Name
Abstract | ||
|---|---|---|
The exploitation of new high revisit frequency earth observations by the future Sentinel-2 satellite is clearly an important opportunity for global agricultural monitoring. In this context, the Sentinel-2Agriculture project aims at producing algorithms working on large geographical areas having different climates and different agricultural systems. In the framework of this project, the construction of a near-real-time deliverable cropland mask product has been studied here. A set of 12 selected test sites are used to benchmark the proposed method with regard to the diversity of agro-ecological context, the various landscape patterns, the different agriculture practices and the actual satellite observation conditions. The classification results yield very promising accuracies achieving around 90 % at the end of the agricultural season. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | Global cropland mapping, Sentinel-2, Near-real-time classification, Random Forests |
Field | DocType | ISSN |
Time series,Satellite,Computer science,Remote sensing,Satellite observation,Feature extraction,Agriculture,Deliverable | Conference | 2153-6996 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Silvia Valero | 1 | 171 | 13.81 |
| David Morin | 2 | 1 | 0.72 |
| Jordi Inglada | 3 | 426 | 43.13 |
| Guadalupe Sepulcre | 4 | 0 | 0.34 |
| Marcela Arias | 5 | 108 | 7.39 |
| Olivier Hagolle | 6 | 177 | 24.29 |
| Gérard Dedieu | 7 | 149 | 30.83 |
| sophie bontemps | 8 | 89 | 7.42 |
| Pierre Defourny | 9 | 167 | 24.70 |