AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINE
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 a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2019-09-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W7/145/2019/isprs-annals-IV-2-W7-145-2019.pdf |
| _version_ | 1818040719919546368 |
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| author | M. Schmitt L. H. Hughes C. Qiu X. X. Zhu X. X. Zhu |
| author_facet | M. Schmitt L. H. Hughes C. Qiu X. X. Zhu X. X. Zhu |
| author_sort | M. Schmitt |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T08:19:00Z |
| format | Article |
| id | doaj.art-7030220a72ee4e789ab8e9fde11ed6bf |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-12-10T08:19:00Z |
| publishDate | 2019-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-7030220a72ee4e789ab8e9fde11ed6bf2022-12-22T01:56:23ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502019-09-01IV-2-W714515210.5194/isprs-annals-IV-2-W7-145-2019AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINEM. Schmitt0L. H. Hughes1C. Qiu2X. X. Zhu3X. X. Zhu4Signal Processing in Earth Observation, Technical University of Munich, Munich, GermanySignal Processing in Earth Observation, Technical University of Munich, Munich, GermanySignal Processing in Earth Observation, Technical University of Munich, Munich, GermanySignal Processing in Earth Observation, Technical University of Munich, Munich, GermanyRemote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Wessling, GermanyCloud 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.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W7/145/2019/isprs-annals-IV-2-W7-145-2019.pdf |
| spellingShingle | M. Schmitt L. H. Hughes C. Qiu X. X. Zhu X. X. Zhu AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINE ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINE |
| title_full | AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINE |
| title_fullStr | AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINE |
| title_full_unstemmed | AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINE |
| title_short | AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINE |
| title_sort | aggregating cloud free sentinel 2 images with google earth engine |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W7/145/2019/isprs-annals-IV-2-W7-145-2019.pdf |
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