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Remote Sensing
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Artificial Intelligence Applications in Remotely Sensed Hydrologic and Water Systems
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Artificial Intelligence Applications in Remotely Sensed Hydrologic and Water Systems

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

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 4716

Special Issue Editors

Dr. Prashanth Reddy Marpu

E-Mail Website
Guest Editor
Institute Center for Water and Environment (iWATER), Masdar Institute of Science and Technology, Abu Dhabi P.O. Box 54224, United Arab Emirates
Interests: image processing; environmental data analysis; hyperspectral imaging; object-based image analysis
Dr. Usman T. Khan

E-Mail Website
Guest Editor
Department of Civil Engineering, York University, Toronto, ON M3J, Canada
Interests: urban hydrology; flood risk assessment; hydroinformatics; data-driven modelling; uncertainty analysis; stormwater management
Special Issues, Collections and Topics in MDPI journals
Dr. Ju-Young Shin

E-Mail Website
Guest Editor
Applied Meteorology Research Division, National Institute of Meteorological Sciences, Seogwipo 63568, Korea
Interests: satellite imagery; rain radar; artificial intenlligenc; biometeorology; hydrometerology; argometerology
Dr. Mohammad H. Alobaidi

E-Mail Website
Guest Editor
Department of Civil Engineering and Applied Mechanics, Faculty of Engineering, McGill University, 817 Rue Sherbrooke Ouest, Montréal, QC H3A 2K6, Canada
Interests: deep learning; ensemble learning; tensor decomposition; optimization; system identification; remote sensing

Special Issue Information

Dear Colleagues, 

As natural and built systems are undergoing major challenges due to the impact of climate change and rapid urban development, new analytics and modeling solutions are required to study such spatiotemporal systems under dynamic conditions and modeling objectives. This also involves the utilization of diverse and large sources of information, amounting to massive databases or new sources of intelligence. Recent advances in artificial intelligence and computational resources facilitate innovative data-driven modeling approaches which can accommodate the changing nature of hydrological systems. The addition of remote sensing techniques enables the use of massive databases and real-time monitoring of hydrologic phenomena.

Remote Sensing is launching a special issue entitled “Artificial Intelligence Applications in Remotely Sensed Hydrologic and Water Systems.” This issue aims to promote state-of-the-art data-driven and machine learning techniques such as deep learning, ensemble learning, and reinforcement learning, using remote sensing in water research spanning hydro-climatology, hydroinformatics, and hydro-meteorology. Applications of interest include, but not limited to, hazard monitoring, forecasting of extreme events, pollution analysis, mapping of renewables, surface water systems, sociotechnical analysis, hydroinformatics, environment and sustainable agriculture applications. Research featuring advances in statistical modeling approaches is also invited. Consideration will be also given to interdisciplinary methodologies in uncertainty analysis, state-estimation, model interpretability, system identification and relational mapping of remotely sensed systems.

Dr. Prashanth Reddy Marpu
Dr. Usman T. Khan
Dr. Ju-Young Shin
Dr. Mohammad H. Alobaidi
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

  • machine learning
  • big data analytics
  • satellite
  • Radar
  • data assimilation
  • spatiotemporal modeling
  • hydrology
  • hydroinformatics

Published Papers (2 papers)

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18 pages, 2063 KiB  
Article
A Transformer-Based Framework for Parameter Learning of a Land Surface Hydrological Process Model
by Klin Li and Yutong Lu
Remote Sens. 2023, 15(14), 3536; https://doi.org/10.3390/rs15143536 - 13 Jul 2023
Cited by 1 | Viewed by 1084
Abstract
The effective representation of land surface hydrological models strongly relies on spatially varying parameters that require calibration. Well-calibrated physical models can effectively propagate observed information to unobserved variables, but traditional calibration methods often result in nonunique solutions. In this paper, we propose a [...] Read more.
The effective representation of land surface hydrological models strongly relies on spatially varying parameters that require calibration. Well-calibrated physical models can effectively propagate observed information to unobserved variables, but traditional calibration methods often result in nonunique solutions. In this paper, we propose a hydrological parameter calibration training framework consisting of a transformer-based parameter learning model (ParaFormer) and a surrogate model based on LSTM. On the one hand, ParaFormer utilizes self-attention mechanisms to learn a global mapping from observed data to the parameters to be calibrated, which captures spatial correlations. On the other hand, the surrogate model takes the calibrated parameters as inputs and simulates the observable variables, such as soil moisture, overcoming the challenges of directly combining complex hydrological models with a deep learning (DL) platform in a hybrid training scheme. Using the variable infiltration capacity model as the reference, we test the performance of ParaFormer on datasets of different resolutions. The results demonstrate that, in predicting soil moisture and transferring calibrated parameters in the task of evapotranspiration prediction, ParaFormer learns more effective and robust parameter mapping patterns compared to traditional and state-of-the-art DL-based parameter calibration methods. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Remotely Sensed Hydrologic and Water Systems)
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14 pages, 3412 KiB  
Technical Note
A High-Precision Water Body Extraction Method Based on Improved Lightweight U-Net
by Shihao An and Xiaoping Rui
Remote Sens. 2022, 14(17), 4127; https://doi.org/10.3390/rs14174127 - 23 Aug 2022
Cited by 14 | Viewed by 2766
Abstract
The rapid, accurate extraction of water body information is critical for water resource management and disaster assessment. Its data foundation was mostly provided by remote sensing images through deep learning methods. However, the methods still require the improvement of recognition accuracy and reduction [...] Read more.
The rapid, accurate extraction of water body information is critical for water resource management and disaster assessment. Its data foundation was mostly provided by remote sensing images through deep learning methods. However, the methods still require the improvement of recognition accuracy and reduction of model size. As a solution, this paper proposed a new high-precision convolutional neural network for water body extraction. This network’s structural design is based on the assumption that the extraction effect of a convolutional neural network is independent from its parameters number, thus the recognition effect could be effectively improved through reasonable adjustment of the network structure according to characteristics of water bodies on high-resolution remote sensing images. It brings two critical improvements. Firstly, the number of downsampling layers was reduced to adapt to the low resolution of remote sensing imagery. Secondly, the bottleneck structure has also been updated to fit the decoder–encoder framework. The improved bottleneck structures were nested to ensure the transmission of water characteristics information in the model. In comparison with the other five commonly used networks, the new network has achieved the best results (average overall accuracy: 98.31%, parameter benefit value: 0.2625), indicating the extremely high practical value of this approach. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Remotely Sensed Hydrologic and Water Systems)
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