Physics-Based Machine Learning Emulator of at-Sensor Radiances for Solar-Induced Fluorescence Retrieval in the O<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula>-A Absorption Band

The successful operation of airborne and space-based spectrometers in recent years holds the promise to map solar-induced fluorescence (SIF) accurately across the globe. Machine learning (ML) can play an important role in this effort, but its application to SIF retrieval methods is in part hindered...

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Principais autores:	Miguel Pato, Jim Buffat, Kevin Alonso, Stefan Auer, Emiliano Carmona, Stefan Maier, Rupert Muller, Patrick Rademske, Uwe Rascher, Hanno Scharr
Format:	Artigo
Jezik:	Inglês
Izdano:	IEEE 2024-01-01
Serija:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Teme:	Hyperspectral sensors machine learning radiative transfer solar-induced fluorescence
Online dostop:	https://ieeexplore.ieee.org/document/10670589/
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