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Hydrology
Special Issues
UAS and Satellite-Based Remote Sensing for Hydrological Observations and Applications
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UAS and Satellite-Based Remote Sensing for Hydrological Observations and Applications

A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Surface Waters and Groundwaters".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 5215

Special Issue Editors

Dr. Silvano Fortunato Dal Sasso

E-Mail Website
Guest Editor
Department of European and Mediterranean Cultures, Architecture, Environment, and Cultural Heritage (DICEM), University of Basilicata, 75100 Matera, Italy
Interests: hydrology; hydraulics; sediment transport; environmental monitoring; UASs
Dr. László Bertalan

E-Mail Website
Guest Editor
Department of Physical Geography and Geoinformatics, University of Debrecen, Debrecen, H-4032 Egyetem tér 1, Hungary
Interests: UAS; hydromorphology; bank erosion; hydrology; agricultural mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Monitoring water resources at high resolution and with large spatial coverage is essential in order to achieve sustainable development at a global scale. Many regions suffer a critical lack of hydrological observations or present discontinuous measurements from sparse in situ monitoring networks. In recent years, advances in Remote Sensing observations allowed us to improve our monitoring capabilities with regard to the hydrological variables of the water cycle, such as precipitation, evapotranspiration, soil moisture and river flow. Satellite missions have been developed to characterize such variables at regional to global scale (with coarse and intermediate resolution), and UAS data are used to bridge the scales from point-to-catchment scale observations (with high spatial and temporal resolution).

This Special Issue will promote advances in Satellite and UAS methodologies for monitoring hydrological variables, exploring uncertainty and sensitivity assessments. This is crucial in order to gain a deeper understanding and modeling of hydrological processes and to address various problems related to monitoring and managing droughts, floods and water availability for different uses.

We welcome contributions with strong relevance to the characterization of hydraulic and hydrological processes and the development of modeling approaches. Studies that propose technical solutions to combine large-scale observations and local observations, as well as modeling and big data analytics tools, are encouraged.

Dr. Silvano Fortunato Dal Sasso
Dr. László Bertalan
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. Hydrology is an international peer-reviewed open access monthly 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 1800 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

  • UAS/UAV
  • environmental monitoring
  • hydrology
  • rivers
  • soil moisture
  • vegetation

Published Papers (2 papers)

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Research

23 pages, 5268 KiB  
Article
Smart Data Blending Framework to Enhance Precipitation Estimation through Interconnected Atmospheric, Satellite, and Surface Variables
by Niloufar Beikahmadi, Antonio Francipane and Leonardo Valerio Noto
Hydrology 2023, 10(6), 128; https://doi.org/10.3390/hydrology10060128 - 05 Jun 2023
Cited by 3 | Viewed by 2354
Abstract
Accurate precipitation estimation remains a challenge, though it is fundamental for most hydrological analyses. In this regard, this study aims to achieve two objectives. Firstly, we evaluate the performance of two precipitation products from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG) [...] Read more.
Accurate precipitation estimation remains a challenge, though it is fundamental for most hydrological analyses. In this regard, this study aims to achieve two objectives. Firstly, we evaluate the performance of two precipitation products from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG) for Sicily, Italy, from 2016 to 2020 by a set of categorical indicators and statistical indices. Analyses indicate the favorable performance of daily estimates, while half-hourly estimates exhibited poorer performance, revealing larger discrepancies between satellite and ground-based measurements at sub-hourly timescales. Secondly, we propose four multi-source merged models within Artificial Neural Network (ANN) and Multivariant Linear Regression (MLR) blending frameworks to seek potential improvement by exploiting different combinations of Soil Moisture (SM) measurements from the Soil Moisture Active Passive (SMAP) mission and atmospheric factor of Precipitable Water Vapor (PWV) estimations, from the Advanced Microwave Scanning Radiometer-2 (AMSR2). Spatial distribution maps of some diagnostic indices used to quantitatively evaluate the quality of models reveal the best performance of ANNs over the entire domain. Assessing variable sensitivity reveals the importance of IMERG satellite precipitation and PWV in non-linear models such as ANNs, which outperform the MLR modeling framework and individual IMERG products. Full article
(This article belongs to the Special Issue UAS and Satellite-Based Remote Sensing for Hydrological Observations and Applications)
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17 pages, 9809 KiB  
Article
Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale
by Georgia Papacharalampous, Hristos Tyralis, Anastasios Doulamis and Nikolaos Doulamis
Hydrology 2023, 10(2), 50; https://doi.org/10.3390/hydrology10020050 - 12 Feb 2023
Cited by 4 | Viewed by 2248
Abstract
Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are more accurate than pure satellite precipitation products. Machine and statistical learning regression algorithms are regularly utilized in this endeavor. At [...] Read more.
Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are more accurate than pure satellite precipitation products. Machine and statistical learning regression algorithms are regularly utilized in this endeavor. At the same time, tree-based ensemble algorithms are adopted in various fields for solving regression problems with high accuracy and low computational costs. Still, information on which tree-based ensemble algorithm to select for correcting satellite precipitation products for the contiguous United States (US) at the daily time scale is missing from the literature. In this study, we worked towards filling this methodological gap by conducting an extensive comparison between three algorithms of the category of interest, specifically between random forests, gradient boosting machines (gbm) and extreme gradient boosting (XGBoost). We used daily data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and the IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets. We also used earth-observed precipitation data from the Global Historical Climatology Network daily (GHCNd) database. The experiments referred to the entire contiguous US and additionally included the application of the linear regression algorithm for benchmarking purposes. The results suggest that XGBoost is the best-performing tree-based ensemble algorithm among those compared. Indeed, the mean relative improvements that it provided with respect to linear regression (for the case that the latter algorithm was run with the same predictors as XGBoost) are equal to 52.66%, 56.26% and 64.55% (for three different predictor sets), while the respective values are 37.57%, 53.99% and 54.39% for random forests, and 34.72%, 47.99% and 62.61% for gbm. Lastly, the results suggest that IMERG is more useful than PERSIANN in the context investigated. Full article
(This article belongs to the Special Issue UAS and Satellite-Based Remote Sensing for Hydrological Observations and Applications)
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