Special Issue "Satellite Remote Sensing for Water Cycle Studies: Sciences and Societal Applications"

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

Deadline for manuscript submissions: 20 December 2021.

Special Issue Editors

Dr. Marouane Temimi
E-Mail Website
Guest Editor
Department of Civil, Environmental, and Ocean Engineering (CEOE), Stevens Institute of Technology, Hoboken, NJ 07030, USA
Interests: hydrology; remote sensing; land-atmosphere interactions; atmpsheric processes
Special Issues and Collections in MDPI journals
Dr. Xiwu Zhan
E-Mail Website
Guest Editor
NOAA-NESDIS Center for Satellite Applications and Research (STAR), NOAA Center for Weather and Climate Predictions (NCWCP), 5830 University Research Court College Park, MD 20740, USA
Interests: remote sensing; modeling; hydrology; meteorology
Special Issues and Collections in MDPI journals
Dr. Jun Wen
E-Mail
Guest Editor
College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
Interests: remote sensing; modeling; hydrology; meteorology
Dr. Huan Wu
E-Mail Website
Guest Editor
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Interests: remote sensing; modeling; hydrology; meteorology
Special Issues and Collections in MDPI journals
Dr. Rong Liu
E-Mail
Guest Editor
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: remote sensing; modeling; hydrology; meteorology

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the development and use of remote sensing observations from different platforms to enhance our understanding of the water cycle, and the use of remote sensing for the development of novel modelling and applications related to water sciences on the local, regional, and global scales. We welcome contributions that introduce new scientific achievements to these fields. In addition, this Special Issue covers new applications of remote sensing data from old or recent sensors to study different components of the water cycle. Since the launches of Landsat, TRMM, Terra ,and Aqua satellites, remote sensing observations of water cycle components, such as precipitation, snow and ice, soil moisture, evapotranspiration, and ground water, have significantly advanced. The advent of new sensors has strengthened the progress achieved with the previous sensors. Examples of the currently flying satellites include JAXA’s GCOM-W1; NASA’s GPM, SMAP, and GRACE-FO; NOAA’s JPSS and GOES-R series satellites; ESA’s SMOS and Sentinel satellites; EUMETSAT’s MetOps; JMA’s Himawari satellites; Korean COMS and GEO-KOMPSAT-2A; and CMA’s Fengyun satellites. Many national and international space agencies, academic, and industrial institutions and organizations have developed various satellite data products for their hydrological and meteorological applications. This Special Issue invites submissions addressing the development, validation, and applications of these data products in recent years. New developments on land surface process observation, data fusion, data assimilation, hydrological hazards monitoring, and climate and environmental changes on regional and global scales are especially encouraged.

Prof. Marouane Temimi
Dr. Xiwu Zhan
Dr. Jun Wen
Dr. Huan Wu
Dr. Rong Liu
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 papers will be 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 2400 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

  • Water cycle
  • Remote sensing
  • Land surface
  • Hydrology
  • Meteorology
  • Atmosphere
  • Precipitation
  • Soil moisture
  • Water storage

Published Papers (4 papers)

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Research

Article
A Comprehensive Evaluation of Near-Real-Time and Research Products of IMERG Precipitation over India for the Southwest Monsoon Period
Remote Sens. 2021, 13(18), 3676; https://doi.org/10.3390/rs13183676 - 15 Sep 2021
Viewed by 494
Abstract
Precipitation is one of the integral components of the global hydrological cycle. Accurate estimation of precipitation is vital for numerous applications ranging from hydrology to climatology. Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, the Integrated Multi-satellite Retrievals for GPM [...] Read more.
Precipitation is one of the integral components of the global hydrological cycle. Accurate estimation of precipitation is vital for numerous applications ranging from hydrology to climatology. Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, the Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product was released. The IMERG provides global precipitation estimates at finer spatiotemporal resolution (e.g., 0.1°/half-hourly) and has shown to be better than other contemporary multi-satellite precipitation products over most parts of the globe. In this study, near-real-time and research products of IMERG have been extensively evaluated against a daily rain-gauge-based precipitation dataset over India for the southwest monsoon period. In addition, the current version 6 of the IMERG research product or Final Run (IMERG-F V6) has been compared with its predecessor, version 5, and error characteristics of IMERG-F V6 for pre-GPM and GPM periods have been assessed. The spatial distributions of different error metrics over the country show that both near-real-time IMERG products (e.g., Early and Late Runs) have similar error characteristics in precipitation estimation. However, near-real-time products have larger errors than IMERG-F V6, as expected. Bias in all-India daily mean rainfall in the near-real-time IMERG products is about 3–4 times larger than research product. Both V5 and V6 IMERG-F estimates show similar error characteristics in daily precipitation estimation over the country. Similarly, both near-real-time and research products show similar characteristics in the detection of rainy days. However, IMERG-F V6 exhibits better performance in precipitation estimation and detection of rainy days during the GPM period (2014–2017) than the pre-GPM period (2010–2013). The contribution of different rainfall intensity intervals to total monsoon rainfall is captured well by the IMERG estimates. Furthermore, results reveal that IMERG estimates under-detect and overestimate light rainfall intensity of 2.5–7.5 mm day−1, which needs to be improved in the next release. The results of this study would be beneficial for end-users to integrate this multi-satellite product in any specific application. Full article
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Article
A Remote Sensing-Based Assessment of Water Resources in the Arabian Peninsula
Remote Sens. 2021, 13(2), 247; https://doi.org/10.3390/rs13020247 - 13 Jan 2021
Cited by 3 | Viewed by 1072
Abstract
A better understanding of the spatiotemporal distribution of water resources is crucial for the sustainable development of hyper-arid regions. Here, we focus on the Arabian Peninsula (AP) and use remotely sensed data to (i) analyze the local climatology of total water storage (TWS), [...] Read more.
A better understanding of the spatiotemporal distribution of water resources is crucial for the sustainable development of hyper-arid regions. Here, we focus on the Arabian Peninsula (AP) and use remotely sensed data to (i) analyze the local climatology of total water storage (TWS), precipitation, and soil moisture; (ii) characterize their temporal variability and spatial distribution; and (iii) infer recent trends and change points within their time series. Remote sensing data for TWS, precipitation, and soil moisture are obtained from the Gravity Recovery and Climate Experiment (GRACE), the Tropical Rainfall Measuring Mission (TRMM), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), respectively. The study relies on trend analysis, the modified Mann–Kendall test, and change point detection statistics. We first derive 10-year (2002–2011) seasonal averages from each of the datasets and intercompare their spatial organization. In the absence of large-scale in situ data, we then compare trends from GRACE TWS retrievals to in situ groundwater observations locally over the subdomain of the United Arab Emirates (UAE). TWS anomalies vary between −6.2 to 3.2 cm/month and −6.8 to −0.3 cm/month during the winter and summer periods, respectively. Trend analysis shows decreasing precipitation trends (−2.3 × 10−4 mm/day) spatially aligned with decreasing soil moisture trends (−1.5 × 10−4 g/cm3/month) over the southern part of the AP, whereas the highest decreasing TWS trends (−8.6 × 10−2 cm/month) are recorded over areas of excessive groundwater extraction in the northern AP. Interestingly, change point detection reveals increasing precipitation trends pre- and post-change point breaks over the entire AP region. Significant spatial dependencies are observed between TRMM and GRACE change points, particularly over Yemen during 2010, revealing the dominant impact of climatic changes on TWS depletion. Full article
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Article
Estimates of Daily Evapotranspiration in the Source Region of the Yellow River Combining Visible/Near-Infrared and Microwave Remote Sensing
Remote Sens. 2021, 13(1), 53; https://doi.org/10.3390/rs13010053 - 25 Dec 2020
Viewed by 657
Abstract
The spatial variation of surface net radiation, soil heat flux, sensible heat flux, and latent heat flux at different times of the day over the northern Tibetan Plateau were estimated using the Surface Energy Balance System algorithm, data from the FY-2G geostationary meteorological [...] Read more.
The spatial variation of surface net radiation, soil heat flux, sensible heat flux, and latent heat flux at different times of the day over the northern Tibetan Plateau were estimated using the Surface Energy Balance System algorithm, data from the FY-2G geostationary meteorological satellite, and microwave data from the FY-3C polar-orbiting meteorological satellite. In addition, the evaporative fraction was analyzed, and the total evapotranspiration (ET) was obtained by the effective evaporative fraction to avoid the error from accumulation. The hourly change of latent heat flux presented a sound unimodal diurnal variation. The results showed the regional ET ranged between 2.0 and 4.0 mm over the Source Region of the Yellow River. The conditional expectations of surface energy components during the experimental period of the study area were statistically analyzed, and the correspondence between different surface temperatures and the effective energy distribution was examined. The effective energy distribution of the surface changed significantly with the increase in temperature; in particular, when the surface temperature exceeded 290 K, the effective energy was mainly used for surface ET. The aim of this study was to avoid the use of surface meteorological observations that are not readily available over large areas, and the findings lay a foundation for the commercialization of land surface evapotranspiration. Full article
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Article
NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture
Remote Sens. 2020, 12(17), 2861; https://doi.org/10.3390/rs12172861 - 03 Sep 2020
Cited by 2 | Viewed by 1228
Abstract
Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric [...] Read more.
Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations. Full article
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