Conference Agenda

The ESA BIOMASS mission, successfully launched on 29 April 2025, is the first spaceborne P-band synthetic aperture radar (SAR) dedicated to continental-scale mapping of forest biomass, height, and disturbance. The first acquired datasets demonstrate excellent radiometric and interferometric quality, confirming the instrument’s unprecedented sensitivity to forest above-ground biomass density (AGBD) and vertical structure.

In this contribution, we present an intercomparison of AGBD maps over the Amazon forest, generated using an updated variant of the global BIOMASS AGBD mapping algorithm. As the primary AGBD predictors, we use three distinct datasets acquired during the commissioning phase:

(i) Level-1B backscatter coefficients,

(ii) Level-2A ground-cancelled backscatter, and

(iii) interferometric phase height at P-band.

As an independent reference, we employ X-band phase-centre height-based AGBD estimates, previously shown to exhibit a sensitivity to biomass comparable to that of airborne lidar.

We evaluate how the inclusion of Level-2A ground-cancellation processing and P-band interferometric phase height enhances the sensitivity of BIOMASS observables to AGBD and improves retrieval robustness relative to conventional backscatter-only approaches. Furthermore, we assess the accuracy and stability of biomass estimates derived directly from the P-band phase-centre height.

These preliminary results provide one of the first demonstrations of the BIOMASS mission’s capability for large-scale AGBD estimation, paving the way for robust, continental-scale biomass mapping and monitoring once the mission enters its operational phase in 2026.

This contribution describes the principles and implementation of a Level 3 (L3) product processor for ESA’s BIOMASS mission. This mission aims at reducing the uncertainty in the worldwide spatial distribution and dynamics of forest biomass, and will achieve this objective using a P-band SAR, providing global maps of forest biomass stocks, forest disturbance and growth [1].

In its dual-baseline interferometric operating phase, the BIOMASS mission will provide at each Global Cycle (GC), i.e. approximately every 7 months, a set of world-wide Level 2b (L2b) products consisting of maps of the Above Ground Biomass (AGB), Forest Height (FH) and Forest Disturbance (FD).

The L2b AGB and FH estimation processes being led independently, and at rather local spatial and temporal scales, it is very likely that output maps show some variability, related to the intrinsic uncertainty of L2b estimators, but also to potential exceptionally unfavorable factors, such as severe meteorological conditions (rain, wind), problematic propagation effects, or non-optimal baseline configuration.

The objective of L3 processing is to improve the consistency of L2b maps, by enforcing geophysical constraints through an iterative statistical regularization process. Three kinds of constraints are considered:

- spatial consistency is derived by comparing the spatial statistics of each product with autocorrelation function features computed over a wider neighborhood. Significantly different behaviors are to be penalized in order to guarantee spatially homogeneous estimates over undisturbed areas.

- temporal consistency is evaluated by observing estimates performed for different GCs, and by limiting the positive change rate (gain velocity) of AGB and FH parameters. One may note maximal gain rates are fixed according the considered geographical location and to the observed type of forest. Unlike gains, AGB and FH losses are not constrained, as they can happen in a very abrupt way.

- allometric consistency allows to mutually regularize AGB and FH fields using local, and forest-class specific relationships [2]. Allometric equations, as well as their associated dispersion, are directly estimated from L2b maps, at each GC, for each of the forest class provided by a land-cover map, and at the scale of an L2b tile, i.e. over regions of about 100 km x 100 km at the equator.

The regularization process is implemented under the form a Maximum Likelihood optimization, aiming to determine AGB and FH space-time maps which maximize a compound likelihood function, composed of losses terms related to L2b, spatial, temporal and allometric statistics [3].

A log-normal framework is adopted, which allows to represent this optimization as a very large, but sparse, system of linear equations.

At each new GC, the optimization process is run considering the freshly estimated L2b parameters in addition to the previously regularized fields. As a consequence, L3 estimate maps are expected to change significantly during the lifetime of the mission, with a quality level that increases with time.

The L3 processor delivers, at each GC and for each processed tile, L3 AGB and FH maps together with their level of confidence [4] and descriptors of their temporal evolution, auxiliary information related to the considered land-cover maps, and parameters of the BIOMASS allometric relationships at all dates and for all forest types.

An example of regularization, built from realistic AGB and FH maps estimated over Gabon, is provided and illustrates the capabilities of the processor to actually improve the consistency of L2b product maps.

[1] Quegan, S. et al. “The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space”, Remote Sensing of Environment, Volume 227, 2019, Pages 44-60, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2019.03.032.

[2] Chave, J. et al. “Improved allometric models to estimate the aboveground biomass of tropical trees”, Global change biology, 2014, 20(10), pp.3177-3190.

[3] Tarantola, A. (2005) Inverse Problem Theory and Methods for Model Parameter Estimation. SIAM: Society for Industrial and Applied Mathematics, 342 p. https://doi.org/10.1137/1.9780898717921

[4] Keener, R.W., 2010. Theoretical statistics: Topics for a core course. Springer Science & Business Media.

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The Product Algorithm Laboratory (PAL) for Biomass mission represents a key asset of the European Space Agency’s Multi-Mission Algorithm and Analysis Platform (ESA MAAP), a cloud-based platform that enables the processing and analysis of Earth observation data across multiple missions.

The PAL is built on CGI’s Insula platform, an advanced environment for Earth Observation (EO) data analytics that combines cutting-edge, production-ready technologies to deliver a flexible and interoperable cloud framework. This infrastructure underpins the ESA MAAP environment, currently supporting the Biomass and EarthCARE missions, while ensuring security, scalability, and efficient orchestration of processing services for a seamless and reliable user experience.

The Biomass MAAP enables scientists and developers to experiment with prototype algorithms, process large-scale mission data, and perform advanced analytical workflows in a shared, scalable, and reproducible infrastructure.

The PAL integrates direct access to the Biomass mission products, auxiliary datasets, and related validation data and provides scalable computing resources. It allows researchers to deploy and execute custom processing chains without managing underlying infrastructure. This capability fosters an iterative approach to algorithm refinement, from prototyping to pre-operational qualification.

Within this environment, researchers can perform comparative studies across multiple algorithm versions or missions, conduct sensitivity analyses to assess the impact of input parameters and assumptions, and perform advanced visualizations and analytics to better interpret data and algorithm behavior.

These capabilities promote knowledge exchange, algorithm harmonization and interoperability across missions, supporting the development of consistent and scientifically robust products within the ESA MAAP where tools, datasets, and users interact efficiently to empower EO data analysis.

By combining collaboration and experimentation, the PAL creates a framework that bridges scientific research and operational implementation, accelerating EO algorithm innovation and ensuring readiness for operational exploitation within ESA’s upcoming missions.

Importantly, the Biomass PAL is fully operational and open to researchers and developers, providing direct access to mission data and computing resources within a ready-to-use environment.