Alonso, Kevin

Auer, Stefan

Buffat, Jim

Carmona, Emiliano

Maier, Stefan W.

Müller, Rupert

Pato, Miguel

Rademske, Patrick

Rascher, Uwe

Scharr, Hanno

Institute of Transport Research:Publications

Emulator-based Neural Network Prediction for SIF Retrieval in the O2-A Absorption Band 12.06.2023Jim Buffat1, Miguel Pato2, Kevin Alonso3, Stefan Auer2, Emiliano Carmona2, Stefan Maier2, Rupert Müller2, Patrick Rademske1, Uwe Rascher1 and Hanno Scharr41 Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences, IBG-2: Plant Sciences  2 German Aerospace Center (DLR), Earth Observation Center, Remote Sensing Technology Institute, Oberpfaffenhofen, Germany  3 RHEA Group c/o European Space Agency (ESA), Largo Galileo Galilei, 00044 Frascati, Italy 4 Forschungszentrum Jülich GmbH, Institute of Advanced Simulations, IAS-8: Data Analytics and Machine LearningFluoMap: a Helmholtz AI funded project12.06.23 2What is Sun-Induced Fluorescence (SIF) ?esa.earth.intflaticon.comAgricultural use casesESA Earth Explorer FLEXGlobal Fluorescence estimationsMohammed et al. 2019Solar irradiance E0Photochemistry HeatSIF12.06.23 3Airborne HyPlant SpectrometerFLUODUAL• FLUO is the airborne demonstrator  for FLEX • 0.24 nm FWHM, 0.11 nm SSI • 5 years of comparable campaign  acquisitions • 773 acquisitions, 384  [2000, 10’000] px • Operational Baseline SIF Retrieval Methods  • Spectral Fitting Method (SFM), Cogliati et al. 2019 • Improved Fraunhofer Line Discrimination (iFLD), Damm et al. 2022×12.06.23 4Airborne HyPlant Spectrometer384Flight direction  [2 × 10 3, 10 4]O2-A  [mW nm sr m]Lat−sensor−1−1−2 [nm]λ• FLUO is the airborne demonstrator  for FLEX • 0.24 nm FWHM, 0.11 nm SSI • 5 years of comparable campaign  acquisitions • 773 acquisitions, 384  [2000, 10’000] px • Operational Baseline SIF Retrieval Methods  • Spectral Fitting Method (SFM), Cogliati et al. 2019 • Improved Fraunhofer Line Discrimination (iFLD), Damm et al. 2022×12.06.23• Conventional Least Squares Optimization  in Spectral Fitting Method (SFM) • Data inefficiency:  no benefits from statistical relationships • Slow:  Individual fit of each spectral pixel • Model failure:  in topographically variable terrain 5Spectral Fitting Methods to Retrieve Fluorescence12.06.23 6Spectral Fitting Method Neural Network (SFMNN)12.06.23 7Spectral Fitting Method Neural Network (SFMNN)12.06.23 8• Inversion under incomplete knowledge of physical process is ill-posed. • Architectural constraint formulation: difference in spatial variation of terms contributing to radiance signalLoss and Constraint FormulationSelf-Supervised Loss Formulation for Signal ReconstructionSmooth variation of atmospheric contributionHigh frequency spectral variationOverall residual SNR weighting Physiological constraint Physical constraint12.06.23Simulation of HyPlant At-sensor Radiance• Radiative Transfer Modelling (MODTRAN) • Extensive coverage of observational conditions • Inclusion of topographic variation913 parameters12.06.23 10Emulation of HyPlant At-sensor Radiance• High-dimensional regression problem (13  349 dims) • A set of regression models were tested  • Polynomial of 4th degree (P4) is precise enough • P4 was used as forward simulator in SFMNN→12.06.23 11Comparison with in-situ measurements• Small set of synchronous SIF ground measurements  • Good correlation • Absolute errors impacted by systematic biases • Might be caused by domain gap between simulations and HyPlant observationsN12.06.23 12SIF Prediction in Topographically Variable TerrainSFM SFMNN12.06.23 13Diurnal SIF Dynamics are phenologically plausibleDay TimeSIFSFMNNSFMSIF03-0.30.1-0.30.1  [mW nm sr m  h]β−1−1−2−2SIF [mW nm sr m ]−1−1−2• Time series from repeated flights • Second order derivative  as a measure for diurnal SIF dynamicsβ   Not plausible β > 0   Okβ < 012.06.23 14Conclusions & Outlook• The emulator-based neural network prediction achieves correlation comparable to SFM on a data set of in-situ measurements.• Further analysis is needed to establish the reasons for systematic errors in absolute SIF prediction.• The impact of the topographic variation on the atmospheric transfer is compensated in SFMNN. • SFMNN predicted diurnal SIF dynamics are physiologically plausible.• The possibility to extend this method to other sensors is currently being evaluated: • DESIS (onboard the ISS) in FluoMap • FLEX, simulated hyperspectral imageryMiguel Pato Kevin Alonso Stefan Auer Emiliano Carmona Stefan Maier Rupert Müller Patrick Rademske Hanno Scharr Uwe Rascher  j.buffat@fz-juelich.de  0174 627 0142FluoMap Project Members

Emulator-based Neural Network Prediction for SIF Retrieval in the O2-A Absorption Band

https://elib.dlr.de/198834/1/presentation_20230612_helmholtz_ai.pdf

Emulator-based Neural Network Prediction for SIF Retrieval in the O2-A Absorption Band

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