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Remote Sensing
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Earth Observation in Support of Sustainable Water Resources Management
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Earth Observation in Support of Sustainable Water Resources Management

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 9946

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

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

17 pages, 3641 KiB  
Article
Changes in Surface and Terrestrial Waters in the China–Pakistan Economic Corridor Due to Climate Change and Human Activities
by Jiayu Bao, Yanfeng Wu, Xiaoran Huang, Peng Qi, Ye Yuan, Tao Li, Tao Yu, Ting Wang, Pengfei Zhang, Vincent Nzabarinda, Sulei Naibi, Jingyu Jin, Gang Long and Shuya Yang
Remote Sens. 2024, 16(8), 1437; https://doi.org/10.3390/rs16081437 - 18 Apr 2024
Viewed by 1068
Abstract
The surface water area (SWA) and terrestrial water storage (TWS) are both essential metrics for assessing regional water resources. However, the combined effects of climate change and human activities on the dynamics of the SWA and TWS have not been extensively researched within [...] Read more.
The surface water area (SWA) and terrestrial water storage (TWS) are both essential metrics for assessing regional water resources. However, the combined effects of climate change and human activities on the dynamics of the SWA and TWS have not been extensively researched within the context of the CPEC. To fill this gap, we first analyzed the annual changes in the SWA and TWS in the China–Pakistan Economic Corridor (CPEC) region in recent decades using the methods of correlation analysis and Geodetector. Our findings indicate that Sindh exhibited the highest increase in the SWA at 8.68 ha/km2, whereas FATA showed the least increase at 0.2 ha/km2 from 2002 to 2018. Punjab exhibited a significant decrease in TWS, with a slope of −0.48 cm/year. Azad Kashmir followed with a decrease in TWS at a rate of −0.36 cm/year. Khyber Pakhtunkhwa and FATA exhibited an insignificant increase in TWS, with values of 0.02 cm/year and 0.11 cm/year, respectively. TWS was significantly positively correlated with the SWA in Balochistan and Khyber Pakhtunkhwa. However, other regions showed inconsistent changes; in particular, a decline was observed in Gilgit–Baltistan. The changes in TWS in Balochistan were primarily influenced by the SWA and climate change, while TWS changes in FATA were mainly affected by climate change. In addition, human activities had a primary impact on the TWS changes in Azad Kashmir, Punjab, and Sindh. The influencing factors of TWS changes in different regions of the CPEC mainly involved a dual-factor enhancement and the nonlinear weakening of single factors. These results highlight that under the effect of climate change and human activities, TWS may not increase as surface water area increases. This study contributes to a better understanding of water resource dynamics and can aid in the development of strategies for the efficient and sustainable use of water resources in the CPEC. Full article
(This article belongs to the Special Issue Earth Observation in Support of Sustainable Water Resources Management)
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24 pages, 9073 KiB  
Article
Toward a Redefinition of Agricultural Drought Periods—A Case Study in a Mediterranean Semi-Arid Region
by Kaoutar Oukaddour, Michel Le Page and Younes Fakir
Remote Sens. 2024, 16(1), 83; https://doi.org/10.3390/rs16010083 - 25 Dec 2023
Cited by 4 | Viewed by 1401
Abstract
Drought is a powerful natural hazard that has significant effects on ecosystems amid the constant threats posed by climate change. This study investigates agricultural drought in a semi-arid Mediterranean basin through the interconnections among four indices: precipitation (meteorological reanalysis), vegetation development, thermal stress, [...] Read more.
Drought is a powerful natural hazard that has significant effects on ecosystems amid the constant threats posed by climate change. This study investigates agricultural drought in a semi-arid Mediterranean basin through the interconnections among four indices: precipitation (meteorological reanalysis), vegetation development, thermal stress, and soil water deficit (remote sensing observations). While drought seems to be a clear concept with effective assessment tools (e.g., SPI and SPEI), the definition of drought periods is blurrier. This article examines the main drivers of agricultural drought, precipitation, soil moisture deficit, incipient vegetation development, and rising soil surface temperature. Their temporal connections in various agrosystems of the basin and the determination of drought periods by revisiting the run theory were investigated. The Pearson correlations at different spatial scales showed a medium to low level of agreement between the indices, which was explained by the geographical heterogeneity and the climatic variability between the agrosystems within the basin. It was also shown that the cascade of impacts expected from lower precipitations was revealed by the cross-correlation analysis. The connection between precipitation deficit and vegetation remains significant for at least one month for most pairs of indices, especially during drought events, suggesting that agricultural drought spells can be connected in time through the three or four selected indices. Short-, mid-, and long-term impacts of precipitation deficiencies on soil moisture, vegetation, and temperature were revealed. As expected, the more instantaneous variables of soil moisture and surface temperature showed no lag with precipitation. Vegetation anomalies at the monthly time step showed a two-month lag with a preceding effect of vegetation to precipitation. Finally, the determination of drought events and stages with varying thresholds on the run theory showed large variability in duration, magnitude, and intensity according to the choice of both normality and dryness thresholds. Full article
(This article belongs to the Special Issue Earth Observation in Support of Sustainable Water Resources Management)
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16 pages, 24096 KiB  
Article
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
by Qiaomei Su, Jin Lv, Jinlong Fan, Weili Zeng, Rong Pan, Yuejiao Liao, Ying Song, Chunliang Zhao, Zhihao Qin and Pierre Defourny
Remote Sens. 2023, 15(18), 4599; https://doi.org/10.3390/rs15184599 - 19 Sep 2023
Cited by 4 | Viewed by 1452
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, [...] Read more.
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)
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20 pages, 10895 KiB  
Article
In-Situ GNSS-R and Radiometer Fusion Soil Moisture Retrieval Model Based on LSTM
by Tianlong Zhang, Lei Yang, Hongtao Nan, Cong Yin, Bo Sun, Dongkai Yang, Xuebao Hong and Ernesto Lopez-Baeza
Remote Sens. 2023, 15(10), 2693; https://doi.org/10.3390/rs15102693 - 22 May 2023
Cited by 1 | Viewed by 1956
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 [...] Read more.
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)
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24 pages, 13116 KiB  
Article
Uncertainties in Prediction of Streamflows Using SWAT Model—Role of Remote Sensing and Precipitation Sources
by Jay Chordia, Urmila R. Panikkar, Roshan Srivastav and Riyaaz Uddien Shaik
Remote Sens. 2022, 14(21), 5385; https://doi.org/10.3390/rs14215385 - 27 Oct 2022
Cited by 8 | Viewed by 2663
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 [...] Read more.
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)
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