%0 Journal Article %1 Bach:2003 %A Bach, H. %A Mauser, W. %D 2003 %J IEEE Transactions on Geoscience and Remote Sensing %K BRDF assimilation canopy crops model models optical reflectance remotesensing satellite sensing uncertainty vegetation %P 1629- 1637 %R 10.1109/TGRS.2003.813270 %T Methods and examples for remote sensing data assimilation in land surface process modeling %U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1221824 %V 41 %X Land surface process models describe the energy, water, carbon, and nutrient fluxes on a local to regional scale using a set of environmental land surface parameters and variables. They need time series of spatially distributed inputs to account for the large spatial and temporal variability of land surface processes. In principle many of these inputs can be derived through remote sensing using both optical and microwave sensors. New approaches in four-dimensional data-assimilation (4DDA) form the basis to combine remote sensing data and spatially explicit land surface process models more effectively. This paper describes basic techniques for 4DDA in land surface process modeling. Two case studies were carried out to demonstrate different successful approaches of remote sensing data assimilation into land surface process models. The assimilation of surface soil moisture estimates from European Remote Sensing (ERS) synthetic aperture radar data in a flood forecasting scheme is presented, as well as the combination of a land surface process model and a radiative transfer model to improve the accuracy of land surface parameter retrieval from optical data Landsat Thematic Mapper (TM).

@article{Bach:2003, abstract = {Land surface process models describe the energy, water, carbon, and nutrient fluxes on a local to regional scale using a set of environmental land surface parameters and variables. They need time series of spatially distributed inputs to account for the large spatial and temporal variability of land surface processes. In principle many of these inputs can be derived through remote sensing using both optical and microwave sensors. New approaches in four-dimensional data-assimilation (4DDA) form the basis to combine remote sensing data and spatially explicit land surface process models more effectively. This paper describes basic techniques for 4DDA in land surface process modeling. Two case studies were carried out to demonstrate different successful approaches of remote sensing data assimilation into land surface process models. The assimilation of surface soil moisture estimates from European Remote Sensing (ERS) synthetic aperture radar data in a flood forecasting scheme is presented, as well as the combination of a land surface process model and a radiative transfer model to improve the accuracy of land surface parameter retrieval from optical data [Landsat Thematic Mapper (TM)].}, added-at = {2008-05-15T14:26:11.000+0200}, author = {Bach, H. and Mauser, W.}, biburl = {https://www.bibsonomy.org/bibtex/251b9d391f143522e70101f62e7c61f0f/jgomezdans}, description = {Welcome to IEEE Xplore 2.0: Methods and examples for remote sensing data assimilation in land surface process modeling}, doi = {10.1109/TGRS.2003.813270}, interhash = {4c122b081bcbe04549bc3f29d1280196}, intrahash = {51b9d391f143522e70101f62e7c61f0f}, issn = {0196-2892}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, keywords = {BRDF assimilation canopy crops model models optical reflectance remotesensing satellite sensing uncertainty vegetation}, pages = {1629- 1637}, timestamp = {2008-05-15T14:26:12.000+0200}, title = {Methods and examples for remote sensing data assimilation in land surface process modeling}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1221824}, volume = 41, year = 2003 }