Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6347114 | Remote Sensing of Environment | 2013 | 12 Pages |
Abstract
The results of this study are an important contribution to further improve the regularization of ill-posed RT model inversions. The proposed approach allows reducing uncertainties of estimated vegetation variables, which is essential to support various environmental applications. The definition of objects and a priori data in cases where less extensive ground data are available, as well as the definition of the observation covariance matrix, are critical issues which require further research.
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Computers in Earth Sciences
Authors
Valérie C.E. Laurent, Wout Verhoef, Alexander Damm, Michael E. Schaepman, Jan G.P.W. Clevers,