High-Resolution NDVI from Planet's Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture

Handle URI:
http://hdl.handle.net/10754/620952
Title:
High-Resolution NDVI from Planet's Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture
Authors:
Houborg, Rasmus; McCabe, Matthew ( 0000-0002-1279-5272 )
Abstract:
Planet Labs ("Planet") operate the largest fleet of active nano-satellites in orbit, offering an unprecedented monitoring capacity of daily and global RGB image capture at 3-5 m resolution. However, limitations in spectral resolution and lack of accurate radiometric sensor calibration impact the utility of this rich information source. In this study, Planet's RGB imagery was translated into a Normalized Difference Vegetation Index (NDVI): a common metric for vegetation growth and condition. Our framework employs a data mining approach to build a set of rule-based regression models that relate RGB data to atmospherically corrected Landsat-8 NDVI. The approach was evaluated over a desert agricultural landscape in Saudi Arabia where the use of near-coincident (within five days) Planet and Landsat-8 acquisitions in the training of the regression models resulted in NDVI predictabilities with an r2 of approximately 0.97 and a Mean Absolute Deviation (MAD) on the order of 0.014 (~9%). The MAD increased to 0.021 (~14%) when the Landsat NDVI training image was further away (i.e., 11-16 days) from the corrected Planet image. In these cases, the use of MODIS observations to inform on the change in NDVI occurring between overpasses was shown to significantly improve prediction accuracies. MAD levels ranged from 0.002 to 0.011 (3.9% to 9.1%) for the best performing 80% of the data. The technique is generic and extendable to any region of interest, increasing the utility of Planet's dense time-series of RGB imagery.
KAUST Department:
Water Desalination & Reuse Research Cntr; Biological and Environmental Sciences and Engineering (BESE) Division
Citation:
Houborg R, McCabe MF. High-Resolution NDVI from Planet’s Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture. Remote Sensing. 2016; 8(9):768.
Publisher:
MDPI AG
Journal:
Remote Sensing
Issue Date:
19-Sep-2016
DOI:
10.3390/rs8090768
Type:
Article
ISSN:
2072-4292
Sponsors:
Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). We acknowledge Planet’s Ambassadors program for providing access to their imagery archive as well as the outreach efforts of Planet’s Nuno Vilaça and Joseph Mascaro. We greatly appreciate the logistical, equipment, and scientific support offered to our team by Jack King, Alan King, and employees of the Tawdeehiya Farm in Al Kharj, Saudi Arabia, without whom this research would not have been possible.
Additional Links:
http://www.mdpi.com/2072-4292/8/9/768
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorHouborg, Rasmusen
dc.contributor.authorMcCabe, Matthewen
dc.date.accessioned2016-10-12T10:22:29Z-
dc.date.available2016-10-12T10:22:29Z-
dc.date.issued2016-09-19-
dc.identifier.citationHouborg R, McCabe MF. High-Resolution NDVI from Planet’s Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture. Remote Sensing. 2016; 8(9):768.en
dc.identifier.issn2072-4292-
dc.identifier.doi10.3390/rs8090768-
dc.identifier.urihttp://hdl.handle.net/10754/620952-
dc.description.abstractPlanet Labs ("Planet") operate the largest fleet of active nano-satellites in orbit, offering an unprecedented monitoring capacity of daily and global RGB image capture at 3-5 m resolution. However, limitations in spectral resolution and lack of accurate radiometric sensor calibration impact the utility of this rich information source. In this study, Planet's RGB imagery was translated into a Normalized Difference Vegetation Index (NDVI): a common metric for vegetation growth and condition. Our framework employs a data mining approach to build a set of rule-based regression models that relate RGB data to atmospherically corrected Landsat-8 NDVI. The approach was evaluated over a desert agricultural landscape in Saudi Arabia where the use of near-coincident (within five days) Planet and Landsat-8 acquisitions in the training of the regression models resulted in NDVI predictabilities with an r2 of approximately 0.97 and a Mean Absolute Deviation (MAD) on the order of 0.014 (~9%). The MAD increased to 0.021 (~14%) when the Landsat NDVI training image was further away (i.e., 11-16 days) from the corrected Planet image. In these cases, the use of MODIS observations to inform on the change in NDVI occurring between overpasses was shown to significantly improve prediction accuracies. MAD levels ranged from 0.002 to 0.011 (3.9% to 9.1%) for the best performing 80% of the data. The technique is generic and extendable to any region of interest, increasing the utility of Planet's dense time-series of RGB imagery.en
dc.description.sponsorshipResearch reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). We acknowledge Planet’s Ambassadors program for providing access to their imagery archive as well as the outreach efforts of Planet’s Nuno Vilaça and Joseph Mascaro. We greatly appreciate the logistical, equipment, and scientific support offered to our team by Jack King, Alan King, and employees of the Tawdeehiya Farm in Al Kharj, Saudi Arabia, without whom this research would not have been possible.en
dc.language.isoenen
dc.publisherMDPI AGen
dc.relation.urlhttp://www.mdpi.com/2072-4292/8/9/768en
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License (CC BY 4.0).en
dc.subjectplanet labsen
dc.subjectLandsaten
dc.subjectdata miningen
dc.subjectNDVIen
dc.subjectprecision agricultureen
dc.subjectRGBen
dc.subjectnano-satellitesen
dc.titleHigh-Resolution NDVI from Planet's Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agricultureen
dc.typeArticleen
dc.contributor.departmentWater Desalination & Reuse Research Cntren
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.identifier.journalRemote Sensingen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
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