Bayesian Latent Variable Co-kriging Model in Remote Sensing for Quality Flagged Observations
Bayesian Latent Variable Co-kriging Model in Remote Sensing for Quality Flagged Observations
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DOI：
10.1007/s13253-023-00530-9
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发表时间：
2022-08
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影响因子：
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通讯作者：
B. Konomi;E. Kang;Ayat Almomani;J. Hobbs
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作者：
B. Konomi;E. Kang;Ayat Almomani;J. Hobbs
遥感数据产品通常包括质量标志，告知用户相关观测的质量是好的、可接受的还是不可靠的。然而，在遥感数据分析中，这种关于数据保真度的信息并没有得到一致的考虑。受美国宇航局AQUA卫星上的大气红外测深仪(AIRS)观测的启发，我们提出了一个可分离的高斯过程的潜变量协同克立格模型来分析大的有质量标志的遥感数据集及其相关的质量信息。我们通过一种补偿机制来增强后验分布，以利用大协方差矩阵的输入结构将其分解成独立的计算有效分量。在增广的后验分布下，我们发展了一种马尔可夫链蒙特卡罗(MCMC)方法，它主要由条件分布的直接模拟组成。此外，我们还提出了一种计算高效的递归预测方法。我们将该方法应用于AIRS仪器的气温数据。我们表明，与没有考虑质量标志的模型相比，在我们提出的模型中加入质量标志信息显著提高了预测性能。
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 consistently 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.Supplementary materials accompanying this paper appear online.