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Recent Advances in Water Resources and Water Environmental Monitoring with Remote Sensing Techniques

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (15 March 2025) | Viewed by 8140

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Special Issue Editors

Prof. Dr. Xuchu Yu

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Guest Editor

College of Surveying and Geo-informatics, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Interests: Photogrammetry; remote sensing; pattern recognition

Dr. Bing Liu

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Guest Editor

Department of Big Data Analysis, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
Interests: hyperspectral remote sensing; change detection; water extraction from remote sensing images

Dr. Qingyu Li

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Guest Editor

Data Science in Earth Observation, Technische Universität München (TUM), 80333 Munich, Germany
Interests: remote sensing image understanding; remote sensing application; urban analysis; deep learning algorithms
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Special Issue Information

Dear Colleagues,

Human beings live by water, and civilization thrives on water. The monitoring of water resources and the water environment is of great significance regarding the scientific use of water resources. Remote sensing data can provide rich spectral and spatial information for ground objects, making it possible to quantitatively monitor surface water resources and the water environment on a large scale. In this context, the processing and analysis techniques employed to obtain massive remote sensing images are crucial for monitoring water resources and the water environment.

Water resource and water environment monitoring includes the monitoring of water quality, water quantity and hydrology. With the rapid development of remote sensing technology and artificial intelligence, many novel technologies and methods have emerged regarding the application of water quality, water quantity and hydrology monitoring. This Special Issue aims to present studies that address the various uses of remote sensing data and techniques in water quality, water quantity and hydrology monitoring.

Water resources and water environment monitoring is one of the typical application scenarios of remote sensing technology. The research in this direction will promote the development and application of remote sensing technology. Therefore, the subject is suitable for the scope of Remote Sensing.

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

Intelligent estimation method of water level and water volume.
Deep learning for hydrology
Data-driven hydrologic process learning.
Intelligent extraction of waters with remote sensing images.
Inversion models of water quality parameters.
Detection and analysis of water changes with remote sensing images.
Water pollution identification with remote sensing images.
Novel application of remote sensing techniques in water resources and water environment monitoring.
Deep learning and large model applied to water resources and water environment monitoring.
Novel application of geographic information systems in water resources and environmental monitoring.

Prof. Dr. Xuchu Yu
Dr. Bing Liu
Dr. Qingyu Li
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

remote sensing hydrological model
hydrologic process
water level monitoring
water volume monitoring
water body extraction
inversion models
change detection
deep learning
large model
water pollution identification
geographic information systems

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Published Papers (6 papers)

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Research

24 pages, 11288 KiB

Open AccessArticle

Satellite Data Revealed That the Expansion of China’s Lakes Is Accompanied by Rising Temperatures and Wider Temperature Differences

by Yibo Jiao, Zifan Lu and Mengmeng Wang

Remote Sens. 2025, 17(9), 1546; https://doi.org/10.3390/rs17091546 - 26 Apr 2025

Viewed by 174

Abstract

Lake surface water area (LSWA) and lake surface water temperature (LSWT) are critical indicators of climate change, responding rapidly to global warming. However, studies on the synergistic variations of LSWA and LSWT are scarce, and the coupling relationships among lakes with different environmental characteristics remain unclear. In this study, the relative growth rate of LSWA (RK_LSWA); the absolute growth rates of annual maximum, mean, and minimum LSWTs (i.e., K_{LSWT_max}, K_{LSWT_mean}, K_{LSWT_min}); and the absolute growth rates of the difference between maximum and minimum LSWT (LSWT_mmd) (K_{LSWT_mmd}) were investigated across more than 4000 lakes in China using long-term Landsat data, and their coupling relationships among different lake types (i.e., permafrost and non-permafrost recharge, endorheic or exorheic lakes, and natural and artificial lakes) were comprehensively analyzed. Results indicate significant differences in the trends of LSWA and LSWT, as well as their interrelationships across various regions and lake types. In the Qinghai–Tibet Plateau (QTP), 57.8% of lakes showed an increasing trend in LSWA, with 2.4% of the lakes showing moderate expansion (RK_LSWA values of 0.1–0.2), while over 27.5% of lakes in the South China (SC) region displayed shrinkage in LSWA (RK_LSWA values were between −0.1~0%/year). Regarding LSWTs, 49.8% of lakes in the QTP exhibited a K_{LSWT_max} greater than 0, and 47.9% of lakes showed a K_{LSWT_mean} greater than 0. In contrast, 48.1% of lakes in the Middle and Lower Yangtze River Plain (MLYP) had a K_{LSWT_max} less than 0, and 48.5% of lakes had a K_{LSWT_mean} less than 0. Additionally, lakes supplied by permanent permafrost demonstrated more significant growth in both LSWA and LSWT than those supplied by non-permanent permafrost. Further analysis revealed that approximately 20.2% of the lakes experienced a concurrent increase in both mean LSWT and LSWA, whereas around 18.9% of the lakes exhibited a simultaneous rise in both LSWT_mmd and LSWA. This suggests that the expansion of lakes in China is correlated with both rising temperatures and greater temperature differences. This study provides deeper insights into the response of Chinese lakes to climate change and offers important references for lake resource management and ecological conservation. Full article

(This article belongs to the Special Issue Recent Advances in Water Resources and Water Environmental Monitoring with Remote Sensing Techniques)

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63 pages, 46694 KiB

Open AccessArticle

Leveraging Ice, Cloud, and Land Elevation Satellite-2 Laser Altimetry and Surface Water Ocean Topography Radar Altimetry for Error Diagnosis in Hydraulic Models: A Case Study of the Chao Phraya River

by Theerapol Charoensuk, Jakob Luchner and Peter Bauer-Gottwein

Remote Sens. 2025, 17(4), 621; https://doi.org/10.3390/rs17040621 - 11 Feb 2025

Viewed by 1206

Abstract

Recent advancements in satellite Earth observation (EO) technology have significantly improved the accuracy and density of data available for monitoring rivers and streams, as well as for diagnosing errors in hydraulic models. Laser and radar altimetry missions, such as ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) and SWOT (Surface Water and Ocean Topography), offer high-resolution measurements of land and water surface elevation (WSE), covering entire river reaches and providing high-resolution WSE profiles along the river chainage, which can be directly compared to hydraulic model results. In this study, we implemented a workflow to assess the accuracy of simulated WSE and evaluate the performance of hydraulic models in the Chao Phraya (CPY) River, using WSE data from ICESat-2 and SWOT. The evaluation of ICESat-2, SWOT, and simulated WSE from the model, compared to in situ data, resulted in root mean square error (RMSE) values of 0.34 m, 0.35 m, and 0.37 m, respectively. Despite this, both ICESat-2 and SWOT data proved effective for error detection and performance evaluation along the CPY river in point, profile, and spatial map comparisons, with overall RMSE values of 0.36 m and 0.33 m, respectively, when compared with simulated WSE. This paper demonstrates that ICESat-2 and SWOT are valuable tools for diagnosing errors and improving hydraulic model performance, providing critical insights for river monitoring and model validation. Full article

(This article belongs to the Special Issue Recent Advances in Water Resources and Water Environmental Monitoring with Remote Sensing Techniques)

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23 pages, 8203 KiB

Open AccessArticle

Optimal Hyperspectral Characteristic Parameters Construction and Concentration Retrieval for Inland Water Chlorophyll-a Under Different Motion States

by Jie Yu, Zhonghan Zhang, Yi Lin, Yuguan Zhang, Qin Ye, Xuefei Zhou, Hongtao Wang, Mingzhi Qu and Wenwei Ren

Remote Sens. 2024, 16(22), 4323; https://doi.org/10.3390/rs16224323 - 20 Nov 2024

Viewed by 1159

Abstract

In recent decades, the rapid expansion of phytoplankton blooms caused by lake eutrophication has led to severe ecological destruction and impeded the sustainable economic development of local regions. Chlorophyll-a (Chl-a) is commonly used as a biological indicator to detect phytoplankton blooms due to its ease of detection. To improve the accuracy of Chl-a estimation in aquatic systems, an accurate understanding of its true spectral characteristics is imperative. In this study, a comprehensive and realistic experimental scheme was designed from the perspective of real algal strains and real water states. Both in situ and laboratory-based hyperspectral data were collected and analyzed. The results show that there are huge spectral differences not only between laboratory-cultured and real algae strains, but also between static and disturbed water surface conditions. A total of ten different categories of spectral characteristics were selected in both disturbed and static states. Then, six parameters with the best models to the Chl-a concentration were identified. Finally, two linear models of the Chl-a concentration at peaks of 810 nm and 700 nm were identified as the best estimation models for the static and disturbed states, respectively. The results provide a scientific reference for the large-scale retrieval of the Chl-a concentration using satellite remote sensing data. This advancement benefits inland water monitoring and management efforts. Full article

(This article belongs to the Special Issue Recent Advances in Water Resources and Water Environmental Monitoring with Remote Sensing Techniques)

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26 pages, 8224 KiB

Open AccessArticle

SPFDNet: Water Extraction Method Based on Spatial Partition and Feature Decoupling

by Xuejun Cheng, Kuikui Han, Jian Xu, Guozhong Li, Xiao Xiao, Wengang Zhao and Xianjun Gao

Remote Sens. 2024, 16(21), 3959; https://doi.org/10.3390/rs16213959 - 24 Oct 2024

Cited by 2 | Viewed by 1023

Abstract

Extracting water information from remote-sensing images is of great research significance for applications such as water resource protection and flood monitoring. Current water extraction methods aggregated richer multi-level features to enhance the output results. In fact, there is a difference in the requirements for the water body and the water boundary. Indiscriminate multi-feature fusion can lead to perturbation and competition of information between these two types of features during the optimization. Consequently, models cannot accurately locate the internal vacancies within the water body with the external boundary. Therefore, this paper proposes a water feature extraction network with spatial partitioning and feature decoupling. To ensure that the water features are extracted with deep semantic features and stable spatial information before decoupling, we first design a chunked multi-scale feature aggregation module (CMFAM) to construct a context path for obtaining deep semantic information. Then, an information interaction module (IIM) is designed to exchange information between two spatial paths with two fixed resolution intervals and the two paths through. During decoding, a feature decoupling module (FDM) is developed to utilize internal flow prediction to acquire the main body features, and erasing techniques are employed to obtain boundary features. Therefore, the deep features of the water body and the detailed boundary information are supplemented, strengthening the decoupled body and boundary features. Furthermore, the integrated expansion recoupling module (IERM) module is designed for the recoupling stage. The IERM expands the water body and boundary features using expansion and adaptively compensates the transition region between the water body and boundary through information guidance. Finally, multi-level constraints are combined to realize the supervision of the decoupled features. Thus, the water body and boundaries can be extracted more accurately. A comparative validation analysis is conducted on the public datasets, including the gaofen image dataset (GID) and the gaofen2020 challenge dataset (GF2020). By comparing with seven SOTAs, the results show that the proposed method achieves the best results, with IOUs of 91.22 and 78.93, especially in the localization of water bodies and boundaries. By applying the proposed method in different scenarios, the results show the stable capability of the proposed method for extracting water with various shapes and areas. Full article

(This article belongs to the Special Issue Recent Advances in Water Resources and Water Environmental Monitoring with Remote Sensing Techniques)

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18 pages, 5080 KiB

Open AccessArticle

SPT-UNet: A Superpixel-Level Feature Fusion Network for Water Extraction from SAR Imagery

by Teng Zhao, Xiaoping Du, Chen Xu, Hongdeng Jian, Zhipeng Pei, Junjie Zhu, Zhenzhen Yan and Xiangtao Fan

Remote Sens. 2024, 16(14), 2636; https://doi.org/10.3390/rs16142636 - 18 Jul 2024

Cited by 2 | Viewed by 1429

Abstract

Extracting water bodies from synthetic aperture radar (SAR) images plays a crucial role in the management of water resources, flood monitoring, and other applications. Recently, transformer-based models have been extensively utilized in the remote sensing domain. However, due to regular patch-partition and weak inductive bias, transformer-based models face challenges such as edge serration and high data dependency when used for water body extraction from SAR images. To address these challenges, we introduce a new model, the Superpixel-based Transformer (SPT), based on the adaptive characteristic of superpixels and knowledge constraints of the adjacency matrix. (1) To mitigate edge serration, the SPT replaces regular patch partition with superpixel segmentation to fully utilize the internal homogeneity of superpixels. (2) To reduce data dependency, the SPT incorporates a normalized adjacency matrix between superpixels into the Multi-Layer Perceptron (MLP) to impose knowledge constraints. (3) Additionally, to integrate superpixel-level learning from the SPT with pixel-level learning from the CNN, we combine these two deep networks to form SPT-UNet for water body extraction. The results show that our SPT-UNet is competitive compared with other state-of-the-art extraction models, both in terms of quantitative metrics and visual effects. Full article

(This article belongs to the Special Issue Recent Advances in Water Resources and Water Environmental Monitoring with Remote Sensing Techniques)

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25 pages, 19977 KiB

Open AccessArticle

Different Vegetation Covers Leading to the Uncertainty and Consistency of ET Estimation: A Case Study Assessment with Extended Triple Collocation

by Xiaoxiao Li, Huaiwei Sun, Yong Yang, Xunlai Sun, Ming Xiong, Shuo Ouyang, Haichen Li, Hui Qin and Wenxin Zhang

Remote Sens. 2024, 16(13), 2484; https://doi.org/10.3390/rs16132484 - 6 Jul 2024

Viewed by 1467

Abstract

Accurate and reliable estimation of actual evapotranspiration (AET) is essential for various hydrological studies, including drought prediction, water resource management, and the analysis of atmospheric–terrestrial carbon exchanges. Gridded AET products offer potential for application in ungauged areas, but their uncertainties may be significant, making it difficult to identify the best products for specific regions. While in situ data directly estimate gridded ET products, their applicability is limited in ungauged areas that require FLUXNET data. This paper employs an Extended Triple Collocation (ETC) method to estimate the uncertainty of Global Land Evaporation Amsterdam Model (GLEAM), Famine Early Warning Systems Network (FLDAS), and Maximum Entropy Production (MEP) AET product without requiring prior information. Subsequently, a merged ET product is generated by combining ET estimates from three original products. Furthermore, the study quantifies the uncertainty of each individual product across different vegetation covers and then compares three original products and the Merged ET with data from 645 in situ sites. The results indicate that GLEAM covers the largest area, accounting for 39.1% based on the correlation coefficient criterion and 39.9% based on the error variation criterion. Meanwhile, FLDAS and MEP exhibit similar performance characteristics. The merged ET derived from the ETC method demonstrates the ability to mitigate uncertainty in ET estimates in North American (NA) and European (EU) regions, as well as tundra, forest, grassland, and shrubland areas. This merged ET could be effectively utilized to reduce uncertainty in AET estimates from multiple products for ungauged areas. Full article

(This article belongs to the Special Issue Recent Advances in Water Resources and Water Environmental Monitoring with Remote Sensing Techniques)

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