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 : 001229 ( Istex/Corpus ); précédent : 001228; suivant : 001230Integration 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 ; Dennis D. Baldocchi ; Hideki Kobayashi ; Catharine Van Ingen ; Jie Li ; T. Andy Black ; Jason Beringer ; Eva Van Gorsel ; Alexander Knohl ; Beverly E. Law ; Olivier RoupsardSource :
- Global Biogeochemical Cycles [ 0886-6236 ] ; 2011-12.
English descriptors
- KwdEn :
- Actual temperature, Aerosol product, Aerosol type, Agric, Albedo, Algorithm, Appl, Atmospheric radiative transfer, Atmospheric radiative transfer model, Available energy, Azure storage, Baldocchi, Bess, Bess model, Bioclimatic, Bioclimatic zones, Biol, Biophysical model, Boreal, Boreal forests, British columbia, Canopy, Canopy conductance, Canopy depth, Canopy fluxes, Canopy radiative transfer, Canopy radiative transfer model, Canopy reflectance, Clumped canopy, Clumping, Clumping index, Coefficient, Collatz, Conductance, Cool summer, Data gaps, Deciduous, Deciduous forest, Diffuse, Direct beam, Ecol, Ecosystem, Energy fluxes, Environ, Environmental science, Evaporation, Evapotranspiration, Evergreen, Extinction, Extinction coefficient, Farquhar, Flux tower, Flux tower data, Flux towers, Fluxnet, Foliar, Geophys, Global, Global biogeochem, Global change biol, Global climate classification, Global estimates, Global land, Global scale, Good agreement, Houborg, Humid, Ieee trans, Incoming longwave radiation, Incoming shortwave radiation, Input data, Input variables, Irradiance, Jung, Kobayashi, Land products, Land surface models, Land surface temperature, Latent heat flux, Leaf, Leaf area index, Leaf photosynthesis, Leuning, Longwave, Longwave radiation, Meteorol, Meteorological variables, Microsoft, Mmol, Modeling, Modis, Modis data, Modis land, Ncep data, Nearest neighborhood method, Nonlinear processes, Photosynthesis, Photosynthetic, Photosynthetic capacity, Physiol, Pixel, Plant cell environ, Point temperature, Previous studies, Primary productivity, Pury, Qnsh, Qnsun, Qpsh, Qpsun, Radiative, Rain forests, Reanalysis, Recent advances, Reflectance, Reichstein, Relative bias, Relative rmse, Remote sens, Rmse, Seasonal pattern, Seasonal variation, Section canopy radiative transfer model, Sens, Sensitivity analysis, Shade leaf, Shortwave, Sinusoidal, Sinusoidal projection, Sinusoidal tiles, Snir, Soil reflectance, Soil surface, Solar irradiance, Solar zenith angle, Spatial resolution, Stomatal, Stomatal conductance, Sunlit, Sunlit leaf, Temperate, Temporal resolution, Thickness modis, Tree physiol, Tropical forests, Upscaling, Vapor pressure, Vapor pressure deficit, Wang, Warm summer, Water balance, Wind speed, Yuan, Zhao.
- Teeft :
- Actual temperature, Aerosol product, Aerosol type, Agric, Albedo, Algorithm, Appl, Atmospheric radiative transfer, Atmospheric radiative transfer model, Available energy, Azure storage, Baldocchi, Bess, Bess model, Bioclimatic, Bioclimatic zones, Biol, Biophysical model, Boreal, Boreal forests, British columbia, Canopy, Canopy conductance, Canopy depth, Canopy fluxes, Canopy radiative transfer, Canopy radiative transfer model, Canopy reflectance, Clumped canopy, Clumping, Clumping index, Coefficient, Collatz, Conductance, Cool summer, Data gaps, Deciduous, Deciduous forest, Diffuse, Direct beam, Ecol, Ecosystem, Energy fluxes, Environ, Environmental science, Evaporation, Evapotranspiration, Evergreen, Extinction, Extinction coefficient, Farquhar, Flux tower, Flux tower data, Flux towers, Fluxnet, Foliar, Geophys, Global, Global biogeochem, Global change biol, Global climate classification, Global estimates, Global land, Global scale, Good agreement, Houborg, Humid, Ieee trans, Incoming longwave radiation, Incoming shortwave radiation, Input data, Input variables, Irradiance, Jung, Kobayashi, Land products, Land surface models, Land surface temperature, Latent heat flux, Leaf, Leaf area index, Leaf photosynthesis, Leuning, Longwave, Longwave radiation, Meteorol, Meteorological variables, Microsoft, Mmol, Modeling, Modis, Modis data, Modis land, Ncep data, Nearest neighborhood method, Nonlinear processes, Photosynthesis, Photosynthetic, Photosynthetic capacity, Physiol, Pixel, Plant cell environ, Point temperature, Previous studies, Primary productivity, Pury, Qnsh, Qnsun, Qpsh, Qpsun, Radiative, Rain forests, Reanalysis, Recent advances, Reflectance, Reichstein, Relative bias, Relative rmse, Remote sens, Rmse, Seasonal pattern, Seasonal variation, Section canopy radiative transfer model, Sens, Sensitivity analysis, Shade leaf, Shortwave, Sinusoidal, Sinusoidal projection, Sinusoidal tiles, Snir, Soil reflectance, Soil surface, Solar irradiance, Solar zenith angle, Spatial resolution, Stomatal, Stomatal conductance, Sunlit, Sunlit leaf, Temperate, Temporal resolution, Thickness modis, Tree physiol, Tropical forests, Upscaling, Vapor pressure, Vapor pressure deficit, Wang, Warm summer, Water balance, Wind speed, Yuan, Zhao.
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
Links to Exploration step
ISTEX:61487300701F4319BF47883021417FAC46229385Le document en format XML
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<affiliation><mods:affiliation>Department of Environmental Science, Policy and Management, University of California, Berkeley, California, USA</mods:affiliation>
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<affiliation><mods:affiliation>Now at Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, South Korea</mods:affiliation>
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<affiliation><mods:affiliation>E-mail: yryu@snu.ac.kr</mods:affiliation>
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<author><name sortKey="Van Ingen, Catharine" sort="Van Ingen, Catharine" uniqKey="Van Ingen C" first="Catharine" last="Van Ingen">Catharine Van Ingen</name>
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<author><name sortKey="Li, Jie" sort="Li, Jie" uniqKey="Li J" first="Jie" last="Li">Jie Li</name>
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<author><name sortKey="Ryu, Youngryel" sort="Ryu, Youngryel" uniqKey="Ryu Y" first="Youngryel" last="Ryu">Youngryel Ryu</name>
<affiliation><mods:affiliation>E-mail: yryu@snu.ac.kr</mods:affiliation>
</affiliation>
<affiliation><mods:affiliation>Department of Environmental Science, Policy and Management, University of California, Berkeley, California, USA</mods:affiliation>
</affiliation>
<affiliation><mods:affiliation>Now at Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, South Korea</mods:affiliation>
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<author><name sortKey="Baldocchi, Dennis D" sort="Baldocchi, Dennis D" uniqKey="Baldocchi D" first="Dennis D." last="Baldocchi">Dennis D. Baldocchi</name>
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<author><name sortKey="Kobayashi, Hideki" sort="Kobayashi, Hideki" uniqKey="Kobayashi H" first="Hideki" last="Kobayashi">Hideki Kobayashi</name>
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<author><name sortKey="Van Ingen, Catharine" sort="Van Ingen, Catharine" uniqKey="Van Ingen C" first="Catharine" last="Van Ingen">Catharine Van Ingen</name>
<affiliation><mods:affiliation>eScience Group, Microsoft Research, San Francisco, California, USA</mods:affiliation>
</affiliation>
</author>
<author><name sortKey="Li, Jie" sort="Li, Jie" uniqKey="Li J" first="Jie" last="Li">Jie Li</name>
<affiliation><mods:affiliation>Department of Computer Science, University of Virginia, Charlottesville, Virginia, USA</mods:affiliation>
</affiliation>
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<author><name sortKey="Black, T Andy" sort="Black, T Andy" uniqKey="Black T" first="T. Andy" last="Black">T. Andy Black</name>
<affiliation><mods:affiliation>Faculty of Land and Food Systems, University of British Columbia, Vancouver, British Columbia, Canada</mods:affiliation>
</affiliation>
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<author><name sortKey="Beringer, Jason" sort="Beringer, Jason" uniqKey="Beringer J" first="Jason" last="Beringer">Jason Beringer</name>
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<author><name sortKey="Van Gorsel, Eva" sort="Van Gorsel, Eva" uniqKey="Van Gorsel E" first="Eva" last="Van Gorsel">Eva Van Gorsel</name>
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<author><name sortKey="Knohl, Alexander" sort="Knohl, Alexander" uniqKey="Knohl A" first="Alexander" last="Knohl">Alexander Knohl</name>
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</affiliation>
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<author><name sortKey="Law, Beverly E" sort="Law, Beverly E" uniqKey="Law B" first="Beverly E." last="Law">Beverly E. Law</name>
<affiliation><mods:affiliation>Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon, USA</mods:affiliation>
</affiliation>
</author>
<author><name sortKey="Roupsard, Olivier" sort="Roupsard, Olivier" uniqKey="Roupsard O" first="Olivier" last="Roupsard">Olivier Roupsard</name>
<affiliation><mods:affiliation>CIRAD, UMR Ecologie Fonctionnelle and Biogéochimie des Sols and Agroécosystèmes, SupAgro-CIRAD-INRA-IRD, Montpellier, France</mods:affiliation>
</affiliation>
<affiliation><mods:affiliation>Centro Agronómico Tropical de Investigación y Enseñanza, Turrialba, Costa Rica</mods:affiliation>
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<term>Aerosol product</term>
<term>Aerosol type</term>
<term>Agric</term>
<term>Albedo</term>
<term>Algorithm</term>
<term>Appl</term>
<term>Atmospheric radiative transfer</term>
<term>Atmospheric radiative transfer model</term>
<term>Available energy</term>
<term>Azure storage</term>
<term>Baldocchi</term>
<term>Bess</term>
<term>Bess model</term>
<term>Bioclimatic</term>
<term>Bioclimatic zones</term>
<term>Biol</term>
<term>Biophysical model</term>
<term>Boreal</term>
<term>Boreal forests</term>
<term>British columbia</term>
<term>Canopy</term>
<term>Canopy conductance</term>
<term>Canopy depth</term>
<term>Canopy fluxes</term>
<term>Canopy radiative transfer</term>
<term>Canopy radiative transfer model</term>
<term>Canopy reflectance</term>
<term>Clumped canopy</term>
<term>Clumping</term>
<term>Clumping index</term>
<term>Coefficient</term>
<term>Collatz</term>
<term>Conductance</term>
<term>Cool summer</term>
<term>Data gaps</term>
<term>Deciduous</term>
<term>Deciduous forest</term>
<term>Diffuse</term>
<term>Direct beam</term>
<term>Ecol</term>
<term>Ecosystem</term>
<term>Energy fluxes</term>
<term>Environ</term>
<term>Environmental science</term>
<term>Evaporation</term>
<term>Evapotranspiration</term>
<term>Evergreen</term>
<term>Extinction</term>
<term>Extinction coefficient</term>
<term>Farquhar</term>
<term>Flux tower</term>
<term>Flux tower data</term>
<term>Flux towers</term>
<term>Fluxnet</term>
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<term>Global biogeochem</term>
<term>Global change biol</term>
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<term>Qnsh</term>
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<term>Recent advances</term>
<term>Reflectance</term>
<term>Reichstein</term>
<term>Relative bias</term>
<term>Relative rmse</term>
<term>Remote sens</term>
<term>Rmse</term>
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<term>Seasonal variation</term>
<term>Section canopy radiative transfer model</term>
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<term>Sensitivity analysis</term>
<term>Shade leaf</term>
<term>Shortwave</term>
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<term>Sinusoidal projection</term>
<term>Sinusoidal tiles</term>
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<term>Soil reflectance</term>
<term>Soil surface</term>
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<term>Solar zenith angle</term>
<term>Spatial resolution</term>
<term>Stomatal</term>
<term>Stomatal conductance</term>
<term>Sunlit</term>
<term>Sunlit leaf</term>
<term>Temperate</term>
<term>Temporal resolution</term>
<term>Thickness modis</term>
<term>Tree physiol</term>
<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>
</keywords>
<keywords scheme="Teeft" xml:lang="en"><term>Actual temperature</term>
<term>Aerosol product</term>
<term>Aerosol type</term>
<term>Agric</term>
<term>Albedo</term>
<term>Algorithm</term>
<term>Appl</term>
<term>Atmospheric radiative transfer</term>
<term>Atmospheric radiative transfer model</term>
<term>Available energy</term>
<term>Azure storage</term>
<term>Baldocchi</term>
<term>Bess</term>
<term>Bess model</term>
<term>Bioclimatic</term>
<term>Bioclimatic zones</term>
<term>Biol</term>
<term>Biophysical model</term>
<term>Boreal</term>
<term>Boreal forests</term>
<term>British columbia</term>
<term>Canopy</term>
<term>Canopy conductance</term>
<term>Canopy depth</term>
<term>Canopy fluxes</term>
<term>Canopy radiative transfer</term>
<term>Canopy radiative transfer model</term>
<term>Canopy reflectance</term>
<term>Clumped canopy</term>
<term>Clumping</term>
<term>Clumping index</term>
<term>Coefficient</term>
<term>Collatz</term>
<term>Conductance</term>
<term>Cool summer</term>
<term>Data gaps</term>
<term>Deciduous</term>
<term>Deciduous forest</term>
<term>Diffuse</term>
<term>Direct beam</term>
<term>Ecol</term>
<term>Ecosystem</term>
<term>Energy fluxes</term>
<term>Environ</term>
<term>Environmental science</term>
<term>Evaporation</term>
<term>Evapotranspiration</term>
<term>Evergreen</term>
<term>Extinction</term>
<term>Extinction coefficient</term>
<term>Farquhar</term>
<term>Flux tower</term>
<term>Flux tower data</term>
<term>Flux towers</term>
<term>Fluxnet</term>
<term>Foliar</term>
<term>Geophys</term>
<term>Global</term>
<term>Global biogeochem</term>
<term>Global change biol</term>
<term>Global climate classification</term>
<term>Global estimates</term>
<term>Global land</term>
<term>Global scale</term>
<term>Good agreement</term>
<term>Houborg</term>
<term>Humid</term>
<term>Ieee trans</term>
<term>Incoming longwave radiation</term>
<term>Incoming shortwave radiation</term>
<term>Input data</term>
<term>Input variables</term>
<term>Irradiance</term>
<term>Jung</term>
<term>Kobayashi</term>
<term>Land products</term>
<term>Land surface models</term>
<term>Land surface temperature</term>
<term>Latent heat flux</term>
<term>Leaf</term>
<term>Leaf area index</term>
<term>Leaf photosynthesis</term>
<term>Leuning</term>
<term>Longwave</term>
<term>Longwave radiation</term>
<term>Meteorol</term>
<term>Meteorological variables</term>
<term>Microsoft</term>
<term>Mmol</term>
<term>Modeling</term>
<term>Modis</term>
<term>Modis data</term>
<term>Modis land</term>
<term>Ncep data</term>
<term>Nearest neighborhood method</term>
<term>Nonlinear processes</term>
<term>Photosynthesis</term>
<term>Photosynthetic</term>
<term>Photosynthetic capacity</term>
<term>Physiol</term>
<term>Pixel</term>
<term>Plant cell environ</term>
<term>Point temperature</term>
<term>Previous studies</term>
<term>Primary productivity</term>
<term>Pury</term>
<term>Qnsh</term>
<term>Qnsun</term>
<term>Qpsh</term>
<term>Qpsun</term>
<term>Radiative</term>
<term>Rain forests</term>
<term>Reanalysis</term>
<term>Recent advances</term>
<term>Reflectance</term>
<term>Reichstein</term>
<term>Relative bias</term>
<term>Relative rmse</term>
<term>Remote sens</term>
<term>Rmse</term>
<term>Seasonal pattern</term>
<term>Seasonal variation</term>
<term>Section canopy radiative transfer model</term>
<term>Sens</term>
<term>Sensitivity analysis</term>
<term>Shade leaf</term>
<term>Shortwave</term>
<term>Sinusoidal</term>
<term>Sinusoidal projection</term>
<term>Sinusoidal tiles</term>
<term>Snir</term>
<term>Soil reflectance</term>
<term>Soil surface</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>
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<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.</abstract>
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<affiliation>Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon, USA<address><country key="US"></country>
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<affiliation>Centro Agronómico Tropical de Investigación y Enseñanza, Turrialba, Costa Rica<address><country key="CR"></country>
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<profileDesc><abstract style="main"><p xml:id="gbc1823-para-0004">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 (r<hi rend="superscript">2</hi>
= 0.95, relative bias: 8%), gross primary productivity (r<hi rend="superscript">2</hi>
= 0.86, relative bias: 5%) and evapotranspiration (r<hi rend="superscript">2</hi>
= 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<hi rend="superscript">−1</hi>
and 500 ± 104 mm yr<hi rend="superscript">−1</hi>
(equivalent to 63,000 ± 13,100 km<hi rend="superscript">3</hi>
yr<hi rend="superscript">−1</hi>
), 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.</p>
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<item>Mean annual global land GPP and ET are 118 PgC yr‐1 and 63000 km3, respectively</item>
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<keyword xml:id="gbc1823-kwd-0003">gross primary productivity</keyword>
<keyword xml:id="gbc1823-kwd-0004">upscaling</keyword>
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<fundingInfo><fundingAgency>NASA, DOE</fundingAgency>
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<fundingNumber>DE‐FG02‐06ER64308</fundingNumber>
<fundingNumber>DE‐FG02‐04ER63917</fundingNumber>
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<p xml:id="gbc1823-para-0002">Auxiliary material files may require downloading to a local drive depending on platform, browser, configuration, and size. To open auxiliary materials in a browser, click on the label. To download, Right‐click and select “Save Target As…” (PC) or CTRL‐click and select “Download Link to Disk” (Mac).</p>
<p xml:id="gbc1823-para-0003">Additional file information is provided in the readme.txt.</p>
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<caption>Table S2. Look up table of maximum carboxylation rate at 25C and nitrogen use efficiency.</caption>
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<abstractGroup><abstract type="main"><p xml:id="gbc1823-para-0004" label="1">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 (r<sup>2</sup>
= 0.95, relative bias: 8%), gross primary productivity (r<sup>2</sup>
= 0.86, relative bias: 5%) and evapotranspiration (r<sup>2</sup>
= 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<sup>−1</sup>
and 500 ± 104 mm yr<sup>−1</sup>
(equivalent to 63,000 ± 13,100 km<sup>3</sup>
yr<sup>−1</sup>
), 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.</p>
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<abstract type="short"><title type="main">Key Points</title>
<p xml:id="gbc1823-para-0005"><list style="bulleted"><listItem>A process model that couples atmosphere and canopy processes is reported</listItem>
<listItem>The model shows a good performance across a range of spatial and temporal scales</listItem>
<listItem>Mean annual global land GPP and ET are 118 PgC yr‐1 and 63000 km3, respectively</listItem>
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<titleInfo type="abbreviated" lang="en"><title>GLOBAL GPP AND ET</title>
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<name type="personal"><namePart type="given">Youngryel</namePart>
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<affiliation>E-mail: yryu@snu.ac.kr</affiliation>
<affiliation>Department of Environmental Science, Policy and Management, University of California, Berkeley, California, USA</affiliation>
<affiliation>Now at Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, South Korea</affiliation>
<affiliation>E-mail: yryu@snu.ac.kr</affiliation>
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<name type="personal"><namePart type="given">Dennis D.</namePart>
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<name type="personal"><namePart type="given">Hideki</namePart>
<namePart type="family">Kobayashi</namePart>
<affiliation>Department of Environmental Science, Policy and Management, University of California, Berkeley, California, USA</affiliation>
<role><roleTerm type="text">author</roleTerm>
</role>
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<name type="personal"><namePart type="given">Catharine</namePart>
<namePart type="family">van Ingen</namePart>
<affiliation>eScience Group, Microsoft Research, San Francisco, California, USA</affiliation>
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</role>
</name>
<name type="personal"><namePart type="given">Jie</namePart>
<namePart type="family">Li</namePart>
<affiliation>Department of Computer Science, University of Virginia, Charlottesville, Virginia, USA</affiliation>
<role><roleTerm type="text">author</roleTerm>
</role>
</name>
<name type="personal"><namePart type="given">T. Andy</namePart>
<namePart type="family">Black</namePart>
<affiliation>Faculty of Land and Food Systems, University of British Columbia, Vancouver, British Columbia, Canada</affiliation>
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<name type="personal"><namePart type="given">Jason</namePart>
<namePart type="family">Beringer</namePart>
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<name type="personal"><namePart type="given">Eva</namePart>
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<name type="personal"><namePart type="given">Alexander</namePart>
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<name type="personal"><namePart type="given">Beverly E.</namePart>
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<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.</abstract>
<abstract type="short">A process model that couples atmosphere and canopy processes is reported The model shows a good performance across a range of spatial and temporal scales Mean annual global land GPP and ET are 118 PgC yr‐1 and 63000 km3, respectively</abstract>
<note type="funding">NASA, DOE - No. NNX08AU25H; No. DE‐FG02‐06ER64308; No. DE‐FG02‐04ER63917; No. DE‐FG02‐04ER63911; </note>
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<topic>upscaling</topic>
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<note type="content"> Auxiliary material for this article contains three tables showing parameters used in BESS model and site information used for testing BESS model. Auxiliary material files may require downloading to a local drive depending on platform, browser, configuration, and size. To open auxiliary materials in a browser, click on the label. To download, Right‐click and select “Save Target As…” (PC) or CTRL‐click and select “Download Link to Disk” (Mac). Additional file information is provided in the readme.txt. Auxiliary material for this article contains three tables showing parameters used in BESS model and site information used for testing BESS model. Auxiliary material files may require downloading to a local drive depending on platform, browser, configuration, and size. To open auxiliary materials in a browser, click on the label. To download, Right‐click and select “Save Target As…” (PC) or CTRL‐click and select “Download Link to Disk” (Mac). Additional file information is provided in the readme.txt. Auxiliary material for this article contains three tables showing parameters used in BESS model and site information used for testing BESS model. Auxiliary material files may require downloading to a local drive depending on platform, browser, configuration, and size. To open auxiliary materials in a browser, click on the label. To download, Right‐click and select “Save Target As…” (PC) or CTRL‐click and select “Download Link to Disk” (Mac). Additional file information is provided in the readme.txt.Supporting Info Item: readme.txt - Table S1. List of spectral parameters and canopy height for each plant functional type. - Table S2. Look up table of maximum carboxylation rate at 25C and nitrogen use efficiency. - Table S3. Flux tower site information. - Tab‐delimited Table 1. - Tab‐delimited Table 2. - Tab‐delimited Table A1. - </note>
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<topic authorityURI="http://psi.agu.org/taxonomy5/1600">GLOBAL CHANGE</topic>
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<topic authorityURI="http://psi.agu.org/taxonomy5/4800">OCEANOGRAPHY: BIOLOGICAL AND CHEMICAL</topic>
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<topic authorityURI="http://psi.agu.org/taxonomy5/4912">Biogeochemical cycles, processes, and modeling</topic>
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<identifier type="DOI">10.1002/(ISSN)1944-9224</identifier>
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<identifier type="PublisherID">GBC</identifier>
<part><date>2011</date>
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<number>25</number>
</detail>
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