Effects of foliage clumping on the estimation of global terrestrial gross primary productivity
Identifieur interne : 001528 ( Istex/Curation ); précédent : 001527; suivant : 001529Effects of foliage clumping on the estimation of global terrestrial gross primary productivity
Auteurs : Jing M. Chen [République populaire de Chine, Canada] ; Gang Mo [Canada] ; Jan Pisek [Canada, Estonie] ; Jane Liu [Canada] ; Feng Deng [République populaire de Chine] ; Misa Ishizawa [Canada] ; Douglas Chan [Canada]Source :
- Global Biogeochemical Cycles [ 0886-6236 ] ; 2012-03.
Abstract
Sunlit and shaded leaf separation proposed by Norman (1982) is an effective way to upscale from leaf to canopy in modeling vegetation photosynthesis. The Boreal Ecosystem Productivity Simulator (BEPS) makes use of this methodology, and has been shown to be reliable in modeling the gross primary productivity (GPP) derived from CO2flux and tree ring measurements. In this study, we use BEPS to investigate the effect of canopy architecture on the global distribution of GPP. For this purpose, we use not only leaf area index (LAI) but also the first ever global map of the foliage clumping index derived from the multiangle satellite sensor POLDER at 6 km resolution. The clumping index, which characterizes the degree of the deviation of 3‐dimensional leaf spatial distributions from the random case, is used to separate sunlit and shaded LAI values for a given LAI. Our model results show that global GPP in 2003 was 132 ± 22 Pg C. Relative to this baseline case, our results also show: (1) global GPP is overestimated by 12% when accurate LAI is available but clumping is ignored, and (2) global GPP is underestimated by 9% when the effective LAI is available and clumping is ignored. The clumping effects in both cases are statistically significant (p < 0.001). The effective LAI is often derived from remote sensing by inverting the measured canopy gap fraction to LAI without considering the clumping. Global GPP would therefore be generally underestimated when remotely sensed LAI (actually effective LAI by our definition) is used. This is due to the underestimation of the shaded LAI and therefore the contribution of shaded leaves to GPP. We found that shaded leaves contribute 50%, 38%, 37%, 39%, 26%, 29% and 21% to the total GPP for broadleaf evergreen forest, broadleaf deciduous forest, evergreen conifer forest, deciduous conifer forest, shrub, C4 vegetation, and other vegetation, respectively. The global average of this ratio is 35%.
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DOI: 10.1029/2010GB003996
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Chen</name><affiliation wicri:level="1"><mods:affiliation>International Institute of Earth System Science, Nanjing University, Nanjing, China</mods:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>International Institute of Earth System Science, Nanjing University, Nanjing</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>Department of Geography, University of Toronto, Toronto, Ontario, Canada</mods:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Geography, University of Toronto, Toronto, Ontario</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: chenj@geog.utoronto.ca</mods:affiliation><country wicri:rule="url">Canada</country></affiliation></author><author><name sortKey="Mo, Gang" sort="Mo, Gang" uniqKey="Mo G" first="Gang" last="Mo">Gang Mo</name><affiliation wicri:level="1"><mods:affiliation>Department of Geography, University of Toronto, Toronto, Ontario, Canada</mods:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Geography, University of Toronto, Toronto, Ontario</wicri:regionArea></affiliation></author><author><name sortKey="Pisek, Jan" sort="Pisek, Jan" uniqKey="Pisek J" first="Jan" last="Pisek">Jan Pisek</name><affiliation wicri:level="1"><mods:affiliation>Department of Geography, University of Toronto, Toronto, Ontario, Canada</mods:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Geography, University of Toronto, Toronto, Ontario</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>Tartu Observatory 61602, Toravere, Estonia</mods:affiliation><country xml:lang="fr">Estonie</country><wicri:regionArea>Tartu Observatory 61602, Toravere</wicri:regionArea></affiliation></author><author><name sortKey="Liu, Jane" sort="Liu, Jane" uniqKey="Liu J" first="Jane" last="Liu">Jane Liu</name><affiliation wicri:level="1"><mods:affiliation>Department of Geography, University of Toronto, Toronto, Ontario, Canada</mods:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Geography, University of Toronto, Toronto, Ontario</wicri:regionArea></affiliation></author><author><name sortKey="Deng, Feng" sort="Deng, Feng" uniqKey="Deng F" first="Feng" last="Deng">Feng Deng</name><affiliation wicri:level="1"><mods:affiliation>International Institute of Earth System Science, Nanjing University, Nanjing, China</mods:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>International Institute of Earth System Science, Nanjing University, Nanjing</wicri:regionArea></affiliation></author><author><name sortKey="Ishizawa, Misa" sort="Ishizawa, Misa" uniqKey="Ishizawa M" first="Misa" last="Ishizawa">Misa Ishizawa</name><affiliation wicri:level="1"><mods:affiliation>Meteorological Service of Canada, Environment Canada, Toronto, Ontario, Canada</mods:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Meteorological Service of Canada, Environment Canada, Toronto, Ontario</wicri:regionArea></affiliation></author><author><name sortKey="Chan, Douglas" sort="Chan, Douglas" uniqKey="Chan D" first="Douglas" last="Chan">Douglas Chan</name><affiliation wicri:level="1"><mods:affiliation>Meteorological Service of Canada, Environment Canada, Toronto, Ontario, Canada</mods:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Meteorological Service of Canada, Environment Canada, Toronto, Ontario</wicri:regionArea></affiliation></author></analytic><monogr></monogr><series><title level="j">Global Biogeochemical Cycles</title><title level="j" type="abbrev">Global Biogeochem. Cycles</title><idno type="ISSN">0886-6236</idno><idno type="eISSN">1944-9224</idno><imprint><publisher>Blackwell Publishing Ltd</publisher><date type="published" when="2012-03">2012-03</date><biblScope unit="volume">26</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="/">n/a</biblScope><biblScope unit="page" to="/">n/a</biblScope></imprint><idno type="ISSN">0886-6236</idno></series><idno type="istex">1BE91345AC1E39E7C900AB319CB7B6B9B953E9E6</idno><idno type="DOI">10.1029/2010GB003996</idno><idno type="ArticleID">2010GB003996</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0886-6236</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract">Sunlit and shaded leaf separation proposed by Norman (1982) is an effective way to upscale from leaf to canopy in modeling vegetation photosynthesis. The Boreal Ecosystem Productivity Simulator (BEPS) makes use of this methodology, and has been shown to be reliable in modeling the gross primary productivity (GPP) derived from CO2flux and tree ring measurements. In this study, we use BEPS to investigate the effect of canopy architecture on the global distribution of GPP. For this purpose, we use not only leaf area index (LAI) but also the first ever global map of the foliage clumping index derived from the multiangle satellite sensor POLDER at 6 km resolution. The clumping index, which characterizes the degree of the deviation of 3‐dimensional leaf spatial distributions from the random case, is used to separate sunlit and shaded LAI values for a given LAI. Our model results show that global GPP in 2003 was 132 ± 22 Pg C. Relative to this baseline case, our results also show: (1) global GPP is overestimated by 12% when accurate LAI is available but clumping is ignored, and (2) global GPP is underestimated by 9% when the effective LAI is available and clumping is ignored. The clumping effects in both cases are statistically significant (p < 0.001). The effective LAI is often derived from remote sensing by inverting the measured canopy gap fraction to LAI without considering the clumping. Global GPP would therefore be generally underestimated when remotely sensed LAI (actually effective LAI by our definition) is used. This is due to the underestimation of the shaded LAI and therefore the contribution of shaded leaves to GPP. We found that shaded leaves contribute 50%, 38%, 37%, 39%, 26%, 29% and 21% to the total GPP for broadleaf evergreen forest, broadleaf deciduous forest, evergreen conifer forest, deciduous conifer forest, shrub, C4 vegetation, and other vegetation, respectively. The global average of this ratio is 35%.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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