Emulation of Sun-Induced Fluorescence from Radiance Data Recorded by the HyPlant Airborne Imaging Spectrometer.

Miguel Morata,Jochem Verrelst,Juan Pablo Rivera-Caicedo,Pablo Morcillo-Pallarés,Bastian Siegmann

doi:10.3390/rs13214368

Abstract

The retrieval of sun-induced fluorescence (SIF) from hyperspectral radiance data grew to maturity with research activities around the FLuorescence EXplorer satellite mission FLEX, yet full-spectrum estimation methods such as the spectral fitting method (SFM) are computationally expensive. To bypass this computational load, this work aims to approximate the SFM-based SIF retrieval by means of statistical learning, i.e., emulation. While emulators emerged as fast surrogate models of simulators, the accuracy-speedup trade-offs are still to be analyzed when the emulation concept is applied to experimental data. We evaluated the possibility of approximating the SFM-like SIF output directly based on radiance data while minimizing the loss in precision as opposed to SFM-based SIF. To do so, we implemented a double principal component analysis (PCA) dimensionality reduction, i.e., in both input and output, to achieve emulation of multispectral SIF output based on hyperspectral radiance data. We then evaluated systematically: (1) multiple machine learning regression algorithms, (2) number of principal components, (3) number of training samples, and (4) quality of training samples. The best performing SIF emulator was then applied to a HyPlant flight line containing at sensor radiance information, and the results were compared to the SFM SIF map of the same flight line. The emulated SIF map was quasi-instantaneously generated, and a good agreement against the reference SFM map was obtained with a R2 of 0.88 and NRMSE of 3.77%. The SIF emulator was subsequently applied to 7 HyPlant flight lines to evaluate its robustness and portability, leading to a R2 between 0.68 and 0.95, and a NRMSE between 6.42% and 4.13%. Emulated SIF maps proved to be consistent while processing time was in the order of 3 min. In comparison, the original SFM needed approximately 78 min to complete the SIF processing. Our results suggest that emulation can be used to efficiently reduce computational loads of SIF retrieval methods.

Highlights

IntroductionEssential indicators related to the plant’s actual health status can be derived by retrieving sun-induced fluorescence (SIF) from remotely sensed hyperspectral radiometric data, as seen in [1]
While the spectral fitting method (SFM) enables retrieval of multispectral sun-induced fluorescence (SIF) output in the O2 A absorption band from TOA radiance data, the method is computationally costly given the many iterations involved. To bypass this computational burden, here we evaluated the idea of statistical learning, i.e., emulation, to approximate SFM-like multispectral SIF output coming directly from at-sensor radiance data
Experimental data came from the 2018 FLEXSense campaign where, from hyperspectral HyPlant acquisitions, the SFM method was applied for the retrieval of SIF outputs

Summary

Introduction

Essential indicators related to the plant’s actual health status can be derived by retrieving sun-induced fluorescence (SIF) from remotely sensed hyperspectral radiometric data, as seen in [1]. To this end, for the last few decades multiple SIF retrieval methods have been developed, most of them focusing on the retrieval of a single SIF estimate in specific absorption features such as Fraunhofer lines or in oxygen absorption regions (see [1,2,3] for reviews). As for any other time-consuming processing step, there is a high demand for faster alternatives providing similar accuracy

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