Developing a diagnostic model for estimating terrestrial vegetation gross primary productivity using the photosynthetic quantum yield and Earth Observation data

被引:40
|
作者
Ogutu, Booker O. [1 ]
Dash, Jadunandan [2 ]
Dawson, Terence P. [3 ]
机构
[1] Univ Leicester, Dept Geog, Leicester LE1 7RH, Leics, England
[2] Univ Southampton, Southampton SO17 1BJ, Hants, England
[3] Univ Dundee, Sch Environm, Dundee DD1 4HN, Scotland
关键词
C-3; and C-4 plants; fraction of absorbed photosynthetically active radiation; gross primary productivity; light use efficiency; photosynthetic quantum yield; SCARF model; NET PRIMARY PRODUCTION; CARBON-DIOXIDE EXCHANGE; LIGHT-USE EFFICIENCY; ECOSYSTEM CO2 EXCHANGE; ATMOSPHERIC CO2; OLD-GROWTH; INTERANNUAL VARIABILITY; STOMATAL CONDUCTANCE; GLOBAL DISTRIBUTION; CHLOROPHYLL INDEX;
D O I
10.1111/gcb.12261
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
This article develops a new carbon exchange diagnostic model [i.e. Southampton CARbon Flux (SCARF) model] for estimating daily gross primary productivity (GPP). The model exploits the maximum quantum yields of two key photosynthetic pathways (i.e. C-3 and C-4) to estimate the conversion of absorbed photosynthetically active radiation into GPP. Furthermore, this is the first model to use only the fraction of photosynthetically active radiation absorbed by photosynthetic elements of the canopy (i.e. FAPARps) rather than total canopy, to predict GPP. The GPP predicted by the SCARF model was comparable to in situ GPP measurements (R-2 > 0.7) in most of the evaluated biomes. Overall, the SCARF model predicted high GPP in regions dominated by forests and croplands, and low GPP in shrublands and dry-grasslands across USA and Europe. The spatial distribution of GPP from the SCARF model over Europe and conterminous USA was comparable to those from the MOD17 GPP product except in regions dominated by croplands. The SCARF model GPP predictions were positively correlated (R-2 > 0.5) to climatic and biophysical input variables indicating its sensitivity to factors controlling vegetation productivity. The new model has three advantages, first, it prescribes only two quantum yield terms rather than species specific light use efficiency terms; second, it uses only the fraction of PAR absorbed by photosynthetic elements of the canopy (FAPARps) hence capturing the actual PAR used in photosynthesis; and third, it does not need a detailed land cover map that is a major source of uncertainty in most remote sensing based GPP models. The Sentinel satellites planned for launch in 2014 by the European Space Agency have adequate spectral channels to derive FAPARps at relatively high spatial resolution (20 m). This provides a unique opportunity to produce global GPP operationally using the Southampton CARbon Flux (SCARF) model at high spatial resolution.
引用
收藏
页码:2878 / 2892
页数:15
相关论文
共 36 条
  • [21] A path analysis approach to model the gross primary productivity of mangroves using climate data and optical indices
    Manne, Mounika
    Rajitha, K.
    Chakraborty, Supriyo
    Gnanamoorthy, Palingamoorthy
    MODELING EARTH SYSTEMS AND ENVIRONMENT, 2024, 10 (01) : 509 - 522
  • [22] Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data
    Zhang, Yulong
    Song, Conghe
    Sun, Ge
    Band, Lawrence E.
    McNulty, Steven
    Noormets, Asko
    Zhang, Quanfa
    Zhang, Zhiqiang
    AGRICULTURAL AND FOREST METEOROLOGY, 2016, 223 : 116 - 131
  • [23] Continuity of Global MODIS Terrestrial Primary Productivity Estimates in the VIIRS Era Using Model-Data Fusion
    Endsley, K. Arthur
    Zhao, Maosheng
    Kimball, John S.
    Devadiga, Sadashiva
    JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES, 2023, 128 (09)
  • [24] Effects of in-situ and reanalysis climate data on estimation of cropland gross primary production using the Vegetation Photosynthesis Model
    Jin, Cui
    Xiao, Xiangming
    Wagle, Pradeep
    Griffis, Timothy
    Dong, Jinwei
    Wu, Chaoyang
    Qin, Yuanwei
    Cook, David R.
    AGRICULTURAL AND FOREST METEOROLOGY, 2015, 213 : 240 - 250
  • [25] Assessments of gross primary productivity estimations with satellite data-driven models using eddy covariance observation sites over the northern hemisphere
    Xie, Xinyao
    Li, Ainong
    Tan, Jianbo
    Jin, Huaan
    Nan, Xi
    Zhang, Zhengjian
    Bian, Jinhu
    Lei, Guangbin
    AGRICULTURAL AND FOREST METEOROLOGY, 2020, 280
  • [26] Improving Estimates of Gross Primary Productivity by Assimilating Solar-Induced Fluorescence Satellite Retrievals in a Terrestrial Biosphere Model Using a Process-Based SIF Model
    Bacour, C.
    Maignan, F.
    MacBean, N.
    Porcar-Castell, A.
    Flexas, J.
    Frankenberg, C.
    Peylin, P.
    Chevallier, F.
    Vuichard, N.
    Bastrikov, V
    JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES, 2019, : 3281 - 3306
  • [27] Estimating Forest Gross Primary Production Using Machine Learning, Light Use Efficiency Model, and Global Eddy Covariance Data
    Tian, Zhenkun
    Fu, Yingying
    Zhou, Tao
    Yi, Chuixiang
    Kutter, Eric
    Zhang, Qin
    Krakauer, Nir Y.
    FORESTS, 2024, 15 (09):
  • [28] Modelling the Gross Primary Productivity of West Africa with the Regional Biomass Model RBM plus , using optimized 250 m MODIS FPAR and fractional vegetation cover information
    Machwitz, Miriam
    Gessner, Ursula
    Conrad, Christopher
    Falk, Ulrike
    Richters, Jochen
    Dech, Stefan
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2015, 43 : 177 - 194
  • [29] Modeling Global Vegetation Gross Primary Productivity, Transpiration and Hyperspectral Canopy Radiative Transfer Simultaneously Using a Next Generation Land Surface Model-CliMA Land
    Wang, Y.
    Braghiere, R. K.
    Longo, M.
    Norton, A. J.
    Kohler, P.
    Doughty, R.
    Yin, Y.
    Bloom, A. A.
    Frankenberg, C.
    JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, 2023, 15 (03)
  • [30] Development of Hybrid Models to Estimate Gross Primary Productivity at a Near-Natural Peatland Using Sentinel 2 Data and a Light Use Efficiency Model
    Ingle, Ruchita
    Bhatnagar, Saheba
    Ghosh, Bidisha
    Gill, Laurence
    Regan, Shane
    Connolly, John
    Saunders, Matthew
    REMOTE SENSING, 2023, 15 (06)