Conference Agenda

The aim of this project focuses on the development and application of processing methodologies to address the 3D characterization of sub-surface targets using stacks of spaceborne SAR data acquired over natural scenarios. The investigated applications will include 3D imagery of forests, ice sheets, and desert areas, and are therefore mapped into Dragon topic Solid Earth - Subsurface target detection. The topics above are of fundamental importance in the context of present and future spaceborne SAR missions, which will allow increasingly more systematic use of multiple acquisitions thanks to improved hardware stability and more strict orbital control. Specifically, the proposed activities are intended to support use of multi-pass data stacks from:

• the upcoming P-Band mission BIOMASS.
• in-orbit L-Band missions, such as the Argentinian SAOCOM constellation, the Chinese missions LuTan-1 & LuTan-4, and potentially ALOS-4 and NISAR.

Research activities will consider SAR data stacks acquired by P- and L-band spaceborne SARs over dense tropical forests, ice sheets, and desert areas, as well as campaign data from ESA campaigns such as TomoSense, AfriSAR, AlpTomoSAR, IceSAR and the Second Tibetan Plateau Scientific Expedition and Research led by the Chinese Academy of Sciences (CAS). The activities will be concentrated on processing SAR image stacks to extract information about vertical vegetation structure and sub-surface terrain topography in forested areas, and also about the internal structure of sand dunes in desert area as well as snow-ice volume in glacier area. Estimation and compensation of ionospheric and tropospheric propagation effects will be investigated as well. Leveraging the unprecedented availability of P-Band spaceborne data from the BIOMASS mission, the research will as well be extend to investigating the 3D of the ionosphere. Whenever possible, validation activities will exploit the availability of reference data gathered at campaign sites, for which we plan to analyze spaceborne acquisitions at the same sites

The first year of the Dragon 6 project focused on the application of satellite remote sensing methods to monitor geohazards and support agricultural water use assessment. Activities were carried out in Brazil, Portugal China, and Pakistan, combining Synthetic Aperture Radar (SAR), optical data, ground measurements, and artificial intelligence models.

In Maceió, Brazil, ground deformation due to underground salt mining was analyzed using 145 Sentinel-1A images acquired between June 2019 and April 2024. Initial Persistent Scatterer Interferometry (PSI) identified general deformation trends but showed limitations in low-coherence areas. To address this, a Quasi-Persistent Scatterer (QPS-InSAR) approach was applied, increasing point density by over 400% (from 4,494 to 23,460 scatterers) and revealing cumulative vertical displacements ranging from −1,750 mm to +10 mm. Notably, 17 scatterers exhibited a loss of amplitude stability in 2023, with 11 of these changes occurring in December—correlating with the collapse of Mine 18. These results support the use of amplitude time series as an indicator of pre-failure instability.

In the Jamari National Forest, also in Brazil, Sentinel-1 SAR data were used to detect the impact of selective logging in tropical forest areas. Backscatter-based change detection methods were applied to areas logged in 2017 and 2020 and a control area. The detected canopy loss was 6.90% and 4.11% in the logged plots, respectively, while the undisturbed area showed only 0.16% change. Spatial overlap with mapped logging infrastructure reached 60.94% and 51.65%, confirming the method’s suitability for operational-scale forest monitoring.

In Portugal, a study in the Vilariça Valley addressed soil moisture estimation for irrigation management in olive orchards. Sentinel-1 SAR imagery (VV and VH polarizations) was combined with field measurements taken every 30 minutes from July 2020 to December 2021 at 10 cm depth. Two Artificial Neural Network (ANN) models were trained: one using 161 ascending images (D1), and another combining 246 ascending and descending images (D2). The D1 model achieved higher performance (RMSE: 2.78%, R²: 0.69, MAPE: 8.26%) compared to D2 (RMSE: 3.96%, R²: 0.59, MAPE: 12.41%), highlighting the importance of acquisition geometry consistency.

Collaborative work conducted by the Chinese team included surface deformation analysis in the Gilgit section of the China–Pakistan Highway and in the North China Plain (NCP). Multi-temporal InSAR, GNSS, and hydraulic head data from 2015–2019 were used to characterize aquifer behavior and land subsidence in NCP. Seasonal vertical displacements of up to 25 mm were identified, with long-term subsidence averaging 29 mm/year and localized peaks reaching 120 mm/year. Variability across regions was linked to differences in groundwater management and the influence of the South-to-North Water Diversion (SNWD) infrastructure.

These results demonstrate the effectiveness of combining EO data, ground observations, and AI models to monitor dynamic environmental processes. The activities developed during this first year contribute to building operational workflows for deformation risk detection, support tools for irrigation optimization, and future developments such as early warning systems and integration of heterogeneous data sources.