Optimized Application of Biome‐BGC for Modeling the Daily GPP of Natural Vegetation Over Peninsular Spain

[1]  T. Vesala,et al.  On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm , 2005 .

[2]  María Amparo Gilabert,et al.  Mapping daily global solar irradiation over Spain: A comparative study of selected approaches , 2011 .

[3]  H. R. Haise,et al.  Estimating evapotranspiration from solar radiation , 1963 .

[4]  R. Myneni,et al.  On the relationship between FAPAR and NDVI , 1994 .

[5]  María Amparo Gilabert,et al.  Validation of daily global solar irradiation images from MSG over Spain , 2013 .

[6]  H. A. Mooney,et al.  Maximum rooting depth of vegetation types at the global scale , 1996, Oecologia.

[7]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[8]  S. Running,et al.  8 – Generalization of a Forest Ecosystem Process Model for Other Biomes, BIOME-BGC, and an Application for Global-Scale Models , 1993 .

[9]  E. Davidson,et al.  Satellite-based modeling of gross primary production in an evergreen needleleaf forest , 2004 .

[10]  María Amparo Gilabert,et al.  Annual Gross Primary Production from Vegetation Indices: A Theoretically Sound Approach , 2017, Remote. Sens..

[11]  Peter E. Thornton,et al.  Parameterization and Sensitivity Analysis of the BIOME–BGC Terrestrial Ecosystem Model: Net Primary Production Controls , 2000 .

[12]  G. Montserrat-Martí,et al.  Effect of root system morphology on root-sprouting and shoot-rooting abilities in 123 plant species from eroded lands in North-east Spain. , 2006, Annals of botany.

[13]  Adriaan A. Van de Griend,et al.  Mediterranean Landsurface Processes Assessed From Space , 2006 .

[14]  R. B. Jackson,et al.  Mapping the global distribution of deep roots in relation to climate and soil characteristics , 2005 .

[15]  Xubin Zeng,et al.  Global Vegetation Root Distribution for Land Modeling , 2001 .

[16]  John S. Kimball,et al.  BIOME-BGC simulations of stand hydrologic processes for BOREAS , 1997 .

[17]  Marta Chiesi,et al.  Adaptation of a modelling strategy to predict the NPP of even-aged forest stands , 2011, European Journal of Forest Research.

[18]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[19]  C. Ballabio,et al.  Mapping topsoil physical properties at European scale using the LUCAS database , 2016 .

[20]  F. Veroustraete,et al.  Estimation of carbon mass fluxes over Europe using the C-Fix model and Euroflux data , 2002 .

[21]  G. Churkina,et al.  Development of the Biome-BGC model for simulation of managed herbaceous ecosystems , 2012 .

[22]  David A. Seal,et al.  The Shuttle Radar Topography Mission , 2007 .

[23]  Peter E. Thornton,et al.  Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: an application over complex terrain in Austria. , 2000 .

[24]  Marco Bindi,et al.  Modelling the forest carbon budget of a Mediterranean region through the integration of ground and satellite data , 2009 .

[25]  Arnaud Carrara,et al.  Daily GPP estimates in Mediterranean ecosystems by combining remote sensing and meteorological data , 2015 .

[26]  R. B. Jackson,et al.  Rooting depths, lateral root spreads and below‐ground/above‐ground allometries of plants in water‐limited ecosystems , 2002 .

[27]  J. Monteith SOLAR RADIATION AND PRODUCTIVITY IN TROPICAL ECOSYSTEMS , 1972 .

[28]  J. Berry,et al.  A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.

[29]  A. Guswa Effect of plant uptake strategy on the water−optimal root depth , 2010 .

[30]  Piermaria Corona,et al.  Combining remote sensing and ancillary data to monitor the gross productivity of water-limited forest ecosystems , 2009 .

[31]  Peter E. Thornton,et al.  Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests , 2002 .

[32]  Dario Papale,et al.  Simulation of grassland productivity by the combination of ground and satellite data , 2013 .

[33]  J. Randerson,et al.  Terrestrial ecosystem production: A process model based on global satellite and surface data , 1993 .

[34]  Testing the applicability of BIOME-BGC to simulate beech gross primary production in Europe using a new continental weather dataset , 2016, Annals of Forest Science.

[35]  Steven W. Running,et al.  Quantifying water stress effect on daily light use efficiency in Mediterranean ecosystems using satellite data , 2017, Int. J. Digit. Earth.

[36]  A. Kleidon Global Datasets of Rooting Zone Depth Inferred from Inverse Methods , 2004 .

[37]  Jonas Ardö,et al.  Patterns and controls of the variability of radiation use efficiency and primary productivity across terrestrial ecosystems , 2010 .

[38]  Tim R. McVicar,et al.  Global estimation of effective plant rooting depth: Implications for hydrological modeling , 2016 .

[39]  Ramakrishna R. Nemani,et al.  Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[40]  P. Corona,et al.  Estimating daily forest carbon fluxes using a combination of ground and remotely sensed data , 2016 .

[41]  S. Wofsy,et al.  Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data , 2004 .

[42]  A. Arneth,et al.  Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations , 2011 .

[43]  H. Hasenauer,et al.  Modeling effects of hydrological changes on the carbon and nitrogen balance of oak in floodplains. , 2003, Tree physiology.

[44]  Marco Bindi,et al.  Application of BIOME-BGC to simulate Mediterranean forest processes , 2007 .

[45]  Scott J. Goetz,et al.  Remotely Sensed Interannual Variations and Trends in Terrestrial Net Primary Productivity 1981–2000 , 2004, Ecosystems.

[46]  María Amparo Gilabert,et al.  Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter , 2014, Remote. Sens..