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The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation

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Aresys, 20132 Milan, Italy
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Centre d’Etudes Spatiales de la Biosphère, 31400 Toulouse, France
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Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
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German Aerospace Center (DLR), 82234 Wessling, Germany
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School of Mathematics and Statistics, University of Sheffield, Sheffield S10 2TG, UK
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European Space Agency, 2201 AZ Noordwijk, The Netherlands
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MJ Soja Consulting, Hobart, 7000 Tasmania, Australia
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School of Technology, University of Tasmania, Environments and Design, Hobart, 7000 Tasmania, Australia
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Department of Space, Earth and Environment, Chalmers University of Technology, S-412 96 Gothenburg, Sweden
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 985; https://doi.org/10.3390/rs12060985
Received: 21 January 2020 / Revised: 9 March 2020 / Accepted: 12 March 2020 / Published: 19 March 2020
(This article belongs to the Special Issue Estimation of Forest Biomass from SAR)
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. View Full-Text
Keywords: BIOMASS; SAR; polarimetry; tomography; interferometry; forest height; forest disturbance; earth explorer; DTM BIOMASS; SAR; polarimetry; tomography; interferometry; forest height; forest disturbance; earth explorer; DTM
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Banda, F.; Giudici, D.; Le Toan, T.; Mariotti d’Alessandro, M.; Papathanassiou, K.; Quegan, S.; Riembauer, G.; Scipal, K.; Soja, M.; Tebaldini, S.; Ulander, L.; Villard, L. The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation. Remote Sens. 2020, 12, 985.

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