Physics-Based Machine Learning Emulator of at-Sensor Radiances for Solar-Induced Fluorescence Retrieval in the O$_{2}$-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 by the need for time-consuming radiative transfer modeling to account for atmospheric effects. In this work, we address this difficulty and develop a fast and accurate physics-based ML emulator of at-sensor radiances around the O $_{2}$ -A absorption band for the space-based DESIS and the airborne HyPlant spectrometers. Different ML models are trained on an extensive set of simulated spectra encompassing a wide range of atmosphere, geometry, surface, and sensor configurations. A fourth-degree polynomial model is found to perform best, presenting errors at or below 10% of typical SIF at-sensor radiances and a prediction time per sample spectrum of 10 $-$ 20 μs. Using data acquired with the HyPlant instrument, the proposed model is also shown to be able to match very closely the measured spectra. We illustrate how to improve further the accuracy of the emulator and how to generalize it to other sensors using the particular case of ESA's FLEX space mission. Our findings suggest that physics-based emulators can be efficiently used for the development of ML-based SIF retrieval methods by generating large training datasets in short time and by enabling a fast simulation step for self-supervised retrieval schemes.