Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data

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

“…Validation of the MODIS GPP product was mainly undertaken in form of time series comparisons between GPP estimated from eddy covariance flux tower data and GPP from MODIS for one or more 1-km 2 cells surrounding the tower (Turner et al, 2003a,b;Xiao et al, 2004). While some of these comparisons have shown reasonable agreement between tower based estimates of CO 2 fluxes and MODIS land cover products, also numerous limitations were found and issues identified for further research: The largest error associated with the land cover classification is the simplifying assumption that each 1x1 km pixel only contains a single land cover class (Heinsch et al, 2006).…”

A satellite‐based biosphere parameterization for net ecosystem CO2 exchange: Vegetation Photosynthesis and Respiration Model (VPRM)

“…[4] Since the inception of inverse modeling of CO 2 , it has been recognized that surface flux submodels must accurately represent relevant spatiotemporal variations of NEE [Fung et al, 1987;Ruimy et al, 1995;Sellers et al, 1996;Goetz and Prince, 1999;Xiao et al, 2002Xiao et al, , 2004aXiao et al, , 2004b. A priori surface flux models must have a low order of parameterization, so that the optimization process is well constrained [Denning et al, 1995;Lin et al, 2004], while retaining the required fine spatial and temporal resolution.…”

“…Model structure is made very simple to facilitate subsequent inverse analysis. Formulation of the VPRM starts from the Vegetation Photosynthesis Model (VPM) of Xiao et al [2004aXiao et al [ , 2004b, which estimates Gross Ecosystem Exchange (GEE) using satellite-based vegetation indices and environmental data, adding respiration (R) to provide NEE and a nonlinear function to account for the response of GEE to light. The Enhanced Vegetation Index (EVI) [Huete et al, 1997[Huete et al, , 2002 estimates of the Fraction of Photosynthetically Active Radiation (PAR) absorbed by photosynthetically active parts of the vegetation (FAPAR PAV ) [Xiao et al, 2004a[Xiao et al, , 2004b and the Land Surface Water Index (LSWI) help capture the effects of water stress and leaf phenology [Xiao et al, 2004a[Xiao et al, , 2004b, especially for vegetation that becomes dormant in summer (e.g., grasslands).…”

Separating physiologically and directionally induced changes in PRI using BRDF models

“…Determined by any of a large number of environmental stresses restraining the photochemical reaction process, such as nutrition supply, water, and temperature, depends on vegetation types and varies greatly over space and time (Field and Mooney 1986, Prince and Goward 1995, Turner et al 2003. Whilst recent years have seen considerable progress detecting using high spectral resolution remote sensing instrumentation at the leaf, canopy, and stand scales, the angular, spatial, spectral and temporal requirements for up-scaling such observations to landscape and global levels using satellite remote sensing remain less well understood (Hall et al, 1995, Xiao et al, 2004.…”