This is the public archive with ID c9c747a5c5c93883597a86548f644f81 created on 2024-07-15 10:48:24 by Gyula Mate Kovács, IGN <gmk@ign.ku.dk>.
Archive Meta Data
Author(s)
Gyula Mate Kovács, Xiaoye Tong, Stefan Oehmcke, Dimitri Gominski, Stéphanie Horion, Rasmus Fensholt
Title
Manuscript Data 2024-07-14140
Description
### Dataset Metadata Description Dataset Title: Major European Wetland Types at 10-meter resolution Summary: In 2018, we utilized 10-meter resolution satellite data and machine learning to map six wetland types across Europe, achieving a 94±0.5% accuracy. The dataset includes optical data from Sentinel-2 MSI and SAR imagery from Sentinel-1, processed via Google Earth Engine. We employed the CORINE Land Cover (CLC) dataset covering 38 European countries. For a pixel-based classification, we used the XGBoost model, which effectively handles multi-class problems and noisy labels. Evaluation on 10,075 samples showed an accuracy of 94±0.5%, with a mean precision of 85% and recall of 88%. Precision ranged from 67% (inland marshes) to 96% (salines), and recall from 75% (moors & heathland) to 87% (inland marshes). F1 scores ranged from 73% to 95%. Data Sources: - Optical Data: Sentinel-2 MSI, Level-1C TOA reflectance, 10-meter resolution. - SAR Imagery: Sentinel-1, 10-meter resolution. - Land Cover Data: CORINE Land Cover (CLC) dataset, covering 38 European countries. Ancillary Data: - Land surface temperature from MOD11A1 V6.1 product, 1-kilometer resolution, 2000-2020. - Total precipitation data from ERA5, 27-kilometer resolution, 2000-2020. - Digital Elevation Model (EU-DEM 1.1), 25-meter resolution and derivatives. - Proximity layer to significant soil types and parent materials from the European Soil Database v2.0, including dystric histosols organic parent materials from Level-3 FAO Soil Classification. - Spatial information on terrestrial biomes from the RESOLVE Ecoregions dataset. Machine Learning Model: XGBoost, optimized for multi-class classification and robust to noisy labels. Performance Metrics: - Model Accuracy: 94±0.5% - Mean Precision: 85% - Mean Recall: 88% - Precision Range: 67% (inland marshes) to 96% (salines) - Recall Range: 75% (moors & heathland) to 87% (inland marshes) - F1-Score Range: 73% (moors & heathland) to 95% (salines)
Archive Files
| Name | Date | Size | MD5 Checksum | SHA1 Checksum | SHA224 Checksum | SHA256 Checksum | SHA384 Checksum | SHA512 Checksum |
|---|