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Remote Sensing
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Advanced Techniques for Water-Related Remote Sensing (Second Edition)
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Advanced Techniques for Water-Related Remote Sensing (Second Edition)

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

Deadline for manuscript submissions: 15 September 2025 | Viewed by 2005

Special Issue Editors

Dr. Xiaobo Li

E-Mail Website
Guest Editor
School of Marine Science and Technology, Tianjin University, Tianjin 300054, China
Interests: polarization optics (polarimetry and polarimetric imaging); oceanic optics; deep learning and signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haofeng Hu

E-Mail Website
Guest Editor
China School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin, China
Interests: polarimetric imaging; polarimetry; deep learning; ocean optics
Special Issues, Collections and Topics in MDPI journals
Dr. Jianhua Guo

E-Mail Website
Guest Editor
Department of Aerospace and Geodesy, Data Science in Earth Observation, Technical University of Munich, 80333 Munich, Germany
Interests: remote sensing; computer vision; deep learning; urban ecosystem services
Special Issues, Collections and Topics in MDPI journals
Dr. Zhitong Xiong

E-Mail Website
Guest Editor
Data Science in Earth Observation, Technical University of Munich (TUM), 80333 Munich, Germany
Interests: deep learning; remote sensing; multimodal foundation models
Special Issues, Collections and Topics in MDPI journals
Dr. Igor Ogashawara

E-Mail Website
Guest Editor
Department of Experimental Limnology, Leibniz-Institute for Freshwater Ecology and Inland Fisheries, D-16775 Stechlin, Germany
Interests: bio-optical modeling; water quality; optical remote sensing; water quality sensors; fluorescence; climatology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

“Water-related” refers to anything related to water, such as oceans, rivers, lakes, floods, clouds, rain, mist, snow, and ice. The research objects of water-related remote sensing include all water bodies that serve as either local or overall light, microwave, and acoustic wave transmission paths. By studying their characteristics in liquid, gas, and solid states, in addition to the propagation mechanism of light/microwave/acoustic waves in water and across media, various problems related to intelligent data acquisition, information transmission, and intelligent signal processing in water-related fields can be addressed. The theories, sensors/platforms, interpretation methods, and advanced processing techniques applied to water-related light/microwave/acoustic wave remote sensing are continually evolving. Therefore, the introduction of novel techniques and the exploration of related applications are necessary in order to address existing challenges and expand the potential of remote sensing.

This Special Issue aims to provide a platform for researchers to share and discuss important discoveries, theoretical and experimental advances, technical breakthroughs, methodological innovations, application developments, viewpoints, and perspectives with the water-related remote sensing community. All theoretical, numerical, and experimental results are welcome. The scope of this Special Issue includes, but is not limited to, the following topics: 

  • Ocean observation;
  • Water/flood detection/monitoring/mapping;
  • Underwater imaging/optical sensing;
  • Underwater acoustics and Sonar;
  • Sea ice/polar glacier detection/monitoring/mapping;
  • Cloud detection/removal;
  • Fog/haze removal;
  • Water-related signal processing;
  • Underwater in situ observation;
  • Soil water monitoring.

Dr. Xiaobo Li
Prof. Dr. Haofeng Hu
Dr. Jianhua Guo
Dr. Zhitong Xiong
Dr. Igor Ogashawara
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
  • water-related optics
  • sensing/imaging techniques
  • sensors/platforms
  • deep learning
  • signal processing

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Related Special Issue

Published Papers (2 papers)

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Research

20 pages, 27964 KiB  
Article
Delving into Underwater Image Utility: Benchmark Dataset and Prediction Model
by Jiapeng Liu, Yi Liu and Qiuping Jiang
Remote Sens. 2025, 17(11), 1906; https://doi.org/10.3390/rs17111906 - 30 May 2025
Viewed by 160
Abstract
High-quality underwater images are essential for both human visual perception and machine analysis in marine vision applications. Although significant progress has been achieved in Underwater Image Quality Assessment (UIQA), almost all existing UIQA methods focus on the visual perception-oriented image quality issue and [...] Read more.
High-quality underwater images are essential for both human visual perception and machine analysis in marine vision applications. Although significant progress has been achieved in Underwater Image Quality Assessment (UIQA), almost all existing UIQA methods focus on the visual perception-oriented image quality issue and cannot be used to gauge the utility of underwater images for the use in machine vision applications. To address this issue, in this work, we focus on the problem of automatic underwater image utility assessment (UIUA). On the one hand, we first construct a large-scale Object Detection-oriented Underwater Image Utility Assessment (OD-UIUA) dataset, which includes 1200 raw underwater images, corresponding to 12,000 enhanced results by 10 representative underwater image enhancement (UIE) algorithms and 13,200 underwater image utility scores (UIUSs) for all raw and enhanced underwater images in the dataset. On the other hand, based on this newly constructed OD-UIUA dataset, we train a deep UIUA network (DeepUIUA) that can automatically and accurately predict UIUS. To the best of our knowledge, this is the first benchmark dataset for UIUA and also the first model focusing on the specific UIUA problem. We comprehensively compare the performance of our proposed DeepUIUA model with that of 14 state-of-the-art no-reference image quality assessment (NR-IQA) methods by using the OD-UIUA dataset as the benchmark. Extensive experiments showcase that our proposed DeepUIUA model has superior performance compared with the existing NR-IQA methods in assessing UIUS. The OD-UIUA dataset and the source code of our DeepUIUA model will be released. Full article
(This article belongs to the Special Issue Advanced Techniques for Water-Related Remote Sensing (Second Edition))
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37 pages, 10558 KiB  
Article
Climate Impact on Evapotranspiration in the Yellow River Basin: Interpretable Forecasting with Advanced Time Series Models and Explainable AI
by Sheheryar Khan, Huiliang Wang, Umer Nauman, Rabia Dars, Muhammad Waseem Boota and Zening Wu
Remote Sens. 2025, 17(1), 115; https://doi.org/10.3390/rs17010115 - 1 Jan 2025
Cited by 1 | Viewed by 1206
Abstract
Evapotranspiration (ET) plays a crucial role in the hydrological cycle, significantly impacting agricultural productivity and water resource management, particularly in water-scarce areas. This study explores the effects of key climate variables temperature, precipitation, solar radiation, wind speed, and humidity on ET from 2000 [...] Read more.
Evapotranspiration (ET) plays a crucial role in the hydrological cycle, significantly impacting agricultural productivity and water resource management, particularly in water-scarce areas. This study explores the effects of key climate variables temperature, precipitation, solar radiation, wind speed, and humidity on ET from 2000 to 2020, with forecasts extended to 2030. Advanced data preprocessing techniques, including Yeo-Johnson and Box-Cox transformations, Savitzky–Golay smoothing, and outlier elimination, were applied to improve data quality. Datasets from MODIS, TRMM, GLDAS, and ERA5 were utilized to enhance model accuracy. The predictive performance of various time series forecasting models, including Prophet, SARIMA, STL + ARIMA, TBATS, ARIMAX, and ETS, was systematically evaluated. This study also introduces novel algorithms for Explainable AI (XAI) and SHAP (SHapley Additive exPlanations), enhancing the interpretability of model predictions and improving understanding of how climate variables affect ET. This comprehensive methodology not only accurately forecasts ET but also offers a transparent approach to understanding climatic effects on ET. The results indicate that Prophet and ETS models demonstrate superior prediction accuracy compared to other models. The ETS model achieved the lowest Mean Absolute Error (MAE) values of 0.60 for precipitation, 0.51 for wind speed, and 0.48 for solar radiation. Prophet excelled with the lowest Root Mean Squared Error (RMSE) values of 0.62 for solar radiation, 0.67 for wind speed, and 0.74 for precipitation. SHAP analysis indicates that temperature has the strongest impact on ET predictions, with SHAP values ranging from −1.5 to 1.0, followed by wind speed (−0.75 to 0.75) and solar radiation (−0.5 to 0.5). Full article
(This article belongs to the Special Issue Advanced Techniques for Water-Related Remote Sensing (Second Edition))
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