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

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

“…The MOD17 algorithm models using a look-up table containing biome specific information about the maximum light use efficiency max , daily minimum temperature (T Min ) and D for maximum and minimum of each biome type (Running et al 2000;Turner et al 2003a). The biome specific constant max is adjusted using 1°x1.25° estimates of T Min and D, derived from GCMs (e.g.…”

“…The biome specific constant max is adjusted using 1°x1.25° estimates of T Min and D, derived from GCMs (e.g. Potter et al, 1993;Sellers et al, 1996aSellers et al, , 1996b to account for the limiting effects of climatic variables on (Heinsch et al, 2002;Turner et al, 2003a),…”

“…This assumption generally fails to reflect the spatial heterogeneity in land cover, stand age, soil type and canopy structure for most biomes (Goulden et al, 1996). The use of a simple lookup table approach to determining from biome-specific parameters which do not vary in space and time (Running et al, 2000;Heinsch et al, 2002;Turner et al, 2003a), and distinguish only between 11 different vegetation types (Turner et al, 2003a;Heinsch et al, 2006) has been identified as the weak point of the GPP product as it greatly simplifies the existing spatial and temporal variability in . In addition, assigning values of on the basis of biome type assumes between-biome variability to be greater than within biome variability, which is often not realistic (Goetz and Prince, 1996;Landsberg et al, 1997).…”

Towards operational remote sensing of forest carbon balance across Northern Europe

“…The authors state that they are exploring the use of satellite derived air temperature for a robust carbon balance model entirely based on satellite data. The MODIS GPP product, which gives eight day averages of GPP globally at 1 km resolution, was in turn validated by (Turner et al, 2003) who compared MODIS GPP to ground measured GPP for a boreal and a hardwood site in the US. It was found that the product overestimated growing season GPP at the hardwood site while an underestimation was found at the boreal site.…”

“…In general, the main advantages of a LUE-model for estimation of NPP or GPP lies in the possibilities of operational implementation (Running et al, 2000), and that it can output productivity with high accuracy if parameterized correct (Turner et al, 2005). However, a LUE-model usually relies on meteorological data sets for obtaining the LUE-factor.…”