Can Gross Primary Productivity Products be effectively evaluated in regions with few observation data?

ABSTRACT

The dynamics of Gross Primary Productivity (GPP) is key to understand the global carbon cycle. Multiple GPP products are currently available based on remote sensing, Light Use Efficiency model (LUE) or diagnostic biophysical model. However, little knowledge is available on the spatial patterns of the uncertainty of different GPP products and their potential drivers over the Central Asia (CA), a fragile environment for accurate GPP estimation. This study investigates the sensitivity of the 8-day, monthly and yearly GPP uncertainties based on the three-cornered hat (TCH) method and Shapley additive explanation (SHAP) model in terms of vegetation, energy, water, climate and terrain factors in the dryland ecosystem during the 2003–2015 period. Ten GPP products were examined, including one product (FLUXCOM) from machine learning (ML), six products (EC-LUE, FluxSat, LUEopt, MODIS, MuSyQ and VPM) based on the (LUE), two products (GOSIF and NIRv) from satellite-based direct proxies (Proxies) and one product (PML) from the diagnostic biophysical model. The results indicate that the spatial distribution of the ten GPP products in CA showed similar patterns at different time scales, but with values varied at different products and time scales. According to the eddy covariance (EC) observations and the TCH-based uncertainties, the FLUXCOM product showed smaller relative uncertainties than other products. The attribution analysis denotes that the sources of uncertainty of the GPP varied for each product, and thus the improvement strategies for different products should be implemented separately The FLUXCOM should adapt the vegetation- related module to the dryland environment of CA. The LUE model should optimize the LUE parameters for the dryland ecosystem and incorporate the water related variables in the model. The Proxies model should incorporate the water and energy variables (such as soil moisture and radiation) as input data to improve their performance in CA. The diagnostic model should consider the elevation variable as input data, which may improve the performance of the PML in CA. Our results do not only provide an important basis for the selection of GPP products in the study of the carbon cycle in CA, but also offer a new insight into the GPP model development and improvement for the dryland ecosystem.

KEYWORDS:

• Gross Primary Productivity
• Central Asia
• Three-cornered hat (TCH) method
• Uncertainties quantification
• Attribution analysis

Acknowledgments

We thank the Research Center for Ecology and Environment of Central Asia，Chinese Academy of Sciences for the support of data for this study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study (i.e. the TCH results of ten GPP products) are available at figshare (https://doi.org/10.6084/m9.figshare.21505017.v2).

Additional information

Funding

 This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA20060302), the Tianshan Talent Cultivation (Grant No. 2022TSYCLJ0001), the High-End Foreign Experts Project, the Natural Science Foundation of Sichuan (Grant No. 2022NSFCSC1124), and the National Natural Science Foundation of China (42205127).