9th INTERNATIONAL WORKSHOP ON RETRIEVAL OF BIO- & GEO- PHYSICAL PARAMETERS FROM SAR DATA FOR LAND APPLICATIONS

Session

Forest - Modelling and Retrieval II

18 Nov 2026, 11:10

Aula Master (Universitat Politecnica de Catalunya )

20. Paving the Way for Uncertainty-Aware Forestry Products Using TanDEM-X and AI

Daniel Carcereri (German Aerospace Center (DLR))

18/11/2026, 11:10

Forestry

ORAL

Accurate characterization and continuous monitoring of forest canopy height are essential to guide conservation efforts, evaluate temporal stability, and support climatological analyses under increasing anthropogenic pressures. While deep learning (DL) models applied to interferometric synthetic aperture radar (InSAR) data have been shown to achieve state-of-the-art performance, their...

60. New Polarimetric Change Detectors: Application to Tropical Forest Monitoring Using BIOMASS SAR Data

ayoub hamza

18/11/2026, 11:30

Forestry

ORAL

Detecting changes between polarimetric radar observations is important for monitoring the temporal evolution of the biogeophysical properties of natural scenes. The long wavelength of BIOMASS’s fully polarimetric P-band radar provides sensitivity to forest properties and their change over time.
Under the standard complex Gaussian model for single-look complex SAR data, polarimetric covariance...

63. Observation of Ground Surface and Dihedral Scattering Signatures from BIOMASS Pol-InSAR Data: From Local Dynamics to Large-Scale Mapping

Noelia Romero-Puig (German Aerospace Center (DLR) / Hisdesat)

18/11/2026, 11:50

Forestry

ORAL

Understanding forest structural dynamics is crucial for accurate forest biomass estimation, ecosystem monitoring, and carbon cycle modelling. The launch of the European Space Agency’s (ESA) BIOMASS mission on April 29, 2025, opened a new era as the first spaceborne P-band synthetic aperture radar (SAR) continuously operating in quad-polarimetric, multi-baseline interferometric, and tomographic...

64. Forest Height Estimation Based on Sentinel-1 Interferometric Coherence and Meteorological Data

Mr Marc Herrera-Giménez

18/11/2026, 12:10

Forestry

This work analyses the possibility of estimating forest tree height (TH) at high and low spatial resolution using Machine Learning (ML) models and multi-source Earth Observation (EO) data. Estimating forest biophysical parameters is a must to quantify climate change effects, and the challenge with EO is the complexity and volume of data. Building on the observation that a direct relation...