Steven W. Running

Net primary production (NPP) is an important metric of the global carbon cycle, critical for monitoring climate stabilization and for societal economic products. Global NPP should be monitored continuously to adapt societal policy to emerging declines.

Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO₂ and mitigating global climate change.

Terrestrial ecosystems have continued to provide the critical service of slowing the atmospheric CO₂growth rate. Terrestrial net primary productivity (NPP) is thought to be a major contributing factor to this trend. Yet our ability to estimate NPP at the regional scale remains limited due to large uncertainties in the response of NPP to multiple interacting climate factors and uncertainties in the driver data sets needed to estimate NPP. In this study, we introduced an improved NPP algorithm that used local driver data sets and parameters in China. We found that bias decreased by 30% for gross primary production (GPP) and 17% for NPP compared with the widely used global GPP and NPP products, respectively. From 2000 to 2012, a pixel‐level analysis of our improved NPP for the region of China showed an overall decreasing NPP trend of 4.65 Tg C a⁻¹. Reductions in NPP were largest for the southern forests of China (−5.38 Tg C a⁻¹), whereas minor increases in NPP were found for North China (0.65 Tg C a⁻¹). Surprisingly, reductions in NPP were largely due to decreases in solar radiation (82%), rather than the more commonly expected effects of drought (18%). This was because for southern China, the interannual variability of NPP was more sensitive to solar radiation (R²in 0.29–0.59) relative to precipitation (R² < 0.13). These findings update our previous knowledge of carbon uptake responses to climate change in terrestrial ecosystems of China and highlight the importance of shortwave radiation in driving vegetation productivity for the region, especially for tropical forests.

Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO₂ between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPP_NDVI3g), GIMMS NDVI1g (GPP_NDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPP_MOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPP_MOD17). Based on validation with flux tower derived GPP estimates the results show that GPP_NDVI3g is more accurate than GPP_NDVI1g and is comparable in accuracy with GPP_MOD15. In addition, GPP_NDVI3g and GPP_MOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics.

Forty years ago, Meadows et al. published a landmark first analysis of global limits to human activity (1). Based on a primitive computer model of the Earth system, they concluded that by the early decades of the 21st century, tangible limits to key global resources would begin to emerge. A reanalysis of the original results in 2008 found that the original global resource depletion projections were remarkably accurate (2). Since then, Rockström et al. (3) have defined a new term—planetary boundaries—to describe nine variables of high importance to habitability of Earth, including climate change, ocean acidification, land-use change, and biodiversity loss. These metrics are compelling conceptually, but many are not easily measured globally; explicitly defining a critical boundary is even more challenging. I suggest a new planetary boundary, terrestrial net primary (plant) production (NPP), that may be as compelling conceptually, integrates many of the currently defined variables, and is supported by an existing global data set for defining boundaries.

Models of climate change effects should incorporate land-use changes and episodic disturbances such as fires and insect epidemics.