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.

Water use efficiency (WUE) is a useful indicator to illustrate the interaction of carbon and water cycles in terrestrial ecosystems. MODIS gross primary production (GPP) and evapotranspiration (ET) products have been used to analyze the spatial and temporal patterns of WUE and their relationships with environmental factors at regional and global scales. Although MODIS GPP and ET products have been evaluated using eddy covariance flux measurements, the accuracy of WUE estimated from MODIS products has not been well quantified. In this paper, we evaluated WUE estimated from MODIS GPP and ET products against eddy covariance measurements of GPP and ET during 2003–2008 at eight sites of the Chinese flux observation and research network (ChinaFLUX) and conducted sensitivity analysis to investigate the possible key contributors to the bias of MODIS products. Results show that MODIS products underestimate eight-day water use efficiency in four forest ecosystems and one cropland ecosystem with the bias from −0.36–−2.28 g·C·kg⁻¹ H₂O, while overestimating it in three grassland ecosystems with the bias from 0.26–1.11 g·C·kg⁻¹ H₂O. Mean annual WUE was underestimated by 14%–54% at four forest sites, 45% at one cropland site and 7% at an alpine grassland site, but overestimated by 66% and 9% at a temperate grassland site and an alpine meadow site, respectively. The underestimation of WUE by MODIS data results from underestimated GPP and overestimated ET at four forest sites, while MODIS WUE values are significantly overvalued mainly due to underestimated ET in the three grassland ecosystems. The maximum light use efficiency and fraction of photosynthetically-active radiation (FPAR) were the two most sensitive factors to the estimation of WUE derived from the MODIS GPP and ET algorithms. The error in meteorological data partly caused the overestimation of ET and accordingly underestimation in WUE in subtropical and tropical forests. The bias of MODIS-produced WUE was also derived from the uncertainties in eddy flux data due to gap-filling processes and unbalanced surface energy issue. Their contributions to the uncertainty in estimated WUE at both eight-day and annual scales still need to be further quantified.

The karst region of southwest China showed a significant increase in vegetation cover and vegetation carbon stocks under the implementation of a series of ecological restoration projects. However, the relative contribution of ecological restoration projects to terrestrial carbon sequestration in the context of climate change has yet to be well quantified. Here, we used the Community Land Model (CLM4.5) to investigate the trend of net ecosystem productivity (NEP) and attribution to multiple environmental factors in the karst region of southwest China during 2000–2018. The result showed that ecosystems with a significant increasing trend of NEP covered about 46% of the study region, which were mainly located in the peak forest plain region, colliculus region, peak cluster depression region, and middle-high hill region. The simulation experiments suggested that land use change associated with ecological restoration projects caused a large contribution of 53% to the increasing NEP trend, followed by CO₂ fertilization (72%), while climate factors and nitrogen deposition showed minor negative effects. Especially, the NEP trend induced by land use change in the 100 pilot counties with the implementation of rocky desertification control project was significantly higher than that in the other karst area. Moreover, moderate and high levels of restoration efforts invested into recovery led to a larger increasing trend (0.66 gC/m²/yr² and 0.48 gC/m²/yr²) in NEP than the low efforts level (0.22 gC/m²/yr²). Our results highlight the important role of ecological restoration projects in the enhanced terrestrial carbon sequestration in the karst region of southwest China, and recommend a comprehensive assessment of ecological restoration projects for policymaking.