This paper on reports the production of a 1 km spatial resolution land cover classi cation using data for 1992-1993 from the Advanced Very High Resolution Radiometer (AVHRR). This map will be included as an at-launch product of the Moderate Resolution Imaging Spectroradiometer (MODIS) to serve as an input for several algorithms requiring knowledge of land cover type. The methodology was derived from a similar e ort to create a product at 8 km spatial resolution, where high resolution data sets were interpreted in order to derive a coarse-resolution training data set. A set of 37 294Ö 1 km pixels was used within a hierarchical tree structure to classify the AVHRR data into 12 classes. The approach taken involved a hierarchy of pair-wise class trees where a logic based on vegetation form was applied until all classes were depicted. Multitemporal AVHRR metrics were used to predict class memberships. Minimum annual red re ectance, peak annual Normalized Di erence Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not. Comparisons of the nal product with regional digital land cover maps derived from high-resolution remotely sensed data reveal general agreement, except for apparently poor depictions of temperate pastures within areas of agriculture. Distinguishing between forest and non-forest was achieved with agreements ranging from 81 to 92% for these regional subsets. The agreements for all classes varied from an average of 65% when viewing all pixels to an average of 82% when viewing only those 1 km pixels consisting of greater than 90% one class within the high-resolution data sets.

The first results of the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous field algorithm's global percent tree cover are presented. Percent tree cover per 500-m MODIS pixel is

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Accurate depiction of the land and water is critical for the production of land surface parameters from remote sensing data products. Certain parameters, including the land surface temperature, active fires and surface reflectance, can be processed differently when the underlying surface is water as compared with land. Substantial errors in the underlying water mask can then pervade into these products and any products created from them.Historically many global databases have been created to depict global surface water. These databases still fall short of the current needs of the terrestrial remote sensing community working at 250 m spatial resolution. The most recent attempt to address the problem uses the Shuttle Radar Topography Mission (SRTM) data set to create the SRTM Water Body Data set (SWBD 2005). The SWBD represents a good first step but still requires additional work to expand the spatial coverage to include the whole globe and to address some erroneous discontinuities in major river networks.To address this issue a new water mask product has been created using the SWBD in combination with MODIS 250 m data to create a complete global map of surface water at 250 m spatial resolution. This effort is automated and intended to produce a dataset for use in processing of raster data (MODIS and future instruments) and for masking out water in final terrestrial raster data products.This new global dataset is produced from remotely sensed data and provided to the public in digital format, free of charge. The data set can be found on the Global Land Cover Facility (GLCF) website at http://landcover.org. This dataset is expected to be a base set of information to describe the surface of Earth as either land or water which is a fundamental distinction upon which other descriptions can be made.