dorsal/arxiv
View SchemaBayesian Estimation for Land Surface Temperature Retrieval: The Nuisance of Emissivities
| Authors | J. A. Morgan |
|---|---|
| Categories | |
| ArXiv ID | physics/0402099 |
| URL | https://arxiv.org/abs/physics/0402099 |
| DOI | 10.1109/TGRS.2005.845637 |
| Journal | IEEE Transactions on Geoscience and Remote Sensing 43, p. 1279 (June 2005) |
Abstract
An approach to the remote sensing of land surface temperature is developed using the methods of Bayesian inference. The starting point is the maximum entropy estimate for the posterior distribution of radiance in multiple bands. In order to convert this quantity to an estimator for surface temperature and emissivity with Bayes' theorem, it is necessary to obtain the joint prior probability for surface temperature and emissivity, given available prior knowledge. The requirement that any pair of distinct observers be able to relate their descriptions of radiance under arbitrary Lorentz transformations uniquely determines the prior probability. Perhaps surprisingly, surface temperature acts as a scale parameter, while emissivity acts as a location parameter, giving the prior probability P(T,emissivity|K)=const./T dT d(emissivity). Given this result, it is a simple matter to construct estimators for surface temperature and emssivity. Monte Carlo simulations of land surface temeprature retrieval in selected MODIS bands are presented as examples of the utility of the approach.
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"abstract": "An approach to the remote sensing of land surface temperature is developed\nusing the methods of Bayesian inference. The starting point is the maximum\nentropy estimate for the posterior distribution of radiance in multiple bands.\nIn order to convert this quantity to an estimator for surface temperature and\nemissivity with Bayes\u0027 theorem, it is necessary to obtain the joint prior\nprobability for surface temperature and emissivity, given available prior\nknowledge. The requirement that any pair of distinct observers be able to\nrelate their descriptions of radiance under arbitrary Lorentz transformations\nuniquely determines the prior probability. Perhaps surprisingly, surface\ntemperature acts as a scale parameter, while emissivity acts as a location\nparameter, giving the prior probability P(T,emissivity|K)=const./T dT\nd(emissivity). Given this result, it is a simple matter to construct estimators\nfor surface temperature and emssivity. Monte Carlo simulations of land surface\ntemeprature retrieval in selected MODIS bands are presented as examples of the\nutility of the approach.",
"arxiv_id": "physics/0402099",
"authors": [
"J. A. Morgan"
],
"categories": [
"physics.data-an",
"physics.ao-ph",
"physics.geo-ph"
],
"doi": "10.1109/TGRS.2005.845637",
"journal_ref": "IEEE Transactions on Geoscience and Remote Sensing 43, p. 1279\n (June 2005)",
"title": "Bayesian Estimation for Land Surface Temperature Retrieval: The Nuisance of Emissivities",
"url": "https://arxiv.org/abs/physics/0402099"
},
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