Viewed by 12542

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Special Issue Editors

Dr. Frédéric Frappart

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Interactions Sol Plante Atmosphère, UMR 1391 INRAE/Bordeaux Science Agro 71, Avenue Edouard Bourlaux, 33882 Villenave d'Ornon, France
Interests: remote sensing; water cycle; carbon cycle; wetlands
Special Issues, Collections and Topics in MDPI journals

Dr. Luc Bourrel

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Géosciences Environnement Toulouse, UMR 5563, Université de Toulouse, CNRS-IRD-OMP-CNES, 31400 Toulouse, France
Interests: hydroclimatology; water cycle; wetlands; extreme events; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Thibault Catry

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UMR Espace-Dev, Institut de Recherche pour le Développement, Maison de la Télédétection, 500 rue Jean Francois breton, CEDEX 5, 34093 Montpellier, France
Interests: remote sensing; SAR; wetlands; environment

Dr. Pham-Duc Binh

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Guest Editor

Department of Space and Applications, University of Science and Technology of Hanoi, Vietnam Academy of Science and Technology, Hanoi 100000, Vietnam
Interests: remote sensing; surface water; groundwater; environmental sciences; environment monitoring; bibliometric analysis

Special Issue Information

Dear Colleagues,

Water is an essential resource for ecosystems, human life, and anthropogenic activities. In recent years, pressure on water resources has strongly increased, leading to the reduction of surface water storage and the depletion of aquifers worldwide. Current (e.g., satellite images, radar and lidar altimetry, GRACE) and future (e.g., SWOT, THRISHNA, ….) Earth Observation missions have a strong potential for better monitoring the different components of the terrestrial water cycle and, hence, characterizing the vulnerability of water resources at different spatial and temporal scales.

This Special Issue aims to present reviews and recent advances of general interest in the use of remote sensing observations for the characterization of the vulnerability of water resources in the context of global change including climate change, anthropogenic factors, and their feedback.

Manuscripts can be related to any aspect of water resource vulnerability using satellite or AUV observations. They could be related to either new methodological developments or new advances in sensors or original studies related to water resources vulnerability from local to global scales.

Dr. Frédéric Frappart
Dr. Luc Bourrel
Dr. Thibault Catry
Dr. Pham-Duc Binh
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

surface water
surface and root-zone soil moisture
groundwater
vulnerability indices

Published Papers (7 papers)

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Research

23 pages, 5940 KiB

Open AccessArticle

Assessment of Bottom-Up Satellite Precipitation Products on River Streamflow Estimations in the Peruvian Pacific Drainage

by Jonathan Qquenta, Pedro Rau, Luc Bourrel, Frédéric Frappart and Waldo Lavado-Casimiro

Remote Sens. 2024, 16(1), 11; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16010011 - 19 Dec 2023

Viewed by 1194

Abstract

In regions with limited precipitation information, like Peru, many studies rely on precipitation data derived from satellite products (SPP) and model reanalysis. These products provide near-real-time information and offer global spatial coverage, making them attractive for various applications. However, it is essential to consider their uncertainties when conducting hydrological simulations, especially in a key region like the Pacific drainage (Pd), where 56% of the Peruvian population resides (including the capital, Lima). This study, for the first time, assessed the performance of two bottom-up Satellite-based Precipitation Products (SPP), GPM + SM2RAIN and SM2RAIN-ASCAT, and one top-down approach SPP, ERA5-Land, for runoff simulation in the Pacific drainage of Peru. Hydrological modeling was conducted on 30 basins distributed across the Pd, which were grouped into 5 regions (I–V, ordered from south to north). The results showed that SM2RAIN-ASCAT performed well in regions I-III-IV, ERA5-Land in region II, and GPM + SM2RAIN in region V. The hydrological model GR4J was tested, and better efficiency criteria were obtained with SM2RAIN-ASCAT and GPM + SM2RAIN when comparing the simulated versus observed streamflows. The hydrological modeling using SM2RAIN-ASCAT and GPM + SM2RAIN demonstrated satisfactory efficiency metrics (KGE > 0.75; NSE > 0.65). Additionally, ten hydrological signatures were quantified to assess the variability of the simulated streamflows in each basin, with metrics such as Mean Flow (Q mean), 5th Quantile Flow (Q5), and 95th Quantile Flow (Q95) showing an overall better performance. Finally, the results of this study demonstrate the reliability of using bottom-up satellite products in Pd basins. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Vulnerability)

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20 pages, 20493 KiB

Open AccessArticle

Hydrological Response Assessment of Land Cover Change in a Peruvian Amazonian Basin Impacted by Deforestation Using the SWAT Model

by Karla Paiva, Pedro Rau, Cristian Montesinos, Waldo Lavado-Casimiro, Luc Bourrel and Frédéric Frappart

Remote Sens. 2023, 15(24), 5774; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15245774 - 18 Dec 2023

Cited by 1 | Viewed by 1333

Abstract

The watershed hydrologic conditions in the Madre de Dios (MDD) Basin in the Peruvian Amazon have been irreversibly impacted by deforestation and changes in land cover. These changes have also had detrimental effects on the geomorphology, water quality, and aquatic habitat within the basin. However, there is a scarcity of hydrological modeling studies in this area, primarily due to the limited availability of hydrometeorological data. The primary objective of this study was to examine how deforestation impacts the hydrological conditions in the MDD Basin. By implementing the Soil and Water Assessment Tool (SWAT) model, this study determined that replacing 12% of the evergreen broadleaf forest area with bare land resulted in a significant increase in surface runoff, by 38% monthly, a 1% annual reduction of evapotranspiration, and an average monthly streamflow increase of 12%. Changes in spatial patterns reveal that the primary impacted watershed is the Inambari River subbasin, a significant tributary of the Madre de Dios River. This area experiences an annual average surge of 187% in surface runoff generation while witnessing an annual average reduction of 8% in evapotranspiration. These findings have important implications, as they can contribute to instances of flooding and extreme inundation events, which have already occurred in the MDD region. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Vulnerability)

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28 pages, 15240 KiB

Open AccessArticle

Rainfall Erosivity in Peru: A New Gridded Dataset Based on GPM-IMERG and Comprehensive Assessment (2000–2020)

by Leonardo Gutierrez, Adrian Huerta, Evelin Sabino, Luc Bourrel, Frédéric Frappart and Waldo Lavado-Casimiro

Remote Sens. 2023, 15(22), 5432; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15225432 - 20 Nov 2023

Viewed by 1315

Abstract

In soil erosion estimation models, the variables with the greatest impact are rainfall erosivity (

R E

), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (

E D

), which relates

R E

[...] Read more.

In soil erosion estimation models, the variables with the greatest impact are rainfall erosivity (

R E

), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (

E D

), which relates

R E

to precipitation. The

R E

requires high temporal resolution records for its estimation. However, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for

R E

estimation. This study evaluates the performance of a new gridded dataset of

R E

and

E D

in Peru (PISCO_reed) by merging data from the IMERG v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000–2020. By using this method, a correlation of 0.94 was found between PISCO_reed and

R E

obtained by the observed data. An average annual

R E

for Peru of 7840 MJ · mm · ha

^{- 1}

· h

^{- 1}

was estimated with a general increase towards the lowland Amazon regions, and high values were found on the North Pacific Coast area of Peru. The spatial identification of the most at risk areas of erosion was evaluated through a relationship between the

E D

and rainfall. Both erosivity datasets will allow us to expand our fundamental understanding and quantify soil erosion with greater precision. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Vulnerability)

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22 pages, 7259 KiB

Open AccessArticle

Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile

by Lien Rodríguez-López, David Bustos Usta, Iongel Duran-Llacer, Lisandra Bravo Alvarez, Santiago Yépez, Luc Bourrel, Frederic Frappart and Roberto Urrutia

Remote Sens. 2023, 15(17), 4157; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15174157 - 24 Aug 2023

Cited by 4 | Viewed by 2056

Abstract

In this study, we combined machine learning and remote sensing techniques to estimate the value of chlorophyll-a concentration in a freshwater ecosystem in the South American continent (lake in Southern Chile). In a previous study, nine artificial intelligence (AI) algorithms were tested to predict water quality data from measurements during monitoring campaigns. In this study, in addition to field data (Case A), meteorological variables (Case B) and satellite data (Case C) were used to predict chlorophyll-a in Lake Llanquihue. The models used were SARIMAX, LSTM, and RNN, all of which showed generally good statistics for the prediction of the chlorophyll-a variable. Model validation metrics showed that all three models effectively predicted chlorophyll as an indicator of the presence of algae in water bodies. Coefficient of determination values ranging from 0.64 to 0.93 were obtained, with the LSTM model showing the best statistics in any of the cases tested. The LSTM model generally performed well across most stations, with lower values for MSE (<0.260 (μg/L)²), RMSE (<0.510 ug/L), MaxError (<0.730 μg/L), and MAE (<0.442 μg/L). This model, which combines machine learning and remote sensing techniques, is applicable to other Chilean and world lakes that have similar characteristics. In addition, it is a starting point for decision-makers in the protection and conservation of water resource quality. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Vulnerability)

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20 pages, 3260 KiB

Open AccessArticle

Lake Turbidity Mapping Using an OWTs-bp Based Framework and Sentinel-2 Imagery

by Sijia Li, Tiit Kutser, Kaishan Song, Ge Liu and Yong Li

Remote Sens. 2023, 15(10), 2489; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15102489 - 09 May 2023

Cited by 3 | Viewed by 1546

Abstract

Lake turbidity, representing a general indicator of water ‘cloudiness’, is a key parameter in many monitoring programs. It is not possible to cover all lakes with frequent in situ monitoring. Sentinel-2 MultiSpectral Imager (MSI) can help to fill the gaps if a robust turbidity retrieval methodology is developed. Previously published results demonstrated the usefulness of MSI at a limited regional scale, while our aim was to develop methodology that allows monitoring turbidity over the whole of China. We proposed methodology with a reflectance that can be classified into optical water types (OWTs), and then a back propagation neural network model (BP-TURB) is used to estimate turbidity. The reflectance of in situ lake samples extracted from MSI imagery was clustered as three OWTs, and validation performance was satisfactory: R² > 0.81, RMSE < 17.54, and MAE < 11.20. This allowed us to map turbidity in all Chinese lakes, of which the area is larger than 1 km². A larger percentage of clear lakes (53.26%) with low turbidity levels (<10 NTU) was found in 2020 than in 2015 (37.43%). Lakes in the plateau regions generally exhibited lower turbidity than those situated in the plains regions, for which the turbidity patterns were determined by lake volume, averaged depth, and elevation. We demonstrated that the Sentinel-2 MSI data with the novel approach proposed by us allows for mapping lake turbidity over a large variety of lakes and extensive geographic conditions, as well as for revealing temporal changes in these lakes and their links to lake abiotic characteristics. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Vulnerability)

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14 pages, 3345 KiB

Open AccessArticle

Contribution of Sentinel-3A Radar Altimetry Data to the Study of the Water Level Variations in Lake Buyo (West of Côte d’Ivoire)

by Sékouba Oularé, Valère-Carin Jofack Sokeng, Koffi Fernand Kouamé, Christian Armel Kouassi Komenan, Jean Homian Danumah, Benoit Mertens, You Lucette Akpa, Thibault Catry and Benjamin Pillot

Remote Sens. 2022, 14(21), 5602; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215602 - 06 Nov 2022

Cited by 2 | Viewed by 1612

Abstract

The artificial Lake Buyo is an important water reservoir that ensures the availability of water for multiple purposes: drinking water supply, fishing, and energy. In the last five years, this lake has experienced extreme variations in its surface area and water levels, including very significant declines, which has impacted the supply of electricity. This study aimed to assess temporal variations in the water levels of Lake Buyo using radar altimetry. Altimetric data from the Sentinel-3A satellite on Lake Buyo (tracks 16 (orbit 8) and 743 (orbit 372)) were selected over the period from 31 May 2016 to 12 June 2021 and compared to the in situ measurements provided by the Direction de la Production de l’Electricité de Côte d’Ivoire (DPE-CI). The extraction of the time series of the Sentinel-3A altimetric water levels and their corrections (geophysical and environmental corrections) were carried out with the ALTiS software. The results showed an overall agreement between the altimetric water levels and the in situ measurements, with a correlation coefficient (R²) ranging from 0.98 to 0.99 obtained, as well as a Nash–Sutcliffe Efficiency (NSE) coefficient also between 0.98 and 0.99. Further, the bias (0.12 m and 0.13 m) and root mean square error (RMSE) (0.38 and 0.67 m) values showed that the results were acceptable. The analysis of the water levels time series allowed for the identification of two main periods: March to October and November to February. The first period corresponded to a high level period, recording a maximum level of 200.06 m. The second period, from November to March, was characterized by a drop in the water level, recording a minimum level of 187.42 m. The water levels time series provided by Sentinel-3 allowed us to appreciate the respective influences of seasonal and interannual variations on rainfall and the contributions of the Sassandra River tributaries to the water levels of Lake Buyo. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Vulnerability)

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18 pages, 4214 KiB

Open AccessArticle

Monitoring Lake Volume Variation from Space Using Satellite Observations—A Case Study in Thac Mo Reservoir (Vietnam)

by Binh Pham-Duc, Frederic Frappart, Quan Tran-Anh, Son Tong Si, Hien Phan, Son Nguyen Quoc, Anh Pham Le and Bach Do Viet

Remote Sens. 2022, 14(16), 4023; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14164023 - 18 Aug 2022

Cited by 9 | Viewed by 2385

Abstract

This study estimates monthly variation of surface water volume of Thac Mo hydroelectric reservoir (located in South Vietnam), during the 2016–2021 period. Variation of surface water volume is estimated based on variation of surface water extent, derived from Sentinel-1 observations, and variation of surface water level, derived from Jason-3 altimetry data. Except for drought years in 2019 and 2020, surface water extent of Thac Mo reservoir varies in the range 50–100 km², while its water level varies in the range 202–217 m. Correlation between these two components is high (R = 0.948), as well as correlation between surface water maps derived from Sentinel-1 and free-cloud Sentinel-2 observations (R = 0.98), and correlation between surface water level derived from Jason-3 altimetry data and from in situ measurement (R = 0.99; RMSE = 0.86 m). We showed that water volume of Thac Mo reservoir varies between −0.3 and 0.4 km³ month⁻¹, and it is in a very good agreement with in situ measurement (R = 0.95; RMSE = 0.0682 km³ month⁻¹). This study highlights the advantages in using different types of satellite observations and data for monitoring variation of lakes’ water storage, which is very important for regional hydrological models. Similar research can be applied to monitor lakes in remote areas where in situ measurements are not available, or cannot be accessed freely. Full article

(This article belongs to the Special Issue Remote Sensing of Water Resources Vulnerability)

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