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Open AccessEditor’s ChoiceArticle

CubeSats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture

Water Desalination and Reuse Center, King Abdullah University of Science of Technology, Thuwal 23955, Saudi Arabia
Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA
Department of Geography, South Dakota State University, Brookings, SD 57007, USA
Corteva Agriscience, Agriculture Division of DowDuPont 8325 NW 62nd Ave, P.O. Box 7062, Johnston, IA 50131, USA
Theiss Research, 7411 Eads Ave., La Jolla, CA 92037, USA
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(12), 1867;
Received: 28 September 2018 / Revised: 19 November 2018 / Accepted: 20 November 2018 / Published: 22 November 2018
(This article belongs to the Special Issue Advances in the Remote Sensing of Terrestrial Evaporation)
Remote 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. View Full-Text
Keywords: CubeSats; evapotranspiration; PT-JPL; remote sensing; Saudi Arabia; high-resolution; precision agriculture CubeSats; evapotranspiration; PT-JPL; remote sensing; Saudi Arabia; high-resolution; precision agriculture
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MDPI and ACS Style

Aragon, B.; Houborg, R.; Tu, K.; Fisher, J.B.; McCabe, M. CubeSats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture. Remote Sens. 2018, 10, 1867.

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