Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO2 trends

Zhongyi Sun; Xiufeng Wang; Xirui Zhang; Hiroshi Tani; Enliang Guo; Shuai Yin; Tianyou Zhang

doi:10.1016/j.scitotenv.2019.03.025

Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO₂ trends

Sci Total Environ. 2019 Jun 10:668:696-713. doi: 10.1016/j.scitotenv.2019.03.025. Epub 2019 Mar 4.

Authors

Zhongyi Sun¹, Xiufeng Wang², Xirui Zhang³, Hiroshi Tani², Enliang Guo⁴, Shuai Yin⁵, Tianyou Zhang⁶

Affiliations

¹ Hokkaido University, Graduate School of Agriculture, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan. Electronic address: sunzy025@env.agr.hokudai.ac.jp.
² Hokkaido University, Research Faculty of Agriculture, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan.
³ School of Mechanics and Electrics Engineering, Hainan University, Haikou 570228, China.
⁴ Inner Mongolia Normal University, College of Geographic Science, Hohhot 010022, China.
⁵ Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba 3058506, Japan.
⁶ College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.

PMID: 30856578
DOI: 10.1016/j.scitotenv.2019.03.025

Abstract

Remote sensing (RS)-based models play an important role in estimating and monitoring terrestrial ecosystem gross primary productivity (GPP). Several RS-based GPP models have been developed using different criteria, yet the sensitivities to environmental factors vary among models; thus, a comparison of model sensitivity is necessary for analyzing and interpreting results and for choosing suitable models. In this study, we globally evaluated and compared the sensitivities of 14 RS-based models (2 process-, 4 vegetation-index-, 5 light-use-efficiency, and 3 machine-learning-based models) and benchmarked them against GPP responses to climatic factors measured at flux sites and to elevated CO₂ concentrations measured at free-air CO₂ enrichment experiment sites. The results demonstrated that the models with relatively high sensitivity to increasing atmospheric CO₂ concentrations showed a higher increasing GPP trend. The fundamental difference in the CO₂ effect in the models' algorithm either considers the effect of CO₂ through changes in greenness indices (nine models) or introduces the influences on photosynthesis (three models). The overall effects of temperature and radiation, in terms of both magnitude and sign, vary among the models, while the models respond relatively consistently to variations in precipitation. Spatially, larger differences among model sensitivity to climatic factors occur in the tropics; at high latitudes, models have a consistent and obvious positive response to variations in temperature and radiation, and precipitation significantly enhances the GPP in mid-latitudes. Compared with the results calculated by flux-site measurements, the model performance differed substantially among different sites. However, the sensitivities of most models are basically within the confidence interval of the flux-site results. In general, the comparison revealed that models differed substantially in the effect of environmental regulations, particularly CO₂ fertilization and water stress, on GPP, and none of the models performed consistently better across the different ecosystems and under the various external conditions.

Keywords: CO(2) fertilization effect; Gross primary production; Model sensitivity.

Publication types

Review