TY - JOUR
T1 - Evergreen broadleaf greenness and its relationship with leaf flushing, aging, and water fluxes
AU - Luo, Yunpeng
AU - Pacheco-Labrador, Javier
AU - Richardson, Andrew D.
AU - Seyednasrollah, Bijan
AU - Perez-Priego, Oscar
AU - Gonzalez-Cascon, Rosario
AU - Martín, M. Pilar
AU - Moreno, Gerardo
AU - Nair, Richard
AU - Wutzler, Thomas
AU - Bucher, Solveig Franziska
AU - Carrara, Arnaud
AU - Cremonese, Edoardo
AU - El-Madany, Tarek S.
AU - Filippa, Gianluca
AU - Galvagno, Marta
AU - Hammer, Tiana
AU - Ma, Xuanlong
AU - Martini, David
AU - Zhang, Qian
AU - Reichstein, Markus
AU - Menzel, Annette
AU - Römermann, Christine
AU - Migliavacca, Mirco
N1 - Funding Information:
The authors acknowledge the Alexander von Humboldt Foundation for supporting this research with the Max Planck Prize to Markus Reichstein. Yunpeng Luo and Mirco Migliavacca gratefully acknowledge the financial support from the China Scholarship Council. ADR acknowledges support for the PhenoCam network from the National Science Foundation ( DEB- 1702697 ). Javier Pacheco-Labrador and Mirco Migliavacca acknowledge the German Aerospace Center (DLR) project OBEF-Accross2 “The Potential of Earth Observations to Capture Patterns of Biodiversity” (Contract No. 50EE1912). The research also received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 721995 and Ministerio de Economíay Competitividad through FLUXPEC CGL2012-34383 and SynerTGE CGL2015-G9095-R (MINECO/FEDER, UE) projects. We are grateful to all the colleagues who contributed to the acquisition and processing of the field data.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/8/15
Y1 - 2022/8/15
N2 - Remote sensing capabilities to monitor evergreen broadleaved vegetation are limited by the low temporal variability in the greenness signal. With canopy greenness computed from digital repeat photography (PhenoCam), we investigated how canopy greenness related to seasonal changes in leaf age and traits as well as variation of trees’ water fluxes (characterized by sap flow and canopy conductance). The results showed that sprouting leaves are mainly responsible for the rapid increase in canopy green chromatic coordinate (GCC) in spring. We found statistically significantly differences in leaf traits and spectral properties among leaves of different leaf ages. Specifically, mean GCC of young leaves was 0.385 ± 0.010 (mean ± SD), while for mature and old leaves was 0.369 ± 0.003, and 0.376 ± 0.004, respectively. Thus, the temporal dynamics of canopy GCC can be explained by changes in leaf spectral properties and leaf age. Sap flow and canopy conductance are both well explained by a combination of environmental drivers and greenness (96% and 87% of the variance explained, respectively). In particular, air temperature and vapor pressure deficit (VPD) explained most of sap flow and canopy conductance variance, respectively. Besides, GCC is an important explanatory variable for variation of canopy conductance may because GCC can represent the leaf ontogeny information. We conclude that PhenoCam GCC can be used to identify the leaf flushing for evergreen broadleaved trees, which carries important information about leaf ontogeny and traits. Thus, it can be helpful for better estimating canopy conductance which constraints water fluxes.
AB - Remote sensing capabilities to monitor evergreen broadleaved vegetation are limited by the low temporal variability in the greenness signal. With canopy greenness computed from digital repeat photography (PhenoCam), we investigated how canopy greenness related to seasonal changes in leaf age and traits as well as variation of trees’ water fluxes (characterized by sap flow and canopy conductance). The results showed that sprouting leaves are mainly responsible for the rapid increase in canopy green chromatic coordinate (GCC) in spring. We found statistically significantly differences in leaf traits and spectral properties among leaves of different leaf ages. Specifically, mean GCC of young leaves was 0.385 ± 0.010 (mean ± SD), while for mature and old leaves was 0.369 ± 0.003, and 0.376 ± 0.004, respectively. Thus, the temporal dynamics of canopy GCC can be explained by changes in leaf spectral properties and leaf age. Sap flow and canopy conductance are both well explained by a combination of environmental drivers and greenness (96% and 87% of the variance explained, respectively). In particular, air temperature and vapor pressure deficit (VPD) explained most of sap flow and canopy conductance variance, respectively. Besides, GCC is an important explanatory variable for variation of canopy conductance may because GCC can represent the leaf ontogeny information. We conclude that PhenoCam GCC can be used to identify the leaf flushing for evergreen broadleaved trees, which carries important information about leaf ontogeny and traits. Thus, it can be helpful for better estimating canopy conductance which constraints water fluxes.
KW - Digital repeat photography
KW - Evergreen broadleaved trees
KW - Green chromatic coordinate (GCC)
KW - Leaf age
KW - PhenoCam
KW - Water fluxes
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U2 - 10.1016/j.agrformet.2022.109060
DO - 10.1016/j.agrformet.2022.109060
M3 - Article
AN - SCOPUS:85132537547
SN - 0168-1923
VL - 323
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
M1 - 109060
ER -