Time series from hyperion to track productivity in pivot agriculture in saudi arabia

Handle URI:
http://hdl.handle.net/10754/626592
Title:
Time series from hyperion to track productivity in pivot agriculture in saudi arabia
Authors:
Houborg, Rasmus; McCabe, Matthew ( 0000-0002-1279-5272 ) ; Angel, Yoseline; Middleton, Elizabeth M.
Abstract:
The hyperspectral satellite sensing capacity is expected to increase substantially in the near future with the planned deployment of hyperspectral systems by both space agencies and commercial companies. These enhanced observational resources will offer new and improved ways to monitor the dynamics and characteristics of terrestrial ecosystems. This study investigates the utility of time series of hyperspectral imagery, acquired by Hyperion onboard EO-1, for quantifying variations in canopy chlorophyll (Chl<sub>c</sub>), plant productivity, and yield over an intensive farming area in the desert of Saudi Arabia. Chl<sub>c</sub> is estimated on the basis of predictive multi-variate empirical models established via a machine learning approach using a training dataset of in-situ measured target variables and explanatory hyperspectral indices. Resulting time series of Chl<sub>c</sub> are translated into Gross Primary Productivity (GPP) and Yield based on semi-empirical relationships, and evaluated against ground-based observations. Results indicate significant benefit in utilizing the full suite of hyperspectral indices over multi-spectral indices constructible from Landsat-8 and Sentinel-2.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Water Desalination and Reuse Research Center (WDRC)
Citation:
Houborg R, McCabe MF, Angel Y, Middleton EM (2017) Time series from hyperion to track productivity in pivot agriculture in saudi arabia. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Available: http://dx.doi.org/10.1109/IGARSS.2017.8127641.
Publisher:
IEEE
Journal:
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Issue Date:
13-Dec-2017
DOI:
10.1109/IGARSS.2017.8127641
Type:
Conference Paper
Additional Links:
http://ieeexplore.ieee.org/document/8127641/
Appears in Collections:
Conference Papers; Water Desalination and Reuse Research Center (WDRC); Biological and Environmental Sciences and Engineering (BESE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHouborg, Rasmusen
dc.contributor.authorMcCabe, Matthewen
dc.contributor.authorAngel, Yoselineen
dc.contributor.authorMiddleton, Elizabeth M.en
dc.date.accessioned2018-01-01T12:19:01Z-
dc.date.available2018-01-01T12:19:01Z-
dc.date.issued2017-12-13en
dc.identifier.citationHouborg R, McCabe MF, Angel Y, Middleton EM (2017) Time series from hyperion to track productivity in pivot agriculture in saudi arabia. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Available: http://dx.doi.org/10.1109/IGARSS.2017.8127641.en
dc.identifier.doi10.1109/IGARSS.2017.8127641en
dc.identifier.urihttp://hdl.handle.net/10754/626592-
dc.description.abstractThe hyperspectral satellite sensing capacity is expected to increase substantially in the near future with the planned deployment of hyperspectral systems by both space agencies and commercial companies. These enhanced observational resources will offer new and improved ways to monitor the dynamics and characteristics of terrestrial ecosystems. This study investigates the utility of time series of hyperspectral imagery, acquired by Hyperion onboard EO-1, for quantifying variations in canopy chlorophyll (Chl<sub>c</sub>), plant productivity, and yield over an intensive farming area in the desert of Saudi Arabia. Chl<sub>c</sub> is estimated on the basis of predictive multi-variate empirical models established via a machine learning approach using a training dataset of in-situ measured target variables and explanatory hyperspectral indices. Resulting time series of Chl<sub>c</sub> are translated into Gross Primary Productivity (GPP) and Yield based on semi-empirical relationships, and evaluated against ground-based observations. Results indicate significant benefit in utilizing the full suite of hyperspectral indices over multi-spectral indices constructible from Landsat-8 and Sentinel-2.en
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/8127641/en
dc.subjectterrain mappingen
dc.subjecttime seriesen
dc.subjectvegetationen
dc.subjectvegetation mappingen
dc.subjectBiological system modelingen
dc.subjectEarthen
dc.subjectHyperspectral imagingen
dc.subjectPredictive modelsen
dc.subjectProductivityen
dc.titleTime series from hyperion to track productivity in pivot agriculture in saudi arabiaen
dc.typeConference Paperen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.identifier.journal2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)en
dc.contributor.institutionNASA Goddard Space Flight Center (GSFC), Greenbelt, Maryland, U.S.A.en
kaust.authorHouborg, Rasmusen
kaust.authorMcCabe, Matthewen
kaust.authorAngel, Yoselineen
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