FULL-PHYSICS INVERSE LEARNING MACHINE FOR SATELLITE REMOTE SENSING OF OZONE PROFILE SHAPES AND TROPOSPHERIC COLUMNS
Characterizing vertical distributions of ozone from nadir-viewing satellite measurements is known to be challenging, particularly the ozone information in the troposphere. A novel retrieval algorithm called Full-Physics Inverse Learning Machine (FP-ILM), has been developed at DLR in order to estimat...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-04-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/1995/2018/isprs-archives-XLII-3-1995-2018.pdf |
| _version_ | 1828234020547198976 |
|---|---|
| author | J. Xu K.-P. Heue M. Coldewey-Egbers F. Romahn A. Doicu D. Loyola |
| author_facet | J. Xu K.-P. Heue M. Coldewey-Egbers F. Romahn A. Doicu D. Loyola |
| author_sort | J. Xu |
| collection | DOAJ |
| description | Characterizing vertical distributions of ozone from nadir-viewing satellite measurements is known to be challenging, particularly the ozone information in the troposphere. A novel retrieval algorithm called Full-Physics Inverse Learning Machine (FP-ILM), has been developed at DLR in order to estimate ozone profile shapes based on machine learning techniques. In contrast to traditional inversion methods, the FP-ILM algorithm formulates the profile shape retrieval as a classification problem. Its implementation comprises a training phase to derive an inverse function from synthetic measurements, and an operational phase in which the inverse function is applied to real measurements. This paper extends the ability of the FP-ILM retrieval to derive tropospheric ozone columns from GOME- 2 measurements. Results of total and tropical tropospheric ozone columns are compared with the ones using the official GOME Data Processing (GDP) product and the convective-cloud-differential (CCD) method, respectively. Furthermore, the FP-ILM framework will be used for the near-real-time processing of the new European Sentinel sensors with their unprecedented spectral and spatial resolution and corresponding large increases in the amount of data. |
| first_indexed | 2024-04-12T19:48:35Z |
| format | Article |
| id | doaj.art-3f68a90d3c6849f18babd9c1223558c6 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-12T19:48:35Z |
| publishDate | 2018-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-3f68a90d3c6849f18babd9c1223558c62022-12-22T03:18:53ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342018-04-01XLII-31995199810.5194/isprs-archives-XLII-3-1995-2018FULL-PHYSICS INVERSE LEARNING MACHINE FOR SATELLITE REMOTE SENSING OF OZONE PROFILE SHAPES AND TROPOSPHERIC COLUMNSJ. Xu0K.-P. Heue1M. Coldewey-Egbers2F. Romahn3A. Doicu4D. Loyola5Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, GermanyRemote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, GermanyRemote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, GermanyRemote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, GermanyRemote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, GermanyRemote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, GermanyCharacterizing vertical distributions of ozone from nadir-viewing satellite measurements is known to be challenging, particularly the ozone information in the troposphere. A novel retrieval algorithm called Full-Physics Inverse Learning Machine (FP-ILM), has been developed at DLR in order to estimate ozone profile shapes based on machine learning techniques. In contrast to traditional inversion methods, the FP-ILM algorithm formulates the profile shape retrieval as a classification problem. Its implementation comprises a training phase to derive an inverse function from synthetic measurements, and an operational phase in which the inverse function is applied to real measurements. This paper extends the ability of the FP-ILM retrieval to derive tropospheric ozone columns from GOME- 2 measurements. Results of total and tropical tropospheric ozone columns are compared with the ones using the official GOME Data Processing (GDP) product and the convective-cloud-differential (CCD) method, respectively. Furthermore, the FP-ILM framework will be used for the near-real-time processing of the new European Sentinel sensors with their unprecedented spectral and spatial resolution and corresponding large increases in the amount of data.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/1995/2018/isprs-archives-XLII-3-1995-2018.pdf |
| spellingShingle | J. Xu K.-P. Heue M. Coldewey-Egbers F. Romahn A. Doicu D. Loyola FULL-PHYSICS INVERSE LEARNING MACHINE FOR SATELLITE REMOTE SENSING OF OZONE PROFILE SHAPES AND TROPOSPHERIC COLUMNS The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | FULL-PHYSICS INVERSE LEARNING MACHINE FOR SATELLITE REMOTE SENSING OF OZONE PROFILE SHAPES AND TROPOSPHERIC COLUMNS |
| title_full | FULL-PHYSICS INVERSE LEARNING MACHINE FOR SATELLITE REMOTE SENSING OF OZONE PROFILE SHAPES AND TROPOSPHERIC COLUMNS |
| title_fullStr | FULL-PHYSICS INVERSE LEARNING MACHINE FOR SATELLITE REMOTE SENSING OF OZONE PROFILE SHAPES AND TROPOSPHERIC COLUMNS |
| title_full_unstemmed | FULL-PHYSICS INVERSE LEARNING MACHINE FOR SATELLITE REMOTE SENSING OF OZONE PROFILE SHAPES AND TROPOSPHERIC COLUMNS |
| title_short | FULL-PHYSICS INVERSE LEARNING MACHINE FOR SATELLITE REMOTE SENSING OF OZONE PROFILE SHAPES AND TROPOSPHERIC COLUMNS |
| title_sort | full physics inverse learning machine for satellite remote sensing of ozone profile shapes and tropospheric columns |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/1995/2018/isprs-archives-XLII-3-1995-2018.pdf |
| work_keys_str_mv | AT jxu fullphysicsinverselearningmachineforsatelliteremotesensingofozoneprofileshapesandtroposphericcolumns AT kpheue fullphysicsinverselearningmachineforsatelliteremotesensingofozoneprofileshapesandtroposphericcolumns AT mcoldeweyegbers fullphysicsinverselearningmachineforsatelliteremotesensingofozoneprofileshapesandtroposphericcolumns AT fromahn fullphysicsinverselearningmachineforsatelliteremotesensingofozoneprofileshapesandtroposphericcolumns AT adoicu fullphysicsinverselearningmachineforsatelliteremotesensingofozoneprofileshapesandtroposphericcolumns AT dloyola fullphysicsinverselearningmachineforsatelliteremotesensingofozoneprofileshapesandtroposphericcolumns |