Philippa Jane Mason

Detecting surface changes on Venus by comparing Magellan synthetic aperture radar (SAR) data with future high‐resolution data from Envision and VERITAS will be essential for constraining present‐day volcanism and characterizing planetary surface processes. However, such inter‐mission SAR change detection requires resampling images to comparable pixel spacing, which results in image pairs with different equivalent numbers of looks (ENLs) and this invalidates classical statistical models that assume the two images are identically distributed. To address this issue, we propose an unsupervised change detection method for SAR image pairs with different ENLs, termed GB2KI. This method models the ratio image with the generalized beta prime distribution (GB2) and incorporates it into the minimum‐error thresholding framework. An entropy‐weighted maximum likelihood estimation method is introduced for robust parameter estimation when the changed class occupies only a small fraction of the scene. Additionally, a multiscale post‐processing technique is developed to suppress isolated false detections in the final change map. Simulations using Magellan Cycle 1 and Cycle 3 images of a volcanic plain on Venus, with synthetic lava‐flow changes varying in intensity, extent, and location, show that GB2KI improves overall detection accuracy and reduces false alarms. Further validation is performed using two Earth analog data sets from the 2014–2015 Holuhraun lava flow in Iceland, including Sentinel‐1 images from different imaging modes (1‐year interval) and Radarsat‐1/Sentinel‐1 images (15‐year interval). Experimental results indicate that GB2KI accurately identifies lava‐flow changes in both cases, supporting inter‐mission comparisons between Magellan and future Venus SAR missions.

This study is based on the best crop classification result generated by the proposed unsupervised Machine Learning (ML) method in Li et al., 2025a, using the spectral indices calculated by the formula with spectral bands from Sentinel-2 image products, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI) and Normalized Difference Moisture Index (NDMI). The patterns and characteristics of these spectral indices, across arable fields with different crop types following the winter growing seasons, have not yet been analyzed in detail. This research aims to provide a comprehensive study of each input spectral index and its impact on the crop classification model. Each spectral index is analyzed across a series of crop fields, using Sentinel-2 images, carefully selected to follow the patterns of winter crop phenology, and the results of unsupervised classification for each crop type in Norfolk, UK are successfully generated and analyzed. The different growing rates between winter barley and wheat have been classified found on a monthly basis using Sentinel-2 RGB images and thus the images during the harvest time, May and June, can support crop classifications. Wild grasses or other plants on the fields led to some crop misclassification from November to March in the Sentinel-2 RGB images. Similarity between winter barley and wheat and the different sowing time among the same type of crop also led to misclassification. In future these misclassifications could be avoided through better understanding of the relation between spectral indices and crop planting cycles.

Venus is the only other Earth-sized planet in our Solar System, volcanically active and rich in volatiles, but extremely hot, dry, and hostile to life. Models suggest either that Venus was always thus [1], or that in principle it could support oceans even today [2]. While the long history of the planet may be recorded in its ancient highlands, the lowland plains host a range of features suggestive of past water. Some canali are clearly lava channels [3], but others appear similar to fluvial [4] or submarine [5] channels, and our mapping of polygonal terrain implies a submarine sedimentary origin of them. The evidence is compelling that Venus once supported oceans and lost them.Possibly the closest terrestrial analogue for these conditions is the Messinian Salinity Crisis (MSC), 5·97 to 5·33 Ma ago, during which the Mediterranean Sea became repeatedly restricted, and evaporated in part or whole [6,7]. Nearly 10⁶ km³ of gypsum and halite, in places several kilometres thick, were precipitated [8], with exposed salt flats covering most of the Mediterranean. With sea level lowered by 2 km or more, the major rivers—notably the Nile, Rhone, Ebron and Po—carved deep canyons into the continental margins, helping to maintain brine pools within the deepest basins. Remarkably, faunal exchanges took place across this inhospitable landscape [9].Conditions on Venus were even more extreme. In the earliest stages of the runaway greenhouse, photochemical sulphur cycling in a hot steam‑rich atmosphere would have resulted in transient, intense episodes of sulphuric acid rainfall. Under these conditions, the subaerial uplands would have experienced extreme chemical weathering and flash flood erosion [10], rapidly depositing smectite-rich clays into saturated brine ocean basins, generating thick piles of salt-rich sediments. Extensive erosion of the Venus uplands and infilling of the plains basins with sediment and salt may in part explain the subdued topography of Venus and the distribution of certain features in the plains.Polygonal terrains are usually located towards the margins of the lowlands, and nearly all the longer canali appear within these areas and terminate in the deeper basins, consistent with an origin as submarine density currents [5] or hypersaline channels [11]. Once the oceans were finally gone, desiccation of these salt-sediment piles under a superheated steam atmosphere [12] resulted in the smaller-scale polygonal fractures superposed on them. Continued heating first lithified and then metamorphosed these sediments.Ignoring the effects of later topographic changes, and the likelihood of repeated cycles of flooding and evaporation, a lower bound for the mass of salt precipitated on Venus can be obtained by assuming that areas below 6051·3 km radius were filled with saturated brine (at approximately 36 wt% NaCl). The mass of anhydrous halite that results is ~2 x 1019 kg, equivalent to a salt layer 64 m thick in those basins. This mass is orders of magnitudes smaller than the salt content of Earth’s oceans, suggesting that the salt deposits on Venus may host far thicker salt deposits across its lowlands.Venus coastlines were likely steep and dynamic, precluding the establishment of long-lived, well-defined shorelines. Nonetheless, a variety of observations suggest former marine environments and possible shorelines close to the 6053 km radius contour (Fig. 1). Streamlined islands and channels can be seen parallel to some coastlines and there are hints of shoreward drainage systems above them. Taking that sea level and modern topography means oceans covered close to 90% of the surface of Venus, and had a volume about 40% of Earth’s oceans, equating to an average ocean depth of 1400 m. Interestingly, dissolving the mass of halite calculated earlier into that ocean volume gives a salinity of 35‰, about the same as terrestrial seawater. Did those oceans also support life?It is hard to comprehend Mediterranean drying out almost completely, and the environmental impact that must have had, let alone the world losing its oceans entirely. Yet the evidence suggests that Venus did once have oceans, perhaps teeming with life, and that it lost them within the last billion years. The implications of such an event for life outside the Solar System, and on our own planet, are profound indeed.References1. T. Constantinou, O. Shorttle, and P. B. Rimmer, Nature Astronomy 2024 9:2 9, 189 (2024)2. M. J. Way and A. D. Del Genio, J. Geophys. Res. Planets 125, e2019JE006276 (2020)3. V. R. Baker, G. Komatsu, T. J. Parker, V. C. Gulick, J. S. Kargel, and J. S. Lewis, J. Geophys. Res. Planets 97, 13421 (1992)4. A. P. Jones and K. T. Pickering, J. Geol. Soc. London. 160, 319 (2003)5. D. Waltham, K. T. Pickering, and V. J. Bray, J. Geophys. Res. Planets 113, 2012 (2008)6. S. Khawja, R. E. Ernst, C. Samson, P. K. Byrne, R. C. Ghail, and L. M. MacLellan, Nat. Commun. 11, 1 (2020)7. J. M. Rouchy and A. Caruso, Sediment. Geol. 188–189, 35 (2006)8. K. J. Hsü, W. B. F. Ryan, and M. B. Cita, Nature 1973 242:5395 242, 240 (1973)9. B. Haq, C. Gorini, J. Baur, J. Moneron, and J. L. Rubino, Glob. Planet. Change 184, (2020)10. J. van der Made, J. Morales, and P. Montoya, Palaeogeogr. Palaeoclimatol. Palaeoecol. 238, 228 (2006)11. M. Roveri, V. Manzi, A. Bergamasco, F. M. Falcieri, R. Gennari, S. Lugli, and B. C. Schreiber, Am. J. Sci. 314, 751 (2014)12. J. F. Kasting, Icarus 74, 472 (1988)FiguresFig. 1 Features consistent with former marine environments: (A) shoreline and wavecut platform; (B) streamlined islands; (C) submarine channel; and (D) drainage channels. Each can be explained by other processes and therefore none is diagnostic of former marine environments. Left-looking Magellan image, color-coded with stereo-derived topography, Mercator conformal projection. The anomalously deep area near the image centre is an erroneous altimeter measurement in...

ESA's EnVision mission will deliver some of the first new high-resolution radar maps of Venus since Magellan, with the VenSAR instrument operating in HH/HV dual-polarisation S-band at 30 m and 10 m resolutions [1]. Venus' surface is dominated by volcanic terrain [3], so understanding what the new missions may 'see' requires analysis of radar signatures at barren, volcanic terrains on Earth. The Askja volcanic system in Iceland is well-suited to isolating surface-roughness effects on radar backscatter [2]: basaltic composition, minimal topography and vegetation cover, and accessibility for ground-truth validation [6,7]. We address whether radar can be used to differentiate, provide age constraint, and characterise the emplacement and modification of volcanic surfaces, and whether these signatures are preserved at VenSAR specifications.Seven lava flow age units erupted from the Askja volcano, ranging in age from the 1961 Vikrahraun to >7,000 BP, were characterised using fully polarimetric Synthetic Aperture Radar (SAR) data acquired using the DLR’s F-SAR system for NASA JPL and the VERITAS team in August 2023 [4,5]. These datasets include X-band (3.1 cm), S-band (9.2 cm), and L-band (22.6 cm) at 2 m spatial resolution. Flow units have been mapped using S-band HH radar imagery, stratigraphic relationships, and field data; absolute ages are from the ÍSOR digital geological database [9] and Sæmundsson et al. [10,11] (Figure 1).Figure 1. Geological map of the Askja volcanic system showing the seven flow units examined in this study [9-11].Across the Askja flow sequence, mean S-band HH γ0 decreases systematically with flow age. The 1961 Vikrahraun flow has a mean γ0 of -7.0 dB; the >7,000 BP Shield Lavas reach -14.0 dB (Figure 3). This backscatter decrease is driven by post-emplacement weathering, smoothing, and mantling (Figure 2). The 5th percentile of the per-pixel distribution drops 8.7 dB across the age sequence; the 95th percentile drops only 4.6 dB. Weathering therefore smooths the smoothest regions preferentially. Backscatter also decreases with increasing incidence angle (10-15 dB decrease from 10° to 80°), consistent with typical radar scattering behaviour from rough surfaces.Figure 2. Three end-members of the Askja chronosequence (1961 Vikrahraun, 2900-4500 BP, and >6100 BP) shown as paired Sentinel-2 optical (left) and F-SAR S-band γ0 HH at 2 m (right).Figure 3. S-band HH γ0 versus flow age at three resolutions: F-SAR 2 m (blue), VenSAR 10 m (orange), VenSAR 30 m (red). Power-law fits with 95% Monte Carlo confidence intervals; inset gives dynamic range and retention per cell.Wavelength comparison reveals progressive increase in age-discrimination dynamic range with wavelength. X-band, available only at VV polarisation, shows the smallest dynamic range (5.64 dB). S-band HH gives 6.07 dB versus 9.59 dB at L-band HH (8.49 vs 11.60 dB at HV). Polarimetric analysis differentiates surface scattering mechanisms and flow morphologies. Smooth pāhoehoe and mantled surfaces produce high HH and HH/HV ratios, reflecting predominantly single-bounce surface scattering. Rough a'ā flows produce high HV backscatter and large volume-scattering decomposition components: the clinker layer of angular rubble clasts acts as a randomly oriented scattering volume, generating multiple-bounce returns that decompose as volume power.Differentiation between pāhoehoe and a'ā facies within a single flow appears in the decomposition, not in the raw intensity. Following the facies-separability protocol of Tolometti et al. [12], dual-pol Mascolo-Cloude m·v [8] matches or slightly exceeds quad-pol Freeman-Durden P_v [13] in discriminating Event 2 a'ā from pāhoehoe within Vikrahraun 1961 (Figure 4). Polarimetric facies discrimination is most powerful on young, well-preserved flow surfaces.Figure 4. Dual-pol decomposition [8] (R = ms, G = mv, B = ms/mv) of Vikrahraun 1961 at 2 m, 10 m, and 30 m. Coloured polygons mark a'ā and pāhoehoe facies, classified following Blasizzo et al. [14]. Per-flow facies separability following Tolometti et al. [12]: MC m·v: O = 0.84, H = 2.6×10⁴; FD P_v: O = 0.89, H = 2.0×10⁴.To address whether these signatures will be mappable in images captured by VenSAR, the 2 m F-SAR S- and L-band covariance was degraded via a slant-range pipeline: incoherent multilook, Gaussian filtering to the target resolution, Gamma speckle at the specified looks (16 at 30 m, 8 at 10 m), and exponentially-distributed thermal noise at the -20 dB NESZ specification. S-band HH retains 79% of the age-discrimination dynamic range at VenSAR 30 m (1.30 dB loss out of 6.07 dB) and 86% at 10 m (0.87 dB loss); regression slope flattens by 16% and 13% respectively. The HV channel is NESZ-limited: only 49% retention at 30 m and 51% at 10 m, with cross-pol signal on flows older than c.5,000 BP sitting within a few dB of the -20 dB floor. The 10 m mode does not rescue HV because NESZ is per-pixel rather than ensemble-averaged. The same asymmetry holds at L-band (74% HH, 44% HV retention at 30 m), indicating the mechanism is wavelength-robust. Quad-pol and dual-pol products at 30 m agree to within ±0.015 dB on per-flow γ0 statistics; VenSAR's HH/HV mode therefore preserves both the age-discrimination signal and the within-flow facies differentiation accessible to fully polarimetric F-SAR.Age-backscatter correlations provide a quantitative dating framework, while polarimetric analysis enables morphological and textural discrimination. For VenSAR specifically, HH at 30 m and 10 m is optimal for age discrimination across the 60–7,000-year range, while HV is optimal for morphology discrimination.References: [1] EnVision Red Book, ESA-SCI-DIR-RP-003, 2023; [2] Adeli et al., 2023; [3] Brossier et al., 2020; [4] Horn et al., 2017; [5] Keller et al., 2024; [6] Mason et al., 2024; [7] Raguso et...

ESA’s Envision mission is a holistic investigation of Venus’ interior, surface and atmosphere, aimed at improving our understanding of the geodynamic, geological and climatic history and activity of our nearest planetary neighbour. To achieve that, we need a series of complimentary observations at global, regional and local scales, from different instruments and in different modes, and repeated observations for change detection. Envision will carry an integrated instrument suite of spectrometers (VenSpec), a Subsurface Radar Sounder (SRS) and a Synthetic Aperture Radar (VenSAR) that will collect these data. This will be further complemented by gravity and radio occultation observations using the satellite communications system to Earth.Whilst we would like to image the entire planet at high resolution using the SAR instrument, that is not feasible in the 6-sidereal-day nominal mission life, hence Envision’s imaging strategy is targeted: collecting nested, multi-modal images which will be used to answer its complex science questions. At least 20% of the surface of Venus will be imaged using the SAR at 30 m spatial resolution (twice and at different angles, for generation of stereo topography for at least 18% of the surface) with a smaller fraction imaged a third time (for change detection) or at dual-polarisation, and an even smaller fraction imaged at 10 m resolution for detailed geomorphological analysis. The stereo-derived topography and Envision’s near-global altimetry data will be vital to answer science questions involving Envision’s other instruments and for its holistic science investigations. Considering EnVision’s overall scientific objectives, as well as individual instrument goals, synergies, and limitations, the key question then becomes how we decide which areas should be imaged and in which modes?The Envision Science Working Team (SWT) formed an ROI Working Group (ROIWG), which was tasked with compiling a list of surface features on Venus, the imaging of which will help address the Envision Science Objectives. The list contains named and located features of interest, of all types (including, but not limited to, tectonic, volcanic, and impact features, and aeolian landforms), which was in turn used to construct Regions of Interest (ROIs), in a semi-automated, semi-objective way. It is anticipated that the imagery collected across the ROIs will capture a representative sample of its geological terrains and key features. These ROIs will thus form the basis of Envision’s SAR and radiometric imaging strategy and planning, as well as the strategy for the subsurface radar high density observations. Here we summarise the process and present an example of Envision’s ROIs (e.g. Figure 1) and how they will be used to optimize the scientific return of the EnVision mission. This target lists will be used by the Envision SWT to construct the mission Science Activity Plan for the Science Measurement Phase. In the future, as the mission advances, this target list will evolve, will be opened to community input, and will remain central to maximizing the value of synergistic, multi-instrument investigations of our sister planet.Figure 1. Illustration of EnVision’s planned Regions of Interest (ROIs) as published in the Definition Study Report or Red Book (ESA-SCI-DIR-RP-00, European Space Agency, Nov 2023) with a selection of known target features as well as the geological terrains they would intersect, overlain on the global Magellan SAR image mosaic.

Synthetic Aperture Radar (SAR) observations provide valuable information for volcanic surface characterisation due to their sensitivity to surface roughness and dielectric properties. Accurate characterisation of volcanic terrains is important for understanding SAR scattering behaviour and surface physical properties, including roughness and dielectric variability, in both terrestrial and planetary environments. Radar remote sensing is particularly valuable for volcanic investigations in regions where optical observations are limited by cloud cover, ash, or dense atmospheres, such as on Venus. Conventional model-based dual-polarimetric SAR decomposition methods, including the framework proposed by Lorenzo Mascolo et al. [1], often overestimate volume scattering in volcanic terrains because depolarised returns arising from rough-surface interactions and multiple scattering are incorrectly interpreted as random volume scattering. Since volcanic surfaces are predominantly non-vegetated and characterised by blocky lava flows, rough interfaces, and coherent depolarisation mechanisms, a significant portion of the surface scattering contribution is absorbed into the estimated volume scattering component. This leads to systematic underestimation of surface scattering power and reduced physical interpretability of decomposition results. To address these limitations, this study proposes a physically constrained model-based dual-polarimetric SAR decomposition framework for volcanic surface characterisation using ALOS PALSAR-2 datasets acquired over terrestrial volcanic analogue sites in Iceland. The proposed approach constrains the unpolarised scattering component using polarisation-state information derived from Stokes parameters. A hybrid formulation combining the degree of polarisation and a phase-sensitive randomness index is introduced to distinguish stochastic volume scattering from coherent rough-surface depolarisation. In addition, an intensity-based constraint is incorporated to suppress residual overestimation in high-backscatter regions while preserving total power conservation. The methodology is particularly relevant for forthcoming dual-polarimetric SAR observations from the ESA EnVision mission, which will enable investigation of volcanic surface scattering behaviour and dielectric variability on planetary surfaces. Experimental results demonstrate improved discrimination of volcanic surface units and more physically consistent decomposition compared with conventional dual-polarimetric approaches.Keywords: Volcanos; Synthetic Aperture Radar; Dual-polarimetric decomposition; ALOS-PALSAR-2; EnVisionReferences[1] L. Mascolo, S. R. Cloude, and J. M. Lopez-Sanchez, “Model-based decomposition of dual-pol SAR data: Application to Sentinel-1,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–19, 2021.Acknowledgement: The authors would like to acknowledge the Japan Aerospace Exploration Agency (JAXA) for providing the ALOS PALSAR-2 datasets to carry out this research work.

Polygons of various sizes cover more than 5% of the surface of Venus, almost as much of the surface as that covered by tesserae, and yet they have been largely ignored. The most recent study [1] identified 204 polygonal terrain locations covering approximately 8 Mm², using an automated algorithm that resulted in a northern hemisphere bias. Nonetheless, they found that 65% of the identified polygonal terrain is associated with small volcanoes, 25% with coronae, 18% with tesserae, and 20% with wrinkle ridges. Polygonal terrain is currently attributed to thermal contraction by cooling, whether of lava flows, or following heating by an intrusion [2], or in response to climate change [3].Our mapping of an additional 16 Mm² (Figure 1) reveals 6 types of polygonal terrain (plus unclassified), broadly divided into irregular (55% by area) and rectilinear patterns (37% by area). There appear to be two distinct size ranges of smaller cells close to the resolution limit (~100 m) and larger cells several km across, sometimes superposed. A thermal contraction origin by cooling is difficult to reconcile with the variety, shapes and sizes of polygons observed.A range of processes in addition to thermal contraction can generate polygonal patterns at varying scales on Earth and Mars including lava lakes, columnar joints, karst, diagenesis, ice wedge polygons (periglaciation), desiccation (mud cracks), evaporation (salt pans), and polygonal fault systems (PFS). The first four generate polygons on the metre scale, smaller than can be resolved in Magellan imagery. The next three can generate polygons from metres up to a few hundred metres across and may therefore have generated the smaller polygons observed on Venus. PFS have so far only been identified in some terrestrial sedimentary basins [4], but they do generate polygons up to several kilometres across, similar to the larger polygons observed on Venus.PFS form networks of small‐displacement normal faults forming laterally extensive layer‑bound polygonal patterns. They occur in fine-grained, smectite-rich sediments, usually within epicontinental marine basins [5]. Their initiation appears to depend on syneresis of smectite-rich clay gels during early diagenesis [6], while hydraulic fracturing by over-pressured pore fluids [5], aided by the low shearing resistance of clays [7], enables them to propagate and grow into larger polygonal shear faults several kilometres across. Initiation and propagation are confined within sediments buried at

The Indian Space Research Organisation’s (ISRO) Chandrayaan-2 lunar mission carries the Dual-Frequency Synthetic Aperture Radar (DFSAR), which acquires fully polarimetric (quad-pol) observations at L- and S-band wavelengths [1]. This format measures the full scattering matrix for each pixel (HH, HV, VH and VV) enabling a range of polarimetric decompositions to be used to characterise surface and near-subsurface scattering mechanisms. At the lunar south pole where low solar incidence angles create many permanently shadowed regions (PSRs), radar provides a key means of investigating these otherwise inaccessible environments.Given the capabilities of DFSAR, Cabeus crater was chosen for this study as it was designated as a site of interest in a study completed by Lemelin et al. [2], and the selected swath also intersects with a large PSR. The analysed area is close to the LCROSS Centaur stage impact site where near-infrared absorption spectra attributed to water vapour and ice, and ultraviolet emissions attributable to hydroxyl radicals were detected in the impact plume debris [3]. The crater itself is one of the coldest craters on the Moon [3], and has a substantial fraction of water equivalent hydrogen (WEH) content of values around 0.5%wt [4].The chosen DFSAR single look complex (SLC) swath was processed within PyPolSARPro to generate Pauli, Cloude-Pottier, Yamaguchi 4-component, and target scattering vector model (TSVM) decompositions, enabling the comparison of the scattering responses. A polarimetric whitening filter (PWF) was also used to create an image where heterogeneous areas were isolated and highlighted against the background. For example, the volumetric component of the Y4R decomposition, originally developed to represent randomly oriented vegetation scatterers, was used to understand the volumetric return of surface and near-subsurface regolith. Finally, a ratio of the Y4R odd and volume components was used to generate a surface classification map identifying areas of dominant surface, dominant volume, and mixed return in a discrete and quantifiable way.As shown in Fig.1, the Pauli RGB image indicates surface scattering as the dominant mechanism across the region. An enhanced volumetric signal is associated with the crater walls, which is consistent with buried boulders, blocky material or potentially volatile-bearing regolith. This is reinforced by the Y4R decomposition, which identifies a reduction in surface scattering and more volume scattering across the crater walls. Regions located south of the crater are characterised by smoother, surface-dominated scattering. In contrast, the region north of the crater exhibits enhanced heterogeneity, with strong volumetric signals associated with the rims of smaller impacts. Analysis of the PWF image further highlights the northern region’s heterogeneity and enhances linear features in the crater walls.Fig.2 presents the resulting surface classification map. The northern region is predominantly volume dominated, whereas the smoother southern region is consistent with the Y4R surface component as seen in Fig.1. By categorising scattering behaviour into discrete regimes, ratio maps enable easy identification of regions where surface and subsurface heterogeneity may indicate volatile-bearing or structurally complex regolith. This can therefore support site prioritisation for future lunar exploration and in situ investigation. We acknowledge the use of data from the Chandrayaan-II, second lunar mission of the Indian Space Research Organisation (ISRO), archived at the Indian Space Science Data Centre (ISSDC). This work was supported by the UK Space Agency and the Science and Technology Facilities Council [grant number: UKRI2555]. [1] Bhiravarasu et al., 2021, Planetary Science Journal; [2] Lemelin et al., 2021, Planetary Science Journal; [3] Colaprete et al., 2010, Science; [4] Sanin et al., 2015, Icarus

In preparation for the Envision mission to Venus, multi-frequency airborne Synthetic Aperture Radar (SAR) data collected in August 2023 over Iceland, by the DLR for NASA JPL and the VERITAS mission team (using the F-SAR system) [2-5], has been analysed across a series of basaltic lava flows of differing ages. Fieldwork in July-August 2026 in the Reykjanes Peninsula has provided some vital ground-truth (surface roughness and very high-resolution topography) for our analysis. Our investigations are aimed at better understanding the scales and scattering characteristics of young basaltic volcanic landscapes, and SAR data-processing algorithms applied to volcanic terrain data.Analysis of the 2023 F-SAR data at Askja enabled characterisation of seven lava flow units from 1961 (Vikrahraun) to >6100 yr BP using the full-polarimetric X-, S-, and L-band SAR with paired pin-profilometer and drone Digital Elevation Model (DEM) data (Figure 1). Across the Askja flow sequence, mean backscatter decreases systematically with flow age, and decomposition techniques partitions the flows sequence by facies. We now extend this framework to the Reykjanes Peninsula. Figure 1. Representative surface units. Left: field photos; centre: drone orthomosaics; right: drone-derived DEMs. (A) Inflated pāhoehoe with pressure ridges; (B) a'ā-pāhoehoe contact; (C) tephra plain with aeolian bedforms. Horizontal scale bars and DEM elevation ranges (m a.s.l.) shown per row.Complementary analysis of field data across the fresher flows at the Reykjanes Peninsula enables bare-versus-mantled backscatter comparison of several flow units because of the occurrence of moss/lichen colonisation in a slightly warmer climate (Figure 2). Five age groups have been sampled: the Sundhnúkur-Svartsengi system, with nine flows emplaced between December 2023 to August 2025 along a c. 10 km fissure north of Grindavík (fieldwork ages 1-2.5 yr [6]); the Fagradalsfjall system comprising the 2021 Geldingadalir flow (5 yr [7]); the Skollahraun and younger Afstapahraun lavas (c. 2000 yr [8]); the Litla Eldborg flow field (>4000 yr [8]); and the Þráinsskjöldur shield (c. 12,500-14,100 yr [8]). The older three groups carry progressively thicker moss and lichen coverage. This combination delivers a bare versus mantled comparison at matched ages within a single field area at near-uniform basaltic composition, and supports lava-flow facies analysis within each flow field. Figure 2 Field morphology and flow-age contrasts. (A) Direct contact between younger a'ā and older moss-covered pāhoehoe; foreground field of view c.3 m. (B) Examples of morphological end-members: rubbly a'ā, inflated pāhoehoe, and sheet pāhoehoe; foreground field of view c.2 m.Our 2026 field investigations yielded a series of important datasets: facies maps of the Sundhnúkur sequence at 1:5000 scale (classifying pāhoehoe, rubbly pāhoehoe, slabby/platy, spiny, and ʻaʻā facies on selected flows); pin-profilometer surface roughness measurements across the Reykjanes flows (distributed by facies, age, and surface-cover) (Figure 3), and drone-derived DEMs spanning the different facies and ages for scaling roughness statistics up to the scale of the spatial resolution of the F-SAR data (2 m). Together, these measurements deliver fine-scale topography (slopes at cm, dm, and m scales) linking millimetre-scale surface roughness to SAR backscatter at S- and X-band, and enable the quantification of fine-scale morphology (channels, breakouts, tumuli, rubble margins) for direct correlation with radar backscatter. Combined with the existing Askja results, these datasets deliver age sequences of basaltic lava flows with bare and mantled patches, co-located ground-truth from mm to m scale, all tied to multi-frequency polarimetric F-SAR data.Figure 3 Ground-truth surface roughness characterisation. (A) Ropy pāhoehoe close-up with lens cap (c.7 cm diameter) for scale. (B) Penn Industries M5-S pin profilometer (1 mm pin spacing) capturing a transverse profile across pāhoehoe ropes.The combined F-SAR and field dataset has supported four types of analysis: (i) comparison of Mascolo dual-pol [9] and Freeman-Durden quad-pol [10] decompositions across the facies and age, across Askja and Reykjanes flow sequences; (2) Comparison of bare and mantled flows to test penetration by long wavelengths through dry mantling materials into lava-roughness scattering; (3) integrated analysis of multi-temporal airborne F-SAR scenes and with; and (iv) progressive degradation of F-SAR products from 2 m to 10 m, 30 m to simulate VenSAR imagery and evaluate the effect of spatial resolution on scattering characteristics.Venus is a barren basaltic landscape where surface modification is driven by chemical weathering and cementation from basalt–atmosphere interactions at 460°C and 92 bar [11], rather than by the rapid aeolian and biological processes that dominate Earth's basaltic surfaces. These reactions progressively modify surface texture and dielectric properties relevant to radar scattering. This terrestrial analogue work cannot directly emulate Venusian weathering, but it can rigorously partition modification mechanisms on Earth and demonstrate systematic backscatter patterns across them.The analysis yields a multi-frequency polarimetric scattering library resolved by flow age and facies at the VenSAR pixel scale, with bare-versus-mantled comparisons of flows of various ages, and a quad-pol versus dual-pol decomposition comparison tuned to VenSAR’s HH+HV configuration. In addition, a quantitative framework is built which links mm- and cm- scale topography to m- and dm- scale backscatter, slopes, and polarimetric signatures over basaltic volcanic landscapes, supporting sub-pixel detection of surface properties.References. [1] EnVision Red Book, ESA-SCI-DIR-RP-003. [2] Keller et al., EUSAR 2024....