Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems

Abstract

Models constitute the primary approaches for predicting terrestrial ecosystem gross primary production (GPP) at regional and global scales. Many satellite-based GPP models have been developed due to the simple algorithms and the low requirements of model inputs. The performances of these models are well documented at the biome level. However, their performances among vegetation subtypes limited by different environmental stresses within a biome remains largely unexplored. Taking grasslands in northern China as an example, we compared the performance of eight satellite-based GPP models, including three light-use efficiency (LUE) models (vegetation photosynthesis model (VPM), modified VPM (MVPM), and moderate resolution imaging spectroradiometer GPP algorithm (MODIS-GPP)) and five statistical models (temperature and greenness model (TG), greenness and radiation model (GR), vegetation index model (VI), alpine vegetation model (AVM), and photosynthetic capacity model (PCM)), between the water-limited temperate steppe and the temperature-limited alpine meadow based on 16 site-year GPP estimates at four eddy covariance (EC) flux towers. The results showed that all the GPP models performed better in the alpine meadow, particularly in the alpine shrub meadow (R2 ≥ 0.84), than in the temperate steppe (R2 ≤ 0.68). The performance varied greatly among the models in the temperate steppe, while slight intermodel differences existed in the alpine meadow. Overall, MVPM (of the LUE models) and VI (of the statistical models) were the two best-performing models in the temperate steppe due to their better representation of the effect of water stress on vegetation productivity. Additionally, we found that the relatively worse model performances in the temperate steppe were seriously exaggerated by drought events, which may occur more frequently in the future. This study highlights the varying performances of satellite-based GPP models among vegetation subtypes of a biome in different precipitation years and suggests priorities for improving the water stress variables of these models in future efforts.

Spatial variation of nutrients and primary productivity in the Rufiji Delta mangroves, Tanzania

“…where K is the light extinction coefficient. The relationship between PAR and photosynthesis is expressed in Eqn 18, following Zhang et al (2019):…”

“…To avoid excess water loss, the stomata in the leaves adjust the transpiration rate by closing. Since stomata are the same pores through which CO 2 enters the leaves, the closure causes a decrease in the flow of CO 2 and ultimately a reduction in photosynthesis, as represented by Eqn 19 (Zhang et al 2019):…”

Contrasting Performance of the Remotely-Derived GPP Products over Different Climate Zones across China

“…For most biome types, the VPM GPP did not show a systematic bias across different terrestrial ecosystems in a large area. Particularly in the temperate steppe, VPM is an optimal model among eight satellite-based GPP models due to their representation of the effect of water stress on vegetation productivity [73]. Except for the evergreen forests, the VPM GPP dataset underestimated GPP to a certain extent [25].…”

Emerging approaches to measure photosynthesis from the leaf to the ecosystem

“…Remote-sensing products that measure GPP are traditionally based on the Light-Use Efficiency (LUE) concept of ecosystem modeling [86] and empirical models that rely on the relationships between remote sensing-derived variables and GPP [87][88][89][90]. These methods provide reasonable estimates of GPP compared with measured EC fluxes, however, new emerging spectral sensing technologies including Solar-Induced chlorophyll Fluorescence (SIF) are providing potential for estimating GPP at the ecosystem scale [91][92][93].…”