中文摘要：

针对GOSAT(Greenhouse gases Observing Satellite)近红外波段数据进行的大气二氧化碳含量反演,提出了贝叶斯理论中误差矩阵的构建方法并进行了分析验证。首先模拟不同的初始估计值、不同的先验信息误差矩阵Sa和测量误差矩阵Sε构建结果对CO2反演的影响,然后据此挑选出Sa和Sε的优劣两种构建结果组合分别对2009年塔克拉玛干沙漠地区的部分GOSAT观测数据进行反演验证。研究结果表明先验信息方差越大或测量误差越小,反演结果趋于一致,反之结果则较为离散,表现出明显的规律性。实际大气遥感研究中准确的误差矩阵难以获取,此研究有助于进一步选取准确值并提高反演精度。

英文摘要：

Based on the retrieval of atmospheric carbon dioxide using infrared spectral data detected by GOSAT, a method of building error matrixes of Bayesian theory was proposed and validated. Firstly, the effect on retrieval results by different initial guesses, different building results of priori information error matrix Sa and measurement error matrix Sε was simulated, and then two combinations of Sa and Sεbuilding results were validated in the retrieval using part of GOSAT measurements of Taklimakan desert during 2009. The result shows that the retrieved results are more concentrate in the case of bigger priori information variance or smaller measurement error, and the retrieved results are dispersed under the converse circumstance. It is difficult to get real error matrixes in atmospheric remote sensing, therefore, this study will be significant for getting more accurate error matrixes and improving retrieval precision.