001047679 001__ 1047679
001047679 005__ 20251217202226.0
001047679 037__ $$aFZJ-2025-04454
001047679 041__ $$aEnglish
001047679 1001_ $$0P:(DE-Juel1)203284$$aMalenovsky, Zbynek$$b0$$eCorresponding author
001047679 1112_ $$aAmerican Geosciences Union$$cNew Orleans$$d2025-12-15 - 2025-12-19$$gAGU 2025$$wUSA
001047679 245__ $$aPhysical modeling and scaling canopy far-red SIF radiance down to leaf and photosystem fluorescence efficiencies
001047679 260__ $$c2025
001047679 3367_ $$033$$2EndNote$$aConference Paper
001047679 3367_ $$2DataCite$$aOther
001047679 3367_ $$2BibTeX$$aINPROCEEDINGS
001047679 3367_ $$2DRIVER$$aconferenceObject
001047679 3367_ $$2ORCID$$aLECTURE_SPEECH
001047679 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1765961482_25186$$xInvited
001047679 520__ $$aRemotely sensed far-red solar-induced fluorescence (SIF) is increasingly used as a proxy for modeling vegetation photosynthetic activity and gross primary production. However, the apparent top-of-canopy (TOC) SIF signal is strongly affected by non-physiological but physical factors, e.g., canopy structure, leaf and ground or atmospheric optical properties. This work demonstrates different TOC SIF normalization approaches, removing the confounding factors and extracting the physiologically relevant part of remotely-sensed SIF.Scaling TOC SIF down to the leaf emission efficiency requires estimation of the SIF escape fraction from the canopy (Fesc). Several optical indices characterizing canopy scattering of far-red SIF radiance were developed as proxies of Fesc. We tested performance of FCVI and two hyperspectral forms of NIRv (i.e., NIRvH1 and NIRvH2) using nadir airborne SIF of summer barley crops. Modeling in 3D Discrete Anisotropic Radiative Transfer (DART) suggested that the most accurate Fesc is estimated with NIRvH1. Consequently, the SIF normalization using NIRvH1 was found to have a superior performance over NIRvH2 and FCVI. Yet, when applied to the experimental drone and airborne nadir canopy SIF data, the obtained leaf chlorophyll fluorescence efficiencies of both NIRvH1 and FCVI were highly similar (R2 = 0.93).To scale far-red TOC SIF down to emissions from photosystems inside chloroplasts (i.e., PSI and PSII), we developed a novel method estimating the fluorescence quantum efficiency (FQE) based on an efficient DART-Lux bidirectional Monte-Carlo photon path tracing. The steady-state FQE is estimated through optimizing the DART-simulated TOC SIF against corresponding field/airborne measurements. When applied on in-situ measurements acquired with the Fluorescence Box (FloX) system, the retrieved FQE diurnal courses correlated significantly with the PSII photosynthetic yield measured by a MiniPAM active fluorometer (r = 0.87, R2 = 0.76 before and r = -0.82, R2 = 0.67 after 2.00 PM). After application on images from the airborne HyPlant spectrometer, the per-pixel FQE estimates formed narrow bell-shaped (near-Gaussian) histograms with a low coefficient of variation, indicating the reduction of spatial heterogeneity in the input TOC SIF radiance caused by confounding factors.
001047679 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
001047679 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x1
001047679 65017 $$0V:(DE-MLZ)GC-170-2016$$2V:(DE-HGF)$$aEarth, Environment and Cultural Heritage$$x0
001047679 7001_ $$0P:(DE-HGF)0$$aRegaieg, Omar$$b1
001047679 7001_ $$0P:(DE-Juel1)186921$$aBendig, Juliane$$b2$$ufzj
001047679 7001_ $$0P:(DE-Juel1)172711$$aSiegmann, Bastian$$b3$$ufzj
001047679 7001_ $$0P:(DE-HGF)0$$aKrämer, Julie$$b4
001047679 7001_ $$0P:(DE-HGF)0$$aLauret, Nicholas$$b5
001047679 7001_ $$0P:(DE-Juel1)188104$$aBuffat, Jim$$b6$$ufzj
001047679 7001_ $$0P:(DE-Juel1)129388$$aRascher, Uwe$$b7$$ufzj
001047679 8564_ $$uhttps://agu.confex.com/agu/agu25/meetingapp.cgi/Paper/1909517
001047679 909CO $$ooai:juser.fz-juelich.de:1047679$$pVDB
001047679 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186921$$aForschungszentrum Jülich$$b2$$kFZJ
001047679 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172711$$aForschungszentrum Jülich$$b3$$kFZJ
001047679 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188104$$aForschungszentrum Jülich$$b6$$kFZJ
001047679 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129388$$aForschungszentrum Jülich$$b7$$kFZJ
001047679 9131_ $$0G:(DE-HGF)POF4-217$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2173$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x0
001047679 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x1
001047679 9141_ $$y2025
001047679 920__ $$lyes
001047679 9201_ $$0I:(DE-Juel1)IBG-2-20101118$$kIBG-2$$lPflanzenwissenschaften$$x0
001047679 9201_ $$0I:(DE-Juel1)IAS-8-20210421$$kIAS-8$$lDatenanalyse und Maschinenlernen$$x1
001047679 980__ $$aconf
001047679 980__ $$aVDB
001047679 980__ $$aI:(DE-Juel1)IBG-2-20101118
001047679 980__ $$aI:(DE-Juel1)IAS-8-20210421
001047679 980__ $$aUNRESTRICTED