|
|
 |
|
|
|
|
|
| |
Information - HS2 Remote sensing retrieval techniques and data assimilation
 |
|
|
|
Event Information |
|
|
|
|
|
|
Remote sensing has proven its usefulness in many fields and applications.
A critical point consists in the fact that the remotely sensed imagery needs
to be converted into relevant data of geophysical interest, such as soil
moisture, leaf area index, evapotranspiration, snow and ice cover, and
wetland delineation. Within this framework, retrieval techniques play a key
role and their development and refinement is important for assessing
uncertainty on the retrieved values.
By integration of the remote sensing information into land surface process
models, the simulations of the process model can be improved and forced to
mirror a more appropriate and realistic land surface state. Uncertainties in
the surface parameter inversion process as well as in the simulations
obtained from the process model should be considered in this context.
Modern data assimilation theory provides methods for optimally merging
remote sensing observation with land surface process models by considering
the process model and observation uncertainties. This allows for the
estimation of the most probable surface state. The session aims at the
evaluation of the current state of art of remote sensing data assimilation
techniques for land surface modelling purposes. This includes examples of
remote sensing data assimilation projects as well as data fusion approaches
to combine the information content of various heterogeneous data sources.
This session therefore welcomes contributions on the following particular
issues:
- retrieval algorithms for hydrological relevant parameters from remote
sensing
- assessment techniques for uncertainty estimation upon retrieval
- remote sensing data assimilation techniques, including technical
background and comparison of different assimilation strategies
- comparative studies, evaluating the benefit of different assimilation
techniques
- examples of remote sensing data assimilation into land surface process
models
- disaggregation of remote sensing derived land surface information using
assimilation techniques
- fusion of different (complementary) data sources to improve surface
parameter retrievals by means of data assimilation
This session is soliciting for presentations on refinement, development and validation of retrieval techniques for a wide range of applications. These techniques can range from physically-based modelling over data-driven approaches (e.g., neural networks, neuro-fuzzy modelling) to the more simple linear regressions, applied to optical, thermal, passive and active microwave, or lidar data. Also, results regarding the uncertainty assessment and error analysis on the retrieved values are strongly solicited.
|
|
|
|
|
|
|
 Back to Session Programme
|
|
|
|
