Literature DB >> 30233123

The Future of Earth Observation in Hydrology.

Matthew F McCabe1, Matthew Rodell2, Douglas E Alsdorf3, Diego G Miralles4, Remko Uijlenhoet5, Wolfgang Wagner6,7, Arko Lucieer8, Rasmus Houborg1, Niko E C Verhoest4, Trenton E Franz9, Jiancheng Shi10, Huilin Gao11, Eric F Wood12.   

Abstract

In just the past five years, the field of Earth observation has progressed beyond the offerings of conventional space agency based platforms to include a plethora of sensing opportunities afforded by CubeSats, Unmanned Aerial Vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically on the order of one billion dollars per satellite and with concept-to-launch timelines on the order of two decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturise sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist five years ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of the cost of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-meter resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen-scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the Internet of Things as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilise and exploit these new observing systems to enhance our understanding of the Earth and its linked processes.

Entities:  

Year:  2017        PMID: 30233123      PMCID: PMC6140349          DOI: 10.5194/hess-21-3879-2017

Source DB:  PubMed          Journal:  Hydrol Earth Syst Sci        ISSN: 1027-5606            Impact factor:   6.617


  14 in total

1.  GRACE measurements of mass variability in the Earth system.

Authors:  Byron D Tapley; Srinivas Bettadpur; John C Ries; Paul F Thompson; Michael M Watkins
Journal:  Science       Date:  2004-07-23       Impact factor: 47.728

2.  An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland.

Authors:  Ravinesh C Deo; Mehmet Şahin
Journal:  Environ Monit Assess       Date:  2016-01-16       Impact factor: 2.513

3.  Terrestrial ecosystem carbon dynamics and climate feedbacks.

Authors:  Martin Heimann; Markus Reichstein
Journal:  Nature       Date:  2008-01-17       Impact factor: 49.962

4.  Environmental science. Digital soil map of the world.

Authors:  Pedro A Sanchez; Sonya Ahamed; Florence Carré; Alfred E Hartemink; Jonathan Hempel; Jeroen Huising; Philippe Lagacherie; Alex B McBratney; Neil J McKenzie; Maria de Lourdes Mendonça-Santos; Budiman Minasny; Luca Montanarella; Peter Okoth; Cheryl A Palm; Jeffrey D Sachs; Keith D Shepherd; Tor-Gunnar Vågen; Bernard Vanlauwe; Markus G Walsh; Leigh A Winowiecki; Gan-Lin Zhang
Journal:  Science       Date:  2009-08-07       Impact factor: 47.728

5.  Scaling, Similarity, and the Fourth Paradigm for Hydrology

Authors:  Christa D Peters-Lidard; Martyn Clark; Luis Samaniego; Niko E C Verhoest; Tim van Emmerik; Remko Uijlenhoet; Kevin Achieng; Trenton E Franz; Ross Woods
Journal:  Hydrol Earth Syst Sci       Date:  2017-07-20       Impact factor: 5.748

6.  Country-wide rainfall maps from cellular communication networks.

Authors:  Aart Overeem; Hidde Leijnse; Remko Uijlenhoet
Journal:  Proc Natl Acad Sci U S A       Date:  2013-02-04       Impact factor: 11.205

7.  A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science.

Authors:  James H Faghmous; Vipin Kumar
Journal:  Big Data       Date:  2014-09-01       Impact factor: 2.128

8.  Global variations in ecosystem-scale isohydricity.

Authors:  Alexandra G Konings; Pierre Gentine
Journal:  Glob Chang Biol       Date:  2016-07-28       Impact factor: 10.863

9.  Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence.

Authors:  Seyed Hamed Alemohammad; Bin Fang; Alexandra G Konings; Filipe Aires; Julia K Green; Jana Kolassa; Diego Miralles; Catherine Prigent; Pierre Gentine
Journal:  Biogeosciences       Date:  2017-09-20       Impact factor: 4.295

10.  Multi-decadal trends in global terrestrial evapotranspiration and its components.

Authors:  Yongqiang Zhang; Jorge L Peña-Arancibia; Tim R McVicar; Francis H S Chiew; Jai Vaze; Changming Liu; Xingjie Lu; Hongxing Zheng; Yingping Wang; Yi Y Liu; Diego G Miralles; Ming Pan
Journal:  Sci Rep       Date:  2016-01-11       Impact factor: 4.379

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  15 in total

1.  Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine.

Authors:  Qiusheng Wua; Charles R Lane; Xuecao Li; Kaiguang Zhao; Yuyu Zhou; Nicholas Clinton; Ben DeVries; Heather E Golden; Megan W Lang
Journal:  Remote Sens Environ       Date:  2019-07-01       Impact factor: 10.164

2.  Scaling, Similarity, and the Fourth Paradigm for Hydrology

Authors:  Christa D Peters-Lidard; Martyn Clark; Luis Samaniego; Niko E C Verhoest; Tim van Emmerik; Remko Uijlenhoet; Kevin Achieng; Trenton E Franz; Ross Woods
Journal:  Hydrol Earth Syst Sci       Date:  2017-07-20       Impact factor: 5.748

3.  High Resolution, Annual Maps of Field Boundaries for Smallholder-Dominated Croplands at National Scales.

Authors:  Lyndon D Estes; Su Ye; Lei Song; Boka Luo; J Ronald Eastman; Zhenhua Meng; Qi Zhang; Dennis McRitchie; Stephanie R Debats; Justus Muhando; Angeline H Amukoa; Brian W Kaloo; Jackson Makuru; Ben K Mbatia; Isaac M Muasa; Julius Mucha; Adelide M Mugami; Judith M Mugami; Francis W Muinde; Fredrick M Mwawaza; Jeff Ochieng; Charles J Oduol; Purent Oduor; Thuo Wanjiku; Joseph G Wanyoike; Ryan B Avery; Kelly K Caylor
Journal:  Front Artif Intell       Date:  2022-02-25

Review 4.  Water Resources in Africa under Global Change: Monitoring Surface Waters from Space.

Authors:  Fabrice Papa; Jean-François Crétaux; Manuela Grippa; Elodie Robert; Mark Trigg; Raphael M Tshimanga; Benjamin Kitambo; Adrien Paris; Andrew Carr; Ayan Santos Fleischmann; Mathilde de Fleury; Paul Gerard Gbetkom; Beatriz Calmettes; Stephane Calmant
Journal:  Surv Geophys       Date:  2022-04-20       Impact factor: 7.965

5.  CubeSats deliver new insights into agricultural water use at daily and 3 m resolutions.

Authors:  Bruno Aragon; Matteo G Ziliani; Rasmus Houborg; Trenton E Franz; Matthew F McCabe
Journal:  Sci Rep       Date:  2021-06-09       Impact factor: 4.379

6.  Detecting Water Diversion Fingerprints in the Danjiangkou Reservoir from Satellite Gravimetry and Altimetry Data.

Authors:  Nengfang Chao; Gang Chen; Zhicai Luo; Xiaoli Su; Zhengtao Wang; Fupeng Li
Journal:  Sensors (Basel)       Date:  2019-08-10       Impact factor: 3.576

7.  Land-atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges.

Authors:  Diego G Miralles; Pierre Gentine; Sonia I Seneviratne; Adriaan J Teuling
Journal:  Ann N Y Acad Sci       Date:  2018-06-25       Impact factor: 5.691

8.  Leveraging Google Earth Engine for Drought Assessment using Global Soil Moisture Data.

Authors:  Nazmus Sazib; Iliana Mladenova; John Bolten
Journal:  Remote Sens (Basel)       Date:  2018-08-11       Impact factor: 4.848

9.  Advances in the Remote Sensing of Terrestrial Evaporation.

Authors:  Matthew F McCabe; Diego Miralles; Thomas R H Holmes; Joshua B Fisher
Journal:  Remote Sens (Basel)       Date:  2019-05-13       Impact factor: 4.848

10.  Identifying ENSO-related interannual and decadal variability on terrestrial water storage.

Authors:  Se-Hyeon Cheon; Benjamin D Hamlington; John T Reager; Hrishikesh A Chandanpurkar
Journal:  Sci Rep       Date:  2021-06-30       Impact factor: 4.379

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