000866592 001__ 866592
000866592 005__ 20210130003533.0
000866592 0247_ $$2doi$$a10.3390/rs11212562
000866592 0247_ $$2Handle$$a2128/23430
000866592 0247_ $$2WOS$$aWOS:000504716700103
000866592 0247_ $$2altmetric$$aaltmetric:71082925
000866592 037__ $$aFZJ-2019-05673
000866592 041__ $$aEnglish
000866592 082__ $$a620
000866592 1001_ $$0P:(DE-HGF)0$$aMartini$$b0$$eCorresponding author
000866592 245__ $$aNitrogen and Phosphorus effect on Sun-Induced Fluorescence and Gross Primary Productivity in Mediterranean Grassland
000866592 260__ $$aBasel$$bMDPI$$c2019
000866592 3367_ $$2DRIVER$$aarticle
000866592 3367_ $$2DataCite$$aOutput Types/Journal article
000866592 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1574344275_25087
000866592 3367_ $$2BibTeX$$aARTICLE
000866592 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000866592 3367_ $$00$$2EndNote$$aJournal Article
000866592 520__ $$aSun-Induced fluorescence at 760 nm (F760) is increasingly being used to predict gross primary production (GPP) through light use efficiency (LUE) modeling, even though the mechanistic processes that link the two are not well understood. We analyzed the effect of nitrogen (N) and phosphorous (P) availability on the processes that link GPP and F760 in a Mediterranean grassland manipulated with nutrient addition. To do so, we used a combination of process-based modeling with Soil-Canopy Observation of Photosynthesis and Energy (SCOPE), and statistical analyses such as path modeling. With this study, we uncover the mechanisms that link the fertilization-driven changes in canopy nitrogen concentration (N%) to the observed changes in F760 and GPP. N addition changed plant community structure and increased canopy chlorophyll content, which jointly led to changes in photosynthetic active radiation (APAR), ultimately affecting both GPP and F760. Changes in the abundance of graminoids, (%graminoids) driven by N addition led to changes in structural properties of the canopy such as leaf angle distribution, that ultimately influenced observed F760 by controlling the escape probability of F760 (Fesc). In particular, we found a change in GPP–F760 relationship between the first and the second year of the experiment that was largely driven by the effect of plant type composition on Fesc, whose best predictor is %graminoids. The P addition led to a statistically significant increase on light use efficiency of fluorescence emission (LUEf), in particular in plots also with N addition, consistent with leaf level studies. The N addition induced changes in the biophysical properties of the canopy that led to a trade-off between surface temperature (Ts), which decreased, and F760 at leaf scale (F760leaf,fw), which increased. We found that Ts is an important predictor of the light use efficiency of photosynthesis, indicating the importance of Ts in LUE modeling approaches to predict GPP.
000866592 536__ $$0G:(DE-HGF)POF3-582$$a582 - Plant Science (POF3-582)$$cPOF3-582$$fPOF III$$x0
000866592 588__ $$aDataset connected to CrossRef
000866592 7001_ $$0P:(DE-HGF)0$$aPacheco-Labrador$$b1
000866592 7001_ $$0P:(DE-HGF)0$$aPerez-Priego$$b2
000866592 7001_ $$0P:(DE-HGF)0$$aTol, van der$$b3
000866592 7001_ $$0P:(DE-HGF)0$$aMadany$$b4
000866592 7001_ $$0P:(DE-HGF)0$$aJulitta$$b5
000866592 7001_ $$0P:(DE-HGF)0$$aRossini$$b6
000866592 7001_ $$0P:(DE-HGF)0$$aReichstein$$b7
000866592 7001_ $$0P:(DE-HGF)0$$aChristiansen$$b8
000866592 7001_ $$0P:(DE-Juel1)129388$$aRascher, Uwe$$b9
000866592 7001_ $$0P:(DE-HGF)0$$aMoreno$$b10
000866592 7001_ $$0P:(DE-HGF)0$$aMartín$$b11
000866592 7001_ $$0P:(DE-HGF)0$$aYang$$b12
000866592 7001_ $$0P:(DE-HGF)0$$aCarrara$$b13
000866592 7001_ $$0P:(DE-HGF)0$$aGuan$$b14
000866592 7001_ $$0P:(DE-HGF)0$$aGonzález-Cascón$$b15
000866592 7001_ $$0P:(DE-HGF)0$$aMigliavacca$$b16
000866592 773__ $$0PERI:(DE-600)2513863-7$$a10.3390/rs11212562$$gVol. 11, no. 21, p. 2562 -$$n21$$p2562 -$$tRemote sensing$$v11$$x2072-4292$$y2019
000866592 8564_ $$uhttps://juser.fz-juelich.de/record/866592/files/remotesensing-11-02562-v3.pdf$$yOpenAccess
000866592 8564_ $$uhttps://juser.fz-juelich.de/record/866592/files/remotesensing-11-02562-v3.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000866592 909CO $$ooai:juser.fz-juelich.de:866592$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000866592 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129388$$aForschungszentrum Jülich$$b9$$kFZJ
000866592 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
000866592 9141_ $$y2019
000866592 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000866592 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000866592 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000866592 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bREMOTE SENS-BASEL : 2017
000866592 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal
000866592 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ
000866592 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000866592 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000866592 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000866592 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000866592 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000866592 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences
000866592 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000866592 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000866592 920__ $$lyes
000866592 9201_ $$0I:(DE-Juel1)IBG-2-20101118$$kIBG-2$$lPflanzenwissenschaften$$x0
000866592 980__ $$ajournal
000866592 980__ $$aVDB
000866592 980__ $$aUNRESTRICTED
000866592 980__ $$aI:(DE-Juel1)IBG-2-20101118
000866592 9801_ $$aFullTexts