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dc.contributor.authorAragon Solorio, Bruno Jose Luis
dc.contributor.authorHouborg, Rasmus
dc.contributor.authorTu, Kevin
dc.contributor.authorFisher, Joshua B.
dc.contributor.authorMcCabe, Matthew
dc.date.accessioned2018-12-05T08:05:32Z
dc.date.available2018-12-05T08:05:32Z
dc.date.issued2018-11-22
dc.identifier.citationAragon B, Houborg R, Tu K, Fisher JB, McCabe M (2018) CubeSats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture. Remote Sensing 10: 1867. Available: http://dx.doi.org/10.3390/rs10121867.
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/rs10121867
dc.identifier.urihttp://hdl.handle.net/10754/630180
dc.description.abstractRemote sensing based estimation of evapotranspiration (ET) provides a direct accounting of the crop water use. However, the use of satellite data has generally required that a compromise between spatial and temporal resolution is made, i.e., one could obtain low spatial resolution data regularly, or high spatial resolution occasionally. As a consequence, this spatiotemporal trade-off has tended to limit the impact of remote sensing for precision agricultural applications. With the recent emergence of constellations of small CubeSat-based satellite systems, these constraints are rapidly being removed, such that daily 3 m resolution optical data are now a reality for earth observation. Such advances provide an opportunity to develop new earth system monitoring and assessment tools. In this manuscript we evaluate the capacity of CubeSats to advance the estimation of ET via application of the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) retrieval model. To take advantage of the high-spatiotemporal resolution afforded by these systems, we have integrated a CubeSat derived leaf area index as a forcing variable into PT-JPL, as well as modified key biophysical model parameters. We evaluate model performance over an irrigated farmland in Saudi Arabia using observations from an eddy covariance tower. Crop water use retrievals were also compared against measured irrigation from an in-line flow meter installed within a center-pivot system. To leverage the high spatial resolution of the CubeSat imagery, PT-JPL retrievals were integrated over the source area of the eddy covariance footprint, to allow an equivalent intercomparison. Apart from offering new precision agricultural insights into farm operations and management, the 3 m resolution ET retrievals were shown to explain 86% of the observed variability and provide a relative RMSE of 32.9% for irrigated maize, comparable to previously reported satellite-based retrievals. An observed underestimation was diagnosed as a possible misrepresentation of the local surface moisture status, highlighting the challenge of high-resolution modeling applications for precision agriculture and informing future research directions.
dc.description.sponsorshipThe research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). R.H. acknowledges research support by the South Dakota State University. K.T. recognizes support by NASA THP. J.B.F. contributed to this research with support from the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged. J.B.F. was supported in part by NASA programs: THP, SUSMAP and ECOSTRESS.
dc.publisherMDPI AG
dc.relation.urlhttps://www.mdpi.com/2072-4292/10/12/1867
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCubeSats
dc.subjectevapotranspiration
dc.subjectPT-JPL
dc.subjectremote sensing
dc.subjectSaudi Arabia
dc.subjecthigh-resolution
dc.subjectprecision agriculture
dc.titleCubeSats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentEarth System Observation and Modelling
dc.contributor.departmentEnvironmental Science and Engineering
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)
dc.identifier.journalRemote Sensing
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Geography, South Dakota State University, Brookings, SD 57007, USA
dc.contributor.institutionGeospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA
dc.contributor.institutionTheiss Research, 7411 Eads Ave., La Jolla, CA 92037, USA
dc.contributor.institutionCorteva Agriscience, Agriculture Division of DowDuPont 8325 NW 62nd Ave, P.O. Box 7062, Johnston, IA 50131, USA
dc.contributor.institutionJet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
kaust.personAragon Solorio, Bruno Jose Luis
kaust.personMcCabe, Matthew
refterms.dateFOA2018-12-05T08:43:59Z


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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Except where otherwise noted, this item's license is described as This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).