The overall goal is to develop and validate new and improved multisensor algorithms for retrieving snow and soil parameters from EO data improved for use in global climate study and hydrology, in particular run off and flood prediction.
The project will enhance the European capability of utilising EO data in operational applications for sustainable management of the environment and make Europe more competitive in the global industry and research market
The following sub-objective have been identified in order to reach the objectives.
- Improve the understanding of microwave interaction with snow cover and thereby develop multiparameter SAR based algorithms for snow and soil parameter retrieval. The main snow parameters for applications in snow hydrology and climatology are: snow covered area (SCA), snow water equivalent (SWE), and snow liquid water content (SLW). Algorithms for retrieval of these parameters from spaceborne SAR data will be developed. Methods for retrieving soil parameters from SAR will also be investigated, because this is of interest as a background signal for snow retrievals and for hydrological model input. The algorithms will be particular suitable for the future multiparameter SAR sensors, such as ENVISAT ASAR and RADARSAT-II.
- Develop optical methods for snow parameter retrieval. Improve the understanding of the relationship between measured optical spectra and the physical snow parameters of interest. These parameters are snow covered area (SCA), snow wetness (SW), and snow spectral reflectance (SSR). New and improved algorithms will be developed for accurate snow-cover-area-fraction retrieval at the sub-pixel level, snow wetness and, if possible, accurate free water estimation. An improved model for the anisotropic spectral reflectance of snow will be developed and applied in the SCA algorithms. Algorithms will be tested on data from ENVISAT's MERIS and AATSR sensors, and Terra's MODIS, MISR and ASTER sensors.
- Develop multisensor and multitemporal algorithms for snow parameter retrieval. The combination of data from several, different sensors may often give more information than data from a single sensor. Algorithms utilising simultaneous observations with several types of sensors (multi-sensor) will be developed, including data on various scales from point-measurements on the surface to high, medium and low-resolution satellite data. Generic algorithms and data management facilities for high-volume multi-data will be built upon this framework.
- Develop and improve methods for inclusion of the remote-sensing derived snow parameters SCA, SWE, SW and albedo in hydrological models. A lumped and model is adapted to the use of remotely sensed data and investigated.
- Implement a prototype of generic distributed and scalable snow information system. A snow information system must be distributed, high performance and scalable in order to handle the large amount of heterogenous data sets and models to be used.
- Verify and demonstrate in real time the new methods in a semi-operational environment. The demonstration will focus on retrospective and near real-time application of earth observation data and methods in combination with hydrological models for seasonal and short-term snow melt runoff prediction.