The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation
BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH)...
Ausführliche Beschreibung
Autor*in: |
Francesco Banda [verfasserIn] Davide Giudici [verfasserIn] Thuy Le Toan [verfasserIn] Mauro Mariotti d’Alessandro [verfasserIn] Kostas Papathanassiou [verfasserIn] Shaun Quegan [verfasserIn] Guido Riembauer [verfasserIn] Klaus Scipal [verfasserIn] Maciej Soja [verfasserIn] Stefano Tebaldini [verfasserIn] Lars Ulander [verfasserIn] Ludovic Villard [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Übergeordnetes Werk: |
In: Remote Sensing - MDPI AG, 2009, 12(2020), 6, p 985 |
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Übergeordnetes Werk: |
volume:12 ; year:2020 ; number:6, p 985 |
Links: |
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DOI / URN: |
10.3390/rs12060985 |
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Katalog-ID: |
DOAJ019793677 |
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10.3390/rs12060985 doi (DE-627)DOAJ019793677 (DE-599)DOAJ8b97186a342d4526877f8eaeeda16a05 DE-627 ger DE-627 rakwb eng Francesco Banda verfasserin aut The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements. biomass sar polarimetry tomography interferometry forest height forest disturbance earth explorer dtm Science Q Davide Giudici verfasserin aut Thuy Le Toan verfasserin aut Mauro Mariotti d’Alessandro verfasserin aut Kostas Papathanassiou verfasserin aut Shaun Quegan verfasserin aut Guido Riembauer verfasserin aut Klaus Scipal verfasserin aut Maciej Soja verfasserin aut Stefano Tebaldini verfasserin aut Lars Ulander verfasserin aut Ludovic Villard verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 6, p 985 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:6, p 985 https://doi.org/10.3390/rs12060985 kostenfrei https://doaj.org/article/8b97186a342d4526877f8eaeeda16a05 kostenfrei https://www.mdpi.com/2072-4292/12/6/985 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 6, p 985 |
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10.3390/rs12060985 doi (DE-627)DOAJ019793677 (DE-599)DOAJ8b97186a342d4526877f8eaeeda16a05 DE-627 ger DE-627 rakwb eng Francesco Banda verfasserin aut The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements. biomass sar polarimetry tomography interferometry forest height forest disturbance earth explorer dtm Science Q Davide Giudici verfasserin aut Thuy Le Toan verfasserin aut Mauro Mariotti d’Alessandro verfasserin aut Kostas Papathanassiou verfasserin aut Shaun Quegan verfasserin aut Guido Riembauer verfasserin aut Klaus Scipal verfasserin aut Maciej Soja verfasserin aut Stefano Tebaldini verfasserin aut Lars Ulander verfasserin aut Ludovic Villard verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 6, p 985 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:6, p 985 https://doi.org/10.3390/rs12060985 kostenfrei https://doaj.org/article/8b97186a342d4526877f8eaeeda16a05 kostenfrei https://www.mdpi.com/2072-4292/12/6/985 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 6, p 985 |
allfields_unstemmed |
10.3390/rs12060985 doi (DE-627)DOAJ019793677 (DE-599)DOAJ8b97186a342d4526877f8eaeeda16a05 DE-627 ger DE-627 rakwb eng Francesco Banda verfasserin aut The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements. biomass sar polarimetry tomography interferometry forest height forest disturbance earth explorer dtm Science Q Davide Giudici verfasserin aut Thuy Le Toan verfasserin aut Mauro Mariotti d’Alessandro verfasserin aut Kostas Papathanassiou verfasserin aut Shaun Quegan verfasserin aut Guido Riembauer verfasserin aut Klaus Scipal verfasserin aut Maciej Soja verfasserin aut Stefano Tebaldini verfasserin aut Lars Ulander verfasserin aut Ludovic Villard verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 6, p 985 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:6, p 985 https://doi.org/10.3390/rs12060985 kostenfrei https://doaj.org/article/8b97186a342d4526877f8eaeeda16a05 kostenfrei https://www.mdpi.com/2072-4292/12/6/985 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 6, p 985 |
allfieldsGer |
10.3390/rs12060985 doi (DE-627)DOAJ019793677 (DE-599)DOAJ8b97186a342d4526877f8eaeeda16a05 DE-627 ger DE-627 rakwb eng Francesco Banda verfasserin aut The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements. biomass sar polarimetry tomography interferometry forest height forest disturbance earth explorer dtm Science Q Davide Giudici verfasserin aut Thuy Le Toan verfasserin aut Mauro Mariotti d’Alessandro verfasserin aut Kostas Papathanassiou verfasserin aut Shaun Quegan verfasserin aut Guido Riembauer verfasserin aut Klaus Scipal verfasserin aut Maciej Soja verfasserin aut Stefano Tebaldini verfasserin aut Lars Ulander verfasserin aut Ludovic Villard verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 6, p 985 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:6, p 985 https://doi.org/10.3390/rs12060985 kostenfrei https://doaj.org/article/8b97186a342d4526877f8eaeeda16a05 kostenfrei https://www.mdpi.com/2072-4292/12/6/985 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 6, p 985 |
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10.3390/rs12060985 doi (DE-627)DOAJ019793677 (DE-599)DOAJ8b97186a342d4526877f8eaeeda16a05 DE-627 ger DE-627 rakwb eng Francesco Banda verfasserin aut The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements. biomass sar polarimetry tomography interferometry forest height forest disturbance earth explorer dtm Science Q Davide Giudici verfasserin aut Thuy Le Toan verfasserin aut Mauro Mariotti d’Alessandro verfasserin aut Kostas Papathanassiou verfasserin aut Shaun Quegan verfasserin aut Guido Riembauer verfasserin aut Klaus Scipal verfasserin aut Maciej Soja verfasserin aut Stefano Tebaldini verfasserin aut Lars Ulander verfasserin aut Ludovic Villard verfasserin aut In Remote Sensing MDPI AG, 2009 12(2020), 6, p 985 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:12 year:2020 number:6, p 985 https://doi.org/10.3390/rs12060985 kostenfrei https://doaj.org/article/8b97186a342d4526877f8eaeeda16a05 kostenfrei https://www.mdpi.com/2072-4292/12/6/985 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 12 2020 6, p 985 |
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The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation |
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BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements. |
abstractGer |
BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements. |
abstract_unstemmed |
BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements. |
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container_issue |
6, p 985 |
title_short |
The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation |
url |
https://doi.org/10.3390/rs12060985 https://doaj.org/article/8b97186a342d4526877f8eaeeda16a05 https://www.mdpi.com/2072-4292/12/6/985 https://doaj.org/toc/2072-4292 |
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true |
author2 |
Davide Giudici Thuy Le Toan Mauro Mariotti d’Alessandro Kostas Papathanassiou Shaun Quegan Guido Riembauer Klaus Scipal Maciej Soja Stefano Tebaldini Lars Ulander Ludovic Villard |
author2Str |
Davide Giudici Thuy Le Toan Mauro Mariotti d’Alessandro Kostas Papathanassiou Shaun Quegan Guido Riembauer Klaus Scipal Maciej Soja Stefano Tebaldini Lars Ulander Ludovic Villard |
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doi_str |
10.3390/rs12060985 |
up_date |
2024-07-04T00:59:02.708Z |
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