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Multi-Source Remote Sensing Data in Hydrology and Water Management

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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: 15 July 2025 | Viewed by 10284

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Special Issue Editors

Dr. Mircea Alexe

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Guest Editor

Department of Physical and Technical Geography, Faculty of Geography, Babes-Bolyai University, 400006 Cluj-Napoca, Romania
Interests: remote sensing; limnology; GIS

Dr. Iulian-Horia Holobâcă

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Guest Editor

Department of Physical and Technical Geography, Faculty of Geography, Babes-Bolyai University, 400006 Cluj-Napoca, Romania
Interests: remote sensing; glaciology; nivology; GIS

Dr. Noel Aloysius

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Guest Editor

Department of Chemical and Biomedical Engineering, School of Natural Resources University of Missouri, Columbia, MO 65211, USA
Interests: terrestrial hydrology; remote sensing; GIS
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Special Issue Information

Dear Colleagues,

Multi-source and multi-temporal remote sensing data refer to the combination of data from different remote sensing platforms and sensors, such as satellites, aircrafts, and ground-based instruments. This integration of data from multiple sources can provide a more comprehensive and accurate view of the Earth's surface, and it is increasingly being used in hydrology and water management.

The use of multi-source remote sensing data in hydrology and water management offers several advantages, including improved accuracy, increased spatial and temporal resolution and coverage, improved data quality and accuracy, increased cost-effectiveness, and broader and cost-effective access to stakeholders.

The aim of this Special Issue is to advance the field of land surface hydrology and water resource management by providing valuable information on key water-related states and fluxes. The results provided by remote sensing applications inform decisions regarding water resource management, and enable the development of effective water management strategies.

Remote sensing applications are targeted in areas such as flood and drought monitoring, water quality monitoring, snow and ice cover monitoring, limnology, land use and land cover change, and have a significant impact on water resources. Remotely sensed data also help to test and validate hydrologic and biogeochemical cycling models, which can greatly aid decision-making in water management.

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

Integration of multi-source remote sensing data for improved hydrologic modeling and forecasting;
Using the potential of remote sensing for mapping and monitoring surface water bodies;
Using the potential of remote sensing for mapping and monitoring snow and ice;
Remote sensing for monitoring and assessing drought and water scarcity;
Using multi-source remote sensing data to improve flood risk assessment and management;
Application of remote sensing in support of water quality monitoring and management;
Application of remotely sensed data to test and validate hydrological and other biophysical models.

Dr. Mircea Alexe
Dr. Iulian-Horia Holobâcă
Dr. Noel Aloysius
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

multi-source and multi-temporal remote sensing data
terrestrial hydrology
water management
hydrologic modeling and forecasting
cryosphere
supporting decision-making in water management
flood
drought and water scarcity

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Published Papers (6 papers)

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Research

28 pages, 21544 KiB

Open AccessArticle

A Comparative Analysis of Different Algorithms for Estimating Evapotranspiration with Limited Observation Variables: A Case Study in Beijing, China

by Di Sun, Hang Zhang, Yanbing Qi, Yanmin Ren, Zhengxian Zhang, Xuemin Li, Yuping Lv and Minghan Cheng

Remote Sens. 2025, 17(4), 636; https://doi.org/10.3390/rs17040636 - 13 Feb 2025

Viewed by 584

Abstract

Evapotranspiration (ET) plays a crucial role in the surface water cycle and energy balance, and accurate ET estimation is essential for study in various domains, including agricultural irrigation, drought monitoring, and water resource management. Remote sensing (RS) technology presents an efficient approach for estimating ET at regional scales; however, existing RS retrieval algorithms for ET are intricate and necessitate a multitude of parameters. The land surface temperature–vegetation index (LST-VI) space method and statistical regression by machine learning (ML) offer the benefits of simplicity and straightforward implementation. This study endeavors to identify the optimal long-term sequence LST-VI space method and ML for ET estimation under conditions of limited observed variables, (LST, VI, and near-surface air temperature). A comparative analysis of their performance is undertaken using ground-based flux observations and MOD16 ET data. The findings can be summarized as follows: (1) Long-term remote sensing data can furnish a more comprehensive background field for the LST-VI space, achieving superior fitting accuracy for wet and dry edges, thereby enabling precise ET estimation with the following metrics: correlation coefficient (r) = 0.68, root mean square error (RMSE) = 0.76 mm/d, mean absolute error (MAE) = 0.49 mm/d, and mean bias error (MBE) = −0.14 mm. (2) ML generally produces more accurate ET estimates, with the Random Forest Regressor (RFR) demonstrating the highest accuracy: r = 0.79, RMSE = 0.61 mm/d, MAE = 0.42 mm/d, and MBE = −0.02 mm. (3) Both ET estimates derived from the LST-VI space and ML exhibit spatial distribution characteristics comparable to those of MOD16 ET data, further attesting to the efficacy of these two algorithms. Nevertheless, when compared to MOD16 data, both approaches exhibit varying degrees of underestimation. The results of this study can contribute to water resource management and offer a fresh perspective on remote sensing estimation methods for ET. Full article

(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Water Management)

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24 pages, 19422 KiB

Open AccessArticle

Enhancing Long-Term Flood Forecasting with SageFormer: A Cascaded Dimensionality Reduction Approach Based on Satellite-Derived Data

by Fatemeh Ghobadi, Amir Saman Tayerani Charmchi and Doosun Kang

Remote Sens. 2025, 17(3), 365; https://doi.org/10.3390/rs17030365 - 22 Jan 2025

Viewed by 685

Abstract

Floods, increasingly exacerbated by climate change, are among the most destructive natural disasters globally, necessitating advancements in long-term forecasting to improve risk management. Traditional models struggle with the complex dependencies of hydroclimatic variables and environmental conditions, thus limiting their reliability. This study introduces a novel framework for enhancing flood forecasting accuracy by integrating geo-spatiotemporal analyses, cascading dimensionality reduction, and SageFormer-based multi-step-ahead predictions. The framework efficiently processes satellite-derived data, addressing the curse of dimensionality and focusing on critical long-range spatiotemporal dependencies. SageFormer captures inter- and intra-dependencies within a compressed feature space, making it particularly effective for long-term forecasting. Performance evaluations against LSTM, Transformer, and Informer across three data fusion scenarios reveal substantial improvements in forecasting accuracy, especially in data-scarce basins. The integration of hydroclimate data with attention-based networks and dimensionality reduction demonstrates significant advancements over traditional approaches. The proposed framework combines cascading dimensionality reduction with advanced deep learning, enhancing both interpretability and precision in capturing complex dependencies. By offering a straightforward and reliable approach, this study advances remote sensing applications in hydrological modeling, providing a robust tool for mitigating the impacts of hydroclimatic extremes. Full article

(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Water Management)

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30 pages, 27337 KiB

Open AccessArticle

Nested Cross-Validation for HBV Conceptual Rainfall–Runoff Model Spatial Stability Analysis in a Semi-Arid Context

by Mohamed El Garnaoui, Abdelghani Boudhar, Karima Nifa, Yousra El Jabiri, Ismail Karaoui, Abdenbi El Aloui, Abdelbasset Midaoui, Morad Karroum, Hassan Mosaid and Abdelghani Chehbouni

Remote Sens. 2024, 16(20), 3756; https://doi.org/10.3390/rs16203756 - 10 Oct 2024

Cited by 1 | Viewed by 1948

Abstract

Accurate and efficient streamflow simulations are necessary for sustainable water management and conservation in arid and semi-arid contexts. Conceptual hydrological models often underperform in these catchments due to the high climatic variability and data scarcity, leading to unstable parameters and biased results. This study evaluates the stability of the HBV model across seven sub-catchments of the Oum Er Rabia river basin (OERB), focusing on the HBV model regionalization process and the effectiveness of Earth Observation data in enhancing predictive capability. Therefore, we developed a nested cross-validation framework for spatiotemporal stability assessment, using optimal parameters from a donor-single-site calibration (DSSC) to inform target-multi-site calibration (TMSC). The results show that the HBV model remains spatially transferable from one basin to another with moderate to high performances (KGE (0.1~0.9 NSE (0.5~0.8)). Furthermore, calibration using KGE improves model stability over NSE. Some parameter sets exhibit spatial instability, but inter-annual parameter behavior remains stable, indicating potential climate change impacts. Model performance declines over time (18–124%) with increasing dryness. As a conclusion, this study presents a framework for analyzing parameter stability in hydrological models and highlights the need for more research on spatial and temporal factors affecting hydrological response variability. Full article

(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Water Management)

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23 pages, 8867 KiB

Open AccessArticle

Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring

by Jose-David Hidalgo-Hidalgo, Antonio-Juan Collados-Lara, David Pulido-Velazquez, Steven R. Fassnacht and C. Husillos

Remote Sens. 2024, 16(19), 3705; https://doi.org/10.3390/rs16193705 - 5 Oct 2024

Cited by 1 | Viewed by 2061

Abstract

This research studies the characteristics of snow-covered area (SCA) from two vastly different sensors: optical (Moderate-Resolution Imaging Spectroradiometer, or MODIS, equipped on board the Terra satellite) and radar (Synthetic Aperture Radar (SAR) on-board Sentinel-1 satellites). The focus are the five mountain ranges of the Iberian Peninsula (Cantabrian System, Central System, Iberian Range, Pyrenees, and Sierra Nevada). The MODIS product was selected to identify SCA dynamics in these ranges using the Probability of Snow Cover Presence Index (PSCPI). In addition, we evaluate the potential advantage of the use of SAR remote sensing to complete optical SCA under cloudy conditions. For this purpose, we utilize the Copernicus High-Resolution Snow and Ice SAR Wet Snow (HRS&I SWS) product. The Pyrenees and the Sierra Nevada showed longer-lasting SCA duration and a higher PSCPI throughout the average year. Moreover, we demonstrate that the latitude gradient has a significant influence on the snowline elevation in the Iberian mountains (R² ≥ 0.84). In the Iberian mountains, a general negative SCA trend is observed due to the recent climate change impacts, with a particularly pronounced decline in the winter months (December and January). Finally, in the Pyrenees, we found that wet snow detection has high potential for the spatial gap-filling of MODIS SCA in spring, contributing above 27% to the total SCA. Notably, the additional SCA provided in winter is also significant. Based on the results obtained in the Pyrenees, we can conclude that implementing techniques that combine SAR and optical satellite sensors for SCA detection may provide valuable additional SCA data for the other Iberian mountains, in which the radar product is not available. Full article

(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Water Management)

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20 pages, 7650 KiB

Open AccessArticle

Evaluation and Correction of GFS Water Vapor Products over United States Using GPS Data

by Hai-Lei Liu, Xiao-Qing Zhou, Yu-Yang Zhu, Min-Zheng Duan, Bing Chen and Sheng-Lan Zhang

Remote Sens. 2024, 16(16), 3043; https://doi.org/10.3390/rs16163043 - 19 Aug 2024

Viewed by 1175

Abstract

Precipitable water vapor (PWV) is one of the most dynamic components of the atmosphere, playing a critical role in precipitation formation, the hydrological cycle, and climate change. This study used SuomiNet Global Positioning System (GPS) data from April 2021 to June 2023 in the United States to comprehensively evaluate 3 and 6 h Global Forecast System (GFS) PWV products (i.e., PWV_3h and PWV_6h). There was high consistency between the GFS PWV and GPS PWV data, with correlation coefficients (Rs) higher than 0.98 and a root mean square error (RMSE) of about 0.23 cm. The PWV_3h product performed slightly better than PWV_6h. PWV tended to be underestimated when PWV > 4 cm, and the degree of underestimation increased with increasing water vapor value. The RMSE showed obvious seasonal and diurnal variations, with the RMSE value in summer (i.e., 0.280 cm) considerably higher than in winter (i.e., 0.158 cm), and nighttime were RMSEs higher than daytime RMSEs. Clear-sky conditions showed smaller RMSEs, while cloudy-sky conditions exhibited a smaller range of monthly RMSEs and higher Rs. PWV demonstrated a clear spatial pattern, with both Rs and RMSEs decreasing with increasing elevation and latitude. Based on these temporal and spatial patterns, Back Propagation neural network and random forest (RF) models were employed, using PWV, Julian day, and geographic information (i.e., latitude, longitude, and elevation) as input data to correct the GFS PWV products. The results indicated that the RF model was more advantageous for water vapor correction, improving overall accuracy by 12.08%. In addition, the accuracy of GFS PWV forecasts during hurricane weather was also evaluated. In this extreme weather, the RMSE of the GFS PWV forecast increased comparably to normal weather, but it remained less than 0.4 cm in most cases. Full article

(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Water Management)

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16 pages, 3823 KiB

Open AccessEditor’s ChoiceArticle

Remote Sensing of Chlorophyll-a and Water Quality over Inland Lakes: How to Alleviate Geo-Location Error and Temporal Discrepancy in Model Training

by Jongmin Park, Sami Khanal, Kaiguang Zhao and Kyuhyun Byun

Remote Sens. 2024, 16(15), 2761; https://doi.org/10.3390/rs16152761 - 29 Jul 2024

Cited by 7 | Viewed by 2349

Abstract

Harmful algal blooms (HABs) threaten lake ecosystems and public health. Early HAB detection is possible by monitoring chlorophyll-a (Chl-a) concentration. Ground-based Chl-a data have limited spatial and temporal coverage but can be geo-registered with temporally coincident satellite imagery to calibrate a remote sensing-based predictive model for regional mapping over time. When matching ground and satellite data, positional and temporal discrepancies are unavoidable due particularly to dynamic lake surfaces, thereby biasing the model calibration. This limitation has long been recognized but so far has not been addressed explicitly. To mitigate such effects of data mismatching, we proposed an Akaike Information Criterion (AIC)-like weighted regression algorithm that relies on an error-based heuristic to automatically favor “good” data points and downplay “bad” points. We evaluated the algorithm for estimating Chl-a over inland lakes in Ohio using Harmonized Landsat Sentinel-2. The AIC-like weighted regression estimates showed superior performance with an R² of 0.91 and an error variance (

σ_{E}^{2}

) of 0.29 μg/L, outperforming linear regression (R² = 0.34,

σ_{E}^{2}

= 2.34 μg/L) and random forest (R² = 0.82,

σ_{E}^{2}

= 0.92 μg/L). We also noticed the poorest performance occurred in the spring due to low reflectance variation in clear water and low Chl-a concentration. Our weighted regression scheme is adaptive and generically applicable. Future studies may adopt our scheme to tackle other remote sensing estimation problems (e.g., terrestrial applications) for alleviating the adverse effects of geolocation errors and temporal discrepancies. Full article

(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Water Management)

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