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

Warren B. Cohen, Thomas K. Maiersperger, Zhiqiang Yang, Stith T. Gower, David P. Turner, William D. Ritts, Mercedes Berterretche, Steven W. Running

Research output: Contribution to journalArticlepeer-review

224 Scopus citations


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-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' 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.

Original languageEnglish
Pages (from-to)233-255
Number of pages23
JournalRemote Sensing of Environment
Issue number3
StatePublished - Dec 15 2003


This research was funded by NASA's Terrestrial Ecology Program. We greatly thank Robert Kennedy for his contributions to the project, and to all of those who helped collect the field data, including Al Kirschbaum, John Lennon Campbell, Drew Feldkirschner, and Sean Burrows. We owe much appreciation to Jeff Morisette and Jaime Nickeson for their help in so effectively coordinating MODIS validation activities, and to several people on the various MODIS science teams who are too numerous to list here. Thanks to Ramakrishna Nemani and three anonymous reviewers for their extremely helpful and constructive comments on the manuscript.

FundersFunder number
National Aeronautics and Space Administration


    • BigFoot
    • ETM+
    • IGBP
    • Land cover
    • Leaf area index
    • MODIS
    • Validation


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