guest ::
| Home > Publications database > Emulator-Based Neural Network Prediction for the Retrieval Of Sun-Induced Fluorescence in the O2-A Absorption Band > print |
| Home > Publications database > Emulator-Based Neural Network Prediction for the Retrieval Of Sun-Induced Fluorescence in the O2-A Absorption Band > print |
| 001 | 1018123 | ||
| 005 | 20241213210705.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2023-04567 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2023-04567 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Buffat, Jim |0 P:(DE-Juel1)188104 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a Helmholtz AI Conference |c Hamburg |d 2023-06-12 - 2023-06-14 |w Germany |
| 245 | _ | _ | |a Emulator-Based Neural Network Prediction for the Retrieval Of Sun-Induced Fluorescence in the O2-A Absorption Band |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1734081429_7858 |2 PUB:(DE-HGF) |x After Call |
| 500 | _ | _ | |a The authors gratefully acknowledge the computing time granted through JARA on the supercomputer JURECA[1] at Forschungszentrum Jülich.[1] Jülich Supercomputing Centre. (2021). JURECA: Data Centric and Booster Modules implementing the Modular Supercomputing Architecture at Jülich Supercomputing Centre Journal of large-scale research facilities, 7, A182. http://dx.doi.org/10.17815/jlsrf-7-182 |
| 520 | _ | _ | |a Remote sensing applications rely on precise and efficient corrections of atmospheric effects to retrieve surface parameters such as reflectance, physiological quantities or emission features. Often the retrieval is made more difficult by the need for additional correction of the illumination-viewing geometry. A well known approach to solve such inversion tasks is to generate look-up tables of simulated at-sensor radiance spectra with precise radiative transfer models. The retrieval may then be expressed as a spectral fitting of the observations. This contribution investigates the performance of an encoder-decoder neural network architecture to learn this optimization step in the context of the retrieval of Sun-induced fluorescence (SIF) in the O2-A oxygen absorption band for the ISS- based DESIS spectrometer [1] and the airborne HyPlant instrument [2].Recently, SIF has gained much interest in the wake of the selection of the FLEX satellite mission by the European Space Agency to be the first dedicated Earth Explorer satellite mission for global SIF retrieval. The value of SIF maps at multiple spatial scales to environmental and agricultural use- cases are due to the close causal link between SIF and plant photosynthesis. The present work aims at show-casing the possibility to tightly integrate a neural network with the domain knowledge of radiative transfer codes. While DESIS has not been designed a-priori 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 two-module simulation tool to simulate DESIS and HyPlant at-sensor radiance spectra in the O2-A band, which is particularly sensitive to SIF changes. The first module runs MODTRAN6 on a set of atmosphere and geometry parameters from which atmospheric functions can be derived. The second module models the SIF emission as well as surface and sensor properties. A detailed analysis of appropriate input parameter ranges and dense sampling allowed to generate spectra covering the most common acquisition conditions of HyPlant and DESIS.We tightly integrate the physical domain knowledge intrinsic in the simulation data base and the network architecture by using an emulator of the simulations as non-trainable output layer. Simple emulator models have been tested in order to reduce the complexity of the regression problem due to its high dimensionality (11d -> 13d for DESIS, 13d -> 349d for HyPlant). The training of our encoder-decoder network on simulated and observed at-sensor spectra is conducted in a semi- supervised fashion, where we use the network to estimate appropriate emulator input parameters. The label-free loss evaluates the residuals between the corresponding emulator outputs and the data. This semi-supervised training set-up allows a direct comparison of the performance on simulated and observed data, where no SIF labels are available. Moreover, it has the advantage of allowing emulator transformations to reduce domain gaps between simulations and observations. A combined training on simulated and observed data with label regularization is left for future work.[1] K. Alonso et al, “Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS)”, Sensors, 19(20), 4471, 2019.[2] B. Siegmann et al, “The high-performance airborne imaging spectrometer HyPlant – From raw images to top-of-canopy reflectance and fluorescence products: Introduction of an automatized processing chain.”, Remote Sensing, 11, 2760, 2019.s |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217) |0 G:(DE-HGF)POF4-2173 |c POF4-217 |f POF IV |x 1 |
| 700 | 1 | _ | |a Pato, Miguel |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Alonso, Kevin |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Auer, Stefan |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Carmona, Emiliano |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Maier, Stefan |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Müller, Rupert |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Rademske, Patrick |0 P:(DE-Juel1)162306 |b 7 |
| 700 | 1 | _ | |a Rascher, Uwe |0 P:(DE-Juel1)129388 |b 8 |
| 700 | 1 | _ | |a Scharr, Hanno |0 P:(DE-Juel1)129394 |b 9 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1018123/files/abstract.pdf |y Restricted |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1018123/files/presentation.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1018123 |p openaire |p open_access |p VDB |p driver |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)188104 |
| 910 | 1 | _ | |a DLR, Remote Sensing Technology Institute |0 I:(DE-HGF)0 |b 1 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a RHEA Group c/o European Space Agency (ESA) |0 I:(DE-HGF)0 |b 2 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a DLR, Remote Sensing Technology Institute |0 I:(DE-HGF)0 |b 3 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a DLR, Remote Sensing Technology Institute |0 I:(DE-HGF)0 |b 4 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a DLR, Remote Sensing Technology Institute |0 I:(DE-HGF)0 |b 5 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a DLR, Remote Sensing Technology Institute |0 I:(DE-HGF)0 |b 6 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 7 |6 P:(DE-Juel1)162306 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 8 |6 P:(DE-Juel1)129388 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 9 |6 P:(DE-Juel1)129394 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 913 | 1 | _ | |a DE-HGF |b Forschungsbereich Erde und Umwelt |l Erde im Wandel – Unsere Zukunft nachhaltig gestalten |1 G:(DE-HGF)POF4-210 |0 G:(DE-HGF)POF4-217 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-200 |4 G:(DE-HGF)POF |v Für eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten |9 G:(DE-HGF)POF4-2173 |x 1 |
| 914 | 1 | _ | |y 2023 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IBG-2-20101118 |k IBG-2 |l Pflanzenwissenschaften |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-8-20210421 |k IAS-8 |l Datenanalyse und Maschinenlernen |x 1 |
| 980 | _ | _ | |a conf |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)IBG-2-20101118 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-8-20210421 |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|