Remote sensing data products often include quality flags that inform users whether the associated observations are of good, acceptable or unreliable qualities. However, such information on data fidelity is not considered in remote sensing data analyses. Motivated by observations from the Atmospheric Infrared Sounder (AIRS) instrument on board NASA's Aqua satellite, we propose a latent variable co-kriging model with separable Gaussian processes to analyze large quality-flagged remote sensing data sets together with their associated quality information. We augment the posterior distribution by an imputation mechanism to decompose large covariance matrices into separate computationally efficient components taking advantage of their input structure. Within the augmented posterior, we develop a Markov chain Monte Carlo (MCMC) procedure that mostly consists of direct simulations from conditional distributions. In addition, we propose a computationally efficient recursive prediction procedure. We apply the proposed method to air temperature data from the AIRS instrument. We show that incorporating quality flag information in our proposed model substantially improves the prediction performance compared to models that do not account for quality flags.
翻译:遥感数据产品通常包括高质量的旗帜,使用户了解相关观测是否具有良好、可接受或不可靠的质量。然而,遥感数据分析中并未考虑到这类数据忠诚性信息。受美国航天局Aqua卫星上大气红外线索德(AIRS)仪器观测的启发,我们提出了一个潜伏的可变联钩式模型,该模型配有分解高尔夫(Gaussian)流程,以分析大量质量标签的遥感数据集及其相关质量信息。我们通过一种估算机制,将大型共变差矩阵分解成单独的计算效率高的组件,以利用输入结构。我们在扩大的后方模型中开发了马可夫链蒙特卡洛(Monte Carlo)程序,该程序主要由有条件分布的直接模拟组成。此外,我们提出了一种计算高效的循环预测程序。我们将拟议方法应用于来自AIRS仪器的空气温度数据。我们表明,将高质量的国旗信息纳入拟议模型,大大改进了预测性,而不是考虑到质量旗帜的模型。