Photosynthetically active radiation and foliage clumping improve satellite-based NIRv estimates of gross primary production
Filella, Iolanda (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Descals, Adrià (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Balzarolo, Manuela (Universiteit Antwerpen. Departement Biologie)
Liu, Guoxiang (Southwest Jiaotong University. Faculty of Geosciences and Environmental Engineering)
Verger, Aleixandre (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Fang, Hongliang (Chinese Academy of Sciences. Institute of Geographic Sciences and Natural Resources Research)
Peñuelas, Josep (Centre de Recerca Ecològica i d'Aplicacions Forestals)

Data:	2023
Resum:	Monitoring gross primary production (GPP) is necessary for quantifying the terrestrial carbon balance. The near-infrared reflectance of vegetation (NIRv) has been proven to be a good predictor of GPP. Given that radiation powers photosynthesis, we hypothesized that (i) the addition of photosynthetic photon flux density (PPFD) information to NIRv would improve estimates of GPP and that (ii) a further improvement would be obtained by incorporating the estimates of radiation distribution in the canopy provided by the foliar clumping index (CI). Thus, we used GPP data from FLUXNET sites to test these possible improvements by comparing the performance of a model based solely on NIRv with two other models, one combining NIRv and PPFD and the other combining NIRv, PPFD and the CI of each vegetation cover type. We tested the performance of these models for different types of vegetation cover, at various latitudes and over the different seasons. Our results demonstrate that the addition of daily radiation information and the clumping index for each vegetation cover type to the NIRv improves its ability to estimate GPP. The improvement was related to foliage organization, given that the foliar distribution in the canopy (CI) affects radiation distribution and use and that radiation drives productivity. Evergreen needleleaf forests are the vegetation cover type with the greatest improvement in GPP estimation after the addition of CI information, likely as a result of their greater radiation constraints. Vegetation type was more determinant of the sensitivity to PPFD changes than latitude or seasonality. We advocate for the incorporation of PPFD and CI into NIRv algorithms and GPP models to improve GPP estimates.
Ajuts:	Ministerio de Ciencia e Innovación TED2021-132627B-I00 Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/SGR-01333
Nota:	Altres ajuts: the Fundación Ramón Areces grant CIVP20A662
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	GPP ; Clumping index ; NIRv ; Photosynthetically active radiation ; Evergreen needleleaf forest ; Vegetation cover type
Publicat a:	Remote sensing (Basel), Vol. 15, Issue 8 (April 2023) , art. 2207, ISSN 2072-4292

DOI: 10.3390/rs15082207

12 p, 1.7 MB

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Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències > CREAF (Centre de Recerca Ecològica i d'Aplicacions Forestals)
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