Abstract
Cloud coverage is one of the biggest concerns in spaceborne optical remote sensing, because it hampers a continuous monitoring of the Earth's surface. Based on Google Earth Engine, a web-and cloud-based platform for the analysis and visualization of large-scale geospatial data, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for user-defined areas of interest and time periods, which can be significantly shorter than the one-year time frames that are commonly used in other multi-temporal image aggregation approaches. We demonstrate the feasibility of our workflow for several cities spread around the globe and affected by different amounts of average cloud cover. The experimental results confirm that our results are better than the results achieved by standard approaches for cloud-free image aggregation.
| Original language | English |
|---|---|
| Pages (from-to) | 145-152 |
| Number of pages | 8 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 4 |
| Issue number | 2/W7 |
| DOIs | |
| State | Published - 16 Sep 2019 |
| Event | 1st Photogrammetric Image Analysis and Munich Remote Sensing Symposium, PIA 2019+MRSS 2019 - Munich, Germany Duration: 18 Sep 2019 → 20 Sep 2019 |
Keywords
- Big Data
- Cloud Coverage
- Google Earth Engine
- Sentinel-2