2001—2018年中国总初级生产力时空变化的遥感研究

总初级生产力（GPP）是绿色植被吸收大气中CO₂进行光合作用生产的有机质，是陆地生态系统碳循环研究的一个关键参数。利用遥感数据和气象数据驱动的双叶光能利用率DTEC模型计算了2001-2018年中国逐月GPP，并结合日光诱导叶绿素荧光（SIF）反演的GOSIF GPP数据集，分析了中国陆地生态系统2001-2018年GPP的时空变化特征。结果表明：（1） GOSIF和DTEC模拟的中国多年GPP平均值分别为7.23 Pg C和6.93 Pg C，在空间分布上呈现东南部高西北部低的特征；（2）2001-2018年，中国GPP呈显著增长（P<0.01），年增长幅度分别为0.094 PgC/a （GOSIF）和0.073 PgC/a （DTEC）。而已有研究估计的中国GPP年增长幅度约为0.02-0.057 PgC/a，低估了GPP增长趋势。（3）在中国通量网6个通量站的GPP验证表明，两种模型精度高、表现好，都能较好地模拟观测站的GPP季节变化。（4） GOSIF GPP的精度优于DTEC GPP模型，这可能是由于SIF与GPP存在直接机理联系。GOSIF GPP算法能客观地反映植被生产力状况，而DTEC模型更适合自然条件下植被生产力的模拟。

Analysis of spatio-temporal changes of gross primary productivity in China from 2001 to 2018 based on Romote Sensing

Gross primary productivity (GPP) of vegetation, which is the total organic matter produced by green plants through absorbing atmospheric CO₂ for photosynthesis, is a key parameter for carbon cycle studies in terrestrial ecosystems. Accurate quantification of GPP is a research hotspot in the field of earth system science, and GPP has been extensively explored in China using a big leaf remote sensing light use efficiency model. In this paper, we calculated the month by month GPP in China from 2001to 2018 using a diffuse-fraction-based two-leaf light use efficiency model (DTEC) driven by remote sensing data and meteorological data, and analyzed the spatio-temporal characteristics of GPP during 2001-2018 by combining the GOSIF GPP dataset derived from a new global OCO-2-based solar-induced chlorophyll fluorescence dataset (GOSIF). The results show that (1) the multi-year average GPP in China, simulated by GOSIF and DTEC, was 7.23 Pg C and 6.93 Pg C respectively, which featured higher values in the southeast regions and lower values in the northwest regions. (2) From 2001 to 2018, it showed a significant overall increase in GPP (P<0.01) estimated by GOSIF and DTEC model with annual increase trend of 0.094 PgC/a and 0.073 PgC/a, respectively. Among them, the growth rate of GPP in the southeast and southwest regions is the largest, followed by the large and small Xing''an Mountains in the northeast, and the annual GPP in the northwest and the Qinghai Tibet Plateau shows a slight upward trend, and some areas showed a downward trend. Whereas the annual GPP growth rate in China has been estimated from previous studies to be 0.02-0.057 PgC/a, which may underestimate the growth trend of China''s GPP. (3) The evaluation of GPP at six flux stations of the China flux network showed that the two models had high accuracy and good performance, as well as could simulate the seasonal variations in the observed GPP. The two models perform best in the forest site. (4) The accuracy of GOSIF GPP was higher than that of DTEC GPP model, which may be due to the direct mechanistic link between the SIF information and GPP adopted by GOSIF GPP model. The GOSIF GPP algorithm can objectively reflect the vegetation productivity status, while the DTEC model is more suitable for the simulation of vegetation productivity under natural conditions. Using various types of GPP models to carry out large-scale GPP research can reduce the uncertainty of terrestrial ecosystems carbon cycle research.