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Remote Sensing for Hydrological Management

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1684

Special Issue Editors


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Guest Editor
College of Agriculture, Food and Natural Resources, Prairie View A&M University, Prairie View, TX 77446, USA
Interests: climate change; water

Special Issue Information

Dear Colleagues,

Recent advancements in remote sensing technologies, such as the European Space Agency's Sentinel series and NASA's Soil Moisture Active Passive (SMAP) mission, have significantly improved our capacity to monitor the Earth's water cycle from space. These innovations enable data collection across vast and often inaccessible areas, delivering high-resolution spatial and temporal information that is essential for effectively understanding and managing water resources. Thus, remote sensing can be used for hydrological management in the following ways: (1) the data can be used for monitoring and forecasting hydrological elements itself, and (2) the data can be used as input for hydrological models, as well as to calibrate and validate hydrological models in data-scarce regions. With this Special Issue, we aim to advance the field of hydrological management and underscore the importance of remote sensing as an indispensable tool for safeguarding water resources for future generations.

This Special Issue aims to leverage the potential of remote sensing technologies to study key hydrological parameters, including precipitation, soil moisture, evapotranspiration, snow cover, water quality, and water quantity. Traditional hydrological monitoring methods, such as ground-based measurements, often fall short in terms of spatial coverage and frequency, limiting their effectiveness in addressing the complex challenges of contemporary water management. This Special Issue is crucial for enhancing hydrological models, improving water availability forecasts, assessing droughts and floods, monitoring water quality, and ultimately developing robust and informed water resource management strategies.

This Special Issue invites manuscripts that explore the pivotal role of remote sensing in hydrological management. We seek contributions on a range of topics, including the role of remote sensing in enhancing hydrological monitoring and prediction, monitoring hydrological extremes, evaluating water quality, and managing hydrology and water resources. The Special Issue will also include studies on evaluating remote sensing products under hydrological models and improving hydrological model predictions by calibrating and validating the models using remote sensing products. Submissions on other relevant thematic areas that leverage remote sensing technologies for improved hydrological management are also welcome.

Dr. Ram Ray
Dr. Gebrekidan Tefera
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 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • hydrological monitoring
  • hydrological extremes
  • hydrological models
  • soil moisture
  • water quality
  • water management

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Published Papers (2 papers)

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Research

23 pages, 5672 KB  
Article
Validation of SMAP Surface Soil Moisture Using In Situ Measurements in Diverse Agroecosystems Across Texas, US
by Sanjita Gurau, Gebrekidan W. Tefera and Ram L. Ray
Remote Sens. 2026, 18(7), 994; https://doi.org/10.3390/rs18070994 - 25 Mar 2026
Viewed by 490
Abstract
Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from [...] Read more.
Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP’s performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R2), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R2 = 0.57, RMSE = 2.29%), followed by Austin (R2 = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R2 = 0.28, RMSE = 11.28%) and Kingsville (R2 = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions. Full article
(This article belongs to the Special Issue Remote Sensing for Hydrological Management)
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29 pages, 8973 KB  
Article
High-Resolution Daily Evapotranspiration Estimation in Arid Agricultural Regions Based on Remote Sensing via an Improved PT-JPL and CUWFM Fusion Framework
by Hongwei Liu, Xiaoqin Wang, Hongyu Zhang, Mengmeng Li and Qunyong Wu
Remote Sens. 2026, 18(2), 291; https://doi.org/10.3390/rs18020291 - 15 Jan 2026
Viewed by 417
Abstract
Evapotranspiration (ET) plays a crucial role in the terrestrial water cycle, especially in arid and semi-arid agricultural regions where precise water management is essential. However, the limited spatial resolution and temporal frequency of existing ET products hinder their application in fine-scale agricultural monitoring. [...] Read more.
Evapotranspiration (ET) plays a crucial role in the terrestrial water cycle, especially in arid and semi-arid agricultural regions where precise water management is essential. However, the limited spatial resolution and temporal frequency of existing ET products hinder their application in fine-scale agricultural monitoring. In this study, we first improved the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model by replacing the relative humidity-based soil moisture constraint with the land surface water index (LSWI), aiming to enhance model performance in water-limited environments. Second, we developed a Crop Unmixing and Weight Fusion Model for ET (CUWFM) to generate daily ET products at a 30 m spatial resolution by integrating high-resolution but infrequent PT-JPL-ET data with coarse-resolution but frequent PML-V2-ET data. The CUWFM employs a hybrid approach combining sub-pixel crop fraction decomposition with similarity-weighted regression, allowing for more accurate ET estimation over heterogeneous agricultural landscapes. The proposed methods were evaluated in the Changji region of Xinjiang, China, using field-measured ET data from two-flux-tower sites. The results show that the improved PT-JPL model increased ET estimation accuracy compared with the original version, with higher R2 and Nash–Sutcliffe efficiency (NSE), and lower root mean square error (RMSE). The CUWFM outperformed benchmark spatiotemporal fusion methods, including STARFM, ESTARFM, and Fit-FC, in both pixel- and field-scale assessments, achieving the highest overall performance scores based on the All-round Performance Assessment (APA) framework. This study demonstrates the potential of integrating vegetation indices and crop-specific spatial decomposition into ET modeling, providing a feasible pathway for producing high spatiotemporal resolution ET datasets to support precision agriculture in arid and semi-arid regions. Full article
(This article belongs to the Special Issue Remote Sensing for Hydrological Management)
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