@article {KNZ001152,
	title = {Evaluation of MODIS NPP and GPP products across multiple biomes},
	journal = {Remote Sensing of Environment},
	volume = {102},
	year = {2006},
	pages = {282 -292},
	abstract = {Estimates of daily gross primary production (GPP) and annual net primary production (NPP) at the 1 km spatial resolution are now produced operationally for the global terrestrial surface using imagery from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Ecosystem-level measurements of GPP at eddy covariance flux towers and plot-level measurements of NPP over the surrounding landscape offer opportunities for validating the MODIS NPP and GPP products, but these flux measurements must be scaled over areas on the order of 25 km2 to make effective comparisons to the MODIS products. Here, we report results for such comparisons at 9 sites varying widely in biome type and land use. The sites included arctic tundra, boreal forest, temperate hardwood forest, temperate conifer forest, tropical rain forest, tallgrass prairie, desert grassland, and cropland. The ground-based NPP and GPP surfaces were generated by application of the Biome-BGC carbon cycle process model in a spatially-distributed mode. Model inputs of land cover and leaf area index were derived from Landsat data. The MODIS NPP and GPP products showed no overall bias. They tended to be overestimates at low productivity sites {\textemdash} often because of artificially high values of MODIS FPAR (fraction of photosynthetically active radiation absorbed by the canopy), a critical input to the MODIS GPP algorithm. In contrast, the MODIS products tended to be underestimates in high productivity sites {\textemdash} often a function of relatively low values for vegetation light use efficiency in the MODIS GPP algorithm. A global network of sites where both NPP and GPP are measured and scaled over the local landscape is needed to more comprehensively validate the MODIS NPP and GPP products and to potentially calibrate the MODIS NPP/GPP algorithm parameters.},
	keywords = {LTER-KNZ, biomes, Global, Gross primary production, Landsat, MODIS, Monitoring, Net primary production, Validation},
	doi = {10.1016/j.rse.2006.02.017},
	author = {Turner, D.P. and Ritts, W.D. and Cohen, W.B. and Gower, S.T. and Running, SW. and Zhao, M. and Costa, M.H. and Kirschbaum, A. and J.M. Ham and Saleska, S. and Ahl, D.E.}
}
@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 {KNZ001150,
	title = {Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products},
	journal = {Remote Sensing of Environment},
	volume = {88},
	year = {2003},
	pages = {221 -362},
	abstract = {The MODIS land science team produces a number of standard products, including land cover and leaf area index (LAI). Critical to the success of MODIS and other sensor products is an independent evaluation of product quality. In that context, we describe a study using field data and Landsat ETM+ to map land cover and LAI at four 49-km2 sites in North America containing agricultural cropland (AGRO), prairie grassland (KONZ), boreal needleleaf forest, and temperate mixed forest. The purpose was to: (1) develop accurate maps of land cover, based on the MODIS IGBP (International Geosphere{\textendash}Biosphere Programme) land cover classification scheme; (2) derive continuous surfaces of LAI that capture the mean and variability of the LAI field measurements; and (3) conduct initial MODIS validation exercises to assess the quality of early (i.e., provisional) MODIS products. ETM+ land cover maps varied in overall accuracy from 81\% to 95\%. The boreal forest was the most spatially complex, had the greatest number of classes, and the lowest accuracy. The intensive agricultural cropland had the simplest spatial structure, the least number of classes, and the highest overall accuracy. At each site, mapped LAI patterns generally followed patterns of land cover across the site. Predicted versus observed LAI indicated a high degree of correspondence between field-based measures and ETM+ predictions of LAI. Direct comparisons of ETM+ land cover maps with Collection 3 MODIS cover maps revealed several important distinctions and similarities. One obvious difference was associated with image/map resolution. ETM+ captured much of the spatial complexity of land cover at the sites. In contrast, the relatively coarse resolution of MODIS did not allow for that level of spatial detail. Over the extent of all sites, the greatest difference was an overprediction by MODIS of evergreen needleleaf forest cover at the boreal forest site, which consisted largely of open shrubland, woody savanna, and savanna. At the agricultural, temperate mixed forest, and prairie grassland sites, ETM+ and MODIS cover estimates were similar. Collection 3 MODIS-based LAI estimates were considerably higher (up to 4 m2 m-2) than those based on ETM+ LAI at each site. There are numerous probable reasons for this, the most important being the algorithms{\textquoteright} sensitivity to MODIS reflectance calibration, its use of a prelaunch AVHRR-based land cover map, and its apparent reliance on mainly red and near-IR reflectance. Samples of Collection 4 LAI products were examined and found to consist of significantly improved LAI predictions for KONZ, and to some extent for AGRO, but not for the other two sites. In this study, we demonstrate that MODIS reflectance data are highly correlated with LAI across three study sites, with relationships increasing in strength from 500 to 1000 m spatial resolution, when shortwave-infrared bands are included.},
	keywords = {LTER-KNZ, BigFoot, ETM, IGBP, Land cover, Leaf area index, MODIS, Validation},
	doi = {10.1016/j.rse.2003.06.006},
	author = {Cohen, W.B. and Maiersperger, T.K. and Yang, Z. and Gower, S.T. and Turner, D.P. and Ritts, W.D. and Berterretche, M. and Running, S.W.}
}
@article {KNZ00701,
	title = {Multiscale assessment of binary and continuous landcover variables for MODIS validation, mapping, and modeling applications},
	journal = {Remote Sensing of the Environment},
	volume = {70},
	year = {1999},
	pages = {82 -98},
	abstract = {Validation, mapping, and modeling efforts require accurate methods to transform process rates and ecosystem attributes estimated from small field plots to the 250{\textendash}1000-m-wide cells used by a new generation of land cover mapping sensors. We provide alternative scale transformations, each with attendant assumptions and limitations. The choice of method depends on spatial characteristics of the land cover variables in question and consequently may vary between biomes or with the intended application. We extend the fractal similarity dimension renormalization method, previously developed for binary maps, to continuous variables. The method can preserve both the mean and the multifractal properties of the image, thereby satisfying a major goal, namely, to provide accurate areal estimates without sacrificing information about within-site variation. The scale transformation enables the multifractal scaling exponents of landscapes or individual spectral bands to be brought in and out of register with each other, thereby opening another dimension upon which to detect the scales at which various land use or terrain processes operate. Alternatively, landscapes can be selectively rescaled to highlight patterns due to particular processes. We recommend geostatistical procedures with which to assess spatial characteristics both within a site and within individual image cells. We recommend that aggregation of fine-grain measurements during validation of the Moderate Resolution Imaging Spectrometer (MODIS) products be based on continuous variables to reduce errors that originate from uncertainties in binary maps.},
	keywords = {LTER-KNZ},
	doi = {10.1016/S0034-4257(99)00059-0},
	author = {Milne, B.T. and Cohen, W.B.}
}
@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}
}
