中国陆地植被净初级生产力估算模型优化与分析——基于中国生态系统研究网络数据

该研究基于中国生态系统研究网络(CERN)数据对传统CASA模型进行优化，对比两叶模型与优化CASA模型在站点尺度和像元尺度对于8个典型生态站点的植被净初级生产力(NPP)估算精度，选择在像元尺度表现更好的优化CASA模型，结合中国土地覆被数据(ChinaCover)开展2000—2019年中国陆地植被NPP监测与分析。研究结果表明：(1)基于FY2D PAR的优化方案能够有效避免空间插值导致的不确定性问题，显著提高了PAR估算精度；(2)在站点尺度上，两叶模型用于估算典型森林、草地生态系统的NPP表现更好，而在像元尺度上优化CASA模型估算精度更高；(3)在全国尺度上，优化了最大光能利用率、水分胁迫系数以及光合有效辐射计算方法的CASA模型能够较好地模拟中国陆地植被NPP,近20年中国陆地植被NPP变化范围为2.703—2.882 PgC/a,在空间上呈西北低东南高的格局，在时间上呈现波动中缓慢增加的趋势。

Optimization of net primary productivity estimation model for terrestrial vegetation in China based on CERN data

In this study, we optimized the traditional Carnegie-Ames-Stanford Approach (CASA) model based on China Ecosystem Research Network (CERN) datasets, and compared the estimation accuracy of the two-leaf model and the optimized CASA model at the site scale and pixel scale at eight CERN sites covering major ecosystem types. The optimized CASA model with better performance at the pixel scale combined with China Land Cover Data (ChinaCover) were employed for mapping and monitoring the spatio-temporal changes of terrestrial vegetation net primary production (NPP) in China from 2000 to 2019. The results show that:(1) the optimization for model input parameter of photosynthetically active radiation based on FY2D PAR can effectively avoid the uncertainty caused by the spatial interpolation, and significantly improve the accuracy of PAR estimation; (2) The two-leaf model shows higher NPP estimation accuracy at the site scale, while the optimized CASA model performs better for the NPP estimation at the pixel scale; (3) At the national scale, the CASA model with optimized maximum light energy use efficiency, water stress coefficient and photosynthetically active radiation can better simulate China''s terrestrial vegetation NPP. The estimated total NPP in Chinese terrestrial vegetation ranges from 2.703 PgC/a to 2.882 PgC/a in the past 20 years and indicates a fluctuated and slow increasing trend. The spatial distribution of the NPP in China shows a general pattern of gradually increasing from northwest to southeast.