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Satellite Hydrological Data Products and Their Applications

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 21422

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Special Issue Editors

Dr. Jifu Yin

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Guest Editor

ESSIC/CISESS, University of Maryland, College Park, MD 20740, USA
Interests: remote sensing; data assimilation; drought; land surface model

Dr. Xiwu Zhan

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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, Collections and Topics in MDPI journals

Dr. Tarendra Lakhankar

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Guest Editor

NOAA-CESSRST, The City College of New York, City University of New York, New York, NY, USA
Interests: remote sensing; snow and soil moisture; flood; drought
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hamid Moradkhani

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Guest Editor

Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
Interests: hydrologic modeling and data assimilation; machine learning; food-energy-water nexus; remote sensing; uncertainty and risk analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water is crucial for living creatures on Earth and the changes of the Earth’s hydrosphere, biosphere and atmosphere. Accurately observing the regional and global water cycle is very important to hydrological, atmospheric and climatological sciences. Many government agencies have thus invested significantly in developing high quality satellite hydrological data products including precipitation, soil moisture, evapotranspiration, snow and ice, irrigation and ground water, etc. These products offer great opportunities to enhance monitoring and predicting hydroclimatic extremes such as storm, flood, drought, heatwave and fire. This special issue will highlight advancements in understanding, diagnosing, monitoring and predicting hydroclimatic extremes through the application of remote sensing hydrological data products.

We sincerely invite the authors to contribute original review and research manuscripts focused on developing remote sensing hydrological data products and investigating their roles in water and carbon cycle, as well as water and carbon imbalances under changing climate. Potential topics include but are not limited to the following:

The development of satellite hydrological data products; Hydrological products;
Land surface process observations;
Calibration and validation studies;
Fusion of in-situ and remotely-sensed hydrological data products;
Integration of ground and remote sensing observations;
Satellite hydrological data assimilation;
Understanding the physical mechanism and manifestation of hydroclimatic extremes;
Improvements in monitoring and predicting hydroclimatic extremes;
Characteristics of hydrological variables in climate and environmental changes;
Big data analytics for hydrological, atmospheric and climatological sciences;
The use of machine learning for generating hydrological data products and simulation;

Dr. Jifu Yin
Dr. Xiwu Zhan
Dr. Tarendra Lakhankar
Prof. Hamid Moradkhani
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

Remote Sensing
Hydrological products
Hydroclimatic extremes
Data assimilation
Big data
Machine Learning

Published Papers (7 papers)

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Research

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20 pages, 8762 KiB

Open AccessArticle

Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data

by Qin Li, Xiuguo Liu, Yulong Zhong, Mengmeng Wang and Shuang Zhu

Remote Sens. 2021, 13(16), 3304; https://doi.org/10.3390/rs13163304 - 20 Aug 2021

Cited by 4 | Viewed by 2765

Abstract

Terrestrial water storage changes (TWSCs) retrieved from the Gravity Recovery and Climate Experiment (GRACE) satellite mission have been extensively evaluated in previous studies over large basin scales. However, monitoring the TWSC at small basin scales is still poorly understood. This study presented a new method for calculating TWSCs at the small basin scales based on the water balance equation, using hydrometeorological and multi-source data. First, the basin was divided into several sub-basins through the slope runoff simulation algorithm. Secondly, we simulated the evapotranspiration (ET) and outbound runoff of each sub-basin using the PML_V2 and SWAT. Lastly, through the water balance equation, the TWSC of each sub-basin was obtained. Based on the estimated results, we analyzed the temporal and spatial variations in precipitation, ET, outbound runoff, and TWSC in the Ganjiang River Basin (GRB) from 2002 to 2018. The results showed that by comparing with GRACE products, in situ groundwater levels data, and soil moisture storage, the TWSC calculated by this study is in good agreement with these three data. During the study period, the spatial and temporal variations in precipitation and runoff in the GRB were similar, with a minimum in 2011 and maximum in 2016. The annual ET changed gently, while the TWSC fluctuated greatly. The findings of this study could provide some new information for improving the estimate of the TWSC at small basin scales. Full article

(This article belongs to the Special Issue Satellite Hydrological Data Products and Their Applications)

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27 pages, 15008 KiB

Open AccessArticle

Lake Phenology of Freeze-Thaw Cycles Using Random Forest: A Case Study of Qinghai Lake

by Weixiao Han, Chunlin Huang, Hongtao Duan, Juan Gu and Jinliang Hou

Remote Sens. 2020, 12(24), 4098; https://doi.org/10.3390/rs12244098 - 15 Dec 2020

Cited by 10 | Viewed by 2483

Abstract

Lake phenology is essential for understanding the lake freeze-thaw cycle effects on terrestrial hydrological processes. The Qinghai-Tibetan Plateau (QTP) has the most extensive ice reserve outside of the Arctic and Antarctic poles and is a sensitive indicator of global climate changes. Qinghai Lake, the largest lake in the QTP, plays a critical role in climate change. The freeze-thaw cycles of lakes were studied using daily Moderate Resolution Imaging Spectroradiometer (MODIS) data ranging from 2000–2018 in the Google Earth Engine (GEE) platform. Surface water/ice area, coverage, critical dates, surface water, and ice cover duration were extracted. Random forest (RF) was applied with a classifier accuracy of 0.9965 and a validation accuracy of 0.8072. Compared with six common water indexes (tasseled cap wetness (TCW), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), automated water extraction index (AWEI), water index 2015 (WI2015) and multiband water index (MBWI)) and ice threshold value methods, the critical freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS), and break-up end (BUE) dates were extracted by RF and validated by visual interpretation. The results showed an R² of 0.99, RMSE of 3.81 days, FUS and BUS overestimations of 2.50 days, and FUE and BUE underestimations of 0.85 days. RF performed well for lake freeze-thaw cycles. From 2000 to 2018, the FUS and FUE dates were delayed by 11.21 and 8.21 days, respectively, and the BUS and BUE dates were 8.59 and 1.26 days early, respectively. Two novel key indicators, namely date of the first negative land surface temperature (DFNLST) and date of the first positive land surface temperature (DFPLST), were proposed to comprehensively delineate lake phenology: DFNLST was approximately 37 days before FUS, and DFPLST was approximately 20 days before BUS, revealing that the first negative and first positive land surface temperatures occur increasingly earlier. Full article

(This article belongs to the Special Issue Satellite Hydrological Data Products and Their Applications)

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15 pages, 7684 KiB

Open AccessArticle

Ground-based Assessment of Snowfall Detection over Land Using Polarimetric High Frequency Microwave Measurements

by Cezar Kongoli, Huan Meng, Jun Dong and Ralph Ferraro

Remote Sens. 2020, 12(20), 3441; https://doi.org/10.3390/rs12203441 - 20 Oct 2020

Cited by 2 | Viewed by 2437

Abstract

This paper explores the capability of high frequency microwave measurements at vertical and horizontal polarizations in detecting snowfall over land. Surface in-situ meteorological data were collected over Conterminous US during two winter seasons in 2014–2015 and 2015–2016. Statistical analysis of the in-situ data, matched with Global Precipitation Measurement (GPM) Microwave Imager (GMI) measurements on board NASA/JAXA Core Observatory, showed that the polarization difference at 166 GHz had the highest correlation to measured snowfall rate compared to the single channel high frequency measurements and the polarization difference at 89 GHz. A logistic regression model applied to the match-up data, using the polarization difference at 166 and 89 GHz as predictors, yielded an overall snowfall classification rate of 69.0%, with the largest contribution coming from the polarization difference at 166 GHz. Logistic regression using the four single channels as predictors (at 89 and 166 GHz, horizontal and vertical polarizations) further indicated that the horizontal polarization at 166 GHz was the most important contributor. An overall classification rate of 73% was achieved by including the 183.31 ± 3 GHz and 183.31 ± 7 GHz vertical polarization channels in the final logistic regression model. Evaluation of the final algorithm demonstrated skill in snowfall detection of two significant events. Full article

(This article belongs to the Special Issue Satellite Hydrological Data Products and Their Applications)

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20 pages, 577 KiB

Open AccessArticle

Comparing the Assimilation of SMOS Brightness Temperatures and Soil Moisture Products on Hydrological Simulation in the Canadian Land Surface Scheme

by Manoj K. Nambiar, Jaison Thomas Ambadan, Tracy Rowlandson, Paul Bartlett, Erica Tetlock and Aaron A. Berg

Remote Sens. 2020, 12(20), 3405; https://doi.org/10.3390/rs12203405 - 16 Oct 2020

Cited by 3 | Viewed by 2114

Abstract

Soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Therefore, there is widespread interest in the use of soil moisture retrievals from passive microwave satellites. In the assimilation of satellite soil moisture data into land surface models, two approaches are commonly used. In the first approach brightness temperature (TB) data are assimilated, while in the second approach retrieved soil moisture (SM) data from the satellite are assimilated. However, there is not a significant body of literature comparing the differences between these two approaches, and it is not known whether there is any advantage in using a particular approach over the other. In this study, TB and SM L2 retrieval products from the Soil Moisture and Ocean Salinity (SMOS) satellite are assimilated into the Canadian Land Surface Scheme (CLASS), for improved soil moisture estimation over an agricultural region in Saskatchewan. CLASS is the land surface component of the Canadian Earth System Model (CESM), and the Canadian Seasonal and Interannual Prediction System (CanSIPS). Our results indicated that assimilating the SMOS products improved the soil moisture simulation skill of the CLASS. Near surface soil moisture assimilation also resulted in improved forecasts of root zone soil moisture (RZSM) values. Although both techniques resulted in improved forecasts of RZSM, assimilation of TB resulted in the superior estimates. Full article

(This article belongs to the Special Issue Satellite Hydrological Data Products and Their Applications)

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18 pages, 5567 KiB

Open AccessArticle

Biases Characteristics Assessment of the Advanced Geosynchronous Radiation Imager (AGRI) Measurement on Board Fengyun–4A Geostationary Satellite

by Jia Zhu, Jiong Shu and Wei Guo

Remote Sens. 2020, 12(18), 2871; https://doi.org/10.3390/rs12182871 - 04 Sep 2020

Cited by 14 | Viewed by 3052

Abstract

The Chinese Fengyun–4A geostationary meteorological satellite was successfully launched on 11 December 2016, carrying an Advanced Geostationary Radiation Imager (AGRI) to provide the observations of visible, near infrared, and infrared bands with improved spectral, spatial, and temporal resolution. The AGRI infrared observations can be assimilated into numerical weather prediction (NWP) data assimilation systems to improve the atmospheric analysis and weather forecasting capabilities. To achieve data assimilation, the first and crucial step is to characterize and evaluate the biases of the AGRI brightness temperatures in infrared channels 8–14. This study conducts the assessment of clear–sky AGRI full–disk infrared observation biases by coupling the RTTOV model and ERA Interim analysis. The AGRI observations are generally in good agreement with the model simulations. It is found that the biases over the ocean and land are less than 1.4 and 1.6 K, respectively. For bias difference between land and ocean, channels 11–13 are more obvious than water vapor channels 9–10. The fitting coefficient of linear regression tests between AGRI biases and sensor zenith angles manifests no obvious scan angle–dependent biases over ocean. All infrared channels observations are scene temperature–dependent over the ocean and land. Full article

(This article belongs to the Special Issue Satellite Hydrological Data Products and Their Applications)

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15 pages, 2113 KiB

Open AccessTechnical Note

An LSWI-Based Method for Mapping Irrigated Areas in China Using Moderate-Resolution Satellite Data

by Kunlun Xiang, Wenping Yuan, Liwen Wang and Yujiao Deng

Remote Sens. 2020, 12(24), 4181; https://doi.org/10.3390/rs12244181 - 21 Dec 2020

Cited by 16 | Viewed by 5257

Abstract

Accurate spatial information about irrigation is crucial to a variety of applications, such as water resources management, water exchange between the land surface and atmosphere, climate change, hydrological cycle, food security, and agricultural planning. Our study proposes a new method for extracting cropland irrigation information using statistical data, mean annual precipitation and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type data and surface reflectance data. The approach is based on comparing the land surface water index (LSWI) of cropland pixels to that of adjacent forest pixels with similar normalized difference vegetation index (NDVI). In our study, we validated the approach over mainland China with 612 reference samples (231 irrigated and 381 non-irrigated) and found the accuracy of 62.09%. Validation with statistical data also showed that our method explained 86.67 and 58.87% of the spatial variation in irrigated area at the provincial and prefecture levels, respectively. We further compared our new map to existing datasets of FAO/UF, IWMI, Zhu and statistical data, and found a good agreement with the irrigated area distribution from Zhu’s dataset. Results show that our method is an effective method apply to mapping irrigated regions and monitoring their yearly changes. Because the method does not depend on training samples, it can be easily repeated to other regions. Full article

(This article belongs to the Special Issue Satellite Hydrological Data Products and Their Applications)

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9 pages, 2101 KiB

Open AccessLetter

Scale Impact of Soil Moisture Observations to Noah-MP Land Surface Model Simulations

by Jifu Yin and Xiwu Zhan

Remote Sens. 2020, 12(7), 1169; https://doi.org/10.3390/rs12071169 - 06 Apr 2020

Viewed by 2429

Abstract

Due to the limitations of satellite antenna technology, current operational microwave soil moisture (SM) data products are typically at tens of kilometers spatial resolutions. Many approaches have thus been proposed to generate finer resolution SM data using ancillary information, but it is still unknown if assimilation of the finer spatial resolution SM data has beneficial impacts on model skills. In this paper, a synthetic experiment is thus conducted to identify the benefits of SM observations at a finer spatial resolution on the Noah-MP land surface model. Results of this study show that the performance of the Noah-MP model is significantly improved with the benefits of assimilating 1 km SM observations in comparison with the assimilation of SM data at coarser resolutions. Downscaling satellite microwave SM observations from coarse spatial resolution to 1 km resolution is recommended, and the assimilation of 1 km remotely sensed SM retrievals is suggested for NOAA National Weather Service and National Water Center. Full article

(This article belongs to the Special Issue Satellite Hydrological Data Products and Their Applications)

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