Radar Backscatter Research Articles (Page 1)

Spaceborne Synthetic Aperture Radar (SAR) can be applied for monitoring tropical cyclones (TCs), but co-polarized C-band SAR suffers from signal saturation such that it is improper for high wind-speed conditions. In contrast, cross-polarized SAR data does not suffer from this issue, but the retrieval algorithm needs more deliberation. Previously, many geophysical model functions (GMFs) have been developed using cross-polarized data, which obtain wind speeds using the complex relationships described by radar backscatter, incidence angle, wind direction, and radar look direction. In this regard, the rapid development of artificial intelligence technology has provided versatile machine learning methods for such a nonlinear inversion problem. In this study, we comprehensively compare the wind-speed retrieval performance of several models including Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN), which were developed based on spatio-temporal matching and correlation analysis of stepped frequency microwave radiometer (SFMR) and dual-polarized Sentinel-1 SAR data after noise removal. A data set with ~2800 samples is generated during TCs for training and validating the inversion model. The generalization ability of different models is tested by the reserved independent data. When using similar parameters with GMFs, RF inversion has the highest accuracy with a Root Mean Square Error (RMSE) of 3.40 m/s and correlation coefficient of 0.94. Furthermore, considering that the sea surface temperature is a crucial factor for generating TCs and influencing ocean backscattering, its effects on the proposed RF model are also explored, the results of which show improved wind-speed retrieval performances.

Abstract At midlatitudes, air‐sea interactions have been documented in numerical models, in situ campaigns and satellite observations down to the ocean mesoscales. However little is known about scales of a few kilometers (the submesoscales). The new satellite mission Surface Water and Ocean Topography (SWOT) provides a global coverage of these scales by measuring sea surface height. Through the radar backscatter coefficient, it also provides surface wind speed at the same resolution. Here, we examine situations in the Gulf Stream and Kuroshio Extension regions where SWOT as well as scatterometer winds and sea surface temperature (SST) at kilometer scale were available. A good correspondence between winds from SWOT and scatterometer is found at the mesoscales. More importantly, the signature of SST anomalies is found in SWOT winds down to 10 km scales, confirming the effect of the ocean on the atmosphere at those scales. SWOT therefore opens new opportunities for the study of submesoscale air‐sea interactions from space.

ABSTRACT Sugarcane is a widely cultivated crop in tropical regions and serves as the source of 90% of China’s sugar production. In recent years, sugarcane planting in Guangxi has fluctuated due to the impacts of other cash crops and climate disasters. Real-time and accurate sugarcane mapping not only has great significance for monitoring sugarcane area and yield, but also ensures the security of sugar production. To date, there has been no research on sugarcane mapping over large areas or long time series simultaneously. In this study, we developed a mapping tool based on radar backscatter phenological information and optical indices to get accurate sugarcane mapping information. First, we analysed the distribution of sugarcane from Sentinel-1 data, extracted the typical phenological features of sugarcane from Sentinel-1 data, and then proposed the sugarcane index for sugarcane and other vegetation surfaces. Second, we combined the new radar index with the traditional optical index to obtain the 10-m resolution sugarcane distribution in Guangxi Zhuang Autonomous Region from 2018 to 2022, with an accuracy rate of 93%. Compared with the county-level statistical area, the gap was within 1%, and the R2 reached 0.99. Based on the highly accurate and stable annual sugarcane distribution map, we obtained the specific distribution locations of the new and reduced sugarcane fields each year for the first time. Our study demonstrated the feasibility of using the sugarcane index and optical vegetation index based on phenological backscattering information for sugarcane extraction, which provides annual high-precision sugarcane extraction results.

Monitoring and mapping surface water dynamics is a key element in studying and modelling their roles in any hydrological system. However, the fast and accurate extraction of these surfaces is a major challenge due to the spatio-temporal variety of surface water bodies. Through its spatial and temporal capabilities and the synoptic view it offers, remote sensing becomes a very powerful tool in this kind of problem. In recent years, Sentinel 2 optical and Sentinel 1 radar data have shown their effectiveness in the study of natural resources and water surfaces in particular. In this context, this study has taken advantage of remote sensing data to detect and extract surface water bodies. The approach adopted in this work is to use indices derived from high-resolution satellite images for the period 2015-2020. For this, this study used a series of Sentinel 2 MSI (Multi-Spectral Imager) multi-spectral images and Sentinel 1 synthetic radar images. These images are acquired over the Merdja Sidi Abed dam located in western Algeria and allowed us to derive spectral indices by combining several bands such as: Normalised Difference Vegetation Index (NDVI), Modified Normalised Difference Water Index (MNDWI), and the radar backscatter coefficient (????0). The automatic extraction of the dam extent shows a significant degradation of the surface, which has fallen from 815 ha in 2015 to 28 ha in 2020. The signal sensitivity of radar sensors such as Sentinel 1 and the better spatial (10 m) and temporal (5 days) resolution of the Sentinel 2 MSI sensor are very practical ways to track water bodies over time.

ABSTRACT Fall Armyworm (FAW) is a widespread invasive pest in maize crops. This study aimed at detecting and mapping FAW infestations in maize fields across Bangladesh, using freely available Sentinel-1 and Sentinel-2 data. Field observations were conducted during the 2019–2020 maize growing season in 579 maize fields across six administrative divisions of Bangladesh. The study covered both infested and non-infested sites across four crop growth phases, namely vegetative phases 9 (V9) and 12 (V12), as well as the silking and maturing phases. Synthetic Aperture Radar backscatter values, spectral reflectance profiles, and eight vegetation indices were extracted from the Sentinel data and analysed using non-parametric statistical tests to identify differences between infested and non-infested fields. Machine learning models, specifically Random Forest - and Support Vector Machine, were then used to classify infestation severity based on five model input data combinations: (i) Sentinel-1, (ii) Sentinel-2, (iii) Sentinel-2 with vegetation indices, (iv) Sentinel-1 and Sentinel-2, and (v) Sentinel-1, Sentinel-2, and vegetation indices. The results indicated that infested maize fields exhibited reduced near-infrared reflectance and distinct backscatter patterns in σVH o, with notable variations at silking and maturity phases. The red edge (740 nm), near-infrared (865 nm) and shortwave infrared (1610–2190 nm) bands were particularly effective in distinguishing infestation levels across all growing phases. Among the studied vegetation indices, the Normalized Difference Vegetation Index , Modified Chlorophyll Absorption in Reflectance Index, Red Edge Simple Ratio, and Modified Simple Ratio - were identified as the most significant indicators for discriminating between non-infested and infested maize classes at all growing phases. RF achieved 94–96% accuracy (96% in V9) versus SVM’s 78–80% using only Sentinel‐1 data. Multi‐source (Sentinel-1, Sentinel-2 and Vegetation Indices) integration improved both models in most cases. These results underscore the potential of integrating multi-source remote sensing data for scalable and accurate pest detection. Freely available Sentinel data is a valuable source of information for early pest detection and management aiding policymakers in identifying high-risk areas, implementing timely interventions, and promoting sustainable pest management strategies to protect maize production and reduce economic losses.

ABSTRACTThe plant communities of Poyang Lake, constituting the foundational element of the wetland ecosystem, are integral to crucial ecological processes including energy flow, biodiversity sustenance, water purification and hydrological regulation. Consequently, they serve an irreplaceable function in preserving the stability and ecosystem services of the region. This study uses Landsat 8, Sentinel‐2 optical images and Sentinel‐1 SAR images as data sources to extract spectral reflectance, index features and texture features from optical images as well as radar backscattering features from SAR images, constructing a multidimensional feature dataset. The Recursive Feature Elimination algorithm is employed to perform feature optimization on the dataset. Three classification schemes with different feature combinations are designed, and based on the random forest classifier, the impacts of multisource data fusion and feature optimization on the accuracy of plant community identification are investigated. The results demonstrate that the feature optimization‐based classification scheme attains the highest accuracy, reaching 93.42% overall accuracy with a Kappa coefficient of 0.93. Meanwhile, the optical‐SAR data fusion scheme shows significantly superior performance compared with the optical‐only scheme, delivering a 13.04% enhancement in overall classification accuracy. This study provides a scientific reference for remote sensing classification of wetland plant communities and supports biodiversity conservation and ecological management in the Poyang Lake wetland.

Abstract. In this work, the T-matrix approach is exploited to produce simulations of spectral polarimetric variables (spectral differential reflectivity, sZDR, spectral differential scattering phase, sδHV, and spectral correlation coefficient, sρHV) for observations of rain acquired from slant-looking W-band cloud radar. The spectral polarimetric variables are simulated with two different methodologies, taking into account instrument noise and the stochastic movement of the raindrops, introduced by raindrop oscillations and by turbulence. The simulated results are then compared with rain Doppler spectra observations from W-band radar for moderate rain rate conditions. Two cases, differing in levels of turbulence, are considered. While the comparison of the simulations with the measurements presents a reasonable agreement for equi-volume diameters less than 2.25 mm, large discrepancies are found in the amplitude (but not the position) of the maxima and minima of sZDR and, more mildly, of sδHV. This pinpoints a general weakness in approximating raindrop as spheroids to simulate radar backscattering properties at the W-band.

Cloud cover can delay landslide detection in optical satellite imagery for weeks, complicating disaster response. Synthetic Aperture Radar (SAR) backscatter imagery, which is widely used for monitoring floods and avalanches, remains underutilised for landslide detection due to a limited understanding of landslide signatures in SAR data. We developed a conceptual model of landslide expression in SAR backscatter (σ°) change images through iterative investigation of over 1000 landslides across 30 diverse study areas. Using multi-temporal composites and dense time series Sentinel-1 C-band SAR data, we identified characteristic patterns linked to land cover, terrain, and landslide material. The results showed either increased or decreased backscatter depending on environmental conditions, with reduced visibility in urban or mixed vegetation areas. Detection was also hindered by geometric distortions and snow cover. The diversity of landslide expression illustrates the need to consider local variability and multi-track (ascending and descending) satellite data in designing representative training datasets for automated detection models. The conceptual model was applied to three recent disaster events using the first post-event Sentinel-1 image, successfully identifying previously unknown landslides before optical imagery became available in two cases. This study provides a theoretical foundation for interpreting landslides in SAR imagery and demonstrates its utility for rapid landslide detection. The findings support further exploration of rapid landslides in SAR backscatter data and future development of automated detection models, offering a valuable tool for disaster response.

ABSTRACT Accurate quantification of forest biomass carbon (C) stocks is essential for regional carbon accounting and climate change mitigation. However, high-resolution mapping remained challenging as contemporary satellite LiDAR-based methods faced an inherent trade-off between spatial resolution and prediction accuracy due to their limited spatial coverage. In this study, we proposed ForestCarbonNet, a scatter-supervised image-to-image U-Net framework that integrated in-situ measurements with terrain corrected GEDI LiDAR observations, Landsat imagery, and ALOS-2 PALSAR-2 radar backscatter data to overcome this resolution-accuracy compromise. We generated high-resolution (30-m) forest aboveground C density maps and achieved robust model performance (R2 = 0.83, %RMSE = 24.02%) across temperate forests in northeast China for 2020. Our approach offered three key advantages: (1) the locally calibrated allometric model effectively bridged in-situ measurements with terrain-corrected GEDI observations (3.69 million samples), enabling robust C density estimation at GEDI footprint scale; (2) GEDI expanded C density sampling across climatic gradients while mitigating saturation effects in high-biomass forests, reducing RMSE by 24.8%; and (3) ForestCarbonNet preserved spatial contextual information and outperformed traditional pixel-based methods, reducing RMSE by 12.3%. We estimated the total forest aboveground biomass carbon stock to be 2.74 ± 0.38 PgC, with projected carbon sink peak at 4.21–5.08 TgC year−1 in the 2060s but decline to only 32% of peak levels by 2100s. The proposed method enables accurate quantification of forest carbon stocks, providing critical data that strengthens evidence-based forest management strategies and informs climate policy decisions.

ABSTRACT This study investigates the use of polarimetric ALOS PALSAR data for estimating biomass semi-arid dry tropical deciduous Sariska Tiger Reserve (STR) forest in the eastern Rajasthan, India. Radar backscatter intensity HH, HV, and HH+HV were correlated with forest structure and measured biomass for field sampled plots within the STR. Measured above ground biomass (AGB) was also estimated for these plots using volumetric equation of the tree species. Field measured AGB was estimated using an allometric equation based on tree heights, Diameter at Breast’s Height (DBH) and the wood-specific gravity of the sampled tree. Predicted biomass was also estimated for the field sampled plots using allometric relationships developed through field measured AGB and polarization. Annual mosaic PALSAR derived HH, HV and HH+HV polarizations backscatter were assessed for the biomass and it is found that HH+HV had the highest regression co-efficient (R2 0.56, p < 0.001) with ground-based biomass measurements followed by HH (R2 0.55, p < 0.001) and HV (R2 0.46, p < 0.0025) in logarithmic model respectively. Moreover, backscatter of the HH polarization had the highest regression co-efficient (R2 0.301) with ground-based basal area measurements followed by HH+HV (R2 0.272), while HV had the lowest regression co-efficient (R2 0.204) in linear models. MIMICS simulation outcome indicated that both co-polarized and cross-polarized backscatter is strongly influenced by AGB and dependent on it, demonstrating greater sensitivity to AGB at stand level within STR. The HH polarization exhibited a lower backscatter dynamic range (~5 dB), whereas HV polarization showed a slightly higher range (~6 dB). Across all polarizations, L-band backscatter increased with biomass up to approximately 100 Mg ha−1 in all polarizations, beyond which sensitivity remains unchanged or saturation is observed. Thus, the outcome of the present study highlighted the significance of ALOS PALSAR backscattering potential and implementation for an improved forest structure and AGB quantification in homogeneous biomass of tropical deciduous forest of STR.

This study aims to reconstruct NDI45 missing values due to cloud cover while outlining the importance of vegetation health for the climate–carbon cycle and the benefits of the NDI45 index for high canopy area indices. The methods include a novel hybrid framework that combines a deterministic Kalman filter (KF) and a clustering-based LSTM network to generate gap-free NDI45 series with 20 m spatial and 5-day temporal resolution. The innovation of the applied method relies on achieving a single-sensor workflow, provides a pixel-level uncertainty map, and minimizes LSTM overfitting through clustering based on a correlation threshold. In the northern Pampas (South America), this hybrid approach reduces the MAE by 22–35% on average and narrows the 95% confidence interval by 25–40% compared to the Kalman filter or LSTM alone. The three-dimensional spatio-temporal analysis demonstrates that the KF–LSTM hybrid provides better spatial homogeneity and reliability across the entire study area. The proposed framework can generate gap-free, high-resolution NDI45 time series with quantified uncertainties, enabling more reliable detection of vegetation stress, yield fluctuations, and long-term resilience trends. These capabilities make the method directly applicable to operational drought monitoring, crop insurance modeling, and climate risk assessment in agricultural systems, particularly in regions prone to frequent cloud cover. The framework can be further extended by including radar backscatter and multi-model ensembles, thus providing a promising basis for the reconstruction of global, high-resolution vegetation time series.

Abstract The subject of this research is the analysis of flood dynamics in the Ukrina River Basin, Bosnia and Herzegovina, using a remote sensing-based approach and geographic information systems during the period 2016–2019. The aim is to identify the spatial and temporal patterns of floods by integrating satellite-derived precipitation data, hydrological data, and Sentinel-1 imagery processed through Google Earth Engine. The methodology included the use of CHIRPS precipitation data and the Standardized Precipitation Index (SPI) for identifying meteorological anomalies, while Sentinel-1 SAR data were used to map flood extent based on radar backscatter change detection. The approach combined temporal analysis with spatial overlays of land use and administrative boundaries to assess affected areas. Flood events were identified in January 2016 (89.98 ha), March 2017 (179.85 ha), March 2018 (58.81 ha), and May 2019 (195.38 ha), coinciding with periods of above-average precipitation (&gt;125%), positive SPI values, and elevated water levels. The spatial analysis of flooded areas, overlaid with land use data, revealed that agricultural land was the most affected category, with 79.21 ha flooded in 2016, 169.15 ha in 2017, 48.89 ha in 2018, and 184.90 ha in 2019. Built-up areas were also significantly impacted, posing risks to infrastructure and economic stability. The cities and municipalities of Derventa, Prnjavor, and Stanari were most frequently affected by floods during the study period. The findings highlight the role of cumulative precipitation and hydrological conditions in triggering flood events and provide insights for flood risk management, including adaptive strategies, early warning, and sustainable land use planning.

Climate change is generally having a negative impact on forest health by inducing drought stress and favouring the spread of pest species, such as bark beetles. The terrestrial monitoring of bark beetle infestation is very time-consuming, especially in the early stages, and therefore not feasible for extensive areas, emphasising the need for a comprehensive approach based on remote sensing. Although numerous studies have researched the use of optical data for this task, radar data remains comparatively underexplored. Therefore, this study uses the weekly and cloud-free acquisitions of Sentinel-1 in the Bavarian Forest National Park. Time series analysis within a Multi-SAR framework using Random Forest enables the monitoring of moisture content loss and, consequently, the assessment of tree vitality, which is crucial for the detection of stress conditions conducive to bark beetle outbreaks. High accuracies are achieved in predicting future bark beetle infestation (R2 of 0.83–0.89). These results demonstrate that forest vitality trends ranging from healthy to bark beetle-affected states can be mapped, supporting early intervention strategies. The standard deviation of 0.44 to 0.76 years indicates that the model deviates on average by half a year, mainly due to the uncertainty in the reference data. This temporal uncertainty is acceptable, as half a year provides a sufficient window to identify stressed forest areas and implement targeted management actions before bark beetle damage occurs. The successful application of this technique to extensive test sites in the state of North Rhine-Westphalia proves its transferability. For the first time, the results clearly demonstrate the expected relationship between radar backscatter expressed in the Kennaugh elements K0 and K1 and bark beetle infestation, thereby providing an opportunity for the continuous and cost-effective monitoring of forest health from space.

Abstract This study presents an enhanced method for reconstructing meteoroid trajectories and speeds using the Belgian RAdio Meteor Stations forward scatter radio network. A novel extension of the pre‐ phase technique, originally developed for backscatter radars, has been adapted for forward scatter continuous wave systems. This method leverages phase data recorded before the meteoroid reaches the specular reflection point to improve speed estimations. Additionally, we combine this newly determined pre‐ speed information with time of flight measurements into the trajectory solver to reduce uncertainties in meteoroid path and speed reconstructions. A Markov Chain Monte Carlo method is employed to propagate measurement uncertainties to the trajectory parameters. The reconstructed trajectories and speeds are validated against optical data from the CAMS‐BeNeLux network. The results show significant improvements in the accuracy and robustness of speed and inclination determination.

This study aims to classify crops on fragmented agricultural land by integrating radar (Sentinel-1) and optical (Sentinel-2) satellite remote sensing data. The research responds to the pressing issue of decreasing agricultural land in Jember Regency due to land conversion, which threatens food security. Feature-level fusion is applied to combine spectral indices (NDVI, NDWI, NDBI) from Sentinel-2 and radar backscatter characteristics (VV, VH) from Sentinel-1. Classification was performed using the Random Forest algorithm in the Google Earth Engine (GEE) platform. The results showed that the combination of both datasets provided high overall accuracy (81.58%) in classifying eight land cover types including agricultural crops such as paddy, corn, sugarcane, and citrus. This integration enables better monitoring of complex agricultural landscapes, offering a practical tool for sustainable land management.

ABSTRACT Wetlands are vital ecosystems that support regional ecological balance and require efficient, accurate, and cost-effective monitoring approaches. This study enhances wetland mapping in the Sanjiang Plain using multi-source (Sentinel-1 SAR and Sentinel-2 optical) and multi-temporal (2018–2021) satellite data on the Google Earth Engine platform. A Random Forest classifier was applied with training samples from high-resolution imagery and field surveys. Input features included spectral bands, vegetation indices, and radar backscatter coefficients. Preprocessing involved cloud masking for Sentinel-2 and speckle filtering for Sentinel-1. Classification accuracy was evaluated using independent validation samples, overall accuracy, and Kappa coefficient. Results indicate October images provide the highest single-year mapping accuracy, with Sentinel-1 and Sentinel-2 data from October 2021 achieving 85.4% accuracy and a Kappa of 0.669. Multi-temporal data improved accuracy to 88.9% (Kappa = 0.749). Sentinel-1 showed greater annual variation in wetland distribution compared to the stable Sentinel-2. Precipitation impacted accuracy, reducing Sentinel-2 performance while variably affecting Sentinel-1. Combining radar and optical data with multi-temporal analysis enhances wetland monitoring, offering guidance for data acquisition in large-scale conservation. Challenges include misclassifications like water-shadow confusion in optical imagery and backscatter interference from vegetation in radar data, requiring further research.

Snow plays a crucial role in global climate regulation, hydrological processes, and environmental change, making the accurate acquisition of snow depth data highly significant. In this study, we used Sentinel-1 radar data and employed a simulated annealing algorithm to select the optimal influencing factors from radar backscatter characteristics and spatiotemporal geographical parameters within the study area. Snow depth retrieval was subsequently performed using both random forest (RF) and Support Vector Machine (SVM) models. The retrieval results were validated against in situ measurements and compared with the long-term daily snow depth dataset of China for the period 2017–2019. The results indicate that the RF model achieves better agreement with the measured data than existing snow depth products. Specifically, in the Xinjiang region, the RF model demonstrates superior performance, with an R2 of 0.92, a root mean square error (RMSE) of 2.61 cm, and a mean absolute error (MAE) of 1.42 cm. In contrast, the SVM regression model shows weaker agreement with the observations, with an R2 lower than that of the existing snow depth product (0.51) in Xinjiang, and it performs poorly in other regions as well. Overall, the SVM model exhibits deficiencies in both predictive accuracy and spatial stability. This study provides a valuable reference for snow depth retrieval research based on active microwave remote sensing techniques.

The presence and storage of meltwater on Antarctic ice shelves has implications for ice-shelf stability and collapse, while meltwater on grounded tributary glaciers, if able to access the bed, could affect their dynamics. Given the significance of Antarctica’s ice shelves for grounded ice contributions to global mean sea levels, there have been many efforts to map their meltwater extents, whereas far fewer efforts have been made to map water on Antarctica’s grounded ice. Most previous mapping has used optical imagery, which is limited to mapping surface water on cloud-free days during the austral summer. Conversely, research into the prevalence of wintertime and shallow subsurface meltwater storage is scarce. Here, we analyse synthetic aperture radar (SAR) backscatter time series between 2015 and 2021 for a selected number of large, late- and early-summer meltwater bodies on George VI Ice Shelf (GVIIS) and surrounding glaciers through intervening winters. Variable rates of surface or shallow subsurface freeze-through and melt onset are identified, alongside two locations where meltwater appears to have been stored throughout the 2019 winter. One of these sites, a large shallow subsurface meltwater body on grounded ice, appears to have retained liquid water throughout all 6 years, including during winter. This site would be valuable for further exploring how surface and shallow subsurface meltwater bodies may influence Antarctic glacier dynamics through drainage to the bed.

This research work aims to analyze hydrocarbon spills along the Ivorian coasts using geospatial techniques. The objective is to geolocate oil spills that occurred from 2018 to 2023 on the Ivorian coast using radar images. These images, available and downloadable from the Copernicus site, processed in ESA SNAP software, have made it possible to detect oil spills, thanks to their ability to distinguish contaminated surfaces from unpolluted water. The techniques used made it possible to carry out radiometric corrections and to remove the speckle noise, followed by the extraction of the spill zones from the differences in water reflectivity. The data processed have been validated thanks to the data from the Project Earth Observation for Sustainable Development (EO4SD)-Marine Resources. The results thus show significant concentrations of spills along the Ivorian coasts, especially in areas close to the oil infrastructures at-49dB in 2018, -48.9 dB in 2019,-57 dB in 2020, -48 dB in 2021,-46 dB in 2022,-48 dB in 2023. These values are low and reflect oil spills. They reduce the Radar backscatter coefficient because of their smoothing effect on the surface of the water. In addition, the spread of flowing hydrocarbons spilled to an east-west preferential direction that is to say from Assinie to Jacqueville. These results highlight the need to improve preventive measures and strengthen environmental policies to reduce the impacts of spills on marine ecosystems. This work opens the way to future research prospects, in particular the automation of the detection of oil spills using artificial intelligence models and the extension of the study to other regions of West Africa. This study is therefore a contribution to environmental monitoring and management of hydrocarbon pollution risks in Côte d&amp;apos;Ivoire.

Abstract Antarctic sea ice has seen recent rapid declines in extent, but it remains unclear whether this is accompanied by thinning. Due to the relative abundance and complexity of overlying snow on sea ice, radar altimetry methods routinely deployed for sea ice thickness estimation in the Arctic are difficult to apply in Antarctica. We present nadir‐looking radar waveforms from the first deployment of the KuKa surface‐based radar on Antarctic sea ice, specifically multi‐year sea ice in the Weddell Sea marginal ice zone with a thick snow cover. Coincident snow pits revealed thick layers of snow which were exposed to the summer melt season and superimposed ice. Our instrument detects only very small amount of co‐polarized radar backscatter from the sea ice surface, suggesting that conventional satellite altimeters may not always range to this interface. However, polarimetric snow depth determination performs well, with of 0.76 between measured and KuKa‐estimated snow depths.