Post hoc Uncertainty Quantification for Remote Sensing Observing Systems

DOI10.1137/19M1304283zbMath1468.62262OpenAlexW3192431161MaRDI QIDQ5010091

Jonathan Hobbs, Michael Gunson, Joaquim Teixeira, Amy Braverman

Publication date: 24 August 2021

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/19m1304283

zbMATH Keywords

uncertainty quantificationremote sensingbootstrap bias correctionGaussian mixture modelingorbiting carbon observatory-2 mission

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to environmental and related topics (62P12) Nonparametric estimation (62G05) Nonparametric tolerance and confidence regions (62G15)

Related Items (1)

Bayesian latent variable co-kriging model in remote sensing for quality flagged observations

Uses Software

• mclust
• corpor
• corpcor
• bootlib

Cites Work

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• Optimal estimation versus MCMC for CO\(_2\) retrievals
• Mixtures of linear regressions
• Statistical unfolding of elementary particle spectra: empirical Bayes estimation and bias-corrected uncertainty quantification
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO$_2$ from Satellite Data
• Inverse Problem Theory and Methods for Model Parameter Estimation
• Mission CO₂ntrol: A Statistical Scientist's Role in Remote Sensing of Atmospheric Carbon Dioxide
• Markov Chain Monte Carlo Methods for High Dimensional Inversion in Remote Sensing

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