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Leaf Nitrogen Content Indirectly Estimated by Leaf Traits Derived From the PROSPECT Model

Wang, Zhihui und Skidmore, Andrew und Darvishzadeh, Roshanak und Heiden, Uta und Heurich, Marco und Wang, Tiejun (2015) Leaf Nitrogen Content Indirectly Estimated by Leaf Traits Derived From the PROSPECT Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (6), Seiten 3172-3182. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2015.2422734. ISSN 1939-1404.

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Offizielle URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7109123

Kurzfassung

Leaf nitrogen content has so far been quantified through empirical techniques using hyperspectral remote sensing. However, it remains a challenge to estimate the nitrogen content in fresh leaves through inversion of physically based models. Leaf nitrogen has been found to correlate with leaf traits (e.g., leaf chlorophyll, dry matter, and water) well through links to the photosynthetic process, which provides potential to estimate nitrogen indirectly. We therefore set out to estimate leaf nitrogen content by using its links to leaf traits that could be retrieved from a physically based model (PROSPECT) inversion. Leaf optical (directional-hemispherical reflectance and transmittance between 350 and 2500 nm) and leaf biochemical (nitrogen, chlorophyll, dry matter, and water) properties were measured. Correlation analysis showed that the area-based nitrogen correlations with leaf traits were higher than mass-based correlations. Hence, simple and multiple linear regression models were established for areabased nitrogen using three leaf traits (leaf chlorophyll content, leaf mass per area, and equivalent water thickness). In addition, the traits were retrieved by the inversion of PROSPECT using an iterative optimization algorithm. The established empirical models and the leaf traits retrieved from PROSPECT were used to estimate leaf nitrogen content. A simple linear regression model using only retrieved equivalent water thickness as a predictor produced the most accurate estimation of nitrogen (R2 = 0.58, normalized RMSE = 0.11). The combination of empirical and physically based models provides a moderately accurate estimation of leaf nitrogen content, which can be transferred to other datasets in a robust and upscalable manner.

elib-URL des Eintrags:https://elib.dlr.de/97655/
Dokumentart:Zeitschriftenbeitrag
Titel:Leaf Nitrogen Content Indirectly Estimated by Leaf Traits Derived From the PROSPECT Model
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Wang, Zhihuiz.wang-1 (at) utwente.nlNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Skidmore, Andrewa.k.skidmore (at) utwente.nlNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Darvishzadeh, Roshanakr.darvish (at) utwente.nlNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Heiden, Utauta.heiden (at) dlr.dehttps://orcid.org/0000-0002-3865-1912NICHT SPEZIFIZIERT
Heurich, MarcoMarco.Heurich (at) npv-bw.bayern.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Wang, Tiejunt.wang (at) utwente.nlNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:2015
Erschienen in:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Referierte Publikation:Ja
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:8
DOI:10.1109/JSTARS.2015.2422734
Seitenbereich:Seiten 3172-3182
Verlag:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:veröffentlicht
Stichwörter:Hyperspectral remote sensing, leaf nitrogen, leaf traits, PROSPECT model
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Vorhaben Fernerkundung der Landoberfläche (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Landoberfläche
Hinterlegt von: Heiden, Dr.rer.nat. Uta
Hinterlegt am:08 Sep 2015 12:04
Letzte Änderung:28 Mär 2023 23:44

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