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A new set of recently developed leaf area index (LAI) algorithms has been employed for producing a global LAI dataset at 1 km resolution and in time-steps of 10 days, using data from the Satellite pour l''observation de la terre (SPOT) VEGETATION (VGT) sensor. In this paper, this new LAI product is compared with the global MODIS Collection 4 LAI product over four validation sites in North America. The accuracy of both LAI products is assessed against seven high resolution ETM+ LAI maps derived from field measurements in 2000, 2001, and 2003. Both products were closely matched outside growing season. The MODIS product tended to be more variable than the VGT product during the summer period when the LAI was maximum. VGT and ETM+ LAI maps agreed well at three out of the four sites. The median relative absolute error of the VGT LAI product varied from 24% to 75% at 1 km scale and it ranged from 34% to 88% for the MODIS LAI product. The importance of correcting field measurements for the clumping effect is illustrated at the deciduous broadleaf forest site (HARV). Inclusion of the sub-pixel land cover information improved the quality of LAI estimates for the prairie grassland KONZ site. Further improvement of the global VGT LAI product is suggested by production and inclusion of pixel-specific global foliage clumping index and forest background reflectance maps that would serve as an input into the VGT LAI algorithms. [Copyright &y& Elsevier]

Observations in the visible and infrared spectral bands from the Imager instrument onboard Geostationary Operational Environmental Satellite (GOES) have been used to derive snow depth. The technique makes use of correlation between depth of the snow pack and satellite-derived subpixel fractional snow cover. Previous efforts to infer snow depth from satellite data with this technique were focused on grasslands and croplands, where the snow depth/snow fraction relationship is most pronounced. In this paper we improve the retrieval algorithm to extend snow depth estimates to forested areas. The enhanced algorithm accounts for the tree cover fraction and for the type of forest, deciduous or coniferous. The developed technique was used to derive maps of snow depth over mid-latitude areas of North America during winter seasons of 2003–2004 and 2004–2005. Satellite-based snow depth maps were produced daily at 4 km spatial resolution. To validate the retrievals we compared them with surface observations of snow depth and with the snow depth analysis prepared at the NOAA National Operational Hydrological Remote Sensing Center (NOHRSC). The estimated retrieval error was about 30% for snow depths below 30 cm and increased to 50% for snow depths ranging from 30 to 50 cm. Snow depth retrievals were limited to scenes with less than 80% deciduous forest cover fraction and less than 50% needle leaf forest cover. [Copyright &y& Elsevier]

Fire disturbance is a significant component of the climate system. Analysis of satellite-derived burned areas has allowed the identification of fire patches and their morphology as a new resource for tracking fire spread to improve fire models used to assess the impact of fires on climate and the carbon cycle. A critical parameter of the flood-fill algorithm used to create fire patches is the cut-off (in days) below which it aggregates two contiguous burned pixels to the same fire patch. However, the current level of validation is insufficient to understand the effect of the cut-off values and sensor resolutions on the subsequent fire-patch morphology. The FRY v1.0 database of functional fire-patch traits (e.g., size, elongation, and direction) emanates from the analyses of two global burned-area products derived from MODIS and MERIS sensors with different spatial and temporal resolutions and with cut-off values of 3, 5, 9, and 14 days. To evaluate whether the FRY products are accurately identifying the spatial features of fire patches and what are the most realistic cut-off values to use in different sub-regions of North America, we propose a new functional diversity trait-based approach, which compares the satellite-derived fire patches to forest service perimeters as reference data. This paper shows the accuracy of the FRY fire patches ≥300 ha in North America during 2005–2011. Our analysis demonstrates that fire patches with a high cut-off of 14 days and those derived from the MODIS sensor, with their high temporal resolution, better identify the fire diversity in North America. In conclusion, our statistical framework can be used for assessing satellite-derived fire patches. Furthermore, the temporal resolution of satellite sensors is the most important factor in identifying fire patches — thus space agencies should consider it when planning the future development of cost-effective climate observation systems. Unlabelled Image • The trait-based method can be used for assessing satellite-derived fire patches. • Functional–structure relationships are complemented by functional dissimilarities. • High temporal resolution is most important for fire-patch identification. • Fire patches with a cut-off of 14 days are better identified in North America. [ABSTRACT FROM AUTHOR]