000872665 001__ 872665
000872665 005__ 20210130004246.0
000872665 037__ $$aFZJ-2020-00156
000872665 041__ $$aEnglish
000872665 1001_ $$0P:(DE-Juel1)172754$$aKrieger, Vera$$b0$$eCorresponding author
000872665 1112_ $$aEARSel SIG Imaging Spectroscopy Workshop$$cBrno$$d2019-02-06 - 2019-02-08$$wCzech
000872665 245__ $$aSystematic Assessment Of Airborne Sun-Induced Fluorescence Maps By The Application Of Quality Criteria
000872665 260__ $$c2019
000872665 3367_ $$033$$2EndNote$$aConference Paper
000872665 3367_ $$2DataCite$$aOther
000872665 3367_ $$2BibTeX$$aINPROCEEDINGS
000872665 3367_ $$2DRIVER$$aconferenceObject
000872665 3367_ $$2ORCID$$aLECTURE_SPEECH
000872665 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1579176578_24850$$xOther
000872665 502__ $$cRheinische Friedrich-Wilhelms-Universität Bonn
000872665 520__ $$aWhen plants  absorb light,  not  all energy is  converted by photosynthesis, but  excess  energy is released as heat or emitted as sun-induced chlorophyll fluorescence (F). This signal, related to the photosynthetic  efficiency  of  plants,  has  been  intensively  studied  and  measured  from  ground, airborne and satellite. However, retrieving sun-induced fluorescence (F) from remote sensing data is challenging because accurate modeling of atmospheric influences is required.. The advent of the airborne imaging spectrometer HyPlant made possible to produce F maps in high-spatial resolution (1-3 meters), which is a valuable tool to better understand F at relevant ecosystem scale. Currently, two  different  algorithms  are  used  routinely  to  retrieve  red  and  far-red  F  from  HyPlant.  Both methods are based on the O 2  absorption bands, but while iFLD method employs a semi-empirical atmospheric correction (i.e., bare-soils), the SFM makes use of a physically-based atmospheric modeling (MODTRAN5 code). A common method of testing the reliability of a remotely sensed F product (in this study airborne F maps) is the comparison with “ground truth” data where the atmosphere can be neglected. In this work we tested another possibility of assessing the quality of the airborne F maps, which does not require ground reference measurements. For this purpose we have developed so-called ’quality criteria’, which should help to find errors and artefacts that have arisen during F retrieval. This method was used to test the quality of the airborne F maps of 2016 campaign.  By applying the quality criteria, clear differences in the performance of two retrievals were found. Although it was shown that both retrievals performed well in F 760  retrieval, even at places with changes from vegetated to non-vegetated sites on pixel scale, iFLD was more robust for retrieving correct absolute values for F 760  and F 687 , while SFM performed less accurate in this term, over- and  underestimating  F  values.  Furthermore,  previously  reported  problems  with  image  pre-processing (deconvolution for correcting PSF) of SFM became clear here. This was causing strong artefacts in F 687  retrievals from SFM. However, SFM proved to be the more suitable method for identifying small differences on pixel scale. Moreover, this algorithm did not show systematic variations over entire flight lines as observed by the use of iFLD. The physically-based approach of atmospheric correction used with SFM thus provided more interference-free F maps than the semi-empirical correction using non-fluorescent surfaces as in iFLD retrieval. Testing F retrievals on vegetation under different illumination conditions showed the necessity to calculate F yield  for quantification of photosynthesis rates. The application of the proposed quality features proved to be a valuable tool for assessing the performance of F retrieval on airborne maps. Therefore we propose to use the quality criteria even when sufficient ground references are available, because even if the quality criteria do not replace ground-truth data, they provide important additional information about the quality of the F product of the respective retrieval method.
000872665 536__ $$0G:(DE-HGF)POF3-582$$a582 - Plant Science (POF3-582)$$cPOF3-582$$fPOF III$$x0
000872665 7001_ $$0P:(DE-HGF)0$$aCogliati$$b1$$eCollaboration author
000872665 7001_ $$0P:(DE-HGF)0$$aDamm, Alexander$$b2$$eCollaboration author
000872665 7001_ $$0P:(DE-Juel1)130098$$aMatveeva, Maria$$b3$$eCorresponding author
000872665 7001_ $$0P:(DE-Juel1)162306$$aRademske, Patrick$$b4$$eContributor
000872665 7001_ $$0P:(DE-Juel1)129388$$aRascher, Uwe$$b5$$eLast author$$ufzj
000872665 909CO $$ooai:juser.fz-juelich.de:872665$$pVDB
000872665 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172754$$aForschungszentrum Jülich$$b0$$kFZJ
000872665 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a  Remote Sensing of Environmental Dynamics Lab., DISAT, University of Milano-Bicocca, Italy$$b1
000872665 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Department  of  Geography,  University  of  Zurich,  Winterthurerstrasse  190,  8057  Zurich,  Switzerland$$b2
000872665 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)130098$$aForschungszentrum Jülich$$b3$$kFZJ
000872665 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162306$$aForschungszentrum Jülich$$b4$$kFZJ
000872665 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129388$$aForschungszentrum Jülich$$b5$$kFZJ
000872665 9131_ $$0G:(DE-HGF)POF3-582$$1G:(DE-HGF)POF3-580$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lKey Technologies for the Bioeconomy$$vPlant Science$$x0
000872665 9141_ $$y2019
000872665 920__ $$lyes
000872665 9201_ $$0I:(DE-Juel1)IBG-2-20101118$$kIBG-2$$lPflanzenwissenschaften$$x0
000872665 980__ $$aconf
000872665 980__ $$aVDB
000872665 980__ $$aI:(DE-Juel1)IBG-2-20101118
000872665 980__ $$aUNRESTRICTED