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TitleValidation of Canada-wide leaf area index maps using ground measurements and high and moderate resolution satellite imagery
AuthorChen, J M; Pavlic, G; Brown, L J; Cihlar, J; Leblanc, S GORCID logo; White, H PORCID logo; Hall, R J; Peddle, D; King, D; Trofymow, J A; Swift, EORCID logo; van der Sanden, J JORCID logo; Pellikka, P
SourceInternational Journal of Remote Sensing 2001.
Alt SeriesEarth Sciences Sector, Contribution Series 20042949
AbstractLeaf area index (LAI) is one of the surface parameters that has importance in climate, weather and ecological studies, and has been routinely estimated from remote sensing measurements. Canada-wide LAI maps are now being produced using cloud-free Advanced Very High Resolution Radiometer (AVHRR) imagery every 10 days at 1 km resolution. The archive of these products began in 1993. LAI maps at the same resolution are also being produced with images from the SPOT VEGETATION sensor. To validate these products across Canada, a group of Canadian scientists acquired LAI measurements during the summer of 1998 in deciduous, conifer and mixed forests and in cropland. Common measurement standards using the commercial TRAC and LAI-2000 instruments were followed. Eight Landsat TM scenes at 30 m resolution were used to locate ground sites and to facilitate the scaling to 1 km pixels. In this paper, examples of Canada-wide LAI maps are presented after an assessment of their accuracy using ground measurements and the eight Landsat scenes. Methodologies for scaling from high to coarse resolution images that consider surface heterogeneity in terms of mixed cover types are evaluated and discussed. Using Landsat LAI images as the standard, it is shown that the accuracy of LAI values of individual AVHRR and VEGETATION pixels was in the range of 50% to 75%. Random and bias errors were both considerable. Bias was mostly caused by uncertainties in atmospheric correction of the Landsat images, but surface heterogeneity in terms of mixed cover types were also found to cause bias in AVHRR and SPOT VEGETATION LAI calculations using non-linear algorithms. Random errors come from many sources, but pixels with mixed cover types are the main cause of random errors. As radiative signals from different vegetation types were quite different at the same LAI, accurate information about subpixel mixture of the various cover types is identified as the key to improving the accuracy of LAI estimates.

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