2009/09/24 by J. A. Morgan, Morgan, J. A.
Earth and Planetary Sciences · Engineering · Environmental Science · Mathematics · Physics and Astronomy · #Algorithm #Atmospheric and Oceanic Physics (physics.ao-ph) #Bayesian inference #Bayesian probability #Building Energy and Comfort Optimization #Climate change and permafrost #Data Analysis #Emissivity #Estimator #FOS: Physical sciences #Geography #Geophysics (physics.geo-ph) #Mathematics #Maximum a posteriori estimation #Maximum likelihood #Optics #Physics #Radiance #Remote sensing #Standard deviation #Statistics #Statistics and Probability (physics.data-an) #Urban Heat Island Mitigation #Zenith #physics.ao-ph #physics.data-an #physics.geo-ph
paper · pdf · doi:10.48550/arxiv.0909.4461
26 pages, 9 figures. Revisions and corrigenda; LaTeX error corrected
openalex publication_date 2009/09/24 · arxiv created 2010/01/22 · arxiv updated 2010/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 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 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∘. 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 ± 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.