Replacing radiative transfer models by surrogate approximations through machine learning

Jochem Verrelst, Juan Pablo Rivera, Jose Gomez-Dans, Gustau Camps-Valls, Jose Moreno
<span title="">2015</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6i67v2zuujhfvocqaapmnsoifm" style="color: black;">2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)</a> </i> &nbsp;
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 &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/igarss.2015.7325843">doi:10.1109/igarss.2015.7325843</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/igarss/VerrelstRGCM15.html">dblp:conf/igarss/VerrelstRGCM15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ppq5ua76cncmjgy67sufwmyzqe">fatcat:ppq5ua76cncmjgy67sufwmyzqe</a> </span>
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