Name
Title | ||
|---|---|---|
A Novel Approach To Improve Spatial Detail In Modeled Soil Moisture Through The Integration Of Remote Sensing Data |
Abstract | ||
|---|---|---|
In this work the possibilities of combining modelled (GEOtop, Hydrological model) and remotely sensed (ENVISAT ASAR WS) soil moisture content (SMC) values were investigated introducing a novel approach for data fusion on a product level. Data fusion was performed through the definition of a correction term for the modelled SMC dataset. For the determination of this term machine learning (Support Vector Regression) was used. As a reference dataset in-situ SMC measurements were considered. The benefit of the proposed method was successfully shown as R-2 between modelled and measured SMC values was improved from 0.11 to 0.61. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | Soil Moisture, Data fusion, hydrological model, machine learning |
Field | DocType | ISSN |
Data integration,Synthetic aperture radar,Computer science,Remote sensing,Support vector machine,Sensor fusion,Soil moisture content,Water content,Image resolution | Conference | 2153-6996 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| f greifeneder | 1 | 24 | 5.69 |
| Claudia Notarnicola | 2 | 166 | 40.15 |
| g bertoldi | 3 | 2 | 1.41 |
| J. Brenner | 4 | 0 | 0.34 |
| Wolfgang Wagner | 5 | 94 | 17.96 |