A Novel Self-Supervised Sun-Induced Fluorescence Retrieval Using Simulated HyPlant and DESIS Data
Creators: name: Buffat, Jim
name: Pato, Miguel
name: Alonso, Kevin
name: Auer, Stefan
name: Carmona, Emiliano
name: Maier, Stefan
name: Müller, Rupert
name: Rademske, Patrick
name: Scharr, Hanno
Publisher: name: Forschungzentrum Jülich
Year: 2023

description: Flex Fluorescence Workshop 2023, Frascati, Italy, 19 Sep 2023 - 21 Sep 2023
description: Operationally efficient retrieval of sun-induced fluorescence (SIF) from remote sensing data requires exact atmospheric correction at low computational cost to compensate for atmospheric effects at play in the radiative transfer of light through the atmosphere. Incomplete knowledge not only of the atmospheric state at acquisition time, but also of surface conditions imply the formulation of SIF retrieval as a simultaneous search of the atmospheric, viewing geometry and surface related variables describing best the observed scene.Such a problem can be addressed by the construction of large look-up tables (LUT) covering the space of possible input parameters describing the hyperspectral observations. The SIF retrieval reduces to spectrally fitting this LUT to the observations. In the present contribution we investigate the use of a neural network to perform this optimization step. We show-case the possibility to tightly integrate a neural network with the domain knowledge of radiative transfer codes simulating observations of the airborne HyPlant instrument and the ISS-based DESIS spectrometer in a spectral window around the O2-A oxygen absorption band (740-780 nm). While DESIS has not been designed for SIF retrieval, HyPlant is a testing instrument for the FLORIS spectrometer onboard FLEX. The present work can thus make use of the insights gained on high-resolution HyPlant data for the more challenging SIF prediction on DESIS acquisitions.We have developed a generic simulation tool based on MODTRAN6 to simulate at-sensor radiance in the O2-A oxygen absorption band. The tool consists of two modules. A primary module simulates distributions of atmospheric functions under varying atmospheric state variables. The high spectral resolution of DESIS (full width at half maximum 3.5 nm) and HyPlant (0.25 nm) required us to run MODTRAN6 at the finest available resolution. A secondary module modeling the sensor and surface characteristics factors in expert knowledge on DESIS and HyPlant as well as constraints on the SIF signal. We sampled an exhaustive range of observable atmospheric and surface conditions based on an extensive sensitivity analysis of key input parameters.Due to its computational complexity and cost the simulation tool can not be included in the network predictor directly. In order to tightly integrate the physical domain knowledge with a neural network predictor we tested a set of models to emulate at-sensor radiance using the simulated database. The high-dimensional nature of the regression problem (11d -&gt; 13d for DESIS, 13d -&gt; 349 for HyPlant) required us to test relatively simple models. We found fourth-degree polynomials to perform best with at-sensor radiance residuals of less than 0.01 mW/cm2/sr/μm on average across our simulated test set for both DESIS and HyPlant. Importantly, the per sample prediction time is of the order 10 μs, i.e. 107 times faster than the original simulation. We integrated this emulator as a prediction head into an encoder-decoder type neural network and trained the network in a first step on observed HyPlant imagery. Application of this approach to DESIS data is left for future work. A self- supervised loss evaluating the radiance residual of the emulated reconstruction and a physiological constraint was implemented to this end. Furthermore, the inclusion of topographic variation in the simulation data base as well as spatial constraints in the network architecture allow for SIF prediction even in topographically complex terrain.Validation of our self-supervised method’s predictions with ground based SIF measurements and comparison with the state-of-the-art Spectral Fitting Method (SFM) and Improved Fraunhofer Line Detection (iFLD) baseline methods revealed that the presented approached yielded comparable correlation scores. Linear calibration is, however, still needed to reach state-of-the-art absolute prediction accuracy as the SIF predictions suffer from systematic biases. Further plausibility studies highlight that the network yields physiologically plausible diurnal SIF dynamics and compensates for changes in the atmospheric path in topographically complex terrain.In summary, we have developed a SIF prediction approach integrating a computationally efficient and precise emulator in a neural network architecture derived from extensive sampling and high precision simulations of observational conditions. Validation and plausibility studies could show- case its successful application to HyPlant imagery.