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.

As the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are still uncertain especially under extreme climate conditions such as droughts. As the only official products of global spatially explicit GPP, MOD17A2H (GPP_MOD) has been widely used to assess the variations of carbon uptake of terrestrial ecosystems. However, systematic assessment of its performance has rarely been conducted especially for the grassland ecosystems where inter-annual variability is high. Based on a collection of GPP datasets (GPP_EC) from a global network of eddy covariance towers (FluxNet), we compared GPP_MOD and GPP_EC at all FluxNet grassland sites with more than five years of observations. We evaluated the performance and robustness of GPP_MOD in different grassland biomes (tropical, temperate, and alpine) by using a bootstrapping method for calculating 95% confident intervals (CI) for the linear regression slope, coefficients of determination (R²), and root mean square errors (RMSE). We found that GPP_MOD generally underestimated GPP by about 34% across all biomes despite a significant relationship (R² = 0.66 (CI, 0.63–0.69), RMSE = 2.46 (2.33–2.58) g Cm⁻² day⁻¹) for the three grassland biomes. GPP_MOD had varied performances with R² values of 0.72 (0.68–0.75) (temperate), 0.64 (0.59–0.68) (alpine), and 0.40 (0.27–0.52) (tropical). Thus, GPP_MOD performed better in low GPP situations (e.g., temperate grassland type), which further indicated that GPP_MOD underestimated GPP. The underestimation of GPP could be partly attributed to the biased maximum light use efficiency (ε_max) values of different grassland biomes. The uncertainty of the fraction of absorbed photosynthetically active radiation (FPAR) and the water scalar based on the vapor pressure deficit (VPD) could have other reasons for the underestimation. Therefore, more accurate estimates of GPP for different grassland biomes should consider improvements in ε_max, FPAR, and the VPD scalar. Our results suggest that the community should be cautious when using MODIS GPP products to examine spatial and temporal variations of carbon fluxes.

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.

Terrestrial ecosystems provide multiple services interacting in complex ways. However, most ecosystem services (ESs) models (e.g., InVEST and ARIES) ignored the relationships among ESs. Process‐based models can overcome this limitation, and the integration of ecological models with remote sensing data could greatly facilitate the investigation of the complex ecological processes. Therefore, based on the Carbon and Exchange between Vegetation, Soil, and Atmosphere (CEVSA) models, we developed a process‐based ES model (CEVSA‐ES) integrating remotely sensed leaf area index to evaluate four important ESs (i.e., productivity provision, carbon sequestration, water retention, and soil retention) at annual timescale in China. Compared to the traditional terrestrial biosphere models, the main innovation of CEVSA‐ES model was the consideration of soil erosion processes and its impact on carbon cycling. The new version also improved the carbon‐water cycle algorithms. Then, the Sobol and DEMC methods that integrated the CEVSA‐ES model with nine flux sites comprising 39 site‐years were used to identify and optimize parameters. Finally, the model using the optimized parameters was validated at 26 field sites comprising 135 site‐years. Simulation results showed good fits with ecosystem processes, explaining 95%, 92%, 76%, and 65% interannual variabilities of gross primary productivity, ecosystem respiration, net ecosystem productivity, and evapotranspiration, respectively. The CEVSA‐ES model performed well for productivity provision and carbon sequestration, which explained 96% and 81% of the spatial‐temporal variations of the observed annual productivity provision and carbon sequestration, respectively. The model also captured the interannual trends of water retention and soil erosion for most sites or basins.

A new model (GLOPEM-CEVSA) to determine terrestrial carbon budgets was developed by coupling remote sensing with ecosystem process simulation, and was validated with reference to the carbon fluxes of three forests. MODIS FPAR (MOD15A2 product) was applied together with meteorological data on flux towers. The seasonal variances of modelled gross primary production and ecosystem respiration were significantly correlated with observed values (correlation coefficient, r > 0.9). The seasonal dynamics of the modelled net ecosystem production over the plant-growth season showed significant agreement with observed values with a r range of 0.64 to 0.87. This work demonstrates the potential of GLOPEM-CEVSA to quantify the spatial patterns and temporal dynamics of terrestrial ecosystem carbon sources and sinks with consideration of the spatial heterogeneity of ecosystems based on remote sensing.