Application of a regularized model inversion system (REGFLEC) to multi-temporal RapidEye imagery for retrieving vegetation characteristics

Handle URI:
http://hdl.handle.net/10754/579920
Title:
Application of a regularized model inversion system (REGFLEC) to multi-temporal RapidEye imagery for retrieving vegetation characteristics
Authors:
Houborg, Rasmus; McCabe, Matthew ( 0000-0002-1279-5272 )
Abstract:
Accurate retrieval of canopy biophysical and leaf biochemical constituents from space observations is critical to diagnosing the functioning and condition of vegetation canopies across spatio-temporal scales. Retrieved vegetation characteristics may serve as important inputs to precision farming applications and as constraints in spatially and temporally distributed model simulations of water and carbon exchange processes. However significant challenges remain in the translation of composite remote sensing signals into useful biochemical, physiological or structural quantities and treatment of confounding factors in spectrum-trait relations. Bands in the red-edge spectrum have particular potential for improving the robustness of retrieved vegetation properties. The development of observationally based vegetation retrieval capacities, effectively constrained by the enhanced information content afforded by bands in the red-edge, is a needed investment towards optimizing the benefit of current and future satellite sensor systems. In this study, a REGularized canopy reFLECtance model (REGFLEC) for joint leaf chlorophyll (Chll) and leaf area index (LAI) retrieval is extended to sensor systems with a band in the red-edge region for the first time. Application to time-series of 5 m resolution multi-spectral RapidEye data is demonstrated over an irrigated agricultural region in central Saudi Arabia, showcasing the value of satellite-derived crop information at this fine scale for precision management. Validation against in-situ measurements in fields of alfalfa, Rhodes grass, carrot and maize indicate improved accuracy of retrieved vegetation properties when exploiting red-edge information in the model inversion process. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).
KAUST Department:
Water Desalination and Reuse Research Center (WDRC); Biological and Environmental Sciences and Engineering (BESE) Division
Citation:
Rasmus Houborg and Matthew F. McCabe " Application of a regularized model inversion system (REGFLEC) to multi-temporal RapidEye imagery for retrieving vegetation characteristics ", Proc. SPIE 9637, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, 963717 (October 14, 2015)
Publisher:
SPIE-Intl Soc Optical Eng
Journal:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII
Conference/Event name:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII
Issue Date:
14-Oct-2015
DOI:
10.1117/12.2196378
Type:
Conference Paper
Additional Links:
http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2196378
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.date.accessioned2015-10-20T09:46:27Zen
dc.date.available2015-10-20T09:46:27Zen
dc.date.issued2015-10-14en
dc.identifier.citationRasmus Houborg and Matthew F. McCabe " Application of a regularized model inversion system (REGFLEC) to multi-temporal RapidEye imagery for retrieving vegetation characteristics ", Proc. SPIE 9637, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, 963717 (October 14, 2015)en
dc.identifier.doi10.1117/12.2196378en
dc.identifier.urihttp://hdl.handle.net/10754/579920en
dc.description.abstractAccurate retrieval of canopy biophysical and leaf biochemical constituents from space observations is critical to diagnosing the functioning and condition of vegetation canopies across spatio-temporal scales. Retrieved vegetation characteristics may serve as important inputs to precision farming applications and as constraints in spatially and temporally distributed model simulations of water and carbon exchange processes. However significant challenges remain in the translation of composite remote sensing signals into useful biochemical, physiological or structural quantities and treatment of confounding factors in spectrum-trait relations. Bands in the red-edge spectrum have particular potential for improving the robustness of retrieved vegetation properties. The development of observationally based vegetation retrieval capacities, effectively constrained by the enhanced information content afforded by bands in the red-edge, is a needed investment towards optimizing the benefit of current and future satellite sensor systems. In this study, a REGularized canopy reFLECtance model (REGFLEC) for joint leaf chlorophyll (Chll) and leaf area index (LAI) retrieval is extended to sensor systems with a band in the red-edge region for the first time. Application to time-series of 5 m resolution multi-spectral RapidEye data is demonstrated over an irrigated agricultural region in central Saudi Arabia, showcasing the value of satellite-derived crop information at this fine scale for precision management. Validation against in-situ measurements in fields of alfalfa, Rhodes grass, carrot and maize indicate improved accuracy of retrieved vegetation properties when exploiting red-edge information in the model inversion process. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).en
dc.language.isoenen
dc.publisherSPIE-Intl Soc Optical Engen
dc.relation.urlhttp://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2196378en
dc.rightsArchived with thanks to Proceedings of SPIEen
dc.titleApplication of a regularized model inversion system (REGFLEC) to multi-temporal RapidEye imagery for retrieving vegetation characteristicsen
dc.typeConference Paperen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.identifier.journalRemote Sensing for Agriculture, Ecosystems, and Hydrology XVIIen
dc.conference.date2015-09-22 to 2015-09-24en
dc.conference.nameRemote Sensing for Agriculture, Ecosystems, and Hydrology XVIIen
dc.conference.locationToulouse, FRAen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorHouborg, Rasmusen
kaust.authorMcCabe, Matthewen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.