Comparison of Bayesian Land Surface Temperature algorithm performance with Terra MODIS observations

Details Comparison of Bayesian Land Surface Temperature algorithm performance with Terra MODIS observations Comparison of Bayesian Land Surface Temperature algorithm performance with Terra MODIS observations

2009-09-24

arxiv.org/abs/0909.4461v3

Physics

Data Analysis, Statistics and Probability

26 pages, 9 figures. Revisions and corrigenda; LaTeX error corrected

Scientific paper

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 \emph{A-Posteriori} (MAP) search for the maximum joint \emph{a-posteriori} probability for LST, given observed sensor aperture radiances and \emph{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 $| ZA | < 30^{\circ}$. 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 $| < \Delta T > | < 0.3 K$, and its standard deviation does not exceed $1 K$. The $\pm 68 %$ 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.

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