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Machine Learning and Automation in Remote Sensing Applied in Hydrological...
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Machine Learning and Automation in Remote Sensing Applied in Hydrological Processes

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

Deadline for manuscript submissions: 15 July 2025 | Viewed by 5188

Special Issue Editors

Dr. Andrei Enea

E-Mail Website
Guest Editor
Department of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Iași, 700505 Iași, Romania
Interests: automation; flood risk; earth science; GIS; land use; hydrology; UAV; drone; remote sensing; structure from motion
Special Issues, Collections and Topics in MDPI journals
Dr. Cristian Constantin Stoleriu

E-Mail Website
Guest Editor
Department of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Iași, 700505 Iași, Romania
Interests: biogeography; hydrology; GIS; remote sensing; geo-informatics; phytogeography; hydrological processes; environmental studies
Special Issues, Collections and Topics in MDPI journals
Dr. Marina Iosub

E-Mail Website
Guest Editor
Department of Geography, Faculty of Geography and Geology, Alexandru Ioan Cuza University of Iași, 700505 Iași, Romania
Interests: flash flood modeling; risk modeling; natural hazards; hydrological modeling and forecasting
Special Issues, Collections and Topics in MDPI journals
Dr. Fiachra O’Loughlin

E-Mail Website
Guest Editor
School of Civil Engineering, University College Dublin, Belfield, Dublin 4, Ireland
Interests: remote sensing; hydrology; flood modelling; hydrological extremes; hazards

Special Issue Information

Dear Colleagues,

As a result of the constantly growing computational power that researchers have access to, and the constant development of AI tools and techniques, there is an increasing demand to automate and develop machine learning tools to study geo-spatial phenomena in a better, faster manner. By including more data, parameters, and different types of remote sensing imagery, studies can be more representative and results can be more easily compared and brought into a “bigger picture” in order to more appropriately provide an assessment of the impact of different hydrological processes and phenomena, such as droughts, flash floods, water balance, soil moisture, evapotranspiration, infiltration, coastal erosion and other water-related topics.

Considering the fact that the changes in hydrological processes and phenomena are more frequent, and manifest significantly faster, in the context of climate change, automated tools and analyses can significantly aid in the rapid decision making prior to (or during) such cases, or even for disaster management and impact mitigation after extreme hydrological events.

The aim of this Special Issue is to provide state-of-the-art knowledge in the field of remote sensing for hydrological processes through the means of machine learning, neural networks, deep learning, artificial intelligence, automation techniques, etc., and promote new approaches and techniques in the field.

This Special Issue addresses (but is not limited to) the following topics:

  • Machine learning for hydrological processes;
  • Neural networks applied in water-related topics;
  • Methodological studies;
  • Remote sensing applied in hydrology;
  • Automation techniques;
  • Tools developed in GIS software (such as ArcGIS, QGIS, Snap, etc.);
  • Google Earth Engine;
  • Drought analyses;
  • Flood risk analyses;
  • Automated and semi-automated classifications;
  • Artificial intelligence in water studies;
  • Water budget analyses;
  • Deep learning applications in hydrology;
  • Morphometric studies;
  • WebGIS platforms for online automation, etc.

Dr. Andrei Enea
Dr. Cristian Constantin Stoleriu
Dr. Marina Iosub
Dr. Fiachra O’Loughlin
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

  • automation
  • artificial intelligence
  • flood risk automation
  • GIS tools
  • Google Earth Engine
  • hydrological processes
  • machine learning
  • neural networks
  • remote sensing automation

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Published Papers (3 papers)

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Research

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19 pages, 9426 KiB  
Article
Ensemble Streamflow Simulations in a Qinghai–Tibet Plateau Basin Using a Deep Learning Method with Remote Sensing Precipitation Data as Input
by Jinqiang Wang, Zhanjie Li, Ling Zhou, Chi Ma and Wenchao Sun
Remote Sens. 2025, 17(6), 967; https://doi.org/10.3390/rs17060967 - 9 Mar 2025
Viewed by 883
Abstract
Satellite and reanalysis-based precipitation products have played a crucial role in addressing the challenges associated with limited ground-based observational data. These products are widely utilized in hydrometeorological research, particularly in data-scarce regions like the Qinghai–Tibetan Plateau (QTP). This study proposed an ensemble streamflow [...] Read more.
Satellite and reanalysis-based precipitation products have played a crucial role in addressing the challenges associated with limited ground-based observational data. These products are widely utilized in hydrometeorological research, particularly in data-scarce regions like the Qinghai–Tibetan Plateau (QTP). This study proposed an ensemble streamflow simulation method using remote sensing precipitation data as input. By employing a 1D Convolutional Neural Networks (1D CNN), streamflow simulations from multiple models are integrated and a Shapley Additive exPlanations (SHAP) interpretability analysis was conducted to examine the contributions of individual models on ensemble streamflow simulation. The method is demonstrated using GPM IMERG (Global Precipitation Measurement Integrated Multi-satellite Retrievals) remote sensing precipitation data for streamflow estimation in the upstream region of the Ganzi gauging station in the Yalong River basin of QTP for the period from 2010 to 2019. Streamflow simulations were carried out using models with diverse structures, including the physically based BTOPMC (Block-wise use of TOPMODEL) and two machine learning models, i.e., Random Forest (RF) and Long Short-Term Memory Neural Networks (LSTM). Furthermore, ensemble simulations were compared: the Simple Average Method (SAM), Weighted Average Method (WAM), and the proposed 1D CNN method. The results revealed that, for the hydrological simulation of each individual models, the Kling–Gupta Efficiency (KGE) values during the validation period were 0.66 for BTOPMC, 0.71 for RF, and 0.74 for LSTM. Among the ensemble approaches, the validation period KGE values for SAM, WAM, and the 1D CNN-based nonlinear method were 0.74, 0.73, and 0.82, respectively, indicating that the nonlinear 1D CNN approach achieved the highest accuracy. The SHAP-based interpretability analysis further demonstrated that RF made the most significant contribution to the ensemble simulation, while LSTM contributed the least. These findings highlight that the proposed 1D CNN ensemble simulation framework has great potential to improve streamflow estimations using remote sensing precipitation data as input and may provide new insight into how deep learning methods advance the application of remote sensing in hydrological research. Full article
(This article belongs to the Special Issue Machine Learning and Automation in Remote Sensing Applied in Hydrological Processes)
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13 pages, 8391 KiB  
Communication
Leveraging Google Earth Engine and Machine Learning to Estimate Evapotranspiration in a Commercial Forest Plantation
by Shaeden Gokool, Richard Kunz, Alistair Clulow and Michele Toucher
Remote Sens. 2024, 16(15), 2726; https://doi.org/10.3390/rs16152726 - 25 Jul 2024
Cited by 2 | Viewed by 1691
Abstract
Estimation of actual evapotranspiration (ETa) based on reference evapotranspiration (ETo) and the crop coefficient (Kc) remains one of the most widely used ETa estimation approaches. However, its application in non-agricultural and natural environments has been limited, [...] Read more.
Estimation of actual evapotranspiration (ETa) based on reference evapotranspiration (ETo) and the crop coefficient (Kc) remains one of the most widely used ETa estimation approaches. However, its application in non-agricultural and natural environments has been limited, largely due to the lack of well-established Kc coefficients in these environments. Alternate Kc estimation approaches have thus been proposed in such instances, with techniques based on the use of leaf area index (LAI) estimates being quite popular. In this study, we utilised satellite-derived estimates of LAI acquired through the Google Earth Engine geospatial cloud computing platform and machine learning to quantify the water use of a commercial forest plantation situated within the eastern region of South Africa. Various machine learning-based models were trained and evaluated to predict Kc as a function of LAI, with the Kc estimates derived from the best-performing model then being used in conjunction with in situ measurements of ETo to estimate ETa. The ET estimates were then evaluated through comparisons against in situ measurements. An ensemble machine learning model showed the best performance, yielding RMSE and R2 values of 0.05 and 0.68, respectively, when compared against measured Kc. Comparisons between estimated and measured ETa yielded RMSE and R2 values of 0.51 mm d−1 and 0.90, respectively. These results were quite promising and further demonstrate the potential of geospatial cloud computing and machine learning-based approaches to provide a robust and efficient means of handling large volumes of data so that they can be optimally utilised to assist planning and management decisions. Full article
(This article belongs to the Special Issue Machine Learning and Automation in Remote Sensing Applied in Hydrological Processes)
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Other

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13 pages, 3455 KiB  
Technical Note
Global Semantic Classification of Fluvial Landscapes with Attention-Based Deep Learning
by Patrice E. Carbonneau
Remote Sens. 2024, 16(24), 4747; https://doi.org/10.3390/rs16244747 - 19 Dec 2024
Viewed by 836
Abstract
Rivers occupy less than 1% of the earth’s surface and yet they perform ecosystem service functions that are crucial to civilisation. Global monitoring of this asset is within reach thanks to the development of big data portals such as Google Earth Engine (GEE) [...] Read more.
Rivers occupy less than 1% of the earth’s surface and yet they perform ecosystem service functions that are crucial to civilisation. Global monitoring of this asset is within reach thanks to the development of big data portals such as Google Earth Engine (GEE) but several challenges relating to output quality and processing efficiency remain. In this technical note, we present a new deep learning pipeline that uses attention-based deep learning to perform state-of-the-art semantic classification of fluvial landscapes with Sentinel-2 imagery accessed via GEE. We train, validate and test the network on a multi-seasonal and multi-annual dataset drawn from a study site that covers 89% of the Earth’s surface. F1-scores for independent test data not used in model training reach 92% for rivers and 96% for lakes. This is achieved without post-processing and significantly reduced computation times, thus making automated global monitoring of rivers achievable. Full article
(This article belongs to the Special Issue Machine Learning and Automation in Remote Sensing Applied in Hydrological Processes)
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