Thickness Retrieval Research Articles

Global evaluation of sentinel 2 level 2A Sen2Cor aerosol optical thickness retrievals

ABSTRACT The present study aims to conduct the first known comprehensive global evaluation of Aerosol Optical Thickness (AOT) retrievals derived from the Sentinel 2 Sen2Cor algorithm between 2018 and 2022, using data from over 400 AERONET stations. The results indicate that Sentinel 2 tends to underestimate AOT, especially at higher aerosol loadings. Although there appears to be a good overall correlation (r = 0.57) with low RMSE (0.16) and relative mean bias (RMB = -2.46%) between AERONET and Sentinel 2 AOT datasets, regional analysis reveals significant variation in performance across regions, with areas like Europe and Americas exhibiting stronger correlations and lower RMSE than others like Indian subcontinent and Southern Africa. Temporal analysis also suggests an improvement in Sentinel 2 performance, particularly in capturing extreme AOT values in 2022. While elevation does not appear to have any noticeable impact on AOT estimation, slight variations could be observed over certain land cover types like sparse vegetation and permanent water bodies. Though improvements are certainly required over certain geographical regions like Indian subcontinent, Sentinel 2 AOT can, nevertheless, be reliably used for aerosol studies in urban areas or at a finer spatial resolution, especially in Europe, Mainland U.S.A. and East Asia.

On sea ice emission modeling for MOSAiC's L-band radiometric measurements

Abstract The retrieval of sea ice thickness using L-band passive remote sensing requires robust models for emission from sea ice. In this work, measurements obtained from surface-based radiometers during the MOSAiC expedition are assessed with the Burke, Wilheit and SMRT radiative transfer models. These models encompass distinct methodologies: radiative transfer with/without wave coherence effects, and with/without scattering. Before running these emission models, the sea ice growth is simulated using the Cumulative Freezing Degree Days (CFDD) model to further compute the evolution of the ice structure during each period. Ice coring profiles done near the instruments are used to obtain the initial state of the computation, along with Digital Thermistor Chain (DTC) data to derive the sea ice temperature during the analyzed periods. The results suggest that the coherent approach used in the Wilheit model results in a better agreement with the horizontal polarization of the in situ measured brightness temperature. The Burke and SMRT incoherent models offer a more robust fit for the vertical component. These models are almost equivalent since the scattering considered in SMRT can be safely neglected at this low frequency, but the Burke model misses an important contribution from the snow layer above sea ice. The results also suggest that a more realistic permittivity falls between the spheres and random needles formulations, with potential for refinement, particularly for L-band applications, through future field measurements.

Quantifying the influence of snow over sea ice morphology on L-band passive microwave satellite observations in the Southern Ocean

Abstract. Antarctic snow on sea ice can contain slush, snow ice, and stratified layers, complicating satellite retrieval processes for snow depth, ice thickness, and sea ice concentration. The presence of moist and brine-wetted snow alters microwave snow emissions and modifies the energy and mass balance of sea ice. This study assesses the impact of brine-wetted snow and slush layers on L-band surface brightness temperatures (TBs) by synergizing a snow stratigraphy model (SNOWPACK) driven by atmospheric reanalysis data and the RAdiative transfer model Developed for Ice and Snow in the L-band (RADIS-L) v1.0 The updated RADIS-L v1.1 further introduces parameterizations for brine-wetted snow and slush layers over Antarctic sea ice. Our findings highlight the importance of including both brine-wetted snow and slush layers in order to accurately simulate L-band brightness temperatures, laying the groundwork for improved satellite retrievals of snow depth and ice thickness using satellite sensors such as Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP). However, biases in modelled and observed L-band brightness temperatures persist, which we attribute to small-scale sea ice heterogeneity and snow stratigraphy. Given the scarcity of comprehensive in situ snow and ice data in the Southern Ocean, ramping up observational initiatives is imperative to not only provide satellite validation datasets but also improve process-level understanding that can scale up to improving the precision of satellite snow and ice thickness retrievals.

Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

Correction of cloud optical thickness retrievals from nadir reflectances in the presence of 3D radiative effects. Part I: concept and tests on 3D RT simulations

3D effects cause substantial underestimation of cloud optical thickness (COT) in airborne and satellite retrievals based on 1D radiative transfer computations (such as in the case of widely used bispectral technique). For a single-layer isolated cloud we propose a simple linear correction of the retrieved COT with the renormalization factor dependent on the cloud’s aspect ratio (the ratio between vertical and horizontal dimensions of the cloud). This is an empirical assumption which we successfully test using synthetic 3D RT data. We introduce a heuristic “block model” of 3D radiative effects and show that the functional form of the renormalization factor is consistent with the process of radiation escape from cloud sides in an essentially 3D geometry. We also extend the block model to the case of single-layer broken cloud field with radiative interaction between the neighboring clouds. In this case the renormalization factor depends also on the distance between clouds.

On the retrieval of cloud optical thickness from spectral radiances - A sensitivity study with high albedo surfaces

Measurements of spectral zenith radiance in the 320–950 nm wavelength range have been carried out since 2021 at the Thule High Arctic Atmospheric Observatory (THAAO, https://www.thuleatmos-it.it/, 76.5° N, 68.8° W, 225 m a.s.l) located in Pituffik, northern Greenland. This study evaluates whether such observations could provide, in principle, scientifically meaningful cloud optical depth (τ) estimates, what would be the limitations, which are the necessary auxiliary measurements, and which radiance wavelengths would be better suited for the goal. Although clouds play a critical role in the Arctic, a climatology of τ in high albedo conditions is particularly difficult to obtain. THAAO might have the instrument capabilities to provide such a long-term dataset. We use a radiative transfer package to simulate visible spectra with different cloud and surface conditions, assuming homogeneous overcast sky and liquid water clouds of fixed geometrical thickness. Simulations are run to reproduce typical conditions encountered at THAAO when measurements are carried out. We find that the assumption of a broadband albedo instead of a spectrally-resolved one is the source of the largest uncertainties. Tests on size and phase of cloud particles showed that a 50% uncertainty in reff leads to a ∼10% error in τ, and that a 10% contamination of ice crystals in a low-level liquid water cloud leads to an error in τ estimates of less than 5%. All the tests showed that the most critical τ range is between the thin and thick cloud regimes (τ ∼ 7–15), where the retrievals can be less reliable. Otherwise, tests suggest that in the environmental conditions that characterize late spring and summer at THAAO, and given the observatory measurements capabilities, estimates of τ for low-level clouds could be accurately retrieved both in high and low surface albedo conditions by means of zenith radiance measurements in the UV-Vis-NIR range.

Retrieval of sea ice thickness from FY-3E data using Random Forest method

In this study, we employ a Random Forest approach to estimate sea ice thickness (SIT) using Fengyun-3E (FY-3E) and Soil Moisture Ocean Salinity (SMOS) data. This method relies on four input parameters: incidence angle (θ), reflectivity (Γ), sea ice salinity (S), and sea ice temperature (T). In addition, FY-3E can receive both Global Positioning System (GPS) and Beidou Navigation Satellite System (BDS) reflected signals. Evaluation for the Arctic region based on data spanning from October 2022 to April 2023 reveals that the proposed models trained on GPS and BDS signals from FY-3E achieve high consistency and low error. Take GPS signals as an example, coefficients of determination are 0.97 and 0.91 and mean absolute errors are 0.019 m and 0.032 m for the training and test sets, respectively. In general, SIT inversion based on GPS signals slightly exhibits a higher accuracy than that based on BDS signals, but both approaches display high performances. The areas with the highest accuracy of SIT estimation based on GPS and BDS signals are the Shelikhov Bay and the Okhotsk Sea, followed by the Bering Sea and the Bering Strait. We conclude that machine learning and data fusion are effective for SIT estimation.

Retrieval of Antarctic sea ice freeboard and thickness from HY-2B satellite altimeter data

Retrieval of Antarctic sea ice freeboard and thickness from HY-2B satellite altimeter data

Lake Ice Thickness Retrieval Method with ICESat-2-Assisted CyroSat-2 Echo Peak Selection

Lake ice thickness (LIT) is one of the key climate variables in the lake ice domain, but there are currently large uncertainties in the retrieval of LIT. We present and validate a new LIT retrieval method that utilizes ICESat-2 data to assist CryoSat-2 echo peak selection, aiming to improve the accuracy of LIT retrieval and enable data acquisition without on-site measurements. The method involves screening out similar ICESat-2 and CryoSat-2 tracks based on time and space constraints. It also involves dynamically adjusting the range constraint window of CryoSat-2 waveforms based on the high-precision lake ice surface ellipsoid height obtained from ICESat-2/ATL06 data. Within this range constraint window, the peak selection strategy is used to determine the scattering interfaces between snow-ice and ice-water. By utilizing the distance between the scattering horizons, the thickness of the lake ice can be determined. We performed the ice thickness retrieval experiment for Baker Lake in winter and verified it against the on-site measurement data. The results showed that the accuracy was about 0.143 m. At the same time, we performed the ice thickness retrieval experiment for Great Bear Lake (GBL), which does not have on-site measurement data, and compared it with the climate change trend of GBL. The results showed that the retrieval results were consistent with the climate change trend of GBL, confirming the validity of the proposed method.

Stand-Alone Retrieval of Sea Ice Thickness From FY-3E GNOS-R Data

Stand-Alone Retrieval of Sea Ice Thickness From FY-3E GNOS-R Data

Aerosol Shortwave Radiative Heating and Cooling by the 2017 and 2023 Chilean Wildfire Smoke Plumes

AbstractThe aerosol shortwave, direct radiative effects of smoke plumes from Chilean wildfires in 2017 and 2023 were derived from satellite observations in both cloud‐free and cloud scenes. At the top of the atmosphere, the aerosol DRE changes sign when aerosol overly clouds or open ocean, confirmed by both measurements and a simulation study. The cloud‐free daily‐mean DRE, computed using an offline radiative transfer model (RTM), was 66 W m−2 in 2023 and 42 W m−2 in 2017, due to absorption by smoke. However, the total radiative effects were larger in 2017 due to a larger plume size compared to 2023. The method presented here provides a new conceptual model to quickly assess the radiative effects of wildfire smoke plumes using satellite measurements and pre‐computed RTM results. The presented estimates are strongly affected by the uncertainty of aerosol optical thickness retrievals from satellite, which can be large in the presence of clouds.

Combined Retrieval of Oil Film Thickness Using Hyperspectral and Thermal Infrared Remote Sensing

An outdoor experiment was conducted to measure the thickness of oil films (0~3000 μm) using an airborne hyperspectral imager and thermal infrared imager, and the spectral response and thermal response of oil films of different thicknesses were analyzed. The classic support vector regression (SVR) model was used to retrieve the oil film thickness. On this basis, the suitable range for retrieving oil film thickness using hyperspectral and thermal infrared remote sensing was explored, and the decision-level fusion algorithm was developed to fuse the retrieval capabilities of hyperspectral and thermal infrared remote sensing for oil film thickness. The following conclusions can be drawn: (1) Based on airborne hyperspectral data and thermal infrared data, the retrieval accuracy of oil films of different thicknesses reached 154.31 μm and 116.59 μm, respectively. (2) The S185 hyperspectral data were beneficial for retrieving thicknesses greater than or equal to 400 μm, and the H20T thermal infrared data were beneficial for retrieving thicknesses greater than 500 μm. (3) The result of the decision-level fusion model based on a fuzzy membership degree was superior to those obtained using a single sensor (hyperspectral or thermal infrared), indicating that it can better integrate the retrieval results of hyperspectral and thermal infrared remote sensing for oil film thickness. Furthermore, the feasibility of using hyperspectral and thermal infrared remote sensing to detect water-in-oil emulsions of different thicknesses was investigated through spectral response and thermal response analysis.

Machine learning based classification of lake ice and open water from Sentinel-3 SAR altimetry waveforms

The aim of the study was to evaluate, for the first time, the capability of different machine learning (ML) algorithms in classifying along-track lake surface conditions (open water and ice types) across ice seasons (freeze-up, ice growth and break-up periods) from Sentinel-3 A/B synthetic aperture radar altimeter (SRAL) data. To achieve this goal, over 107,500 radar waveforms extracted from 11 large lakes across the Northern Hemisphere and three ice seasons (2018–2021) were manually labelled using complementary satellite data (Sentinel-1 imaging Synthetic Aperture Radar (SAR), Sentinel-2 Multispectral Instrument (MSI) Level 1C, and MODIS Aqua/Terra data) for the training and testing of the ML algorithms in discriminating between open water, young (thin) ice, growing ice and melting ice. The four ML algorithms tested include Random Forest (RF), Gradient Boosting Trees (GBT), K Nearest Neighbor (KNN) and Support Vector Machine (SVM). To characterize the waveforms, seven waveform parameters were derived: Leading Edge Width (LEW), Offset Center of Gravity (OCOG) Width, Pulse Peakiness (PP), backscatter coefficient (Sigma0), late tail to peak power (LTPP), early tail to peak power (ETPP) and the maximum value of the echo power (Max). Accuracies >95% were achieved across all classifiers using a 4-parameter combination (Sigma0, PP, OCOG Width, and LEW). Among all waveform parameters, Sigma0, OCOG width and PP were found to be the most important parameters for discriminating between lake ice types and open water. Despite showing comparable classification performances in the overall classification, RF and KNN are found to be a better fit for global lake ice mapping as both are less sensitive to their internal hyperparameters. Additionally, consistent results (>93.7% accuracy in all classifiers) achieved on the accuracy assessment carried out for each lake (out-of-sample testing) revealed the strength of the classifiers for spatial transferability. Implementation of RF and KNN could be valuable in a pre-or post-processing step for identifying lake surface conditions under which the retrieval of water level and ice thickness may be limited or not possible and, therefore, inform algorithms currently used for the generation of operational or research products. While the research focused on 11 of the largest lakes of the Northern Hemisphere, the classification approach presented herein has potential for application on smaller lakes too since data in SAR mode (∼300 m along-track resolution) are used.

An improved active layer thickness retrieval method over Qinghai-Tibet permafrost using InSAR technology: With emphasis on two-dimensional deformation and unfrozen water

An improved active layer thickness retrieval method over Qinghai-Tibet permafrost using InSAR technology: With emphasis on two-dimensional deformation and unfrozen water

Bivariate sea-ice assimilation for global-ocean analysis–reanalysis

Abstract. In the last decade, various satellite missions have been monitoring the status of the cryosphere and its evolution. Besides sea-ice concentration data, available since the 1980s, sea-ice thickness retrievals are now ready to be used in global operational prediction and global reanalysis systems. Nevertheless, while univariate algorithms are commonly used to constrain sea-ice area or volume, multivariate approaches have not yet been employed due to the highly non-Gaussian distribution of sea-ice variables together with the low accuracy of thickness observations. This study extends a 3DVar system, called OceanVar, which is routinely employed in the production of global/regional operational/reanalysis products, to process sea-ice variables. The tangent/adjoint versions of an anamorphosis operator are used to locally transform the sea-ice anomalies into Gaussian control variables and back, minimizing in the latter space. The benefit achieved by such a transformation is described. Several sensitivity experiments are carried out using a suite of diverse datasets. The sole assimilation of the CryoSat-2 provides a good spatial representation of thickness distribution but still overestimates the total volume that requires the inclusion of Soil Moisture and Ocean Salinity (SMOS) mission data to converge towards the observation estimates. The intermittent availability of thickness data can lead to potential jumps in the evolution of the volume and requires a dedicated tuning. The use of the merged L4 product CS2SMOS shows the best skill score when validated against independent measurements during the melting season when satellite data are not available. This new sea-ice module is meant to simplify the future coupling with ocean variables.

Using InSAR for Surface Deformation Monitoring and Active Layer Thickness Retrieval in the Heihe River Basin on the Northeast Qinghai‐Tibet Plateau

AbstractIn cold regions characterized by perennially frozen soil, climate warming has caused permafrost degradation, which is manifested by surface deformation and deepening of the active layer. Interferometric synthetic aperture radar (InSAR) is a common method to obtain large‐scale surface deformation, especially in mountainous terrain where it is difficult to install a large number of monitoring sites. Here, we used Sentinel‐1A SAR data acquired from March 2017 to March 2021 to monitor surface deformation and estimate active layer thickness (ALT) using two methods—temperature‐based and soil moisture‐based—in the permafrost regions of the Heihe River Basin on the northeast Qinghai‐Tibetan Plateau. We find that the seasonal deformation amplitude ranges 10–60 mm, and the annual mean vertical deformation trend ranges from −40 to 30 mm/a. We verify the ALT estimates with observations and find that the method based on soil moisture has higher accuracy and is therefore more applicable in this particular study area. The ALT gradually decreases from the southeast to the northwest, ranging from 0.3 to 6.5 m, with an average value of 2.47 m. These results contribute to better quantifying the response of permafrost change and to understanding the spatial distribution of ALT on a large scale, serving as guidance for engineering and other applications.

Feasibility of retrieving Arctic sea ice thickness from the Chinese HY-2B Ku-band radar altimeter

Abstract. With the continuous development of the China ocean dynamic environment satellite series (Haiyang-2, HY-2), it is urgent to explore the potential application of HY-2B in Arctic sea ice thickness retrievals. In this study, we first derive the Arctic radar freeboard and sea ice thickness during two cycles (from October 2019 to April 2020 and from October 2020 to April 2021) using the HY-2B radar altimeter and compare the results with the Alfred Wegener Institute (AWI) CryoSat-2 (CS-2) products. We evaluate our HY-2B sea ice freeboard and thickness products using Operation IceBridge (OIB) airborne data and Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) products. Finally, we estimate the uncertainties in the HY-2B sea ice freeboard and sea ice thickness. Here, we derive the radar freeboard by calculating the difference between the relative elevation of the floe obtained by subtracting the mean sea surface (MSS) height and sea surface height anomaly (SSHA) determined by an average of the 15 lowest points method. The radar freeboard deviation between HY-2B and CS-2 is within 0.02 m, whereas the sea ice thickness deviation between HY-2B and CS-2 is within 0.2 m. The HY-2B radar freeboards are generally thicker than AWI CS-2, except in spring (March and April). A spring segment likely has more floe points than an early winter segment. We also find that the deviations in radar freeboard and sea ice thickness between HY-2B and CS-2 over multiyear ice (MYI) are larger than those over first-year ice (FYI). The correlation between HY-2B (CS-2) sea ice freeboard retrievals and OIB values is 0.77 (0.84), with a root mean square error (RMSE) of 0.13 (0.10) m and a mean absolute error (MAE) of 0.12 (0.081) m. The correlation between HY-2B (CS-2) sea ice thickness retrievals and OIB values is 0.65 (0.80), with an RMSE of 1.86 (1.00) m and an MAE of 1.72 (0.75) m. The HY-2B sea ice freeboard uncertainty values range from 0.021 to 0.027 m, while the uncertainties in the HY-2B sea ice thickness range from 0.61 to 0.74 m. The future work will include reprocessing the HY-2B L1 data with a dedicated sea ice retracker, and using the radar waveforms to directly identify leads to release products that are more reasonable and suitable for polar sea ice thickness retrieval.

Estimation of Lake Ice Thickness with Satellite Radar Altimeter Waveforms

Background and Relevance: The phenology of seasonally ice covered lakes has been altered with anthropogenic climate change; recent studies point out an overall reduction in the number of days in which the lake is covered with ice [1]. Lake ice is essential for many northern communities in fishing and travel. Reductions in the thickness in ice is a major safety risk, especially when those who travel on ice have historically been able to trust it. Remote sensing efforts have majorly focused on sea ice and glacial changes, and little is known about changes in lake ice thickness besides the seasonal length of ice coverage. Previous researchers have determined that the Ku-band (13 to 17 GHz) is capable of penetrating through freshwater ice and is scattered from both the snow/ice and the ice/water interfaces [1]. A radar altimeter operating at the Ku-band holds the potential to measure the height of the sensor above the snow/ice interface and above the ice/water interface. The difference between these two measurements yields the ice thickness. This method has been confirmed with data from the Cryosat-2 satellite [1], but only tested with arctic lakes. There is a gap in knowledge of how midlatitude lakes have responded to changing climates in terms of their seasonal ice thickness. Objectives and Methods: Ku-band radar altimeter measurements from the JASON series of satellites can be used to determine lake ice thickness by calculating the distance between dualpeak waveforms, representative of snow/ice and ice/water interfaces. A study area will be chosen after a review of the available data coverage and an overall analysis of scattering. Snowpack, bubbles, and wetness of the ice adds noise to the waveform and complicates the ice thickness retrieval. A lake that minimizes these factors will be selected for the study area. Data will be extracted from an orbit passing over the lake for each month of a year and in intervals of 5 years. The JASON series has the following advantages to the Cryosat-2 satellite: Improved temporal resolution – CryoSat-2 covers polar regions, while the JASON Series covers the area between N: 66.15° N and S: -66.15°; and greater coverage – Jason-1/2/3 have the shortest revisit cycle (~10 days) among all existing satellite altimeters and have been operating since 1992 [2]. The radar altimeter measurements will be displayed as the travel distance as a function of mean normalized power. The difference between peaks on the waveform will be calculated and logged as the ice thickness during that time. The results will be validated by comparing the estimated lake ice thickness values with ice fishing logs from similar dates. There is potential for the ice thickness data to be interpolated across the entirety of the lake using ArcGIS Pro, but this is contingence on the specifics of the study area. Anticipated Contributions: This study will: (1) estimate ice thickness in areas where in situ drillhole measurements cannot be taken, (2) introduce another factor in climate change discussions and models, (3) further our ability to monitor biophysical factors with remote sensing at global scales. [1] J. F. Beckers, J. Alec Casey and C. Haas, "Retrievals of Lake Ice Thickness From Great Slave Lake and Great Bear Lake Using CryoSat-2," in IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 3708-3720, July 2017, doi: 10.1109/TGRS.2017.2677583. [2] C. J. Donlon et al., “The Copernicus Sentinel-6 mission: Enhanced continuity of satellite sea level measurements from space,” Remote sensing of environment, vol. 258, p. 112395–, 2021, doi: 10.1016/j.rse.2021.112395.

Estimation of Arctic Sea Ice Thickness from Chinese HY-2B Radar Altimetry Data

Sea ice thickness (SIT) is an important parameter in the study of climate change. During the past 20 years, satellite altimetry has been widely used to observe sea ice thickness. The Chinese Haiyang-2B (HY-2B) radar altimeter, launched in October 2018, can provide data up to 80.6° latitude and can be used as a supplementary means to observe polar sea ice. Reliable HY-2B SIT products will contribute to the sea ice community. In this study, we aimed to assess the Arctic sea ice thickness retrieval ability of the HY-2B radar altimetry data. We processed the HY-2B radar altimetry data from January 2019 to April 2022 and used the processed data to retrieve the Arctic SIT. The Alfred Wegener Institute (AWI) CryoSat-2 (CS-2) SIT products were used to calibrate the HY-2B SIT estimates with a linear regression method. The Goddard Space Flight Center (GSFC) CS-2, Jet Propulsion Laboratory (JPL), and GSFC ICESat-2 (IS-2) SIT products were used to validate the HY-2B calibrated SIT estimates. The HY-2B calibrated SIT estimates have good, consistent spatial distributions with the CS-2 and IS-2 SIT products. The comparison with the IS-2 and IS-2 SIT products shows the root-mean-square error (RMSE) and bias for the HY-2B SIT estimates are significantly reduced after calibration. The HY-2B SIT estimates were also validated using the ice thickness data from Operation IceBridge (OIB) and the ice draft data from the Beaufort Gyre Exploration Project (BGEP). Finally, the monthly variations of the HY-2B SIT estimates were analyzed. Results show that the HY-2B calibrated SIT estimates are reliable, especially when the SIT values are lower than 3 m. The HY-2B altimetry data is a possible source for sea ice thickness data at lower latitudes and will help us better understand the sea ice response to climate change.

Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery

Abstract. Areas of thin sea ice in the polar regions not only are experiencing the highest rate of sea-ice production but also are, therefore, important hot spots for ocean ventilation as well as heat and moisture exchange between the ocean and the atmosphere. Through co-location of (1) an unsupervised waveform classification (UWC) approach applied to CryoSat-2 radar waveforms with (2) Moderate Resolution Imaging Spectroradiometer-derived (MODIS) thin-ice-thickness estimates and (3) Sentinel-1A/B synthetic-aperture radar (SAR) reference data, thin-ice-based waveform shapes are identified, referenced, and discussed with regard to a manifold of waveform shape parameters. Here, strong linear dependencies are found between binned thin-ice thickness up to 25 cm from MODIS and the CryoSat-2 waveform shape parameters that show the possibility of either developing simple correction terms for altimeter ranges over thin ice or directing adjustments to current retracker algorithms specifically for very thin sea ice. This highlights the potential of CryoSat-2-based SAR altimetry to reliably discriminate between occurrences of thick sea ice, open-water leads, and thin ice within recently refrozen leads or areas of thin sea ice. Furthermore, a comparison to the ESA Climate Change Initiative's (CCI) CryoSat-2 surface type classification with classes sea ice, lead, and unknown reveals that the newly found thin-ice-related waveforms are divided up almost equally between unknown (46.3 %) and lead type (53.4 %) classifications. Overall, the UWC results in far fewer unknown classifications (1.4 % to 38.7 %). Thus, UWC provides more usable information for sea-ice freeboard and thickness retrieval and at the same time reduces range biases from thin-ice waveforms processed as regular sea ice in the CCI classification.