A Bayesian Estimator for Linear Calibration Error Effects in Thermal Remote Sensing

Morgan, J. A.

doi:10.1109/lgrs.2005.856755

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Even though the posterior probability for surface T is a closed-form expression, this procedure is computationally intensive. Moreover, extensions to the estimator -such as, for example, a more careful treatment of the forward model, or incorporation of calibration error effects (Morgan 2006) -may deprive us of the comfort of the closed-form solution. The MAP criterion for an estimator promises to dramatically reduce the number of CPU cycles expended per LST estimate, and may yet preserve the ability to rely upon closed-form solutions.…”

Section: Lst Algorithmsmentioning

confidence: 99%

“…We begin by adapting the analysis of Morgan (2005Morgan ( , 2006 to obtain the prior probability for surface meteorological range VSBY and use it to construct posterior probabilities marginalized over a range of VSBY. The starting point is the apparently trivial observation that the meteorological range is a length.…”

Section: Appendix B Insensitivity Of Lst Estimates To Meteorologicalmentioning

confidence: 99%

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Comparison of Bayesian land surface temperature algorithm performance with Terra MODIS observations

Morgan

2011

International Journal of Remote Sensing

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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 (Moderate Resolution Imaging Spectroradiometer) instrument. The algorithm employed requires minimal knowledge of surface emissivity, 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 a posteriori (MAP) search for the maximum joint a posteriori probability for LST, given observed sensor aperture radiances and a priori probabilities for LST and emissivity.Two MODIS data granules each for daytime and night-time 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 • . Level 1b radiances were used to obtain 500 LST estimates per granule 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 | T | < 0.3 K, and its standard deviation does not exceed 1K. The ±68% confidence intervals for individual LST estimates associated with assumed uncertainty in surface emissivity are of order 0.8K.The appendix presents a proof of convergence of the iterative contraction mapping algorithm. The expectation values of surface temperature in multiple bands, and jointly in all bands, converge to a fixed point, within a stipulated convergence criterion. Provided the support [T min , T max ] for the calculation of the expectation value brackets the maximum in the joint posterior probability for LST, the fixed point converges to the MAP LST estimate in the limit T max − T min → 0.

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Section: Lst Algorithmsmentioning

confidence: 99%

Section: Appendix B Insensitivity Of Lst Estimates To Meteorologicalmentioning

confidence: 99%

Comparison of Bayesian land surface temperature algorithm performance with Terra MODIS observations

Morgan

2011

International Journal of Remote Sensing

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A Probabilistic Model and Capturing Device for Remote Simultaneous Estimation of Spectral Emissivity and Temperature of Hot Emissive Materials

et al. 2021

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An Efficient Temperature Calibration Method Based on the Improved Infrared Forward Model and Bayesian Inference

Chu,

Yan,

Zhong

et al. 2024

IEEE Sensors J.

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A Bayesian Estimator for Linear Calibration Error Effects in Thermal Remote Sensing

Cited by 5 publications

References 6 publications

Comparison of Bayesian land surface temperature algorithm performance with Terra MODIS observations

Comparison of Bayesian land surface temperature algorithm performance with Terra MODIS observations

A Probabilistic Model and Capturing Device for Remote Simultaneous Estimation of Spectral Emissivity and Temperature of Hot Emissive Materials

An Efficient Temperature Calibration Method Based on the Improved Infrared Forward Model and Bayesian Inference

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