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)...
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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]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	biomass sar polarimetry tomography interferometry forest height forest disturbance earth explorer dtm

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 12(2020), 6, p 985
Übergeordnetes Werk:	volume:12 ; year:2020 ; number:6, p 985

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs12060985

Katalog-ID:	DOAJ019793677

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allfields	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
spelling	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
allfieldsSound	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
language	English
source	In Remote Sensing 12(2020), 6, p 985 volume:12 year:2020 number:6, p 985
sourceStr	In Remote Sensing 12(2020), 6, p 985 volume:12 year:2020 number:6, p 985
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	biomass sar polarimetry tomography interferometry forest height forest disturbance earth explorer dtm Science Q
isfreeaccess_bool	true
container_title	Remote Sensing
authorswithroles_txt_mv	Francesco Banda @@aut@@ Davide Giudici @@aut@@ Thuy Le Toan @@aut@@ Mauro Mariotti d’Alessandro @@aut@@ Kostas Papathanassiou @@aut@@ Shaun Quegan @@aut@@ Guido Riembauer @@aut@@ Klaus Scipal @@aut@@ Maciej Soja @@aut@@ Stefano Tebaldini @@aut@@ Lars Ulander @@aut@@ Ludovic Villard @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	608937916
id	DOAJ019793677
language_de	englisch
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author	Francesco Banda
spellingShingle	Francesco Banda misc biomass misc sar misc polarimetry misc tomography misc interferometry misc forest height misc forest disturbance misc earth explorer misc dtm misc Science misc Q The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation
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topic_title	The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation biomass sar polarimetry tomography interferometry forest height forest disturbance earth explorer dtm
topic	misc biomass misc sar misc polarimetry misc tomography misc interferometry misc forest height misc forest disturbance misc earth explorer misc dtm misc Science misc Q
topic_unstemmed	misc biomass misc sar misc polarimetry misc tomography misc interferometry misc forest height misc forest disturbance misc earth explorer misc dtm misc Science misc Q
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title	The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation
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title_full	The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation
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author_browse	Francesco Banda 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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title_sort	biomass level 2 prototype processor: design and experimental results of above-ground biomass estimation
title_auth	The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation
abstract	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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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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The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation

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