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Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications

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

Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 66292

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Special Issue Editors

Prof. Dr. Yang Hong

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1. School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019-3072, USA
2. National Weather Center, ARRC Suite 4610, University of Oklahoma, 120 David L. Boren Blvd, Norman, OK 73072, USA
Interests: radar and satellite remote sensing; hydrology and water security; water resource engineering and GIS
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hongjie Xie

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Department of Geological Sciences, University of Texas at San Antonio, San Antonio, TX 78249, USA
Interests: remote sensing of water cycle; cryosphere; and polar regions
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Dr. Wei Wan

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Guest Editor

Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Interests: satellite remote sensing of lakes and reservoirs; GNSS remote sensing

Dr. Emad Hasan

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Guest Editor

Department of Geological Sciences and Environmental Studies, State University of New York (SUNY) at Binghamton OJ124, 4400 Vestal Parkway East, Binghamton, NY 13902, USA
Interests: remote sensing hydrology; GIS applications in water resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global water cycle dynamics involve energy and matter exchange among the atmosphere, hydrosphere, cryosphere, and biosphere. Remote sensing has the unique advantage of continuously acquiring complex water cycle information in time and space. This Special Issue calls contributions to address such a grand challenge. The methods and sensors used to observe and predict the fluxes, storage, and movement of water across a range of space–time scales by integrating advanced remote sensing technology and numerical water models into a theory–data–application, end-to-end framework.

Specifically, emerging ideas, technologies, and paths forward in remote sensing the water cycle involves the following matters:

New monitoring theory and methods, particularly the development and application of airborne sensors and satellite missions, to observe hydrologic components (precipitation, evapotranspiration, soil moisture, water vapor, streamflow, groundwater, wetland, snow, sea ice, glaciers, water bodies, such as lakes and reservoirs, etc.) across a wide range of spatial and temporal scales;
Remote sensing big data and data analytics for gaining a better and comprehensive understanding and mapping of water distribution and variability, in response to climate change and human activities;
Remote sensing data-enabled global and regional hydrological applications and water resources management, to motivate new theories and applications in remote sensing hydrology and offers new ways to predict and resolve global water conflicts.

Prof. Yang Hong
Prof. Hongjie Xie
Dr. Wei Wan
Dr. Emad Hasan
Guest Editors

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Keywords

Remote sensing hydrology
Water cycle
Cryosphere
Water resources
Big data

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Published Papers (12 papers)

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Editorial

Jump to: Research, Other

3 pages, 165 KiB

Open AccessEditorial

Editorial for Special Issue “Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications”

by Wei Wan, Hongjie Xie, Emad Hasan and Yang Hong

Remote Sens. 2019, 11(10), 1210; https://doi.org/10.3390/rs11101210 - 22 May 2019

Cited by 2 | Viewed by 2401

Abstract

Global water cycle dynamics involve the exchange of water and energy matter among the atmosphere, hydrosphere, geosphere, cryosphere, and biosphere [...] Full article

(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)

Research

Jump to: Editorial, Other

18 pages, 5364 KiB

Open AccessArticle

SAR Backscatter and InSAR Coherence for Monitoring Wetland Extent, Flood Pulse and Vegetation: A Study of the Amazon Lowland

by Francis Canisius, Brian Brisco, Kevin Murnaghan, Marco Van Der Kooij and Edwin Keizer

Remote Sens. 2019, 11(6), 720; https://doi.org/10.3390/rs11060720 - 26 Mar 2019

Cited by 51 | Viewed by 7497

Abstract

Synthetic aperture radar (SAR) data have been identified as a potential source of information for monitoring surface water, including open water and flooded vegetation, in frequent time intervals, which is very significant for flood mapping applications. The SAR specular reflectance separates open water and land surface, and its canopy penetration capability allows enhanced backscatter from flooded vegetation. Further, under certain conditions, the SAR signal from flooded vegetation may remain coherent between two acquisitions, which can be exploited using the InSAR technique. With these SAR capabilities in mind, this study examines the use of multi-temporal RADARSAT-2 C band SAR intensity and coherence components to monitor wetland extent, inundation and vegetation of a tropical wetland, such as Amazon lowland. For this study, 22 multi-temporal RADARSAT-2 images (21 pairs) were used for InSAR processing and the pairs in the low water stage (November, December) showed high coherence over the wetland areas. The three-year intensity stack was used for assessing wetland boundary, inundation extent, flood pulse, hydroperiod, and wetland vegetation. In addition to the intensity, derived coherence was used for classifying wetland vegetation. Wetland vegetation types were successfully classified with 86% accuracy using the statistical parameters derived from the multi-temporal intensity and coherence data stacks. We have found that in addition to SAR intensity, coherence provided information about wetland vegetation. In the next year, the Canadian RADARSAT Constellation Mission (RCM), will provide more data with frequent revisits, enhancing the application of SAR intensity and coherence for monitoring these types of wetlands at large scales. Full article

(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)

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18 pages, 6169 KiB

Open AccessArticle

A Methodological Framework to Retrospectively Obtain Downscaled Precipitation Estimates over the Tibetan Plateau

by Kang He, Ziqiang Ma, Ruiying Zhao, Asim Biswas, Hongfen Teng, Junfeng Xu, Wu Yu and Zhou Shi

Remote Sens. 2018, 10(12), 1974; https://doi.org/10.3390/rs10121974 - 7 Dec 2018

Cited by 7 | Viewed by 3145

Abstract

Long-term precipitation estimates with both finer spatial resolution and better quality are vital and highly needed in various related fields. Numerous downscaling algorithms have been investigated based on the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), to obtain precipitation data with finer resolution (~1 km). However, this research was restricted by the time span of the TMPA dataset, as the starting time of TMPA was 1998. In this study, a new methodological framework incorporating wavelet coherence and Cubist was proposed to retrospectively obtain downscaled precipitation estimates (DS) over the Tibetan Plateau (TP), based on TMPA and ground observations, in 1990s. The correlations and similarities of precipitation patterns between the target years, from 1990 to 1999, and reference years, from 2000 to 2013, were firstly determined using wavelet coherence based on ground observations. Following this, the TMPA data in the reference years were regarded as the reference in the corresponding target years, which were adopted to be downscaled using Cubist models and land surface variables, to obtain the DS in the target years. We found that the DS showed continuous trends, which corresponded well with the ground observations. Additionally, the performances of the DS were better than those of the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over the TP. Therefore, this methodological framework has great potential for obtaining precipitation estimates for the period of the 1990s for which TMPA data is inaccessible. Full article

(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)

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20 pages, 5603 KiB

Open AccessFeature PaperArticle

Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau

by Ziqiang Ma, Kang He, Xiao Tan, Jintao Xu, Weizhen Fang, Yu He and Yang Hong

Remote Sens. 2018, 10(12), 1883; https://doi.org/10.3390/rs10121883 - 26 Nov 2018

Cited by 33 | Viewed by 3930

Abstract

Accurate precipitation data is crucial in many applications such as hydrology, meteorology, and ecology. Compared with ground observations, satellite-based precipitation estimates can provide much more spatial information to characterize precipitation. In this study, the satellite-based precipitation products of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) were firstly evaluated over the Tibetan Plateau (TP) in 2015 against ground observations at both annual and monthly scales. Secondly, random forest algorithm was used to obtain the annual downscaled results (~1 km) based on IMERG and TMPA data and the downscaled results were examined against rain gauge data. Thirdly, a disaggregation algorithm was used to obtain the monthly downscaled results based on those at annual scale. The results indicated that (1) IMERG performed better than TMPA at both annual and monthly scales; (2) IMERG had few anomalies while TMPA displayed significant numbers of outliers in central and western parts of the TP; (3) random forest was a promising algorithm in acquiring high resolution precipitation data with improved accuracy; (4) the downscaled results based on IMERG had better performances than those based on TMPA. Full article

(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)

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26 pages, 5335 KiB

Open AccessArticle

Observing Water Vapour in the Planetary Boundary Layer from the Short-Wave Infrared

by Tim Trent, Hartmut Boesch, Peter Somkuti and Noëlle A. Scott

Remote Sens. 2018, 10(9), 1469; https://doi.org/10.3390/rs10091469 - 14 Sep 2018

Cited by 13 | Viewed by 6600

Abstract

Water vapour is a key greenhouse gas in the Earth climate system. In this golden age of satellite remote sensing, global observations of water vapour fields are made from numerous instruments measuring in the ultraviolet/visible, through the infrared bands, to the microwave regions of the electromagnetic spectrum. While these observations provide a wealth of information on columnar, free-tropospheric and upper troposphere/lower stratosphere water vapour amounts, there is still an observational gap regarding resolved bulk planetary boundary layer (PBL) concentrations. In this study we demonstrate the ability of the Greenhouse Gases Observing SATellite (GOSAT) to bridge this gap from highly resolved measurements in the shortwave infrared (SWIR). These new measurements of near surface columnar water vapour are free of topographic artefacts and are interpreted as a proxy for bulk PBL water vapour. Validation (over land surfaces only) of this new data set against global radiosondes show low biases that vary seasonally between −2% to 5%. Analysis on broad latitudinal bands show biases between −3% and 2% moving from high latitudes to the equatorial regions. Finally, with the extension of the GOSAT program out to at least 2027, we discuss the potential for a new GOSAT PBL water vapour Climate Data Record (CDR). Full article

(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)

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27 pages, 3968 KiB

Open AccessArticle

Improved Albedo Estimates Implemented in the METRIC Model for Modeling Energy Balance Fluxes and Evapotranspiration over Agricultural and Natural Areas in the Brazilian Cerrado

by Bruno Silva Oliveira, Elisabete Caria Moraes, Marcos Carrasco-Benavides, Gabriel Bertani and Guilherme Augusto Verola Mataveli

Remote Sens. 2018, 10(8), 1181; https://doi.org/10.3390/rs10081181 - 26 Jul 2018

Cited by 22 | Viewed by 5180

Abstract

In this study we assessed METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model performance to estimate energy balance fluxes and evapotranspiration (ET) in two heterogeneous landscapes in the Brazilian Cerrado, including fluxes and ET in both agricultural and natural vegetation. The estimates were evaluated by comparing them to flux tower data collected over sugarcane (USR site), woody savanna (PDG site) and stricto-sensu savanna (RECOR site) areas. The selection of the study years (2005–2007 for USR/PDG sites and 2011–2015 for RECOR site) was based on the availability of meteorological data (to be used as inputs in METRIC) and of flux tower data for energy balance fluxes and ET comparisons. The broadband albedo submodel was adjusted in order to improve Net Radiation estimates. For this adjustment, we applied at-surface solar radiation simulations obtained from the SMARTS2 model under different conditions of land elevation, precipitable water content and solar angles. We also tested the equivalence between the measured crop coefficient (Kc_{_ec}) and the reference evapotranspiration fraction (ETrF or F), seeking to extrapolate from instantaneous to daily values of actual evapotranspiration (ETa). Surface albedo was underestimated by 10% at the USR site (showing a better performance for full crop coverage), by 15% at the PDG site (following the woody savanna dynamics pattern through dry and wet seasons) and was overestimated by 21% at the RECOR site. METRIC was effective in simulating the spatial and temporal variability of energy balance fluxes and ET over agricultural and natural vegetation in the Brazilian Cerrado, with errors within those reported in the literature. Net radiation (Rn) presented consistent results (coefficient of determination (R²) > 0.94) but it was overestimated by 8% and 9% in sugarcane and woody savanna, respectively. METRIC-derived ET estimates showed an agreement with ground data at USR and PDG sites (R² > 0.88, root mean square error (RMSE) up to 0.87 mm day⁻¹), but at the RECOR site, ET was overestimated by 14% (R² = 0.96, mean absolute error (MAE) = 0.62 mm.day⁻¹ and RMSE = 0.75 mm day⁻¹). Surface energy balance fluxes and ET were marked by seasonality, with direct dependence on available energy, rainfall distribution, soil moisture and other parameters like albedo and NDVI. Full article

(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)

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20 pages, 3738 KiB

Open AccessFeature PaperArticle

Global Satellite Retrievals of the Near-Surface Atmospheric Vapor Pressure Deficit from AMSR-E and AMSR2

by Jinyang Du, John S. Kimball, Rolf H. Reichle, Lucas A. Jones, Jennifer D. Watts and Youngwook Kim

Remote Sens. 2018, 10(8), 1175; https://doi.org/10.3390/rs10081175 - 25 Jul 2018

Cited by 16 | Viewed by 6985

Abstract

Near-surface atmospheric Vapor Pressure Deficit (VPD) is a key environmental variable affecting vegetation water stress, evapotranspiration, and atmospheric moisture demand. Although VPD is readily derived from in situ standard weather station measurements, more spatially continuous global observations for regional monitoring of VPD are lacking. Here, we document a new method to estimate daily (both a.m. and p.m.) global land surface VPD at a 25-km resolution using a satellite passive microwave remotely sensed Land Parameter Data Record (LPDR) derived from the Advanced Microwave Scanning Radiometer (AMSR) sensors. The AMSR-derived VPD record shows strong correspondence (correlation coefficient ≥ 0.80, p-value < 0.001) and overall good performance (0.48 kPa ≤ Root Mean Square Error ≤ 0.69 kPa) against independent VPD observations from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data. The estimated AMSR VPD retrieval uncertainties vary with land cover type, satellite observation time, and underlying LPDR data quality. These results provide new satellite capabilities for global mapping and monitoring of land surface VPD dynamics from ongoing AMSR2 operations. Overall good accuracy and similar observations from both AMSR2 and AMSR-E allow for the development of climate data records documenting recent (from 2002) VPD trends and potential impacts on vegetation, land surface evaporation, and energy budgets. Full article

(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)

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17 pages, 15255 KiB

Open AccessArticle

Improved Hydrological Decision Support System for the Lower Mekong River Basin Using Satellite-Based Earth Observations

by Ibrahim Nourein Mohammed, John D. Bolten, Raghavan Srinivasan and Venkat Lakshmi

Remote Sens. 2018, 10(6), 885; https://doi.org/10.3390/rs10060885 - 6 Jun 2018

Cited by 55 | Viewed by 7818

Abstract

Multiple satellite-based earth observations and traditional station data along with the Soil & Water Assessment Tool (SWAT) hydrologic model were employed to enhance the Lower Mekong River Basin region’s hydrological decision support system. A nearest neighbor approximation methodology was introduced to fill the Integrated Multi-satellite Retrieval for the Global Precipitation Measurement mission (IMERG) grid points from 2001 to 2014, together with the Tropical Rainfall Measurement Mission (TRMM) data points for continuous precipitation forcing for our hydrological decision support system. A software tool to access and format satellite-based earth observation systems of precipitation and minimum and maximum air temperatures was developed and is presented. Our results suggest that the model-simulated streamflow utilizing TRMM and IMERG forcing data was able to capture the variability of the observed streamflow patterns in the Lower Mekong better than model-simulated streamflow with in-situ precipitation station data. We also present satellite-based and in-situ precipitation adjustment maps that can serve to correct precipitation data for the Lower Mekong region for use in other applications. The inconsistency, scarcity, poor spatial representation, difficult access and incompleteness of the available in-situ precipitation data for the Mekong region make it imperative to adopt satellite-based earth observations to pursue hydrologic modeling. Full article

(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)

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22 pages, 5958 KiB

Open AccessArticle

Metrology Assessment of the Accuracy of Precipitable Water Vapor Estimates from GPS Data Acquisition in Tropical Areas: The Tahiti Case

by Fangzhao Zhang, Jean-Pierre Barriot, Guochang Xu and Ta-Kang Yeh

Remote Sens. 2018, 10(5), 758; https://doi.org/10.3390/rs10050758 - 15 May 2018

Cited by 15 | Viewed by 4982

Abstract

High precision Global Positioning System (GPS) receivers, with the advantages of all-weather work and low cost, are now widely used to routinely monitor precipitable water (PW) vapor. They are so successful that the progressive phasing out of the costly and sparse in situ radio soundings (RS) is now a certainty. Nevertheless, the sub-daily to annual monitoring of high levels of the PW by GPS receivers in the tropics and the equatorial area still needs to be asserted in terms of metrology accuracy. This is the subject of this paper, which focuses on a tropical site located in mid-ocean (Tahiti). The metrology assessment was divided into two steps. Firstly, a GPS internal assessment, with an in-house processing based on the Bernese GNSS Software Version 5.2 and a comparison with the Center for Orbit Determination in Europe (CODE) products. Secondly, an external assessment, with a comparison with RS PW estimates. In contrast with previous works that only used PW estimates from the Integrated Global Radiosonde Archive (IGRA) website, we estimated the RS PW from the balloon raw data. This is especially important in tropical areas, where IGRA estimates only consider balloon measurements taken below approximately 5500 m. We show that, in our case, this threshold is one of the main sources of bias between GPS and RS estimates, and that the formula used to translate the GPS zenith wet delays (ZWD) to PW estimates also needs to be revisited for high level water vapor contents in the atmosphere. Full article

(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)

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21 pages, 2769 KiB

Open AccessArticle

Stable Water Isotopologues in the Stratosphere Retrieved from Odin/SMR Measurements

by Tongmei Wang, Qiong Zhang, Stefan Lossow, Léon Chafik, Camille Risi, Donal Murtagh and Abdel Hannachi

Remote Sens. 2018, 10(2), 166; https://doi.org/10.3390/rs10020166 - 25 Jan 2018

Cited by 5 | Viewed by 4972

Abstract

Stable Water Isotopologues (SWIs) are important diagnostic tracers for understanding processes in the atmosphere and the global hydrological cycle. Using eight years (2002–2009) of retrievals from Odin/SMR (Sub-Millimetre Radiometer), the global climatological features of three SWIs, H₂¹⁶O, HDO and H₂¹⁸O, the isotopic composition δD and δ¹⁸O in the stratosphere are analysed for the first time. Spatially, SWIs are found to increase with altitude due to stratospheric methane oxidation. In the tropics, highly depleted SWIs in the lower stratosphere indicate the effect of dehydration when the air comes through the cold tropopause, while, at higher latitudes, more enriched SWIs in the upper stratosphere during summer are produced and transported to the other hemisphere via the Brewer–Dobson circulation. Furthermore, we found that more H₂¹⁶O is produced over summer Northern Hemisphere and more HDO is produced over summer Southern Hemisphere. Temporally, a tape recorder in H₂¹⁶O is observed in the lower tropical stratosphere, in addition to a pronounced downward propagating seasonal signal in SWIs from the upper to the lower stratosphere over the polar regions. These observed features in SWIs are further compared to SWI-enabled model outputs. This helped to identify possible causes of model deficiencies in reproducing main stratospheric features. For instance, choosing a better advection scheme and including methane oxidation process in a specific model immediately capture the main features of stratospheric water vapor. The representation of other features, such as the observed inter-hemispheric difference of isotopic component, is also discussed. Full article

(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)

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10699 KiB

Open AccessArticle

CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities

by Giulia Panegrossi, Jean-François Rysman, Daniele Casella, Anna Cinzia Marra, Paolo Sanò and Mark S. Kulie

Remote Sens. 2017, 9(12), 1263; https://doi.org/10.3390/rs9121263 - 6 Dec 2017

Cited by 53 | Viewed by 6302

Abstract

The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60°N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water, and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166 ∆TB) and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166 ∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW > 3.6 kg·m⁻², IWP > 0.24 kg·m⁻² over land, and SIC > 57%, TPW > 5.1 kg·m⁻² over sea). The complex combined 166 ∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms. Full article

(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)

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9 pages, 3308 KiB

Open AccessLetter

Using CYGNSS Data to Monitor China’s Flood Inundation during Typhoon and Extreme Precipitation Events in 2017

by Wei Wan, Baojian Liu, Ziyue Zeng, Xi Chen, Guiping Wu, Liwen Xu, Xiuwan Chen and Yang Hong

Remote Sens. 2019, 11(7), 854; https://doi.org/10.3390/rs11070854 - 9 Apr 2019

Cited by 64 | Viewed by 4713

Abstract

NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission, launched in 2016, is a small satellite constellation designed to measure the ocean surface wind speed in hurricanes and tropical cyclones. To explore its additional capabilities for applications on the land surface, this study investigated the advantages and limitations of using CYGNSS data to monitor flood inundation during typhoon and extreme precipitation events in southeast China in 2017. The results showed that despite the lack of quantitative evaluation, the CYGNSS-derived surface reflectivity (SR) and flood inundation area was qualitatively consistent with the Global Precipitation Measurement (GPM)-derived precipitation and Soil Moisture Active Passive (SMAP)/Soil Moisture and Ocean Salinity (SMOS)-derived total brightness temperature at circular polarization (

T_{b_{C}}

). The results provide supporting evidence for further designation of Global Navigation Satellite System (GNSS) reflectometry (GNSS-R) constellations to monitor land surface hydrology. Full article

(This article belongs to the Special Issue Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications)

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