Abstract
Spatial scaling is very important to evaluation terrestrial carbon balance. In this paper, Gross primary production (GPP) are calculated using the Region Production Efficiency Model(REG-PEM) based the Moderate Resolution Imaging Spectrometer(MODIS) and Landsat TM data, respectively, and some issues related to spatial scaling are analyzed. The minimum and maximum values of parameters and GPP are significantly different at different scale. The mean values of parameters and GPP based on TM image are smaller than that based on MODIS image.
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References
Potter, C.S., Randerson, J.T., Field, C.B., et al.: Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochemical Cycle 7, 811–842 (1993)
Turner, D.P., Ollinger, S., Smith, M., Krankina, O., Gregory, M.: Scaling net primary production to a MODIS footprint in support of Earth observing system product validation. International Journal of Remote Sensing 25, 1961–1979 (2004)
Collatz, G.J., Ball, J.T., Grivet, C., et al.: Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer. Agricultural and Forest Meteorology 54, 107–136 (1991)
Chen, J.M.: Spatial scaling of a remotely sensed surface parameter by contexture. Remote Sensing of Environment 69, 30–42 (1999)
Prince, S.D., Goward, S.N.: Global primary production: a remote sensing approach. Journal of Biogeography 22, 815–835 (1995)
Li, S., Yan, H., Niu, Z., Xu, W., Yuan, J.: Satellite Estimation of Photosynthetically Active Radiation in Jiangxi Province. In: MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications. Proceedings of SPIE, vol. 6790, p. 67902D (2007)
Li, S., Niu, Z., Yan, H., Xu, W.: Modeling Gross Primary Production in Jiangxi Province Using MODIS Images. In: International Conference on Earth Observation Data Processing and Analysis (ICEODPA). Proceedings of SPIE, vol. 7285, p. 72854G (2008)
Qi, S., Cai, Y.: Mapping and Assessment of Degraded Land in the Heihe River Basin. Arid Northwestern China. Sensors 7, 2565–2578 (2007)
Sasai, T., Okamoto, K., Hiyama, T., Yamaguchi, Y.: Comparing terrestrial carbon fluxes from the scale of a flux tower to the global scale. Ecological Modeling 208, 135–144 (2007)
Yuan, W., Liu, S., Zhou, G., et al.: Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agricultural and Forest Meteorology 143, 189–207 (2007)
Xiao, X., Hollinger, D., Aber, J., et al.: Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sensing of Environment 89, 519–534 (2004a)
Xiao, X., Zhang, Q., Braswell, B., et al.: Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment 91, 256–270 (2004)
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© 2012 Springer-Verlag GmbH Berlin Heidelberg
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Li, S., Xiao, J., Hu, Z., Li, Z., Zhao, L. (2012). Spatial Scaling Analysis in Gross Primary Production Estimation. In: Lee, G. (eds) Advances in Computational Environment Science. Advances in Intelligent and Soft Computing, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27957-7_32
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DOI: https://doi.org/10.1007/978-3-642-27957-7_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27956-0
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