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Remote Sensing for Hydrogeological/Hydrological Modelling and Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 16077

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Dr. Amen Al-Yaari

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Faculty of Science and Engineering, Department of Environment, Transfers and Interactions in Soils and Water Bodies, Sorbonne University, Paris, France
Interests: hydrology; soil moisture; climate; microwave remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaoyong Xu

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Guest Editor

Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada
Interests: watershed hydrology; remote sensing of water resources; hydrologic data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water is essential for all life on Earth. Water security and sustainable water resource management are some of the most urgent challenges that the world faces today, given that the water-cycle behavior and spatial and temporal patterns of water resources are being increasingly impacted by climate change. Recent advances in data collection from satellite-based remote sensing (Earth observation) have opened new opportunities to better understand water cycle behavior and how it is changing in response to climate change.

This Special Issue focuses on Earth observation applications for improving our understanding of variability in water-cycle behavior and water resources, a necessary step toward evidence-based sustainable water resources management and water security. We welcome submissions that are related (but not limited) to the following topics:

Development of retrieval algorithms for various types of satellite hydrologic products (precipitation, soil moisture, snow and ice, terrestrial water storage, evapotranspiration, streamflow, lake or river water levels, etc.);
Validation of satellite hydrologic products using ground measurements;
Monitoring of hydroclimatic extreme events (e.g., floods and droughts) from Earth observation;
Satellite detection of variability in regional or global surface water and groundwater resources as influenced by climate change and/or human activities;
Application of satellite hydrologic products in computational models (e.g., data assimilation, model calibration);
An integrated use of Earth observation, ground measurements, and computational modeling for advancing the understanding of the physical processes that govern water movement in the surface/subsurface domains of the Earth system.

Dr. Amen Al-Yaari
Dr. Xiaoyong Xu
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

earth observation
satellite hydrologic products
water cycle
water resources sustainability
water security
climate change
data assimilation

Published Papers (8 papers)

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Research

24 pages, 8586 KiB

Open AccessArticle

Investigating Correlations and the Validation of SMAP-Sentinel L2 and In Situ Soil Moisture in Thailand

by Apiniti Jotisankasa, Kritanai Torsri, Soravis Supavetch, Kajornsak Sirirodwattanakool, Nuttasit Thonglert, Rati Sawangwattanaphaibun, Apiwat Faikrua, Pattarapoom Peangta and Jakrapop Akaranee

Sensors 2023, 23(21), 8828; https://doi.org/10.3390/s23218828 - 30 Oct 2023

Viewed by 1244

Abstract

Soil moisture plays a crucial role in various hydrological processes and energy partitioning of the global surface. The Soil Moisture Active Passive-Sentinel (SMAP-Sentinel) remote-sensing technology has demonstrated great potential for monitoring soil moisture with a maximum spatial resolution of 1 km. This capability can be applied to improve the weather forecast accuracy, enhance water management for agriculture, and managing climate-related disasters. Despite the techniques being increasingly used worldwide, their accuracy still requires field validation in specific regions like Thailand. In this paper, we report on the extensive in situ monitoring of soil moisture (from surface up to 1 m depth) at 10 stations across Thailand, spanning the years 2021 to 2023. The aim was to validate the SMAP surface-soil moisture (SSM) Level 2 product over a period of two years. Using a one-month averaging approach, the study revealed linear relationships between the two measurement types, with the coefficient of determination (R-squared) varying from 0.13 to 0.58. Notably, areas with more uniform land use and topography such as croplands tended to have a better coefficient of determination. We also conducted detailed soil core characterization, including soil–water retention curves, permeability, porosity, and other physical properties. The basic soil properties were used for estimating the correlation constants between SMAP and in situ soil moistures using multiple linear regression. The results produced R-squared values between 0.933 and 0.847. An upscaling approach to SMAP was proposed that showed promising results when a 3-month average of all measurements in cropland was used together. The finding also suggests that the SMAP-Sentinel remote-sensing technology exhibits significant potential for soil-moisture monitoring in certain applications. Further validation efforts and research, particularly in terms of root-zone depths and area-based assessments, especially in the agricultural sector, can greatly improve the technology’s effectiveness and usefulness in the region. Full article

(This article belongs to the Special Issue Remote Sensing for Hydrogeological/Hydrological Modelling and Applications)

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23 pages, 7646 KiB

Open AccessArticle

Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model

by Hong Zhao, Yijian Zeng, Xujun Han and Zhongbo Su

Sensors 2023, 23(5), 2620; https://doi.org/10.3390/s23052620 - 27 Feb 2023

Cited by 1 | Viewed by 1640

Abstract

This paper coupled a unified passive and active microwave observation operator—namely, an enhanced, physically-based, discrete emission-scattering model—with the community land model (CLM) in a data assimilation (DA) system. By implementing the system default local ensemble transform Kalman filter (LETKF) algorithm, the Soil Moisture [...] Read more.

T_{B}^{p}

(

p

= Horizontal or Vertical polarization) assimilations for only soil property retrieval and both soil properties and soil moisture estimates were investigated with the aid of in situ observations at the Maqu site. The results indicate improved estimates of soil properties of the topmost layer in comparison to measurements, as well as of the profile. Specifically, both assimilations of

T_{B}^{H}

lead to over a 48% reduction in root mean square errors (RMSEs) for the retrieved clay fraction from the background compared to the top layer measurements. Both assimilations of

T_{B}^{V}

reduce RMSEs by 36% for the sand fraction and by 28% for the clay fraction. However, the DA estimated soil moisture and land surface fluxes still exhibit discrepancies when compared to the measurements. The retrieved accurate soil properties alone are inadequate to improve those estimates. The discussed uncertainties (e.g., fixed PTF structures) in the CLM model structures should be mitigated. Full article

(This article belongs to the Special Issue Remote Sensing for Hydrogeological/Hydrological Modelling and Applications)

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31 pages, 7743 KiB

Open AccessArticle

The Application of SWAT Model and Remotely Sensed Products to Characterize the Dynamic of Streamflow and Snow in a Mountainous Watershed in the High Atlas

by Soufiane Taia, Lamia Erraioui, Youssef Arjdal, Jamal Chao, Bouabid El Mansouri and Andrea Scozzari

Sensors 2023, 23(3), 1246; https://doi.org/10.3390/s23031246 - 21 Jan 2023

Cited by 6 | Viewed by 3137

Abstract

Snowfall, snowpack, and snowmelt are among the processes with the greatest influence on the water cycle in mountainous watersheds. Hydrological models may be significantly biased if snow estimations are inaccurate. However, the unavailability of in situ snow data with enough spatiotemporal resolution limits the application of spatially distributed models in snow-fed watersheds. This obliges numerous modellers to reduce their attention to the snowpack and its effect on water distribution, particularly when a portion of the watershed is predominately covered by snow. This research demonstrates the added value of remotely sensed snow cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) in evaluating the performance of hydrological models to estimate seasonal snow dynamics and discharge. The Soil and Water Assessment Tool (SWAT) model was used in this work to simulate discharge and snow processes in the Oued El Abid snow-dominated watershed. The model was calibrated and validated on a daily basis, for a long period (1981–2015), using four discharge-gauging stations. A spatially varied approach (snow parameters are varied spatially) and a lumped approach (snow parameters are unique across the whole watershed) have been compared. Remote sensing data provided by MODIS enabled the evaluation of the snow processes simulated by the SWAT model. Results illustrate that SWAT model discharge simulations were satisfactory to good according to the statistical criteria. In addition, the model was able to reasonably estimate the snow-covered area when comparing it to the MODIS daily snow cover product. When allowing snow parameters to vary spatially, SWAT model results were more consistent with the observed streamflow and the MODIS snow-covered area (MODIS-SCA). This paper provides an example of how hydrological modelling using SWAT and snow coverage products by remote sensing may be used together to examine seasonal snow cover and snow dynamics in the High Atlas watershed. Full article

(This article belongs to the Special Issue Remote Sensing for Hydrogeological/Hydrological Modelling and Applications)

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22 pages, 4159 KiB

Open AccessArticle

A Different Processing of Time-Domain Induced Polarisation: Application for Investigating the Marine Intrusion in a Coastal Aquifer in the SE Iberian Peninsula

by Jesús Díaz-Curiel, Bárbara Biosca, Lucía Arévalo-Lomas and María Jesús Miguel

Sensors 2023, 23(2), 708; https://doi.org/10.3390/s23020708 - 08 Jan 2023

Viewed by 1982

Abstract

This study presents the developments regarding the time-domain induced polarisation method as a supporting tool for resistivity soundings during investigations of coastal detrital aquifers that are salinized by marine intrusion. The interpretation of resistivity measurements in such aquifers, which have variable hydrochemistry and lithology, involves uncertainties owing to the presence of low-resistivity lithologies, such as clays. To reduce these uncertainties, the use of other geophysical parameters is necessary; hence, this study focuses on induced polarisation since it can be measured simultaneously with resistivity. In detail, we propose the determination of induced polarisation using 1D techniques while developing a different algorithm for processing the induced polarisation data. The aim is to extend the results of this phenomenon, using, instead of chargeability, the concepts of polarisability and decay time, which are extracted from the decay curve, given that they represent more intrinsic properties of the various analyzed subsurface media. Results were obtained by applying this methodology to a Quaternary aquifer of the Costa del Sol in the SE Iberian Peninsula (in the province of Almería) during two different campaigns, one in mid-autumn and one late winter (i.e., in October and February, respectively) are presented. The results reveal the position of the saline front during each campaign while reflecting the seasonal movement of the marine intrusion. Full article

(This article belongs to the Special Issue Remote Sensing for Hydrogeological/Hydrological Modelling and Applications)

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19 pages, 10101 KiB

Open AccessArticle

Monitoring Dewatering Fish Spawning Sites in the Reservoir of a Large Hydropower Plant in a Lowland Country Using Unmanned Aerial Vehicles

by Linas Jurevičius, Petras Punys, Raimondas Šadzevičius and Egidijus Kasiulis

Sensors 2023, 23(1), 303; https://doi.org/10.3390/s23010303 - 28 Dec 2022

Cited by 1 | Viewed by 1402

Abstract

This paper presents research concerning dewatered areas in the littoral zones of the Kaunas hydropower plant (HPP) reservoir in Lithuania. It is a multipurpose reservoir that is primarily used by two large hydropower plants for power generation. As a result of the peaking operation regime of the Kaunas HPP, the large quantity of water that is subtracted and released into the reservoir by the Kruonis pumped storage hydropower plant (PSP), and the reservoir morphology, i.e., the shallow, gently sloping littoral zone, significant dewatered areas can appear during drawdown operations. This is especially dangerous during the fish spawning period. Therefore, reservoir operation rules are in force that limit the operation of HPPs and secure other reservoir stakeholder needs. There is a lack of knowledge concerning fish spawning locations, how they change, and what areas are dewatered at different stages of HPP operation. This knowledge is crucial for decision-making and efficient reservoir storage management in order to simultaneously increase power generation and protect the environment. Current assessments of the spawning sites are mostly based on studies that were carried out in the 1990s. Surveying fish spawning sites is typically a difficult task that is usually carried out by performing manual bathymetric measurements due to the limitations of sonar in such conditions. A detailed survey of a small (approximately 5 ha) area containing several potential spawning sites was carried out using Unmanned Aerial Vehicles (UAV) equipped with multispectral and conventional RGB cameras. The captured images were processed using photogrammetry and analyzed using various techniques, including machine learning. In order to highlight water and track changes, various indices were calculated and assessed, such as the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Visible Atmospherically Resistant Index (VARI), and Normalized Green-Red Difference Index (NGRDI). High-resolution multispectral images were used to analyze the spectral footprint of aquatic macrophytes, and the possibility of using the results of this study to identify and map potential spawning sites over the entire reservoir (approximately 63.5 km²) was evaluated. The aim of the study was to investigate and implement modern surveying techniques to improve usage of reservoir storage during hydropower plant drawdown operations. The experimental results show that thresholding of the NGRDI and supervised classification of the NDWI were the best-performing methods for the shoreline detection in the fish spawning sites. Full article

(This article belongs to the Special Issue Remote Sensing for Hydrogeological/Hydrological Modelling and Applications)

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16 pages, 1760 KiB

Open AccessArticle

Evaluating the Performance of Seven Ongoing Satellite Altimetry Missions for Measuring Inland Water Levels of the Great Lakes

by Zhiyuan An, Peng Chen, Fucai Tang, Xueying Yang, Rong Wang and Zhihao Wang

Sensors 2022, 22(24), 9718; https://doi.org/10.3390/s22249718 - 12 Dec 2022

Cited by 5 | Viewed by 1598

Abstract

Satellite altimetry can provide long-term water level time series for water bodies lacking hydrological stations. Few studies have evaluated the performance of HY-2C and Sentinel-6 satellites in inland water bodies, as they have operated for less than 1 and 2 years, respectively. This study evaluated the measured water level accuracy of CryoSat-2, HY-2B, HY-2C, ICESat-2, Jason-3, Sentinel-3A, and Sentinel-6 in the Great Lakes by in-situ data of 12 hydrological stations from 1 January 2021 to 1 April 2022. Jason-3 and Sentinel-6 have the lowest mean root-mean-square-error (RMSE) of measured water level, which is 0.07 m. The measured water level of Sentinel-6 satellite shows a high correlation at all passing stations, and the average value of all correlation coefficients (R) is also the highest among all satellites, reaching 0.94. The mean RMSE of ICESat-2 satellite is slightly lower than Jason-3 and Sentinel-6, which is 0.09 m. The stability of the average deviation (bias) of the ICESat-2 is the best, with the maximum bias only 0.07 m larger than the minimum bias. ICESat-2 satellite has an exceptionally high spatial resolution. It is the only satellite among the seven satellites that has retrieved water levels around twelve stations. HY-2C satellite has the highest temporal resolution, with a temporal resolution of 7.5 days at station 9075014 in Huron Lake and an average of 10 days in the Great Lakes region. The results show that the seven altimetry satellites currently in operation have their own advantages and disadvantages, Jason-3 and Sentinel-6 have the highest accuracy, ICESat-2 has higher accuracy and the highest spatial resolution, and HY-2C has the highest temporal resolution, although it is less accurate. In summary, with full consideration of accuracy and space-time resolution, the ICESat-2 satellite can be used as the benchmark to achieve the unification of multi-source data and establish water level time series. Full article

(This article belongs to the Special Issue Remote Sensing for Hydrogeological/Hydrological Modelling and Applications)

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15 pages, 3026 KiB

Open AccessArticle

Detection of Water Spread Area Changes in Eutrophic Lake Using Landsat Data

by Vaibhav Deoli, Deepak Kumar and Alban Kuriqi

Sensors 2022, 22(18), 6827; https://doi.org/10.3390/s22186827 - 09 Sep 2022

Cited by 32 | Viewed by 2426

Abstract

Adequate water resource management is essential for fulfilling ecosystem and human needs. Nainital Lake is a popular lake in Uttarakhand State in India, attracting lakhs of tourists annually. Locals also use the lake water for domestic purposes and irrigation. The increasing impact of climate change and over-exploration of water from lakes make their regular monitoring key to implementing effective conservation measures and preventing substantial degradation. In this study, dynamic change in the water spread area of Nainital Lake from 2001 to 2018 has been investigated using the multiband rationing indices, namely normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and water ratio index (WRI). The model has been developed in QGIS 3.4 software. A physical GPS survey of the lake was conducted to check the accuracy of these indices. Furthermore, to determine the trend in water surface area for a studied period, a non-parametric Mann–Kendall test was used. San’s slope estimator test determined the magnitude of the trend and total percentage change. The result of the physical survey shows that NDWI was the best method, with an accuracy of 96.94%. Hence, the lake water spread area trend is determined based on calculated NDWI values. The lake water spread area significantly decreased from March to June and July to October at a 5% significance level. The maximum decrease in water spread area has been determined from March to June (7.7%), which was followed by the period July to October (4.67%) and then November to February (2.79%). The study results show that the lake’s water spread area decreased sharply for the analyzed period. The study might be helpful for the government, policymakers, and water experts to make plans for reclaiming and restoring Nainital Lake. This study is very helpful in states such as Uttarakhand, where physical mapping is not possible every time due to its tough topography and climate conditions. Full article

(This article belongs to the Special Issue Remote Sensing for Hydrogeological/Hydrological Modelling and Applications)

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24 pages, 18896 KiB

Open AccessArticle

Technology for Position Correction of Satellite Precipitation and Contributions to Error Reduction—A Case of the ‘720’ Rainstorm in Henan, China

by Wenlong Tian, Xiaoqun Cao and Kecheng Peng

Sensors 2022, 22(15), 5583; https://doi.org/10.3390/s22155583 - 26 Jul 2022

Cited by 3 | Viewed by 1529

Abstract

In July 2021, an extreme precipitation event occurred in Henan, China, causing tremendous damage and deaths; so, it is very important to study the observation technology of extreme precipitation. Surface rain gauge precipitation observations have high accuracy but low resolution and coverage. Satellite remote sensing has high spatial resolution and wide coverage, but has large precipitation accuracy and distribution errors. Therefore, how to merge the above two kinds of precipitation observations effectively to obtain heavy precipitation products with more accurate geographic distributions has become an important but difficult scientific problem. In this paper, a new information fusion method for improving the position accuracy of satellite precipitation estimations is used based on the idea of registration and warping in image processing. The key point is constructing a loss function that includes a term for measuring two information field differences and a term for a warping field constraint. By minimizing the loss function, the purpose of position error correction of quantitative precipitation estimation from FY-4A and Integrated Multisatellite Retrievals of GPM are achieved, respectively, using observations from surface rain gauge stations. The errors of different satellite precipitation products relative to ground stations are compared and analyzed before and after position correction, using the ‘720’ extreme precipitation in Henan, China, as an example. The experimental results show that the final run has the best performance and FY-4A has the worse performance. After position corrections, the precipitation products of the three satellites are improved, among which FY-4A has the largest improvement, IMERG final run has the smallest improvement, and IMERG late run has the best performance and the smallest error. Their mean absolute errors are reduced by 23%, 14%, and 16%, respectively, and their correlation coefficients with rain gauge stations are improved by 63%, 9%, and 16%, respectively. The error decomposition model is used to examine the contributions of each error component to the total error. The results show that the new method improves the precipitation products of GPM primarily in terms of hit bias. However, it does not significantly reduce the hit bias of precipitation products of FY-4A while it reduces the total error by reducing the number of false alarms. Full article

(This article belongs to the Special Issue Remote Sensing for Hydrogeological/Hydrological Modelling and Applications)

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