Land Cover Mapping in Support of LAI and FAPAR Retrievals from EOS-MODIS and MISR: Classification Methods and Sensitivities to Errors

Land cover maps are widely used to parameterize the biophysical properties of plant canopies in models that describe terrestrial biogeochemical processes. In this paper, we describe the use of supervised classification algorithms to generate land cover maps that characterize the vegetation types required for LAI and FAPAR retrievals from MODIS and MISR. As part of this analysis, we examine the sensitivity of remote sensing-based retrievals of LAI and FAPAR to land cover information used to parameterize vegetation canopy radiative transfer models. Specifically, a decision tree classification algorithm is used to generate a land cover map of North America from AVHRR data with 1 km resolution using a 6-biome classification scheme. To do this, a time series of normalized difference vegetation index data from the AVHRR is used in association with extensive site-based training data compiled using Landsat TM and ancillary map sources. Accuracy assessment of the map produced via decision tree classification yields a cross-validated map accuracy of 73%. Results comparing LAI and FAPAR retrievals using maps from different sources show that disagreement in land cover labels generally do not translate into strong disagreement in LAI and FAPAR maps. Further, the main source of disagreement in LAI and FAPAR maps can be attributed to specific biome classes that are cahracterized by a continuum of fractional cover and canopy