Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations

Abstract

The Moderate Resolution Spectroradiometer (MODIS) sensor has provided near real-time estimates of gross primary production (GPP) since March 2000. We compare four years (2000 to 2003) of satellite-based calculations of GPP with tower eddy CO 2 flux-based estimates across diverse land cover types and climate regimes. We examine the potential error contributions from meteorology, leaf area index (LAI)/fPAR, and land cover. The error between annual GPP computed from NASA's Data Assimilation Office's (DAO) and tower-based meteorology is 28%, indicating that NASA's DAO global meteorology plays an important role in the accuracy of the GPP algorithm. Approximately 62% of MOD15-based estimates of LAI were within the estimates based on field optical measurements, although remaining values

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). 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).…”

“…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).…”

“…A further serious limitation is the degree to which the GCM derived inputs represent realistic estimates of climate variables because of the coarse scale of the GCM model outputs (~100 km) (Turner et al, 2003a,b). These issues, together with errors related to propagation from other underlying MODIS products (Heinsch et al, 2006), have been identified as potential sources of the differences found between satellite-based GPP estimates and field measurements for some biomes Coops et al, 2007).…”

The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation

“…Thus whilst some PEM parameters can be estimated from satellite data, for example the fraction of absorbed photosynthetically active radiation (fPAR; Myneni et al, 2003;Prince & Goward 1995), the estimation of others, such as LUE, depend upon the availability of metrological data and vegetation maps. There can, however, be substantial errors in the estimation of GPP from satellitebased PEMs because of the coarseness of the metrological inputs commonly used to scale the LUE parameter and the quality and resolution of the landcover classification on which biome specific maximum LUE values are based (Heinsch et al, 2006;Zhao et al, 2006).As a consequence of the difficulties involved with the parameterisation of both detailed process-based ecosystem exchange models and satellite-driven PEMs, there has been a renewed interest in developing productivity models that are entirely reliant upon satellite data, but which are not based upon the traditional LUE concept. Such models utilise vegetation indices to capture the seasonal dynamics of GPP (e.g.…”

“…Thus whilst some PEM parameters can be estimated from satellite data, for example the fraction of absorbed photosynthetically active radiation (fPAR; Myneni et al, 2003;Prince & Goward 1995), the estimation of others, such as LUE, depend upon the availability of metrological data and vegetation maps. There can, however, be substantial errors in the estimation of GPP from satellitebased PEMs because of the coarseness of the metrological inputs commonly used to scale the LUE parameter and the quality and resolution of the landcover classification on which biome specific maximum LUE values are based (Heinsch et al, 2006;Zhao et al, 2006).…”