Climate and Dynamics
Evaluating land surface parameters in the Biosphere-Atmosphere Transfer Scheme using remotely sensed data sets
Article first published online: 21 SEP 2012
Copyright 2000 by the American Geophysical Union.
Journal of Geophysical Research: Atmospheres (1984–2012)
Volume 105, Issue D6, pages 7275–7293, 27 March 2000
How to Cite
2000), Evaluating land surface parameters in the Biosphere-Atmosphere Transfer Scheme using remotely sensed data sets, J. Geophys. Res., 105(D6), 7275–7293, doi:10.1029/1999JD901041., , and (
- Issue published online: 21 SEP 2012
- Article first published online: 21 SEP 2012
- Manuscript Accepted: 5 OCT 1999
- Manuscript Received: 9 APR 1999
Remotely sensed data sets were used to evaluate the spatial/temporal specification of albedo (α), roughness length (Z0), and green leaf area index (Lg) in the Biosphere-Atmosphere Transfer Scheme (BATS). These parameters were evaluated for 1988 and 1993 in the Upper Mississippi River Basin (UMRB) using data from the International Satellite Land Surface Climatology Project Initiative 1 (ISLSCP 1) and by applying the ISLSCP 1 algorithms to high spatial resolution satellite data (Pathfinder advanced very high resolution radiometer (AVHRR) land (PAL)). ISLSCP 1 Lg was more variable spatially than BATS, particularly in midsummer months. ISLSCP 1 α was generally lower than BATS α. BATS and ISLSCP 1 generally showed small spatial variability in α for a given cover type. ISLSCP 1 Z0 were up to 100% larger than BATS. Spatial/temporal variability in ISLSCP 1 and BATS Z0 was primarily restricted to the spring. Comparisons between the ISLSCP 1 and PAL data indicated substantial differences in estimates of Lg and Z0 because of differences in the transformation of the vegetation index to Lg and grid cell heterogeneity. PAL Lg and Z0 were extremely variable spatially. Large differences between years in the PAL Lg were found, particularly in climate-affected regions. The remotely sensed data indicate that improved parameterizations in BATS are needed to account for spatial variability in Lg and Z0, as well as interannual variability in Lg. However, the comparisons also demonstrate that assumptions inherent in remote sensing algorithms and the impact of these on parameter estimates must be carefully examined.