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TitleApplication of Hyperspectral Remote Sensing for LAI Estimation in Precision Farming
DownloadDownloads (Preprint)
 
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorPacheco, A; Bannari, A; Deguise, J -C; McNairn, H; Staenz, K
SourceProceedings of the 23rd Canadian Symposium on Remote Sensing, Ste-Foy, Québec, August 20-24; 2001 p. 281-287, https://doi.org/10.4095/219855 Open
Access logo Open Access
Year2001
Alt SeriesEarth Sciences Sector, Contribution Series 20043053
Documentbook
Lang.English
Mediapaper; on-line; digital
File formatpdf
Released2001 01 01
AbstractLeaf Area Index (LAI) is a key parameter controlling biophysical processes of the vegetation canopy. LAI helps to estimate productivity of agriculture and forest canopies, which can then serve as input to crop modelling. LAI can be measured using different approaches such as destructive sampling, optical ground-based instruments and optical imagery. Hyperspectral data has the advantage of distinguishing different target types within a pixel using spectral unmixing analysis as a tool to separate such spectral signatures. This paper investigates the relationship between ground-based effective LAI (eLAI) measurements estimated with the LI-COR LAI-2000 and eLAI values derived from Probe-1 hyperspectral surface reflectance data. This data were collected together with ground-based eLAI data during the summer of 1999 in Clinton, an agricultural area in South Western Ontario, Canada. The crops investigated for this study are corn and white beans. Correlations between ground eLAI and eLAI values derived from hyperspectral data produced encouraging results. Correlations were not strong when analysis was done on a single crop type. However, correlation results are good (r = 0.91) when data from all canopies are considered.
GEOSCAN ID219855

 
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