Abstract
Abstract. 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 studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10–80%). The ability of remotely sensed proxies and models to explain interannual variability in 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, or deciduous broadleaf forests. Robust and repeatable characterization of spatiotemporal variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. Our analyses highlight the power of remote sensing-based models, but also provide bounds on the uncertainties associated with these models. Uncertainty in flux tower GPP, and difference between the footprints of MODIS pixels and flux tower measurements are acknowledged as unresolved challenges.
Highlights
Terrestrial ecosystems sequester about 25 % (≈ 2–2.5 Pg C year−1) of the carbon emitted by human activities each year (Canadell et al, 2007)
growing period length (GPL) and enhanced vegetation index (EVI)-area estimates were not extracted for evergreen broadleaf forest (EBF) sites because we assume that GPL is not a significant control on annual Gross primary productivity (GPP) in this biome
We examined six remote sensing-based models in this study (Table 1): the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (MOD17; Running et al, 2004), the temperature and greenness (TG) model (Sims et al, 2008), the vegetation photosynthesis and respiration model (VPRM) (Mahadevan et al, 2008), a non-parametric neural network model (e.g., Beer et al, 2010; Moffat et al, 2010), the MOD17 algorithm calibrated to tower GPP (e.g., Heinsch et al, 2006; hereafter referred to as “MOD17Tower”), and regression models that use one of the four proxies and mean annual temperature or mean annual precipitation as predictors
Summary
Terrestrial ecosystems sequester about 25 % (≈ 2–2.5 Pg C year−1) of the carbon emitted by human activities each year (Canadell et al, 2007). A large number of studies have compared results derived from remote sensing-based models with in situ measurements (e.g., Turner et al, 2006; Heinsch et al, 2006; Yuan et al, 2007; Sims et al, 2008; Mahadevan et al, 2008; Xiao et al 2010) All of these studies are based on relatively small in situ data sets and none have explicitly examined both spatial and temporal variations in remotely sensed proxies (e.g., Hashimoto et al, 2012) and modeled estimates with corresponding variations in in situ measurements of GPP. Our analysis addresses three questions: 1. How well do the selected remote sensing-based methods capture geographic (across sites) and interannual variation (across years) in annual GPP?
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