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Integration of MODIS land and atmosphere products with a coupled‐process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales

Identifieur interne : 000824 ( Istex/Checkpoint ); précédent : 000823; suivant : 000825

Integration of MODIS land and atmosphere products with a coupled‐process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales

Auteurs : Youngryel Ryu [Corée du Sud, États-Unis] ; Dennis D. Baldocchi [États-Unis] ; Hideki Kobayashi [États-Unis] ; Catharine Van Ingen [États-Unis] ; Jie Li [États-Unis] ; T. Andy Black [Canada] ; Jason Beringer [Australie] ; Eva Van Gorsel [Australie] ; Alexander Knohl [Allemagne] ; Beverly E. Law [États-Unis] ; Olivier Roupsard [France, Costa Rica]

Source :

RBID : ISTEX:61487300701F4319BF47883021417FAC46229385

Descripteurs français

English descriptors

Abstract

We propose the Breathing Earth System Simulator (BESS), an upscaling approach to quantify global gross primary productivity and evapotranspiration using MODIS with a spatial resolution of 1–5 km and a temporal resolution of 8 days. This effort is novel because it is the first system that harmonizes and utilizes MODIS Atmosphere and Land products on the same projection and spatial resolution over the global land. This enabled us to use the MODIS Atmosphere products to calculate atmospheric radiative transfer for visual and near infrared radiation wave bands. Then we coupled atmospheric and canopy radiative transfer processes, with models that computed leaf photosynthesis, stomatal conductance and transpiration on the sunlit and shaded portions of the vegetation and soil. At the annual time step, the mass and energy fluxes derived from BESS showed strong linear relations with measurements of solar irradiance (r2 = 0.95, relative bias: 8%), gross primary productivity (r2 = 0.86, relative bias: 5%) and evapotranspiration (r2 = 0.86, relative bias: 15%) in data from 33 flux towers that cover seven plant functional types across arctic to tropical climatic zones. A sensitivity analysis revealed that the gross primary productivity and evapotranspiration computed in BESS were most sensitive to leaf area index and solar irradiance, respectively. We quantified the mean global terrestrial estimates of gross primary productivity and evapotranpiration between 2001 and 2003 as 118 ± 26 PgC yr−1 and 500 ± 104 mm yr−1 (equivalent to 63,000 ± 13,100 km3 yr−1), respectively. BESS‐derived gross primary productivity and evapotranspiration estimates were consistent with the estimates from independent machine‐learning, data‐driven products, but the process‐oriented structure has the advantage of diagnosing sensitivity of mechanisms. The process‐based BESS is able to offer gridded biophysical variables everywhere from local to the total global land scales with an 8‐day interval over multiple years.

Url:
DOI: 10.1029/2011GB004053


Affiliations:


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ISTEX:61487300701F4319BF47883021417FAC46229385

Le document en format XML

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<term>Ncep data</term>
<term>Nearest neighborhood method</term>
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<term>Photosynthetic capacity</term>
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<term>Pixel</term>
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<term>Temporal resolution</term>
<term>Thickness modis</term>
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<term>Tropical forests</term>
<term>Upscaling</term>
<term>Vapor pressure</term>
<term>Vapor pressure deficit</term>
<term>Wang</term>
<term>Warm summer</term>
<term>Water balance</term>
<term>Wind speed</term>
<term>Yuan</term>
<term>Zhao</term>
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<front>
<div type="abstract">We propose the Breathing Earth System Simulator (BESS), an upscaling approach to quantify global gross primary productivity and evapotranspiration using MODIS with a spatial resolution of 1–5 km and a temporal resolution of 8 days. This effort is novel because it is the first system that harmonizes and utilizes MODIS Atmosphere and Land products on the same projection and spatial resolution over the global land. This enabled us to use the MODIS Atmosphere products to calculate atmospheric radiative transfer for visual and near infrared radiation wave bands. Then we coupled atmospheric and canopy radiative transfer processes, with models that computed leaf photosynthesis, stomatal conductance and transpiration on the sunlit and shaded portions of the vegetation and soil. At the annual time step, the mass and energy fluxes derived from BESS showed strong linear relations with measurements of solar irradiance (r2 = 0.95, relative bias: 8%), gross primary productivity (r2 = 0.86, relative bias: 5%) and evapotranspiration (r2 = 0.86, relative bias: 15%) in data from 33 flux towers that cover seven plant functional types across arctic to tropical climatic zones. A sensitivity analysis revealed that the gross primary productivity and evapotranspiration computed in BESS were most sensitive to leaf area index and solar irradiance, respectively. We quantified the mean global terrestrial estimates of gross primary productivity and evapotranpiration between 2001 and 2003 as 118 ± 26 PgC yr−1 and 500 ± 104 mm yr−1 (equivalent to 63,000 ± 13,100 km3 yr−1), respectively. BESS‐derived gross primary productivity and evapotranspiration estimates were consistent with the estimates from independent machine‐learning, data‐driven products, but the process‐oriented structure has the advantage of diagnosing sensitivity of mechanisms. The process‐based BESS is able to offer gridded biophysical variables everywhere from local to the total global land scales with an 8‐day interval over multiple years.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>Allemagne</li>
<li>Australie</li>
<li>Canada</li>
<li>Corée du Sud</li>
<li>Costa Rica</li>
<li>France</li>
<li>États-Unis</li>
</country>
<region>
<li>Basse-Saxe</li>
<li>Californie</li>
<li>Languedoc-Roussillon</li>
<li>Occitanie (région administrative)</li>
<li>Oregon</li>
<li>Région capitale de Séoul</li>
<li>Virginie</li>
</region>
<settlement>
<li>Göttingen</li>
<li>Montpellier</li>
<li>Séoul</li>
</settlement>
<orgName>
<li>Université nationale de Séoul</li>
</orgName>
</list>
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<country name="Corée du Sud">
<noRegion>
<name sortKey="Ryu, Youngryel" sort="Ryu, Youngryel" uniqKey="Ryu Y" first="Youngryel" last="Ryu">Youngryel Ryu</name>
</noRegion>
<name sortKey="Ryu, Youngryel" sort="Ryu, Youngryel" uniqKey="Ryu Y" first="Youngryel" last="Ryu">Youngryel Ryu</name>
<name sortKey="Ryu, Youngryel" sort="Ryu, Youngryel" uniqKey="Ryu Y" first="Youngryel" last="Ryu">Youngryel Ryu</name>
</country>
<country name="États-Unis">
<region name="Californie">
<name sortKey="Ryu, Youngryel" sort="Ryu, Youngryel" uniqKey="Ryu Y" first="Youngryel" last="Ryu">Youngryel Ryu</name>
</region>
<name sortKey="Baldocchi, Dennis D" sort="Baldocchi, Dennis D" uniqKey="Baldocchi D" first="Dennis D." last="Baldocchi">Dennis D. Baldocchi</name>
<name sortKey="Kobayashi, Hideki" sort="Kobayashi, Hideki" uniqKey="Kobayashi H" first="Hideki" last="Kobayashi">Hideki Kobayashi</name>
<name sortKey="Law, Beverly E" sort="Law, Beverly E" uniqKey="Law B" first="Beverly E." last="Law">Beverly E. Law</name>
<name sortKey="Li, Jie" sort="Li, Jie" uniqKey="Li J" first="Jie" last="Li">Jie Li</name>
<name sortKey="Van Ingen, Catharine" sort="Van Ingen, Catharine" uniqKey="Van Ingen C" first="Catharine" last="Van Ingen">Catharine Van Ingen</name>
</country>
<country name="Canada">
<noRegion>
<name sortKey="Black, T Andy" sort="Black, T Andy" uniqKey="Black T" first="T. Andy" last="Black">T. Andy Black</name>
</noRegion>
</country>
<country name="Australie">
<noRegion>
<name sortKey="Beringer, Jason" sort="Beringer, Jason" uniqKey="Beringer J" first="Jason" last="Beringer">Jason Beringer</name>
</noRegion>
<name sortKey="Van Gorsel, Eva" sort="Van Gorsel, Eva" uniqKey="Van Gorsel E" first="Eva" last="Van Gorsel">Eva Van Gorsel</name>
</country>
<country name="Allemagne">
<region name="Basse-Saxe">
<name sortKey="Knohl, Alexander" sort="Knohl, Alexander" uniqKey="Knohl A" first="Alexander" last="Knohl">Alexander Knohl</name>
</region>
</country>
<country name="France">
<region name="Occitanie (région administrative)">
<name sortKey="Roupsard, Olivier" sort="Roupsard, Olivier" uniqKey="Roupsard O" first="Olivier" last="Roupsard">Olivier Roupsard</name>
</region>
</country>
<country name="Costa Rica">
<noRegion>
<name sortKey="Roupsard, Olivier" sort="Roupsard, Olivier" uniqKey="Roupsard O" first="Olivier" last="Roupsard">Olivier Roupsard</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>

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