Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set

Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET "La Thuile" data set, which includes several times more sites (144) and site years (422) than previous efforts have used. Our results show that remotely sensed proxies and modeled GPP are able to capture statistically significant amounts of spatial variation in mean annual GPP in every biome except croplands, but that the total variance explained differed substantially across biomes (R2 ≈ 0.1−0.8). The ability of remotely sensed proxies and models to explain interannual variability GPP was even more limited. Remotely sensed proxies explained 40–60% of interannual variance in annual GPP in moisture-limited biomes including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, and deciduous broadleaf forests. Because important factors that affect year-to-year variation in GPP are not explicitly captured or included in the remote sensing proxies and models we examined (e.g., interactions between biotic and abiotic conditions, and lagged ecosystems responses to environmental process), our results are not surprising. Nevertheless, robust and repeatable characterization of interannual variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. As larger and more comprehensive data sets derived from the FLUXNET community become available, additional systematic assessment and refinement of remote sensing-based methods for monitoring annual GPP is warranted