A modeling approach for upscaling gross ecosystem production to the landscape scale using remote sensing data

Hilker, Thomas, Coops, Nicholas C., Hall, Forrest G., Black, T. Andrew, Chen, Baozhang, Krishnan, Praveena, Wulder, Michael A., Sellers, Piers J., Middleton, Elizabeth M. and Huemmrich, Karl F. (2008) A modeling approach for upscaling gross ecosystem production to the landscape scale using remote sensing data. Journal of Geophysical Research, 113 (G3), 1-15. (doi:10.1029/2007JG000666).

Abstract

[1] Gross ecosystem production (GEP) can be estimated at the global scale and in a spatially continuous mode using models driven by remote sensing. Multiple studies have demonstrated the capability of high resolution optical remote sensing to accurately measure GEP at the leaf and stand level, but upscaling this relationship using satellite data remains challenging. Canopy structure is one of the complicating factors as it not only alters the strength of a measured signal depending on integrated leaf-angle-distribution and sun-observer geometry, but also drives the photosynthetic output and light-use-efficiency (?) of individual leaves. This study introduces a new approach for upscaling multiangular canopy level reflectance measurements to satellite scales which takes account of canopy structure effects by using Light Detection and Ranging (LiDAR). A tower-based spectro-radiometer was used to observe canopy reflectances over an annual period under different look and solar angles. This information was then used to extract sunlit and shaded spectral end-members corresponding to minimum and maximum values of canopy-? over 8-d intervals using a bidirectional reflectance distribution model. Using three-dimensional information of the canopy structure obtained from LiDAR, the canopy light regime and leaf area was modeled over a 12 km2 area and was combined with spectral end-members to derive high resolution maps of GEP. Comparison with eddy covariance data collected at the site shows that the spectrally driven model is able to accurately predict GEP (r2 between 0.75 and 0.91, p < 0.05).

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