6533b861fe1ef96bd12c5023
RESEARCH PRODUCT
Nonlinear statistical retrieval of surface emissivity from IASI data
Jordi Muñoz-maríGustau Camps-vallsValero LaparraXavier CalbetLuis Gómez-chovasubject
0211 other engineering and technologies020206 networking & telecommunications02 engineering and technologyAtmospheric modelInfrared atmospheric sounding interferometerLeast squaresKernel method13. Climate actionKernel (statistics)Linear regression0202 electrical engineering electronic engineering information engineeringEmissivityKernel regressionPhysics::Atmospheric and Oceanic Physics021101 geological & geomatics engineeringRemote sensingMathematicsdescription
Emissivity is one of the most important parameters to improve the determination of the troposphere properties (thermodynamic properties, aerosols and trace gases concentration) and it is essential to estimate the radiative budget. With the second generation of infrared sounders, we can estimate emissivity spectra at high spectral resolution, which gives us a global view and long-term monitoring of continental surfaces. Statistically, this is an ill-posed retrieval problem, with as many output variables as inputs. We here propose nonlinear multi-output statistical regression based on kernel methods to estimate spectral emissivity given the radiances. Kernel methods can cope with high-dimensional input-output spaces efficiently. We give empirical evidence of models performance on Infrared Atmospheric Sounding Interferometer (IASI) simulated data. Kernel regression model largely improves previous least squares linear regression model quantitatively, with an average reduction of 25% in mean-square error.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-07-01 | 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |