Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation

Abstract

The Moderate Resolution Imaging Radiometer (MODIS) is the primary instrument in the NASA Earth Observing System for monitoring the seasonality of global terrestrial vegetation. Estimates of 8-day mean daily gross primary production (GPP) at the 1 km spatial resolution are now operationally produced by the MODIS Land Science Team for the global terrestrial surface using a production efficiency approach. In this study, the 2001 MODIS GPP product was compared with scaled GPP estimates (25 km 2 ) based on ground measurements at two forested sites. The ground-based GPP scaling approach relied on a carbon cycle process model run in a spatially distributed mode. Land cover classification and maximum annual leaf area index, as derived from Landsat ETM+ imagery, were used in model initiation. The model was driven by daily meteorological observations from an eddy covariance flux tower situated at the center of each site. Model simulated GPPs were corroborated with daily GPP estimates from the flux tower. At the hardwood forest site, the MODIS GPP phenology started earlier than was indicated by the scaled GPP, and the summertime GPP from MODIS was generally lower than the scaled GPP values. The fall-off in production at the end of the growing season was similar to the validation data. At the boreal forest site, the GPP phenologies generally agreed because both responded to the strong signal associated with minimum temperature. The midsummer MODIS GPP there was generally higher than the ground-based GPP. The differences between the MODIS GPP products and the ground-based GPPs were driven by differences in the timing of FPAR and the magnitude of light use efficiency as well as by differences in other inputs to the MODIS GPP algorithm-daily incident PAR, minimum temperature, and vapor pressure deficit. Ground-based scaling of GPP has the potential to improve the parameterization of light use efficiency in satellite-based GPP monitoring algorithms. D

Satellite assessment of land surface evapotranspiration for the pan‐Arctic domain

“…Also model biophysical parameters, such as the critical VPD values when stomata are completely inhibited (VPD_close) or under no water stress (VPD_open) are used as constant parameters within a given biome type as defined from a global land cover classification. However, for different species within the same biome type, the differences in these parameters can be large [Turner et al, 2003a[Turner et al, , 2003b. In addition, the RS-ET model treats nonvegetated areas as a uniform evaporating surface.…”

Satellite Ecology (SATECO)—linking ecology, remote sensing and micrometeorology, from plot to regional scale, for the study of ecosystem structure and function

“…Numerous studies have been published to test the accuracy of vegetation indices as the indicator of foliage density (LAI) and its phenology (Tucker et al 2001) or GPP and related parameters (Xiao et al 2004;Nakaji et al 2008) in various terrestrial ecosystems. Especially intensive studies have been conducted in FLUXNET sites to validate such indices and MOD17 with the aid of eddy covariance data (Turner et al 2003(Turner et al , 2005Heinsch et al 2006;Sims et al 2008).…”

Remotely Monitoring Ecosystem Water Use Efficiency of Grassland and Cropland in China’s Arid and Semi-Arid Regions with MODIS Data

“…Jiang and Ryu [60] The mismatch between the actual flux tower footprint with a 1 km 2 approximation used in the comparisons to MODIS GPP and ET products is also an issue. Although the EC-based flux measurement has a larger footprint compared to the field measurements, it remains uncertain owing to random measurement errors, gap-filling errors, the energy balance disclosure issue and the variation in the footprint with wind speed and direction [61,62]. Spatially-averaged MODIS values from a 3 × 3 window over each tower location were used to reduce the effects of spatial heterogeneity, but may also introduce additional uncertainties through regional smoothing of MODIS retrievals relative to tower observations, especially over spatially-complicated vegetation and terrain [47,63].…”