基于GOSAT反演的中国地区二氧化碳浓度时空分布研究

卫星遥感监测大气二氧化碳柱平均干空气体积混合比(XCO₂)是实现碳源汇全球监测的最有效手段,本文对国际上4种应用GOSAT卫星观测的短波红外反演算法进行了介绍和结果分析。首先对于4种反演产品的有效数据量的分析表明:现有单一反演产品还不足以支撑XCO₂时空分布研究。其次利用集合平均方法,综合使用4种反演产品研究了2010年中国地区XCO₂时空分布特征,结果表明:XCO₂呈现显著的地理分布和季节变化,不同地区季节变化趋势基本一致,均在春季达到最高值、夏季达到最低值,多数地区全年高于380 ppm (×10-6);在地理分布上,东部和西部地区存在较明显的差异,东部地区人口密集、工农业生产等人为活动旺盛,周边多被森林和草地覆盖,碳源汇强度大,因此XCO₂季节变化幅度较大,全年约8 ppm;中、西部地区受人类活动影响较少,植被覆盖稀疏,XCO₂全年变化仅5 ppm。

The Spatial and Temporal Distribution of Carbon Dioxide over China Based on GOSAT Observations

Satellite remote sensing is the most efficient way to monitor global CO₂ flux. ‘Full physics’ retrieval algorithms applied to Greenhouse Gases Observing Satellite (GOSAT) observations are introduced in this paper, and the differences among the algorithms are briefly summarized. The quantity of retrieval data from each algorithm is analyzed, and the spatial coverage indicates that use of only one dataset is insufficient for the study of XCO₂(column-averaged CO₂ dry-air mixing ratio). Therefore, an ensemble average method that fuses four datasets is applied, which aims to increase the data spatial coverage indirectly. Using the ensemble average results, the spatial and temporal distribution of XCO₂ over China is studied. The results indicate strong variation of XCO₂, both spatially and temporally. A seasonal trend is identified, with the maximum and minimum appearing in spring and summer, respectively, over the whole of China, and most of the area shows large XCO₂ values>380 ppm (×10-6)]. However, there is a significant difference between east and west. In the east of China, strong CO₂ sources due to high levels of human activity, and sinks due to large areas of vegetation cover, lead to large variation in XCO₂(8 ppm). Whereas, in the west of China, the relatively sparse human population and vegetation cover lead to small variation in XCO₂(5 ppm).