Multiangular crop differentiation and LAI estimation using PROSAIL model inversion

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dc.contributor.authorMazumdar, Deepayan Dutta
dc.contributor.supervisorCoburn, Craig A.
dc.date.accessioned2012-07-18T18:59:49Z
dc.date.available2012-07-18T18:59:49Z
dc.date.issued2011
dc.degree.levelMasters
dc.descriptionxiii, 161 leaves : ill., map ; 29 cmen_US
dc.description.abstractUnderstanding variations in remote sensing data with illumination and sensor angle changes is important in agricultural crop monitoring. This research investigated field bidirectional reflectance factor (BRF) in crop differentiation and PROSAIL leaf area index (LAI) estimation. BRF and LAI data were collected for planophile and erectophile crops at three growth stages. In the solar principal plane, BRF differed optimally at 860 nm 60 days after planting (DAP) for canola and pea, at 860 nm 45 and 60 DAP for wheat and barley, and at 860 nm and 670 nm 45 and 60 DAP for planophiles versus erectophiles. The field BRF data helped better understand PROSAIL LAI estimation. NDVI was preferred for estimating LAI, however the MTVI2 vegetation index showed high sensitivity to view angles, particularly for erectophiles. The hotspot was important for crop differentiation and LAI. Availability of more along-track, off-nadir looking spaceborne sensors was recommended for agricultural crop monitoring.en_US
dc.identifier.urihttps://hdl.handle.net/10133/3103
dc.language.isoen_USen_US
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Geography, c2011en_US
dc.publisher.departmentDepartment of Geographyen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectArtificial satellites in agricultureen_US
dc.subjectAgriculture -- Remote sensing -- Researchen_US
dc.subjectVegetation mapping -- Remote sensing -- Researchen_US
dc.subjectLeaf area index -- Data processingen_US
dc.subjectDissertations, Academicen_US
dc.titleMultiangular crop differentiation and LAI estimation using PROSAIL model inversionen_US
dc.typeThesisen_US
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