基于多元函数泰勒公式和最优控制原理,首先,在塔克拉玛干沙漠部分中心区域(反演区域),利用2014年1月FY-3C微波成像仪10.65 GHz垂直极化的观测亮温、辐射传输模式(CRTM)的模拟亮温等资料,在原有地表发射率与2个影响因子的函数关系基础之上,构建了该区域1月份微波地表发射率与2、4个影响因子之间的线性反演模型。其次,以观测亮温为参考,分别将两种线性反演模型所得的地表发射率提供给CRTM模拟亮温,发现此时模拟亮温的平均偏差分别减少到原地表发射率模拟亮温平均偏差的56.36%和49.37%。最后,在整个塔克拉玛干沙漠地区(检验区域),选取用于反演日期中的1月18日、未用于反演日期中的1月29日,对两种线性反演模型进行了时间和空间上的独立性检验,结果表明,利用两种线性反演模型所得的地表发射率模拟亮温仍比原模拟亮温更接近观测,平均偏差也明显减小;两种线性反演模型对沙漠地区1月份地表发射率的反演具有一定的合理性和普适性,且4个因子的线性反演模型优于2个因子。

Based on the multivariate function Taylor formula and optimal control principle, first, using the 10.65 GHz vertical polarization data of observed brightness temperature from FY-3C microwave radiation imager and simulated brightness temperature from community radiative transfer model (CRTM), the linear retrievals model between the microwave land surface emissivity and two (four) influencing factors are constructed in the central part of the Taklimakan Desert (retrieval area) in January. Second, considering the observed brightness temperature as a reference, the land surface emissivity obtained by two kinds of linear retrieval models are provided to CRTM model to simulate brightness temperature, it is found that the average deviations of the simulated brightness temperatures are reduced to 56.36% and 49.37% of the original simulated brightness temperature, respectively. Finally, in the whole Taklimakan Desert (test area), the time and space independence tests of two kinds of linear retrieval models are carried out by selecting Jan 18, which is used for retrieval date, and Jan 29, which is not used for retrieval date. The results show that the simulated brightness temperatures of the land surface emissivity obtained by two kinds of linear retrieval models are still closer to the observation than the original simulated brightness temperature, and the average deviations are also significantly reduced. The two linear retrieval models are reasonable and universal for the retrieval of land surface emissivity in the desert area in January, and the linear retrieval model with 4 factors is better than 2 factors.