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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.

Mapping tropical forest aboveground biomass (AGB) is important for quantifying emissions from land use change and evaluating climate mitigation strategies but remains a challenging problem for remote sensing observations. Here, we evaluate the capability of mapping AGB across a dense tropical forest using tomographic Synthetic Aperture Radar (TomoSAR) measurements at P-band frequency that will be available from the European Space Agency’s BIOMASS mission in 2024. To retrieve AGB, we compare three different TomoSAR reconstruction algorithms, back-projection (BP), Capon, and MUltiple SIgnal Classification (MUSIC), and validate AGB estimation from models using TomoSAR variables: backscattered power at 30 m height, forest height (FH), backscatter power metric (Q), and their combination. TropiSAR airborne campaign data in French Guiana, inventory plots, and airborne LiDAR measurements are used as reference data to develop models and calculate the AGB estimation uncertainty. We used univariate and multivariate regression models to estimate AGB at 4-ha grid cells, the nominal resolution of the BIOMASS mission. Our results show that the BP-based variables produced better AGB estimates compared to their counterparts, suggesting a more straightforward TomoSAR processing for the mission. The tomographic FH and AGB estimation have an average relative uncertainty of less than 10% with negligible systematic error across the entire biomass range (~ 200–500 Mg ha −1 ). We show that the backscattered power at 30 m height at HV polarization is the best single measurement to estimate AGB with significantly better accuracy than the LiDAR height metrics, and combining it with FH improved the accuracy of AGB estimation to less than 7% of the mean. Our study implies that using multiple information from P-band TomoSAR data from the BIOMASS mission provides a new capability to map tropical forest biomass and its changes accurately.

Synthetic aperture radar (SAR) tomography (TomoSAR) is an emerging technology to image the 3D structure of the illuminated media. TomoSAR exploits the key feature of microwaves to penetrate into vegetation, snow, and ice, hence providing the possibility to see features that are hidden to optical and hyper-spectral systems. The research on the use of P-band waves, in particular, has been largely propelled since 2007 in experimental studies supporting the future spaceborne Mission BIOMASS, to be launched in 2022 with the aim of mapping forest aboveground biomass (AGB) accurately and globally. The results obtained in the frame of these studies demonstrated that TomoSAR can be used for accurate retrieval of geophysical variables such as forest height and terrain topography and, especially in the case of dense tropical forests, to provide a more direct link to AGB. This paper aims at providing the reader with a comprehensive understanding of TomoSAR and its application for remote sensing of forested areas, with special attention to the case of tropical forests. We will introduce the basic physical principles behind TomoSAR, present the most relevant experimental results of the last decade, and discuss the potentials of BIOMASS tomography.

Synthetic Aperture Radar (SAR) Tomography is a technique to provide direct three-dimensional (3D) imaging of the illuminated targets by processing SAR data acquired from different trajectories. In a large part of the literature, 3D imaging is achieved by assuming mono-dimensional (1D) approaches derived from SAR Interferometry, where a vector of pixels from multiple SAR images is transformed into a new vector of pixels representing the vertical profile of scene reflectivity at a given range, azimuth location. However, mono-dimensional approaches are only suited for data acquired from very closely-spaced trajectories, resulting in coarse vertical resolution. In the case of continuous media, such as forests, snow, ice sheets and glaciers, achieving fine vertical resolution is only possible in the presence of largely-spaced trajectories, which involves significant complications concerning the formation of 3D images. The situation gets even more complicated in the presence of irregular trajectories with variable headings, for which the one theoretically exact approach consists of going back to raw SAR data to resolve the targets by 3D back-projection, resulting in a computational burden beyond the capabilities of standard computers. The first aim of this paper is to provide an exhaustive discussion of the conditions under which high-quality tomographic processing can be carried out by assuming a 1D, 2D, or 3D approach to image formation. The case of 3D processing is then further analyzed, and a new processing method is proposed to produce high-quality imaging while largely reducing the computational burden, and without having to process the original raw data. Furthermore, the new method is shown to be easily parallelized and implemented using GPU processing. The analysis is supported by results from numerical simulations as well as from real airborne data from the ESA campaign AlpTomoSAR.

This paper presents an algorithm for simulating tomographic synthetic aperture radar (SAR) data based on another stack actually gathered by a real acquisition system. Through the procedure here proposed, the simulated system can be evaluated according to its capability to image complex natural media rather than reference point targets. This feature is particularly important whenever the biophysical properties of the target of interest must be preserved and cannot be easily modeled. The system to be simulated may be different from the original one concerning resolution, off-nadir angles, bandwidth and central frequency. The algorithm here proposed handles these differences by properly taking into account the wavenumbers of the target illuminated by the real survey and requested by the simulated one. The complex images constituting the synthetic stack are associated with the effective vertical interferometric wavenumber peculiar of the geometry to be simulated, regardless of the original data. Furthermore, the three-dimensional resolution cell of the simulated tomographic system is consistent with the simulated geometry concerning size and spatial orientation. These two latter features cannot be guaranteed by simply filtering the original stack. The simulator here proposed has been used to simulate the tomographic stack expected from the forthcoming European Space Agency (ESA) BIOMASS mission. The relationship between baseline distribution and 3D focusing capability was explored; special attention has been paid to the robustness of tomographic power at being a good proxy for the above ground biomass in tropical regions.

Low-frequency tomographic synthetic aperture radar (TomoSAR) techniques provide an opportunity for quantifying the dynamics of dense tropical forest vertical structures. Here, we compare the performance of different TomoSAR processing, Back-projection (BP), Capon beamforming (CB), and MUltiple SIgnal Classification (MUSIC), and compensation techniques for estimating forest height (FH) and forest vertical profile from the backscattered echoes. The study also examines how polarimetric measurements in linear, compact, hybrid, and dual circular modes influence parameter estimation. The tomographic analysis was carried out using P-band data acquired over the Paracou study site in French Guiana, and the quantitative evaluation was performed using LiDAR-based canopy height measurements taken during the 2009 TropiSAR campaign. Our results show that the relative root mean squared error (RMSE) of height was less than 10%, with negligible systematic errors across the range, with Capon and MUSIC performing better for height estimates. Radiometric compensation, such as slope correction, does not improve tree height estimation. Further, we compare and analyze the impact of the compensation approach on forest vertical profiles and tomographic metrics and the integrated backscattered power. It is observed that radiometric compensation increases the backscatter values of the vertical profile with a slight shift in local maxima of the canopy layer for both the Capon and the MUSIC estimators. Our results suggest that applying the proper processing and compensation techniques on P-band TomoSAR observations from space will allow the monitoring of forest vertical structure and biomass dynamics.

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.

This work is aimed at showing the present capabilities and future potentialities of an imaging radar system that can be mounted onboard flexible aerial platforms, such as helicopters or small airplanes, and may operate in the UHF and VHF frequency bands as Sounder and as Synthetic Aperture Radar (SAR). More specifically, the Sounder operates at 165 MHz, whereas the SAR may operate either at 450 MHz or at 860 MHz. In the work, we present the first results relevant to a set of Sounder and SAR data collected by the radar during a helicopter-borne campaign conducted in 2018 over a desert area in Erfoud, Morocco, just after the conclusion of a system upgrading procedure. In particular, a first analysis of the focusing capabilities of the Sounder mode and of the polarimetric and interferometric capabilities of the SAR mode is conducted. The overall system, originally developed by CO.RI.S.T.A. according to a ASI funding set up in 2010, has been upgraded in the frame of a contract signed in 2015 between ASI and different private and public Italian Research Institutes and Universities, namely CO.RI.S.T.A., IREA-CNR, Politecnico di Milano and University of Trento.

This paper is intended to report on the progresses made during the Dragon-4 project Three and Four-Dimensional Topographic Measurement and Validation (ID: 32278), sub-project Multi-baseline SAR Processing for 3D/4D Reconstruction (ID: 32278_2). The work here reported focuses on two important aspects of SAR remote sensing of tropical forests, namely the retrieval of forest biomass and the assessment of effects due to changing weather conditions. Recent studies have shown that by using SAR tomography the backscattered power at 30m layer above the ground is linearly correlated to the forest Above Ground Biomass (AGB). However, the two parameters that determine this linear relationship might vary for different tropical forest sites. For purpose of solving this problem, we investigate the possibility of using LiDAR derived AGB to help training the two parameters. Experimental results obtained by processing data from the TropiSAR campaign support the feasibility of the proposed concept. This analysis is complemented by an assessment of the impact of changing weather conditions on tomographic imaging, for which we simulate BIOMASS repeat pass tomography using ground-based TropiSCAT data with a revisit time of 3 days and rainy days included. The resulting backscattered power variation at 30m is within 1.5dB. For this forest site, this error is translated into an AGB error of about 50~80t/hm 2, which is 20% or less of forest AGB.

This study presents an extensive analysis of 1,768 well-preserved waterlogged archaeological grape pips covering approximately 7000 years of history. These samples originate from 25 Italian archaeological sites spanning from the Early Neolithic (6 th millemmium BC) to the Medieval period (8 th -14 th centuries AD). Employing geometric morphometrics and linear discriminant analyses, we compared these archaeological grape pips with modern reference collections to differentiate between wild and domestic grape types. Additionally, we analysed phenotypic changes in grape pip length and shape over the studied period to the present day to highlight traits associated with domestication syndrome. During the Early Neolithic, no evidence of morphologically domesticated grapes was observed. Data from Early Bronze Age sites (ca. 2050–1850 BC) display the same trend observed for the Early Neolithic period. The Middle Bronze Age sites (ca. 1600–1300 BC) continue to exhibit a predominance of wild grape pips. However, a notable transition occurs at the end of the Late Bronze Age (ca. 1300–1100 BC), with the majority of grape pips classified as domestic, indicating the definitive establishment of cultivation practices and selection of domestic grape by these communities. In the Iron Age, grape pips from Etruscan sites dating to the 4 th century BC are predominantly domestic, suggesting an advanced viticulture for this period. During the Roman period (1 st -6 th centuries AD), some sites exhibited a high presence of domestic grape pips and intermediate forms between wild and domestic morphotypes, suggesting introgression between local wild and domestic grape allowing the formation of new varieties. Finally, the Medieval period (8 th -14 th centuries AD) sites demonstrate a widespread prevalence of domestic grape pips across archaeological sites, indicating a reduction of intermediate forms between wild and domestic morphotypes and displaying morphometric characteristics entirely similar to modern domestic grape references. Overall, our study provides valuable insights into the evolution of grapevine cultivation in Italy, highlighting the gradual transition from wild to domesticated types over millennia.