Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO_2 trends

摘要

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. (c) 2019 Elsevier B.V. All rights reserved.