6533b7cefe1ef96bd12578c6
RESEARCH PRODUCT
Replacing radiative transfer models by surrogate approximations through machine learning
Juan Pablo RiveraJose MorenoJochem VerrelstJose Gomez-dansGustau Camps-vallssubject
Flexibility (engineering)Atmosphere (unit)Computer sciencebusiness.industryExtrapolationStatistical modelVegetationMachine learningcomputer.software_genreAtmosphereComputational learning theoryRadiative transferArtificial intelligencebusinesscomputerdescription
Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth's surface and their interactions with vegetation and atmosphere. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. They are advantageous in real practice because of the computational efficiency and excellent accuracy and flexibility for extrapolation. We here present an ‘Emulator toolbox’ that enables analyzing three multi-output machine learning regression algorithms (MO-MLRAs) on their ability to approximate an RTM. As a proof of concept, a case study on emulating sun-induced fluorescence (SIF) is presented. The toolbox is foreseen to open new opportunities in the use of advanced RTMs, in which both consistent physical assumptions and data-driven machine learning algorithms live together.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-07-01 | 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |