Estimation of the global net primary productivity using NOAA images and meteorological data: changes between 1988 and 1993

A model for net primary productivity (NPP) estimation was developed based on a relationship between NPP estimated by the Chikugo model and the intensity-sum of the normalized difference vegetation index (NDVI) multiplied by the solar radiation during growth periods. There was a clear linear relationship between the estimated NPP and the intensity-sum (R 2=0.845), whose slope indicated the average light use efficiency (LUE) of global plants. The NPP estimation model (NDVI-based model), which included growth multipliers of optimum air temperature and soil water stress on vegetation growth with LUE, was developed. NDVI anomalies caused by scattering of volcanic ash from Mt Pinatubo were reduced by a correction based on intensity matching of channels 1 and 2 individually. NDVI retrieved a seasonal change pattern in 1991 and 1992 after the correction. Global NPP between 1988 and 1993 was estimated using the NDVI-based model, corrected NDVI, air temperature and soil water content data. There was a linear relationship between the estimated NPP and NPP observed in forests in China. The average global NPP during the 6 years was about 123 Pg dry weight per year, and the maximum and minimum NPP appeared in 1991 and 1988, respectively.

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

[2]  C. Osmond,et al.  Physiological Plant Ecology I , 1981, Encyclopedia of Plant Physiology.

[3]  J. A. Gatlin,et al.  Monitoring vegetation in the Nile Delta with NOAA-6 and NOAA-7 AVHRR imagery , 1982 .

[4]  R. Jackson,et al.  Spectral response of a plant canopy with different soil backgrounds , 1985 .

[5]  H. Seino,et al.  Agroclimatic Evaluation of Net Primary Productivity of Natural Vegetations , 1985 .

[6]  P. Walter.  Vegetation of the Earth and Ecological Systems of the Geo-biosphere , 1985, Heidelberg Science Library.

[7]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[8]  M. S. Moran,et al.  Reflectance- and radiance-based methods for the in-flight absolute calibration of multispectral sensors , 1987 .

[9]  Ramakrishna R. Nemani,et al.  Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates , 1988 .

[10]  Darrel L. Williams.  A comparison of spectral reflectance properties at the needle, branch, and canopy level for selected Conifer species , 1991 .

[11]  Garik Gutman,et al.  Vegetation indices from AVHRR: An update and future prospects , 1991 .

[12]  B. Choudhury,et al.  Spatial heterogeneity in vegetation canopies and remote sensing of absorbed photosynthetically active radiation: A modeling study , 1992 .

[13]  T. V. Gerasimenko,et al.  8 – Photosynthesis, Respiration, and Growth of Plants in the Soviet Arctic , 1992 .

[14]  R. Leemans.  A Systems Analysis of the Global Boreal Forest: The biological component of the simulation model for boreal forest dynamics , 1992 .

[15]  Russell S. Vose,et al.  The Global Historical Climatology Network: Long-term monthly temperature, precipitation, sea level pressure, and station pressure data , 1992 .

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

[17]  Gérard Dedieu,et al.  Methodology for the estimation of terrestrial net primary production from remotely sensed data , 1994 .

[18]  J. Muller,et al.  Terrestrial remote sensing science and algorithms planned for EOS/MODIS , 1994 .

[19]  S. Goward,et al.  Global Primary Production: A Remote Sensing Approach , 1995 .

[20]  C. Rao,et al.  Inter-satellite calibration linkages for the visible and near-infared channels of the Advanced Very High Resolution Radiometer on the NOAA-7, -9, and -11 spacecraft , 1995 .

[21]  International solar resource data at the World Radiation Data Center , 1995 .

[22]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[23]  J. Randerson,et al.  Interannual variation in global‐scale net primary production: Testing model estimates , 1997 .

[24]  Ruth S. DeFries,et al.  The NOAA/NASA pathfinder AVHRR 8-Km land data set , 1997 .

[25]  A. Bondeau,et al.  Comparing global models of terrestrial net primary productivity (NPP): overview and key results , 1999 .

[26]  Pinatubo volcanic aerosol characteristics as observed from a low latitude location in india using a ground-based multiwavelength solar radiometer , 1999 .

[27]  Assaf Anyamba,et al.  Comparison of NOAA/NASA PAL and NOAA GVI Data for Vegetation Change Studies over China , 1999 .

[28]  G.,et al.  Estimation of Total Above-Ground Phytomass Production Using Remotely Sensed Data * , 2002 .

[29]  Eiji Kanda,et al.  A Simple Model to Predict the Developmental Stages of Rice Panicles Using the Effective Accumulative Temperature(Modelling, Information Technology and Environment) , 2002 .

[30]  J. Houghton,et al.  Climate change 2001 : the scientific basis , 2001 .

[31]  Brent N. Holben,et al.  Accuracy of the AVHRR vegetation index as a predictor of biomass, primary productivity and net CO2 flux , 1989, Vegetatio.