@article {KNZ001153,
	title = {MODIS land cover and LAI collection 4 product quality across nine sites in the western hemisphere},
	journal = {IEEE Transactions in Geosciences and Remote Sensing},
	volume = {44},
	year = {2006},
	pages = {1843 -1857},
	abstract = {Global maps of land cover and leaf area index (LAI) derived from the Moderate Resolution Imaging Spectrometer (MODIS) reflectance data are an important resource in studies of global change, but errors in these must be characterized and well understood. Product validation requires careful scaling from ground and related measurements to a grain commensurate with MODIS products. We present an updated BigFoot project protocol for developing 25-m validation data layers over 49-km2 study areas. Results from comparisons of MODIS and BigFoot land cover and LAI products at nine contrasting sites are reported. In terms of proportional coverage, MODIS and BigFoot land cover were in close agreement at six sites. The largest differences were at low tree cover evergreen needleleaf sites and at an Arctic tundra site where the MODIS product overestimated woody cover proportions. At low leaf biomass sites there was reasonable agreement between MODIS and BigFoot LAI products, but there was not a particular MODIS LAI algorithm pathway that consistently compared most favorably. At high leaf biomass sites, MODIS LAI was generally overpredicted by a significant amount. For evergreen needleleaf sites, LAI seasonality was exaggerated by MODIS. Our results suggest incremental improvement from Collection 3 to Collection 4 MODIS products, with some remaining problems that need to be addressed},
	keywords = {LTER-KNZ},
	doi = {10.1109/TGRS.2006.876026},
	author = {Cohen, W.B. and Maiersperger, T.K. and Turner, D.P. and Ritts, W.D. and Pflugmacher, D. and Kennedy, R.E. and Kirschbaum, A. and Running, S.W. and Costa, M. and Gower, S.T.}
}
@article {KNZ001151,
	title = {Comparison of hyperspectral and multispectral data for estimating leaf area index in four biomes},
	journal = {Remote Sensing of Environment},
	volume = {91},
	year = {2004},
	pages = {508 -520},
	abstract = {Motivated by the increasing importance of hyperspectral remote sensing data, this study sought to determine whether current-generation narrow-band hyperspectral remote sensing data could better track vegetation leaf area index (LAI) than traditional broad-band multispectral data. The study takes advantage of a unique dataset, wherein field measurements of LAI were acquired at the same general time and grain size as both Landsat ETM+ and AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) imagery in four different biomes. Biome types sampled included row-crop agriculture, tallgrass prairie, mixed hardwood-conifer forest, and boreal conifer forest. The effects of bandwidth, band placement, and number of bands were isolated from radiometric quality by comparing regression models derived from individual AVIRIS channels with those derived from simulated ETM+ and MODIS channels using the AVIRIS data. Models with selected subsets of individual AVIRIS channels performed better to predict LAI than those based on the broadband datasets, although the potential to overfit models using the large number of available AVIRIS bands is a concern. Models based on actual ETM+ data were generally stronger than those based on simulated ETM+ data, suggesting that, for predicting LAI, ETM+ data suffer no penalty for having lower radiometric quality. NDVI was generally not sensitive to LAI at the four sites. Band placement of broad-band sensors (e.g., simulated ETM+ and MODIS) did not affect relationships with LAI, suggesting that there is no inherent advantage to MODIS spectral properties over those of ETM+ for estimating LAI. Spectral channels in the red-edge and shortwave-infrared regions were generally more important than those in the near-infrared for predicting LAI.},
	keywords = {LTER-KNZ, Hyperspectral, LAI, Multispectral},
	doi = {10.1016/j.rse.2004.04.010},
	author = {Lee, K-S. and Cohen, W.B. and Kennedy, R.E. and Maiersperger, T.K. and Gower, S.T.}
}
@article {KNZ00717,
	title = {Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites},
	journal = {Remote Sensing of the Environment},
	volume = {70},
	year = {1999},
	pages = {52 -68},
	abstract = {Mapping and monitoring of leaf area index (LAI) is important for spatially distributed modeling of vegetation productivity, evapotranspiration, and surface energy balance. Global LAI surfaces will be an early product of the MODIS Land Science Team, and the requirements for LAI validation at selected sites have prompted interest in accurate LAI mapping at a more local scale. While spectral vegetation indices (SVIs) derived from satellite remote sensing have been used to map LAI, vegetation type, and related optical properties, and effects of Sun{\textendash}surface{\textendash}sensor geometry, background reflectance, and atmospheric quality can limit the strength and generality of empirical LAI{\textendash}SVI relationships. In the interest of a preliminary assessment of the variability in LAI{\textendash}SVI relationships across vegetation types, we compared Landsat 5 Thematic Mapper imagery from three temperate zone sites with on-site LAI measurements. The sites differed widely in location, vegetation physiognomy (grass, shrubs, hardwood forest, and conifer forest), and topographic complexity. Comparisons were made using three different red and near-infrared-based SVIs (NDVI, SR, SAVI). Several derivations of the SVIs were examined, including those based on raw digital numbers (DN), radiance, top of the atmosphere reflectance, and atmospherically corrected reflectance. For one of the sites, which had extreme topographic complexity, additional corrections were made for Sun{\textendash}surface{\textendash}sensor geometry. Across all sites, a strong general relationship was preserved, with SVIs increasing up to LAI values of 3 to 5. For all but the coniferous forest site, sensitivity of the SVIs was low at LAI values above 5. In coniferous forests, the SVIs decreased at the highest LAI values because of decreasing near-infrared reflectance associated with the complex canopy in these mature to old-growth stands. The cross-site LAI{\textendash}SVI relationships based on atmospherically corrected imagery were stronger than those based on DN, radiance, or top of atmosphere reflectance. Topographic corrections at the conifer site altered the SVIs in some cases but had little effect on the LAI{\textendash}SVI relationships. Significant effects of vegetation properties on SVIs, which were independent of LAI, were evident. The variability between and around the best fit LAI{\textendash}SVI relationships for this dataset suggests that for local accuracy in development of LAI surfaces it will be desirable to stratify by land cover classes (e.g., physiognomic type and successional stage) and to vary the SVI.},
	keywords = {LTER-KNZ},
	doi = {10.1016/S0034-4257(99)00057-7},
	author = {Turner, D.P. and Cohen, W.B. and Kennedy, R.E. and Fassnacht, K.S. and J. M. Briggs}
}
