贝叶斯陆面温度算法性能与Terra MODIS观测的比较
Comparison of Bayesian Land Surface Temperature algorithm performance with Terra MODIS observations
摘要：
地面温度（LST）的估计方法，依赖于贝叶斯推断已被测试对多波段红外辐射图像从Terra中分辨率成像光谱仪。贝叶斯LST估计重现标准的MODIS产品LST值从一个吝啬的选择（因此，无信息）范围内的先验波段发射率的知识。两种估计方法已经过测试。第一个是迭代压缩映射的联合期望值LST和表面发射率在以前的文件中描述。在第二种方法中，贝叶斯算法被重新表述为最大\emph{A-Posteriori}（MAP）搜索的最大联合\emph{a-posteriori}概率LST，给定观测到的传感器孔径辐射率和\emph{a-priori}概率LST和发射率。两个白天和夜间的MODIS数据颗粒用于比较。颗粒被选择为基本上无云，在传感器天顶角$| ZA | < 30^{\circ}$的颗粒的那些部分中具有有限的垂直起伏。1B级辐射率用于获得LST估计值，以便与2级MODIS LST产品进行比较。贝叶斯LST估计准确地再现标准的MODIS产品LST值。特别是，MAP检索的平均差异为$| < ΔT > | < 0.3 K$，其标准差不超过$1 K$。与假定的表面发射率不确定性相关的单个LST估计值的$\pm 68 %$置信区间为$0.8 K$阶。附录给出了迭代压缩映射算法的收敛性证明。
英文摘要：
An approach to land surface temperature (LST) estimation that relies upon Bayesian inference has been tested against multiband infrared radiometric imagery from the Terra MODIS instrument. Bayesian LST estimators are shown to reproduce standard MODIS product LST values starting from a parsimoniously chosen (hence, uninformative) range of prior band emissivity knowledge. Two estimation methods have been tested. The first is the iterative contraction mapping of joint expectation values for LST and surface emissivity described in a previous paper. In the second method, the Bayesian algorithm is reformulated as a Maximum \emph{A-Posteriori} (MAP) search for the maximum joint \emph{a-posteriori} probability for LST, given observed sensor aperture radiances and \emph{a-priori} probabilities for LST and emissivity. Two MODIS data granules each for daytime and nighttime were used for the comparison. The granules were chosen to be largely cloud-free, with limited vertical relief in those portions of the granules for which the sensor zenith angle $| ZA | < 30^{\circ}$. Level 1B radiances were used to obtain LST estimates for comparison with the Level 2 MODIS LST product. The Bayesian LST estimators accurately reproduce standard MODIS product LST values. In particular, the mean discrepancy for the MAP retrievals is $| < ΔT > | < 0.3 K$, and its standard deviation does not exceed $1 K$. The $\pm 68 %$ confidence intervals for individual LST estimates associated with assumed uncertainty in surface emissivity are of order $0.8 K$. The Appendix presents a proof of convergence of the iterative contraction mapping algorithm.
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