Conference Agenda

The UAVSAR AfriSAR campaigns of 2016 and 2024 represent major milestones in the joint NASA–ESA–DLR effort to advance calibration and validation (Cal/Val) of the NISAR and BIOMASS missions, while laying the foundation for future radar and lidar missions such as NASA’s Surface Topography and Vegetation (STV). These international collaborations demonstrate how coordinated airborne radar and lidar acquisitions can strengthen cross-agency scientific and technical cooperation, fostering new applications of 3D remote sensing for ecosystem, geomorphological , and hydrological research.

The 2016 AfriSAR campaign in Gabon established a benchmark by acquiring extensive multi-baseline PolInSAR and TomoSAR datasets, complemented by airborne and spaceborne lidar. These data enabled groundbreaking studies—over 100 peer-reviewed publications—on tropical forest structure, biomass, and sub-canopy topography. The 2024 AfriSAR-2 campaign, extending coverage across Ghana, Cameroon, Gabon, Democratic Republic of Congo and the Republic of Congo, builds on this success to refine TomoSAR acquisitions and improving retrieval algorithms for L-band (NISAR) and P-band (BIOMASS) measurements across a wider diversity of landscapes. These datasets directly support the ESA BIOMASS Cal/Val project PP0104629.

To meet the growing analytical complexity of these campaigns, new tools such as Kapok (for multi-baseline PolInSAR and TomoSAR analysis) and CAPON (for adaptive tomographic focusing) are being developed to enhance vertical resolution and structural accuracy. These advances help bridge the algorithmic maturity gap between radar and lidar approaches and contribute directly to the scientific and technological objectives of STV, envisioned as a unified mission for global mapping of surface topography and vegetation structure.

Looking ahead, the upcoming TropiSAR 2026 campaign in Peru and Colombia will expand these Cal/Val activities to the Amazon and Chocó-Darien-central America region, providing cross-continental datasets for BIOMASS, NISAR and STV. This effort reinforces the growing NASA–ESA collaboration in algorithm development, data sharing, and applied science. The presentation will survey the datasets acquired during these campaigns, assess current processing and algorithmic capabilities, and outline future development needs—particularly in TomoSAR processing, canopy-ground separation, and hydrologic coupling—to fully realize the potential of radar missions for global forest and water resource monitoring.

Development and use of low frequency (VHF to UHF) imaging radars has increased in recent years, driven by the presence of flagship scientific programs at European level, such as the BIOMASS mission aiming to map forest height and above ground biomass globally, or by various scientific applications requiring solving FOPEN (Foliage Penetration) issues.

More particularly linked to the BIOMASS mission and to support calibration/validation activities in the tomography phase, a new TropiSAR airborne campaign will be conducted by ONERA on 2027. Objective will be to map and characterize the dense tropical forest cover and the underlying surface using tomoSAR mode at low frequency.

SETHI low-frequency SAR sensors are particularly well-adapted for such a mission where a high performance is required for SAR imaging, repeat-pass interferometric and tomographic measurements.

The proposed campaign will fly again over dense French Guyana tropical forest (Paracou, Nouragues and Rochambeau areas) with P-band, L-band and X-band SAR sensors. Possibility to fly simultaneously multi-band SAR sensors is also of main interest for this campaign: We can then compare multi-band results on same area, with same conditions (weather, vegetation state).

This new campaign will benefit our latest developments in softwares to exploit scientific data using PolinSAR and tomography technics to retrieve information on forest height, density and potentially ground topography.

We will expose in this contribution the campaign plan for TropiSAR-2 experiment, including schedule and tracks selection.

* Land vegetation is a large carbon store and represents opportunities to sequester additional carbon. While many Earth Observation missions aim to estimate forest biomass from space, their calibration and validation is critical. Ultimately trust in biomass maps requires accurate ground data. Supporting ground measurements and the people who make them is thus mission-critical for mapping and tracking Earth’s forest carbon. Building on decades of work from the global research community with a strong representation of partners from the Global South, the GEO-TREES initiative funds high quality ground data from a global network of reference sites, and to make these data openly accessible. 

* In this contribution, we report on the progress in community building, data acquisition, processing and delivery at over 40 biomass reference measurement sites. For each biomass reference measurement site, data acquired by the consortium partners includes plot inventory measurements at ≥10 hectares of forest, aerial laser scanning (ALS) coverage over ≥1000 ha of forests, and terrestrial laser scanning of the forest for ≥3 hectares.  

* We intend to provide the following data: (1) a 0.25-ha resolution aboveground biomass density (AGBD, Mg/ha) estimate for each tree inventory subplot, together with a variance estimate; (2) a 0.25-ha resolution canopy height (m) estimate for each tree inventory subplot, together with a variance estimate; (3) a 0.25-ha map of AGBD inferred from ALS and plot data, together with a pixelwise variance estimate; (4) a 0.25-ha map of canopy height inferred from ALS, together with a pixel-wise variance estimate; (5) a 0.25-ha resolution aboveground biomass density (AGBD, Mg/ha) estimate for subplot scanned with TLS, together with a variance estimate; (6) ancillary data for each site. We detail how plot-level and ALS data is processed to account for uncertainty and possible bias, based on open-access pipelines that are both reproducible and that can be used by the broader GEO-TREES community, using the ESA MAAP. When ready, the data will be accessible on the GEO-TREES data portal. 

* We emphasize the importance of involving research scientists associated with the sites in product validation plans. Not only do they provide essential high-quality data, they also offer invaluable insights about the peculiarities of the study sites which a mission validation plan would ignore at its peril. The establishment of GEO-TREES, a coordinated network of validation sites, is crucial for the success of biomass missions. In the future, it could also prove useful for the validation of other Earth observation missions aimed at quantifying forest-related geophysical measurements. 

Accurate estimation of forest carbon stocks is critical for monitoring ecosystem dynamics and assessing climate mitigation strategies. Terrestrial Laser Scanning (TLS) is a powerful tool for quantifying forest structure and aboveground biomass (AGB) with unprecedented detail. TLS point clouds can be used to produce individual-tree metrics (e.g. stem diameter, height) and quantitative structure models (QSM) to estimate total tree volumes. Local allometric models can then be parameterized to predict tree volume and biomass from diameter and height. However, there are still uncertainties in TLS-based biomass estimates, which propagate through successive analytical stages, ultimately affecting carbon stock estimates while scaling them up from tree to plot level. The accurate estimation of carbon stocks is a challenge due to the use of generic and regionally mismatched allometric models, which potentially have high uncertainty (~40%). The uncertainty arises from field measurement errors, structural metrics errors, and statistical model uncertainties. Therefore, the propagation of uncertainty on the biomass estimates travels at different levels.

GEO-TREES is an initiative to provide the calibration and validation data for the satellite biomass products. This study investigates the propagation of TLS measurement errors into carbon stock assessments across a subset of GEO-TREES reference plots (Barro Colorado Island (Panama), Pasoh (Malaysia), Amacayacu (Colombia), and Mpala (kenya)), representative of a range of tropical forest types.

We quantify sources of uncertainty arising from the TLS data acquisition and post-processing (Segmentation of individual trees and QSM-fitting) to the allometric model parametrization and evaluate their cumulative impact on AGB and carbon estimates. Point cloud processing, tree segmentation, and Quantitative Structure Model (QSM) reconstruction can further contribute to structural inaccuracies, particularly in complex or overlapping canopies. Subsequent derivation of individual tree metrics (e.g., stem diameter, height, branch volume) carries both measurement and model-fitting uncertainties. These propagate into the estimation of tree volume and biomass through the use of volumetric or allometric models, respectively. By employing error-propagation frameworks (Monte Carlo propagation), we identify the dominant contributors, such as the QSMs model fitting error (which can systematically overestimate small branches), to total uncertainty under varying forest conditions. The study further proposes a standardized protocol for quantifying uncertainty and collecting data for TLS-based biomass estimation within the GEO-TREES network. Our findings offer critical insights for harmonizing TLS methodologies and enhancing the reliability of ground-based carbon reference data across a wide range of tree sizes for the ESA BIOMASS mission.

Conventional field census measurements, such as diameter at breast height (DBH) or height, capture only limited aspects of three-dimensional distributions in forest structure. These measurements are often converted to aboveground biomass (AGB) estimates using allometric models. AGB estimates through allometric models are often considered as ground truth for the calibration and validation of spaceborne remote sensing products. These tree size-to-mass allometric models are mostly built on a selected sample of harvested biomass data, but are then often applied to trees that fall far outside the size or ecosystem range of the model calibration data. This can result in potential errors in downstream AGB products from satellite data.

Three-dimensional measurements from terrestrial laser scanning (TLS) have demonstrated that they can overcome the typical limitations of current allometric models and capture the spatial distribution of forest biomass. TLS measurements are increasingly becoming more routine, and GEO-TREES is an example of an initiative that builds on and complements existing long-term ecological plot networks by integrating TLS, airborne laser scanning, and forest inventory census to support the upscaling of aboveground biomass using satellite remote sensing.

Using 3D TLS data collected over a range of forest ecosystems, we illustrate the potential impact of the current issue of conventional allometric models. In our case study of Wytham Woods (UK), we demonstrated using TLS that its AGB is 1.77 times more than current allometric model estimates. We will present two solutions to this problem: (a) TLS can be used to estimate the volume of an individual tree and the entire stand in 3D directly. These volume estimates can be converted to AGB using wood density values. This approach also offers full traceability of the AGB of each tree; (b) TLS can be used to generate 3D tree models across the full size range of trees, which can then be used to create new allometric models that do not need to be extrapolated out of sample. We will further illustrate this solution by a recently constructed a new allometric model using TLS for Eucalyptus tereticornis, the dominant species at EucFACE, an ecosystem-scale mature forest free-air CO2 enrichment (FACE) experiment in Australia.

In both solutions (direct or indirect through new allometric models), TLS is essentially used to virtually harvest trees. Whereas the first approach can provide a deeper understanding of the AGB of all trees in a forest stand, it requires significantly more time to collect and process the data. The construction of new allometric models using TLS provides a practical way forward to improve estimates of AGB for calibration and validation of spaceborne AGB estimates using satellites such as ESA BIOMASS.