Title :
Methods and examples for remote sensing data assimilation in land surface process modeling
Author :
Bach, Heike ; Mauser, Wolfram
Author_Institution :
VISTA Remote Sensing in Geosciences GmbH, Munich, Germany
fDate :
7/1/2003 12:00:00 AM
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)].
Keywords :
atmospheric techniques; geophysical signal processing; geophysical techniques; hydrological techniques; remote sensing; terrain mapping; atmosphere; canopy; data assimilation; flood forecasting; four-dimensional data-assimilation; gas transfer; geophysical measurement technique; hydrology; land surface process; meteorology; model; modelling; regional scale; remote sensing; soil moisture; terrain mapping; vegetation mapping; Adaptive optics; Data assimilation; Floods; Land surface; Microwave sensors; Moisture; Optical sensors; Predictive models; Remote sensing; Surface soil;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2003.813270