How to effectively evaluate the mainstream Gross Primary Production Products in regions with few observation data?

The dynamic of Gross Primary Production (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 in understanding spatial patterns of the uncertainty of different GPP products and 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 over the 2003-2015 period. Ten GPP products are involved in this work, including one product (FLUXCOM) from machine learning (ML), six products (EC-LUE, FluxSat, LUEopt, MODIS, MuSyQ and VPM) based on the Light Use Efficiency model (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, while their values varied depending on various products and for different time scales. According to the 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. The installation of EC stations in CA is an effective way to improve the accuracy of all methods. In addition, 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.