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Abstract
In this work, the role of volume scattering obtained from ground and volume decomposition of P-band synthetic aperture radar (SAR) data as a proxy for biomass is investigated. The analysis here presented originates from the BIOMASS L2 activities, part of which were focused on strengthening the physical foundations of the SAR-based retrieval of forest above-ground biomass (AGB). A critical analysis of the observed strong correlation between tomographic intensity and AGB is done in order to propose simplified AGB proxies to be used during the interferometric phase of BIOMASS. In particular, the aim is to discuss whether, and to what extent, volume scattering obtained from ground/volume decomposition can provide a reasonable alternative to tomography. To do this, both are tested on P-band data collected at Paracou during the TropiSAR campaign and cross-validated against in-situ AGB measurements. Results indicate that volume backscattered power as obtained by ground/volume decomposition is weakly correlated to AGB, notwithstanding different solutions for volume scattering are tested, and support the conclusion that forest structure actually plays a non-negligible role in AGB retrieval in dense tropical forests.
Highlights
The retrieval of biomass and forest characteristics from remote sensing data is one of the main topics of interest within the remote-sensing community, especially in this moment due to pressing concerns about climate change and pollution [1]
Based on the scientific evidence from local campaigns, dedicated spaceborne missions have been designed by space agencies in response to the urgent need of monitoring the health status of the forests of our planet: Global Ecosystem Dynamics Investigation Lidar (GEDI) from NASA [6], already operational, the forthcoming NASA-ISRO Synthetic Aperture Radar (NISAR) [7], and the forthcoming European Space Agency (ESA) BIOMASS [8]
A fundamental aspect of the ground/volume decomposition problem is that multiple solutions are possible, meaning that different combinations of ground and volume coherences and polarimetric correlations exist that result in exactly the same correlation between any SLC data pair [35,36]
Summary
The retrieval of biomass and forest characteristics from remote sensing data is one of the main topics of interest within the remote-sensing community, especially in this moment due to pressing concerns about climate change and pollution [1]. Forests have a major role in the carbon cycle, as they act as a sink for approximately one-third of one-third of CO2 coming from the combustion of fossil fuels. Human activities such as land use and deforestation have a negative impact due to the consequent release of carbon into the atmosphere. Time series of synthetic aperture radar (SAR) data are analyzed to estimate biomass and its change over time from radar backscatter, for instance [5]. Based on the scientific evidence from local campaigns, dedicated spaceborne missions have been designed by space agencies in response to the urgent need of monitoring the health status of the forests of our planet: Global Ecosystem Dynamics Investigation Lidar (GEDI) from NASA [6], already operational, the forthcoming NASA-ISRO Synthetic Aperture Radar (NISAR) [7], and the forthcoming European Space Agency (ESA) BIOMASS [8]
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