Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Special Issue "Earth Observation in Support of Sustainable Water Resources Management"

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: 31 March 2024 | Viewed by 3815

Share This Special Issue

Special Issue Editors

Prof. Dr. Ernesto López-Baeza

E-Mail Website
Guest Editor

1. Environmental Remote Sensing Group, Earth Physics & Thermodynamics Department, Faculty of Physics, University of Valencia, Valencia, Spain
2. Albavalor S.L.U., University of Valencia Science Park, Valencia, Spain
Interests: remote sensing; soil moisture; earth observation; validation; vegetation biophysical parameters; water resources management and sustainability
Special Issues, Collections and Topics in MDPI journals

Dr. Ana Perez Hoyos

E-Mail Website
Guest Editor

Albavalor S.L.U., University of Valencia Science Park, Valencia, Spain
Interests: earth observation and geo-information for policy support and international cooperation support (SDGs); food security; satellite image analysis; agricultural applications; groundwater and land cover mapping; vegetation parameters; validation
Special Issues, Collections and Topics in MDPI journals

Dr. Rafael Catany

E-Mail Website
Guest Editor

Albavalor S.L.U., Carrer del Catedràtic Agustín Escardino Benlloch,9, 46980 Paterna, Valencia, Spain
Interests: water cycle; water quality; ocean health; climate change; essential climate variables; ocean and coastal management

Prof. Dr. Angel Udías Moinelo

E-Mail Website
Guest Editor

European Commission, Directorate D – Sustainable Resources – Joint Research Center, via E. Fermi 2749, 21027 Ispra, VA, Italy
Interests: water resources management; environmental impact assessment; decision support systems; multi-criteria analyses; heuristics; machine learning; participatory processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable Development Goal #6 (SDG#6) refers to ensuring access to water and sanitation for all, focusing on the sustainable management of water resources, wastewater and ecosystems, and acknowledging the importance of an enabling environment. In this Special Issue, authors are kindly requested to submit manuscripts on the advanced operational tools and services based on Earth observation data and products to manage water resources in a sustainable way. Earth observation techniques can support and assist in the systematic monitoring and review of progress towards SDG#6 goals and targets by assessing the overall indicator set. Furthermore, remote sensing techniques support and facilitate sustainable integrated water resources management at all levels, which is vital for long-term social, economic and environmental well-being—the three pillars of the 2030 Agenda—and helps to balance competing water demands from across society and the economy.

Specific topics include, but are not limited to:

Applications of Earth observation for water quality, water-use efficiency, water stress and water-related ecosystems, main and significant SDG#6 global indicators;
Assessment of Earth observation in water-related risks such as floods and related water management problems, water scarcity, droughts, desertification, heat waves and forest fires, marine risks, coastal erosion and landslides;
Climate change and water adaptation issues;
Novel and innovative methodologies for detecting water bodies using multi-spectral, hyperspectral, thermal, and microwave sensors;
Remote sensing data assimilation within hydrological models;
Best practices of rational and sustainable water management—case studies;
Monitoring water quality (rivers, lakes, etc.) using remote sensing techniques;
Applications of remote sensing data for a range of hydrological studies at multiple spatiotemporal scales;
Accuracy evaluation and uncertainty analysis of remote sensing data;
Remote sensing data to assess the performance of best management practices in hydrology;
Irrigation information retrievals from remote sensing;
RS-based crop evapotranspiration modeling and optimization of agricultural water demand management.

Review papers are also welcomed.

Prof. Dr. Ernesto Lopez-Baeza
Dr. Ana Perez Hoyos
Dr. Rafael Catany
Prof. Dr. Angel Udías Moinelo
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

active and passive remote sensing
coastal erosion
desertification
droughts
forest fires
heat waves
integrated water resources management
machine and deep learning
multisensory analysis
water
water adaptation to climate change
water management
water quality modelling
water–energy nexus
water footprint
water stress
water-use efficiency

Published Papers (3 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

16 pages, 24096 KiB

Open AccessArticle

Remote Sensing-Based Classification of Winter Irrigation Fields Using the Random Forest Algorithm and GF-1 Data: A Case Study of Jinzhong Basin, North China

and

Remote Sens. 2023, 15(18), 4599; https://doi.org/10.3390/rs15184599 - 19 Sep 2023

Viewed by 477

Abstract

Irrigation is one of the key agricultural management practices of crop cultivation in the world. Irrigation practice is traceable on satellite images. Most irrigated area mapping methods were developed based on time series of NDVI or backscatter coefficient within the growing season. However, it has been found that winter irrigation out of growing season is also dominating in north China. This kind of irrigation aims to increase the soil moisture for coping with spring drought and reduce the wind erosion in spring. This study developed a remote sensing-based classification approach to identify irrigated fields out of growing season with Radom Forest algorithm. Four spectral bands and all Normalized Difference Vegetation Index (NDVI) like indices computed from any two of these four bands for each of the seven scenes of GF-1 satellite data were used as the input features in the building of separated RF models and in applying the built models for the classification. The results showed that the mean of the highest out-of-bag accuracies for seven RF models was 94.9% and the mean of the averaged out-of-bag accuracies in the plateau for seven RF models was 94.1%; the overall accuracy for all seven classified outputs was in the range of 86.8–92.5%, Kappa in the range of 84.0–91.0% and F1-Score in the range of 82.1–90.1%. These results showed that the classification was neither overperformed nor underperformed as the accuracies of all classified images were lower than the model ones. This study also found that irrigation started to be applied as early as in November and irrigated fields were increased and suspended in December and January due to freezing conditions. The newly irrigated fields were found again in March and April when the temperature rose above zero degrees. The area of irrigated fields in the study area were increasing over time with sizes of 98.6, 166.9, 208.0, 292.8, 538.0, 623.1, 653.8 km² from December to April, accounting for 6.1%, 10.4%, 12.9%, 18.2%, 33.4%, 38.7%, and 40.6% of the total irrigatable land in the study area, respectively. The results showed that the method developed in this study performed well. This study found on the satellite images that 40.6% of irrigatable fields were already irrigated before the sowing season and the irrigation authorities were supposed to improve their water supply capacity in the whole year with this information. This study may complement the traditional consideration of retrieving irrigation maps only in growing season with remote sensing images for a large area. Full article

(This article belongs to the Special Issue Earth Observation in Support of Sustainable Water Resources Management)

► Show Figures

Graphical abstract

20 pages, 10895 KiB

Open AccessArticle

In-Situ GNSS-R and Radiometer Fusion Soil Moisture Retrieval Model Based on LSTM

and

Remote Sens. 2023, 15(10), 2693; https://doi.org/10.3390/rs15102693 - 22 May 2023

Viewed by 929

Abstract

Global navigation satellite system reflectometry (GNSS-R) is a remote sensing technology of soil moisture measurement using signals of opportunity from GNSS, which has the advantages of low cost, all-weather detection, and multi-platform application. An in situ GNSS-R and radiometer fusion soil moisture retrieval model based on LSTM (long–short term memory) is proposed to improve accuracy and robustness as to the impacts of vegetation cover and soil surface roughness. The Oceanpal GNSS-R data obtained from the experimental campaign at the Valencia Anchor Station are used as the main input data, and the TB (brightness temperature) and TR (soil roughness and vegetation integrated attenuation coefficient) outputs of the ELBARA-II radiometer are used as auxiliary input data, while field measurements with a Delta-T ML2x ThetaProbe soil moisture sensor were used for reference and validation. The results show that the LSTM model can be used to retrieve soil moisture, and that it performs better in the data fusion scenario with GNSS-R and radiometer. The STD of the multi-satellite fusion model is 0.013. Among the single-satellite models, PRN13, 20, and 32 gave the best retrieval results with STD = 0.011, 0.012, and 0.007, respectively. Full article

(This article belongs to the Special Issue Earth Observation in Support of Sustainable Water Resources Management)

► Show Figures

Graphical abstract

24 pages, 13116 KiB

Open AccessArticle

Uncertainties in Prediction of Streamflows Using SWAT Model—Role of Remote Sensing and Precipitation Sources

and

Remote Sens. 2022, 14(21), 5385; https://doi.org/10.3390/rs14215385 - 27 Oct 2022

Cited by 4 | Viewed by 1600

Abstract

Watershed modelling is crucial for understanding fluctuations in water balance and ensuring sustainable water management. The models’ strength and predictive ability are heavily reliant on inputs such as topography, land use, and climate. This study mainly focuses on quantifying the uncertainty associated with the input sources of the Digital Elevation Model (DEM), Land Use Land Cover (LULC), and precipitation using the Soil and Water Assessment Tool (SWAT) model. Basin-level modelling is being carried out to analyze the impact of source uncertainty in the prediction of streamflow. The sources for DEM used are National Elevation Dataset (NED)-United States Geological Survey (USGS), Shuttle Radar Topographic Mission (SRTM), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), whereas for LULC the sources were the National Land Cover Database (NLCD), Continuous Change Detection Classification (CCDC), and GAP/LANDFIRE National Terrestrial Ecosystems dataset. Observed monitoring stations (Gage), Climate Forecast System Reanalysis (CFSR), and Tropical Rainfall Measuring Mission (TRMM) satellites are the respective precipitation sources. The Nash-Sutcliffe Efficiency (NSE), Coefficient of Determination (R²), Percent Bias (PBIAS), and the ratio of Root Mean Square Error to the standard deviation (RSR) are used to assess the model’s predictive performance. The results indicated that TRMM yielded better performance compared to the CFSR dataset. The USGS DEM performs best in all four case studies with the NLCD and CCDC LULC for all precipitation datasets except Gage. Furthermore, the results show that using a DEM with an appropriate combination can improve the model’s prediction ability by simulating streamflows with lower uncertainties. TheVIKOR MCDM method is used to rank model combinations. It is observed from MCDM analysis that USGS DEM combinations with NLCD/CCDC LULC attained top priority with all precipitation datasets. Furthermore, the rankings obtained from VIKOR MCDM are in accordance with the validation analysis using SWAT. Full article

(This article belongs to the Special Issue Earth Observation in Support of Sustainable Water Resources Management)

► Show Figures

Journal Menu

Journal Browser

Special Issue "Earth Observation in Support of Sustainable Water Resources Management"

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (3 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI