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

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 28965

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Dr. Marouane Temimi

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Department of Civil, Environmental, and Ocean Engineering (CEOE), Stevens Institute of Technology, Hoboken, NJ 07030, USA
Interests: hydrometeorology; remote sensing; water resources
Special Issues, Collections and Topics in MDPI journals

Dr. Xiwu Zhan

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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
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Dr. Jun Wen

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College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
Interests: remote sensing; modeling; hydrology; meteorology
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Dr. Huan Wu

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School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
Interests: remote sensing; modeling; hydrology; meteorology
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Dr. Rong Liu

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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 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

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

Published Papers (9 papers)

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Research

20 pages, 4435 KiB

Open AccessArticle

Assessing the Spatiotemporal Variability of SMAP Soil Moisture Accuracy in a Deciduous Forest Region

by Mohamed Abdelkader, Marouane Temimi, Andreas Colliander, Michael H. Cosh, Vicky R. Kelly, Tarendra Lakhankar and Ali Fares

Remote Sens. 2022, 14(14), 3329; https://doi.org/10.3390/rs14143329 - 11 Jul 2022

Cited by 9 | Viewed by 1975

Abstract

The goal of this study is to assess the temporal variability of the performance of the Soil Moisture Active Passive, SMAP, soil moisture retrievals throughout the seasons as surface conditions change. In-situ soil moisture observations from a network deployed in Millbrook, New York, between 2019 and 2021 are used. The network comprises 25 stations distributed across a 33-km SMAP pixel with a predominantly forest land cover. The in-situ soil moisture observations were collected between 6 and 7 a.m., local time. This article covers the assessment of the temporal accuracy of SMAP soil moisture by incorporating various upscaling methods. Four upscaling methods are used in this study: arithmetic average, Voronoi diagram, topographic wetness index, and land cover weighted average. The agreement between SMAP soil moisture and the upscaled in-situ measurements was gauged using the root-mean-squared difference, the mean difference, and the unbiased root-mean-squared difference. The consistency of the temporal variability of SMAP soil moisture data resulting from the four upscaling methods was analyzed. The results revealed that SMAP retrievals (soil moisture data) are systematically higher than in situ observations during the different seasons. The results indicate that the highest performance of SMAP soil moisture retrievals is in September with an ubRMSD value of 0.03 m³.m⁻³ for the morning and evening overpasses, which can be attributed to a lower vegetation density during the seasonal transition. The agreement with in-situ observations degrades during March–April with ubRMSD values above 0.04 m³.m⁻³, reaching ~0.06 m³.m⁻³ in April, which can be attributed to the non-reliability of in-situ measurements due to freeze\thaw transition and the challenging determination of the soil effective temperature. The ubRMSD is also higher than 0.04 m³.m⁻³ in the months of May–June, which could be due to the introduced vegetation effect during the growth season. These findings are consistent across all the upscaling methods. The average ubRMSD over the study period is 0.055 m³.m⁻³, which falls short of meeting the mission’s performance target. This study proves the need to enhance SMAP retrieval over forest sites. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Water Cycle Studies: Sciences and Societal Applications)

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24 pages, 12389 KiB

Open AccessArticle

Validation of NASA SMAP Satellite Soil Moisture Products over the Desert of Kuwait

by Hala AlJassar, Marouane Temimi, Mohamed Abdelkader, Peter Petrov, Panagiotis Kokkalis, Hussain AlSarraf, Nair Roshni and Hamad Al Hendi

Remote Sens. 2022, 14(14), 3328; https://doi.org/10.3390/rs14143328 - 11 Jul 2022

Cited by 3 | Viewed by 2264

Abstract

The goal of this study is to validate and analyze NASA’s Soil Moisture Active Passive (SMAP) products over the desert of Kuwait. The study period was between April 2015 and April 2020. The study domain includes a mission candidate calibration/validation (Cal/Val) site that comprises six permanent soil moisture stations used to verify SMAP estimates. In addition, intensive field campaigns were conducted within and around the candidate Cal/Val site during the study period to collect additional thermogravimetric samples. The mean difference (MD), root mean squared difference (RMSD), unbiased root mean square difference (ubRMSD), and correlation coefficient (R) were computed to assess the agreement between SMAP SM products and in situ observations. The comparison of the six ground station sensors’ observations with the thermogravimetric samples led to an absolute mean bias (AMB) of 0.034 m³ m⁻³, which was then used to calibrate the sensors and bias-correct their measurements. The temporal consistency of the readings from the test site and calibrated sensors was assessed using the mean relative difference (MRD) and its standard deviation of relative difference (SDRD). Using a sampling density analysis, it was determined that a minimum of four ground stations would be required to validate the test site. Furthermore, the consistency between SMAP satellite soil moisture data and those derived from the Soil Moisture and Ocean Salinity (SMOS) satellite operated by the European Space Agency, and their agreement with in situ samples, was analyzed. The comparison of SMAP and SMOS soil moisture data with in situ observations showed that both satellites successfully captured the spatial and temporal distribution of soil moisture. For SMAP and SMOS, the lowest ubRMSE statistics were 0.043 m³ m⁻³ and 0.045 m³ m⁻³, respectively, which are slightly higher than the mission target of 0.04 m³ m⁻³. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Water Cycle Studies: Sciences and Societal Applications)

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24 pages, 17223 KiB

Open AccessArticle

Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales

by Ravidho Ramadhan, Helmi Yusnaini, Marzuki Marzuki, Robi Muharsyah, Wiwit Suryanto, Sholihun Sholihun, Mutya Vonnisa, Harmadi Harmadi, Ayu Putri Ningsih, Alessandro Battaglia, Hiroyuki Hashiguchi and Ali Tokay

Remote Sens. 2022, 14(5), 1172; https://doi.org/10.3390/rs14051172 - 27 Feb 2022

Cited by 33 | Viewed by 3461

Abstract

Accurate precipitation observations are crucial for water resources management and as inputs for a gamut of hydrometeorological applications. Precipitation data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) have recently been widely used to complement traditional rain gauge systems. However, the satellite precipitation data needs to be validated before being widely used in the applications and this is still missing over the Indonesian maritime continent (IMC). We conducted a validation of the IMERG product version 6 for this region. The evaluation was carried out using gauge data in the period from 2016 to 2020 for three types of IMERG: Early (E), Late (L), and Final (F) from annual, monthly, daily and hourly data. In general, the annual and monthly data from IMERG showed a good correlation with the rain gauge, with the mean correlation coefficient (CC) approximately 0.54–0.78 and 0.62–0.79, respectively. About 80% of stations in the IMC area showed a very good correlation between gauge data and IMERG-F estimates (CC = 0.7–0.9). For the daily assessment, the CC value was in the range of 0.39 to 0.44 and about 40% of stations had a correlation of 0.5–0.7. IMERG had a fairly good ability to detect daily rain in which the average probability of detection (POD) for all stations was above 0.8. However, the false alarm ratio (FAR) value is quite high (<0.5). For hourly data, IMERG’s performance was still poor with CC around 0.03–0.28. For all assessments, IMERG generally overestimated rainfall in comparison with rain gauge. The accuracy of the three types of IMERG in IMC was also influenced by season and topography. The highest and lowest CC values were observed for June–July–August and December–January–February, respectively. However, categorical statistics (POD, FAR and critical success index) did not show any clear seasonal variation. The CC value decreased with higher altitude, but with slight difference for each IMERG type. For all assessments conducted, IMERG-F generally showed the best rainfall observations in IMC, but with slightly difference from IMERG-E and IMERG-L. Thus, IMERG-E and IMERG-L data that had a faster latency than IMERG-F show potential to be used in rainfall observations in IMC. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Water Cycle Studies: Sciences and Societal Applications)

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20 pages, 6054 KiB

Open AccessArticle

The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model

by Azbina Rahman, Viviana Maggioni, Xinxuan Zhang, Paul Houser, Timothy Sauer and David M. Mocko

Remote Sens. 2022, 14(3), 437; https://doi.org/10.3390/rs14030437 - 18 Jan 2022

Cited by 14 | Viewed by 2421

Abstract

This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test this hypothesis across the Contiguous United States from April 2015 to December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). The results show that the assimilation of the leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multivariate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Water Cycle Studies: Sciences and Societal Applications)

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21 pages, 7261 KiB

Open AccessArticle

Water Budget Closure in the Upper Chao Phraya River Basin, Thailand Using Multisource Data

by Abhishek, Tsuyoshi Kinouchi, Ronnie Abolafia-Rosenzweig and Megumi Ito

Remote Sens. 2022, 14(1), 173; https://doi.org/10.3390/rs14010173 - 31 Dec 2021

Cited by 12 | Viewed by 3556

Abstract

Accurate quantification of the terrestrial water cycle relies on combinations of multisource datasets. This analysis uses data from remotely sensed, in-situ, and reanalysis records to quantify the terrestrial water budget/balance and component uncertainties in the upper Chao Phraya River Basin from May 2002 to April 2020. Three closure techniques are applied to merge independent records of water budget components, creating up to 72 probabilistic realizations of the monthly water budget for the upper Chao Phraya River Basin. An artificial neural network (ANN) model is used to gap-fill data in and between GRACE and GRACE-FO-based terrestrial water storage anomalies. The ANN model performed well with r

\geq

0.95, NRMSE

=

0.24 − 0.37, and NSE

\geq

0.89 during the calibration and validation phases. The cumulative residual error in the water budget ensemble mean accounts for ~15% of the ensemble mean for both the precipitation and evapotranspiration. An increasing trend of 0.03 mm month⁻¹ in the residual errors may be partially attributable to increases in human activity and the relative redistribution of biases among other water budget variables. All three closure techniques show similar directions of constraints (i.e., wet or dry bias) in water budget variables with slightly different magnitudes. Our quantification of water budget residual errors may help benchmark regional hydroclimate models for understanding the past, present, and future status of water budget components and effectively manage regional water resources, especially during hydroclimate extremes. Full article

(This article belongs to the Special Issue Satellite Remote Sensing for Water Cycle Studies: Sciences and Societal Applications)

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20 pages, 7058 KiB

Open AccessArticle

A Comprehensive Evaluation of Near-Real-Time and Research Products of IMERG Precipitation over India for the Southwest Monsoon Period

by Satya Prakash and Jayaraman Srinivasan

Remote Sens. 2021, 13(18), 3676; https://doi.org/10.3390/rs13183676 - 15 Sep 2021

Cited by 12 | Viewed by 3218

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 (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

(This article belongs to the Special Issue Satellite Remote Sensing for Water Cycle Studies: Sciences and Societal Applications)

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17 pages, 14347 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Remote Sensing-Based Assessment of Water Resources in the Arabian Peninsula

by Youssef Wehbe and Marouane Temimi

Remote Sens. 2021, 13(2), 247; https://doi.org/10.3390/rs13020247 - 13 Jan 2021

Cited by 29 | Viewed by 4351

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), 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

(This article belongs to the Special Issue Satellite Remote Sensing for Water Cycle Studies: Sciences and Societal Applications)

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15 pages, 4079 KiB

Open AccessArticle

Estimates of Daily Evapotranspiration in the Source Region of the Yellow River Combining Visible/Near-Infrared and Microwave Remote Sensing

by Rong Liu, Jun Wen, Xin Wang, Zuoliang Wang, Yu Liu and Ming Zhang

Remote Sens. 2021, 13(1), 53; https://doi.org/10.3390/rs13010053 - 25 Dec 2020

Cited by 4 | Viewed by 2044

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 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

(This article belongs to the Special Issue Satellite Remote Sensing for Water Cycle Studies: Sciences and Societal Applications)

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15 pages, 7107 KiB

Open AccessArticle

NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

by Jifu Yin, Xiwu Zhan and Jicheng Liu

Remote Sens. 2020, 12(17), 2861; https://doi.org/10.3390/rs12172861 - 03 Sep 2020

Cited by 13 | Viewed by 3323

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 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

(This article belongs to the Special Issue Satellite Remote Sensing for Water Cycle Studies: Sciences and Societal Applications)

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