Conference Agenda

This work is dedicated to a starting Biomass Cal/Val project focused on temporal variability of P-band backscatter. In spite of an important literature on the subject, a comprehensive characterization of temporal variability remains challenging, especially from few observations (as for Biomass mission revisit) and when changes magnitude due to forest growth or loss can be easily confused with vegetation or soil moisture variations.

To address this question, the originality of our approach relies on the use of in-situ radar and flux data, respectively from the TropiScat-2 experiment and the Guyaflux tower located in the Paracou research area (French Guiana). The former consists in multifrequency (P, L and C bands) and quasi continuous (every 15 min) radar acquisitions from the top of the Guyaflux tower (ca 55m high), which enables to mimic satellite based acquisitions but with a much higher revisit.

The Guyaflux tower (labelled by ICOS as GF-Guy) has been generating meteorological and CO2, H2O and energy fluxes data since 2003, in order to determine the drivers of ecosystem CO2 source or sink strengths and evapotranspiration.

The TropiScat-2 instrumentation has been operating since 2018, making possible the development of data-driven and semi-empirical models of the radar backscatter variability with respect to a rather diverse range of meteorological conditions (especially for what concerns the intensity of dry and rainy periods in this tropical environment). These time-series models are mainly dedicated to backscattering intensity and temporal decorrelation, which can both serve to parameterize retrieval methods dedicated to the forest (dry) biomass and height estimation, contributing thereby to Biomass external calibration activities for this type of forest. Several examples of parameterization based on such time-series modeling will be presented, whether with fast and simple analytical formulations or with more complex microwave interaction models such as MIPERS-4D (a fully coherent model based on 3D forest representation).

For what concerns validation, the main objective is to compare the Net Primary Production (NPP) derived from the in-situ flux measurements with the forest biomass change estimates from the L3 products. Several observation periods will be tackled as the products delivery will follow, from which we should be able to gain in spatial resolution and better fit the Guyaflux footprint. This task will be also supported by the cross analysis between temporal variations of BIOMASS higher products (esp. L1) and TropiScat-2 radar data, together with the analysis of meteorological conditions and the closest in-situ forest biomass and height estimates. Given the first product delivery for French Guiana in 2027, our methodology will will be mainly illustrated with Sentinel-1, ALOS Scansar and simulated P-band data.

The ESA BIOMASS mission, recently launched as the first spaceborne P-band synthetic aperture radar (SAR), aims to generate global maps of forest aboveground biomass (AGB) and improve our understanding of forest contributions to the global carbon cycle. Despite its potential, the influence of canopy structural variability on P-band radar backscatter remains insufficiently understood, limiting the physical interpretability of BIOMASS observations.

In this study, we assess the sensitivity of P-band radar backscatter to forest canopy structure using the Michigan Microwave Canopy Scattering (MIMICS) model parameterised with terrestrial laser scanning (TLS) data. Our initial analysis focuses on four savanna woodland plots in Bicuar National Park, Angola, and one tropical rainforest plot in Lopé National Park, Gabon. Individual trees were extracted from the TLS data and reconstructed into quantitative structural models (QSMs) to derive detailed branch-level architectural parameters, which were then used to parameterise MIMICS simulations across all radar polarisations and incidence angles consistent with the BIOMASS observation geometry.

To investigate the relationship between canopy structure and P-band backscatter, we computed integrated metrics capturing fundamental physical properties relevant to electromagnetic interactions. Results indicate that tree size and structural complexity are primary factors of backscatter magnitude, with less structural complex generally producing stronger P-band backscatter. Simulated backscatter from the Gabon rainforest plot was further compared with BIOMASS Level-1 products over a nearby forested region to evaluate model performance and sensitivity.

This modelling framework enables us to explore and quantify the key structural parameters driving variability in the P-band radar response, and to examine potential variations across forest types and sensitivity to environmental factors such as soil moisture. Ultimately, the insights gained from this work will support the development of a physically-informed deep-learning approach aimed at assessing the accuracy and uncertainty of EO-derived biomass estimates from regional to global scales.

Recent studies show that forest degradation partly explain the decline in the forest carbon sink observed by top-down approaches. Tropical forest degradation is estimated to be responsible for 25% of forest carbon emissions, with approximately 20% of tropical forests disturbed by logging activities. In the Brazilian Amazon, CO2 emissions from fires and forest fragmentation reached 88% of gross deforestation emissions (Silva et al., 2021).

Improving knowledge of greenhouse gas emissions from these processes is essential to better understand and seize opportunities to mitigate climate change. However, many aspects of carbon loss associated with forest degradation remain insufficiently understood. Post-perturbation recovery, the impact of recurrent perturbations (especially understory fires and wildfires), and net biomass loss are still subject to active research.

In this paper, we assess the potential of the BIOMASS P-band radar sensor to evaluate the impact of forest degradation on tropical dense forests. In particular, We analyze the long-term impacts of degradation on forest biomass and structure as reflected in P-band backscatter, measuring post-perturbation resilience as a function of perturbation type, recurrence, and edge effects.

We use the extensive Deter system archive over the Brazilian Amazon as reference perturbation data. This dataset, produced by the National Institute for Space Research (INPE), comprises over 300,000 deforestation warnings and 180,000 degradation warnings spanning 2015-2025. This temporal coverage enables a chronosequence approach to build post-perturbation recovery curves for each perturbation type. By leveraging the Deter archive and chronosequence analysis, we aim to characterize recovery trajectories following various perturbations and assess the compounding effects of recurrent disturbances and edge effects on forest biomass. These results are expected to improve carbon emission estimates from forest degradation and enhance understanding of forest resilience in the Brazilian Amazon, with implications for REDD+ monitoring and tropical forest management.

Ongoing Earth observation missions bring unprecedented detail and comprehensiveness for understanding and quantifying the role of terrestrial ecosystem on the global carbon cycle. Yet, limited for longer term analysis given their contemporaneous shorter period in orbit. Legacy missions and datasets are essential to study dynamics and processes at longer time scales. Here, analysing global long-term datasets on vegetation aboveground biomass dynamics, spanning from 1992 to 2019, and atmospheric CO2 measurements, we study (1) the contribution of biomass dynamics to the atmospheric CO2 growth rate and (2) the CO2 fertilization effect on plant biomass.

Adopting a fully data-driven three-box model that simulates carbon dynamics within live vegetation, woody debris and soil organic carbon pools, and considers wildfires and spatio-temporal changes in primary productivity, we are able to explain over 60% of the observed variability in atmospheric CO2 growth rate over the period of 1997-2019 (R = 0.78, p-value < 0.05), with a low RMSE of 1.0 PgC yr-1. Our results show that, globally, lagged effects from heterotrophic pools account for 50% of the variability in atmospheric CO2 growth rate, exceeding three times the direct contribution of transient effects from the live biomass pool. These findings highlight the importance of quantifying tree mortality and cascading carbon release from litter and soils in shaping the terrestrial carbon balance.

We further leverage these Earth observations to isolate the specific contribution of elevated CO2 concentration to the biomass dynamics using both local multiple regression and residual methods. The approach is evaluated across an ensemble of dynamic global vegetation model simulations, showing low errors (RMSE: 0.04 and 0.02) and high correlation (R2: 0.79 and 0.88; p-value < 0.005). Globally, satellite-derived estimates indicate a global increase in AGB of 16.9% [13.9–18.8%] per 100 ppm rise in CO2 concentration. These observation-based estimates are close to those estimated by current land surface models (16.3 ± 5.0 %) but exceed estimates from global Earth system models (12.7 ± 6.5% for CMIP5, 13.2 ± 4.6% for CMIP6), suggesting an underestimation of Earth system models on the contribution of the land ecosystems in dampening anthropogenic CO2 emissions.

Overall, vegetation-atmosphere interactions from annual to decadal time scales show both the strong role of carbon loss processes and legacy dynamics, alongside a modest though larger CO2 fertilization effect on biomass when compared to global models. Ultimately, we contextualize these results on the possible future benefits from integrating BIOMASS, NISAR and the GEDI missions to better quantify and understand processes controlling growth, disturbance and recovery processes in terrestrial ecosystems.

An improved representation of the carbon and water cycle dynamics in terrestrial ecosystems underpins a large uncertainty reduction in modeling Earth system dynamics. The climate sensitivity of ecosystem processes controls land-atmosphere interactions and the overall atmospheric carbon uptake and release dynamics across scales. Local and Earth observations of vegetation dynamics are key for the evaluation of our understanding and support the quantification of process representation in model development. Previous research has shown the importance in undermining equifinality using multi-variate observation constraints, focusing water and carbon fluxes and stocks.

Long-wavelength radar backscatter provides unique insights into the dynamics of plant water and carbon dynamics when compared to optical EO products, as such, embeds the potential for constraining various parameters controlling local climate vegetation responses. In this study, we present an approach for assimilating Earth observation backscatter data in a terrestrial ecosystem model to improve estimates of vegetation parameters turnover rates. Among others, we focus on the information content of L-band ALOS PALSAR data in constraining vegetation dynamics at selected FLUXNET sites, where carbon and water fluxes and stocks are observed. Using a radar observation operator, a standard radiative transfer model, we design a model-data integration experiment to investigate the benefits of multiple backscatter observations versus unique above ground biomass to constrain model parameters.

The experimental setup focuses on the trade-off between information content from backscatter and uncertainties from observation operators versus sparse above ground biomass observations to constrain parameters controlling leaf and wood pool dynamics in vegetation. Current results indicate that the assimilation improves the estimation of aboveground biomass and constraints on turnover rates for both foliage and woody pools. Ultimately, data sparsity and availability exert control on model performance and prior model uncertainty on parameter constraints. Ultimately, this study highlights the potential of L-band backscatter to enhance vegetation carbon cycle modeling, emphasizes the added value of the upcoming ESA BIOMASS mission, and underscores the importance of integrating vegetation water dynamics into carbon models.