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Remote Sensing of Spatial-Temporal Variation in Surface Water
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Remote Sensing of Spatial-Temporal Variation in Surface Water

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: 25 July 2025 | Viewed by 459

Special Issue Editors

Dr. Zheng Lu

E-Mail Website
Guest Editor
1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2. School of Natural Resources, Beijing Normal University, Beijing 100875, China
Interests: hydrology; hydrogeology; remote sensing; soil moisture; machine learning; groundwater–surface water interactions
Dr. Xiang Li

E-Mail Website
Guest Editor
College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 830046, China
Interests: scale transformation; remote sensing product validation; uncertainty quantification; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yongkai An

E-Mail Website
Guest Editor
School of Water and Environment, Chang’an University, Xi’an 710054, China
Interests: hydrology; hydrogeology; numerical simulation; uncertainty analysis; machine learning; data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Surface water observation is fundamental in the study of hydrological and ecological processes. Understanding the spatial–temporal characteristics of surface water and its responses to climatic factors is critical for advancing knowledge of the water budget and the ecological environment. Satellite-based sensors have proven to be efficient tools for monitoring surface water at regional and global scales. Recent advancements in Earth Observation missions have significantly enhanced our ability to monitor various components of the terrestrial water cycle, thereby providing valuable insights into the vulnerability of water resources across different spatial and temporal scales. These developments have ushered in a new era for “sensing” surface water. However, critical knowledge gaps persist, particularly in quantifying the spatial–temporal variations in water cycle elements and their driving mechanisms, as well as in discerning the impacts of climate change and human activities on surface water dynamics.

This Special Issue aims to present reviews and recent advancements in the application of remote sensing for the characterization of spatial–temporal variations in surface water. We welcome the submission of articles that focus on new theories, methodologies, and the integration of remote sensing data with hydrological modeling to enhance the understanding of complex hydrological and socio-hydrological systems.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Algorithms for surface water mapping using diverse remote sensing datasets (e.g., microwave and optical) across various scales;
  • Methods to address cloud interference in surface water monitoring;
  • The integration of multisource remote sensing data for improved surface water monitoring or the characterization of surface water–groundwater interactions;
  • Assessing the impacts of climate change and human activities on surface water dynamics;
  • Characterizing uncertainties in surface water retrieval from remote sensing;
  • Precise estimations of water cycle elements (e.g., surface water bodies, precipitation, snow, overland flow, infiltration, transpiration, and evaporation);
  • The quantification of the water budget using innovative methods and technologies;
  • The application of surface water monitoring in rivers, lakes, reservoirs, canals, and wetlands.

Dr. Zheng Lu
Dr. Xiang Li
Dr. Yongkai An
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. Water 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 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

  • surface water
  • remote sensing
  • spatial-temporal dynamics
  • water cycle
  • data fusion
  • uncertainty

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Published Papers (2 papers)

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Research

28 pages, 8465 KiB  
Article
Analysis of Precipitation Variation Characteristics in Typical Chinese Regions Within the Indian Ocean and Pacific Monsoon Convergence Zone
by Junjie Wu, Liqun Zhong, Daichun Liu, Xuhua Tan, Hongzhen Pu, Bolin Chen, Chunyong Li and Hongbo Zhang
Water 2025, 17(12), 1812; https://doi.org/10.3390/w17121812 - 17 Jun 2025
Viewed by 59
Abstract
With climate warming, the global precipitation patterns have undergone significant changes, which will profoundly impact flood–drought disaster regimes and socioeconomic development in key regions of human activity worldwide. The convergence zone of the Indian Ocean monsoon and Pacific monsoon in China covers most [...] Read more.
With climate warming, the global precipitation patterns have undergone significant changes, which will profoundly impact flood–drought disaster regimes and socioeconomic development in key regions of human activity worldwide. The convergence zone of the Indian Ocean monsoon and Pacific monsoon in China covers most of the middle and lower reaches of the Yangtze River (MLRYR), which is located in the transitional area of the second and third steps of China’s terrain. Changes in precipitation patterns in this region will significantly impact flood and drought control in the MLRYR, as well as the socioeconomic development of the MLRYR Economic Belt. In this study, Huaihua area in China was selected as the study area to study the characteristics of regional precipitation change, and to analyze the evolution in the trends in annual precipitation, extreme precipitation events, and their spatiotemporal distribution, so as to provide a reference for the study of precipitation change patterns in the intersection zone. This study utilizes precipitation data from meteorological stations and the China Meteorological Forcing Dataset (CMFD) reanalysis data for the period 1979–2023 in Huaihua region. The spatiotemporal variation in precipitation in the study area was analyzed by using linear regression, the Mann–Kendall trend test, the moving average method, the Mann–Kendall–Sneyers test, wavelet analysis, and R/S analysis. The results demonstrate the following: (1) The annual precipitation in the study area is on the rise as a whole, the climate tendency rate is 9 mm/10 a, and the precipitation fluctuates greatly, showing an alternating change of “dry–wet–dry–wet”. (2) Wavelet analysis reveals that there are 28-year, 9-year, and 4-year main cycles in annual precipitation, and the precipitation patterns at different timescales are different. (3) The results of R/S analysis show that the future precipitation trend will continue to increase, with a strong long-term memory. (4) Extreme precipitation events generally show an upward trend, indicating that their intensity and frequency have increased. (5) Spatial distribution analysis shows that the precipitation in the study area is mainly concentrated in the northeast and south of Jingzhou and Tongdao, and the precipitation level in the west is lower. The comprehensive analysis shows that the annual precipitation in the study area is on the rise and has a certain periodic precipitation law. The spatial distribution is greatly affected by other factors and the distribution is uneven. Extreme precipitation events show an increasing trend, which may lead to increased flood risk in the region and downstream areas. In the future, it is necessary to strengthen countermeasures to reduce the impact of changes in precipitation patterns on local and downstream economic and social activities. Full article
(This article belongs to the Special Issue Remote Sensing of Spatial-Temporal Variation in Surface Water)
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28 pages, 4019 KiB  
Article
Study of the Applicability of CMADS Data Based on the BTOPMC Model in the South Yunnan Region—An Example from the Jinping River Basin
by Hongbo Zhang, Chunyong Li, Junjie Wu, Ban Yin, Hongbin Liu, Guliang Xie, Yanglin Xie and Ting Yang
Water 2025, 17(12), 1802; https://doi.org/10.3390/w17121802 - 16 Jun 2025
Viewed by 89
Abstract
Data-driven distributed hydrological models utilizing atmospheric assimilation are crucial for simulating hydrological processes, particularly in regions lacking historical observational data, and for managing and developing local water resources due to the impacts of climate change and human activities. The southern part of Yunnan [...] Read more.
Data-driven distributed hydrological models utilizing atmospheric assimilation are crucial for simulating hydrological processes, particularly in regions lacking historical observational data, and for managing and developing local water resources due to the impacts of climate change and human activities. The southern part of Yunnan is located at the southwestern border of China, and the small number of observation stations poses a major obstacle to local water-resource management and hydrological research. This paper carries out an evaluation of the accuracy of the China Atmospheric-Assimilation Dataset (CMADS) in southern Yunnan and uses CMADS data and measured data to drive the BTOPMC model to investigate hydrological processes in the Jinping River basin, a representative local sub-basin. The study shows that the probability density function statistic (SS) between CMADS data and the measured precipitation data is 0.941, and their probability density curves of precipitation are basically the same. The relative error of daily precipitation is −19%, with 90% of the daily precipitation error concentrated within ±10 mm/day, which increases as daily precipitation increases. This paper examines three precipitation scenarios to drive the hydrological model, resulting in Nash–Sutcliffe efficiency (NSE) coefficients of 66.8%, 81.0%, and 83.9% for calibration, and 54.5%, 70.2%, and 74.5% for validation. These results indicate that CMADS data possesses a certain degree of applicable accuracy in southern Yunnan. Furthermore, the CMADS-driven BTOPMC model is suitable for simulating hydrological processes and conducting water-resource research in the region. The integration of CMADS data with actual measurement data can enhance the accuracy of hydrological simulations. Overall, the CMADS data have good applicability in southern Yunnan, and the CMADS-driven BTOPMC model can be used for hydrological modeling studies and water-resource management applications in southern Yunnan. Full article
(This article belongs to the Special Issue Remote Sensing of Spatial-Temporal Variation in Surface Water)
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