Replacing radiative transfer models by surrogate approximations through machine learning

Jochem Verrelst, Juan Pablo Rivera, Jose Gomez-Dans, Gustau Camps-Valls, Jose Moreno
2015 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
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
more » ... ause of the computational efficiency and excellent accuracy and flexibility for extrapolation. We here present an 'Emulator toolbox' that enables analyzing three multioutput 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.
doi:10.1109/igarss.2015.7325843 dblp:conf/igarss/VerrelstRGCM15 fatcat:ppq5ua76cncmjgy67sufwmyzqe