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

作者：Wenqiang Zhang, Geping Luo, Rafiq Hamdi, Xiumei Ma, Yuzhen Li, Xiuliang Yuan, Chaofan Li, Qing Ling, Olaf Hellwich, Piet Termonia, Philippe De Maeyer · 发表于：GIScience & Remote Sensing · 年份：2023 · DOI：10.1080/15481603.2023.2213489 · 被引用次数：22 · 研究领域：Solar Radiation and Photovoltaics、Remote Sensing in Agriculture、Plant Water Relations and Carbon Dynamics

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 f...