Thickness Retrieval Research Articles

Synoptic Variability in Satellite Altimeter‐Derived Radar Freeboard of Arctic Sea Ice

AbstractSatellite observations of sea ice freeboard are integral to the estimation of sea ice thickness. It is commonly assumed that radar pulses from satellite‐mounted Ku‐band altimeters penetrate through the snow and reflect from the snow‐ice interface. We would therefore expect a negative correlation between snow accumulation and radar freeboard measurements, as increased snow loading weighs the ice floe down. In this study we produce daily resolution radar freeboard products from the CryoSat‐2 and Sentinel‐3 altimeters via a recently developed optimal interpolation scheme. We find statistically significant (p < 0.05) positive correlations between radar freeboard anomalies and modeled snow accumulation. This suggests that, in the period after snowfall, radar pulses are not scattering from the snow‐ice interface as commonly assumed. Our results offer satellite‐based evidence of winter Ku‐band radar scattering above the snow‐ice interface, violating a key assumption in sea ice thickness retrievals.

On the Scattering-Angle Dependence of the Spectral Consistency of Ice Cloud Optical Thickness Retrievals Based on Geostationary Satellite Observations

On the Scattering-Angle Dependence of the Spectral Consistency of Ice Cloud Optical Thickness Retrievals Based on Geostationary Satellite Observations

Forward modelling of synthetic aperture radar backscatter from lake ice over Canadian Subarctic Lakes

Lake ice provides important social and economic services to local communities, in addition to being a sensitive indicator of climate change. The reduction of ground observations of freshwater ice has led to an increased reliance on the use of satellite remote sensing data. There is currently interest in the retrieval of lake ice properties (e.g., ice thickness, bubble radius, roughness) using synthetic aperture radar (SAR). Roughness at the ice-water interface is particularly important as it has been identified as the dominant mechanism for increasing SAR backscatter throughout the ice season and must be considered in numerical radiative transfer models. Therefore, this study determines optimal ice-water interface roughness height for two subarctic lakes in northern Canada and models backscatter throughout two ice seasons using the snow microwave radiative transfer (SMRT) model. The two lakes for this study are Noell Lake and Malcolm Ramsay Lake. Field observations of ice thickness, snow depth, snow density, and the Canadian Lake Ice Model (CLIMo) are used to parameterize SMRT. Modelled L, C, and X-band backscatter at different incidence angles is assessed using SAR imagery from multiple satellite missions. Root mean square errors ranged from 0.38 to 1.45 dB for Noell Lake and 0.70 to 2.33 dB for Malcolm Ramsay Lake. Discrepancies between modelled and observed backscatter were found to be connected to the representation of roughness at different interfaces within the ice column and changes that occurred during freeze-melt events. These results provide insight into how changes in ice properties impact backscatter throughout the ice season. SMRT is valuable for modelling backscatter from lake ice during the cold season and could be used to develop retrieval algorithms for estimating ice-water interface roughness. This would allow for the development of other inversion models for retrieval of surface ice conditions and ice thickness.

Impact and Correction of Sea Ice, Snow, and Seawater Density on Arctic Sea-Ice Thickness Retrieval From Ku-Band SAR Altimeters

Satellite radar altimeters (RAs) can measure sea-ice thickness on a large scale. According to the assumption of isostatic equilibrium, knowing the density of water, snow, and ice is crucial to estimating the thickness of sea ice from altimeters. Therefore, using inaccurate density values to estimate sea-ice thickness from RAs will lead to unreliable results. This article proposes a method to evaluate and correct density ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> ) ratios, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho _{w}/{(}\rho _{w}-\rho _{i}{)}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho _{s}/{(}\rho _{w}-\rho _{i}{)}$ </tex-math></inline-formula> , where subscripts <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${w}$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${s}$ </tex-math></inline-formula> represent sea ice, water, and snow, respectively, by using sea ice freeboard of CryoSat-2 (CS-2) and Sentinel-3A (S3), Operational IceBridge ice thickness, and modified climatological snow depth data. In addition to comparing the calculated density ratio (DR) with the DR commonly used in the previous studies, we also investigate how input density parameters affect the accuracy of freeboard-to-thickness conversion of the satellite RA. Our results indicate that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho _{i}$ </tex-math></inline-formula> of first-year ice (FYI) region used by the ESA sea ice climate change initiative product (ESA SICCI) is generally large, whereas <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho _{s}$ </tex-math></inline-formula> is significantly small. For a more accurate inversion of Arctic sea-ice thickness, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho _{w}/{(}\rho _{w}-\rho _{i}{)} = 9.01$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho _{s}/{(}\rho _{w}-\rho _{i}{)} = 3.52$ </tex-math></inline-formula> can be used for FYI, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho _{w}/{(}\rho _{w}-\rho _{i}{)} = 7.20$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho _{s}/(\rho _{w}-\rho _{i}) =2.30$ </tex-math></inline-formula> can be used for multiyear ice (MYI) region. Because <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho _{w}$ </tex-math></inline-formula> is relatively stable and easy to observe, the proposed method also can be used to invert sea ice density <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho _{i}$ </tex-math></inline-formula> and snow layer density <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho _{s}$ </tex-math></inline-formula> from estimated DRs.

Monitoring the leaf equivalent water thickness of kiwifruit in high temperature using leaf spectral reflectance

Leaf water status information is highly needed for assessing high temperature stress. Leaf equivalent water thickness (fresh weight − dry weight)/area) is commonly used functional plant trait. Retrieval of leaf equivalent water thickness based on hyperspectral remote sensing has been shown to be rapid and accurate. However, a universal index that is applicable to various plants remains a considerable challenge. Few attempts had been made to monitor kiwifruit leaf equivalent water thickness using hyperspectral vegetation indexes under high temperature stress. In this study, a high temperature stress experiment was conducted in ambient conditions on 29 July–1 August 2021 during the kiwifruit expansion period to obtain a leaf spectrum dataset in the laboratory after the high temperature (above 35 °C) duration time for 1, 2, 3, and 4 days. This study systematically analyzed the performance of hyperspectral vegetation indexes of kiwifruit in estimating leaf equivalent water thickness with the range of 0.02–0.04 g/cm2. Newly developed and published spectral indexes were calculated to estimate kiwifruit leaf equivalent water thickness. Results showed that the newly developed three-band vegetation index (R2039 − R2438)/R752 generated the optimal testing performance for estimating kiwifruit leaf equivalent water thickness, with the coefficient of determination of 0.771, root mean square error of 0.0024 g/cm2, and residual prediction deviation of 2.09, respectively. This study suggests that the kiwifruit leaf equivalent water thickness under high temperature stress could be accurately estimated based on leaf level spectral indexes.

Ice thickness distribution of Himalayan glaciers inferred from DInSAR-based glacier surface velocity.

Retrieval of glacier ice thickness is extremely important for monitoring water resources and predicting glacier dynamics and changes. The inter-annual glacier ice thickness observations (more than 5years) exploit the glacier mass changes. Ice thickness is one of the important parameters to predict the future sea-level rise. Without adequate knowledge and precise information of glacier ice thickness distribution, future sea-level changes cannot be accurately assessed. In this study, we use an existing flow model to estimate the ice thickness of the High Mountain Asia (HMA) glaciers, using remote sensing techniques. The glacier ice velocity is one of the significant parameters in the Laminar flow model to retrieve the ice thickness. The glacier ice velocity is derived by utilizing the Differential SAR Interferometry (DInSAR) technique. The most optimum DInSAR data (ALOS-2/PALSAR-2) is used for estimating the ice velocity of the HMA glaciers. The ice thickness is mainly estimated for five different states in the HMA region, namely Himachal Pradesh, Uttarakhand, Sikkim, Bhutan, and Arunachal Pradesh. Most of the states are observed with a mean ice thickness of 100m. Five benchmark glaciers (Samudra Tapu, Bara Shigri, Chhota Shigri, Sakchum, and Gangotri glaciers) are also selected for validating our results with the existing thickness information. The issues related to velocity-based ice thickness inversion are also emphasized in this study. The high-velocity rate due to the influx of melting water from adjacent glaciers causes an increment in the flow rate. This abnormal velocity derives erroneous ice thickness measurements. This is one of the major problems to be considered in the velocity-based thickness-derived procedures. Finally, the investigation suggests the inclusion of the velocity influencing parameters in the physical-based models for an accurate ice thickness inversion.

Solution of the Radiative Transfer Equation for Vertically Inhomogeneous Media by Numerical Integration Solvers: Comparative Analysis

An efficiency of the singular value decomposition (SVD) method and ordinary differential equation (ODE) solvers in finding the reflection matrix are compared. A reflection matrix can be found by solving the one-dimensional radiative transfer equation. The latter’s solution based on the discrete ordinate method leads to the singular value decomposition (SVD) method. Alternative approach consists in transforming the original problem into a matrix Riccati equation written specifically for the reflection matrix. The matrix Riccati equation is solved using numerical integration techniques for ordinary differential equations (ODEs). It is found that for a single layer case, the SVD approach is faster than the ODE solvers by an order of magnitude. Yet as the number of layers increases, the ODE solvers become more efficient than the SVD approach. In addition, they outperform the SVD method when a solution for a set of optical thicknesses of the medium should be found or when retrieval of optical thickness should be performed. The comparison between different ODE solvers is performed, as well.

First results of the ARIEL L-band radiometer on the MOSAiC Arctic Expedition during the late summer and autumn period

Arctic sea ice is changing rapidly. Its retreat significantly impacts Arctic heat fluxes, ocean currents, and ecology, warranting the continuous monitoring and tracking of changes to sea ice extent and thickness. L-band (1.4 GHz) microwave radiometry can measure sea ice thickness for thin ice ≤1 m, depending on salinity and temperature. The sensitivity to thin ice makes L-band measurements complementary to radar altimetry which can measure the thickness of thick ice with reasonable accuracy. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we deployed the mobile ARIEL L-band radiometer on the sea ice floe next to research vessel Polarstern to study the sensitivity of the L-band to different sea ice parameters (e.g., snow and ice thickness, ice salinity, ice and snow temperature), with the aim to help improve/validate current microwave emission models. Our results show that ARIEL is sensitive to different types of surfaces (ice, leads, and melt ponds) and to ice thickness up to 70 cm when the salinity of the sea ice is low. The measurements can be reproduced with the Burke emission model when in situ snow and ice measurements for the autumn transects were used as model input. The correlation coefficient for modeled Burke brightness temperature (BT) versus ARIEL measurements was approximately 0.8. The discrepancy between the measurements and the model is about 5%, depending on the transects analyzed. No explicit dependence on snow depth was detected. We present a qualitative analysis for thin ice observations on leads. We have demonstrated that the ARIEL radiometer is an excellent field instrument for quantifying the sensitivity of L-band radiometry to ice and snow parameters, leading to insights that can enhance sea ice thickness retrievals from L-band radiometer satellites (such as Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP)) and improve estimates of Arctic sea-ice thickness changes on a larger scale.

Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network

Abstract. We introduce a new machine learning approach to retrieve cloud optical thickness (COT) fields from visible passive imagery. In contrast to the heritage independent pixel approximation (IPA), our convolutional neural network (CNN) retrieval takes the spatial context of a pixel into account and thereby reduces artifacts arising from net horizontal photon transfer, which is commonly known as independent pixel (IP) bias. The CNN maps radiance fields acquired by imaging radiometers at a single wavelength channel to COT fields. It is trained with a low-complexity and therefore fast U-Net architecture with which the mapping is implemented as a segmentation problem with 36 COT classes. As a training data set, we use a single radiance channel (600 nm) generated from a 3D radiative transfer model using large eddy simulations (LESs) from the Sulu Sea. We study the CNN model under various conditions based on different permutations of cloud aspect ratio and morphology, and we use appropriate cloud morphology metrics to measure the performance of the retrievals. Additionally, we test the general applicability of the CNN on a new geographic location with LES data from the equatorial Atlantic. Results indicate that the CNN is broadly successful in overcoming the IP bias and outperforms IPA retrievals across all morphologies. Over the Atlantic, the CNN tends to overestimate the COT but shows promise in regions with high cloud fractions and high optical thicknesses, despite being outside the general training envelope. This work is intended to be used as a baseline for future implementations of the CNN that can enable generalization to different regions, scales, wavelengths, and sun-sensor geometries with limited training.

A Review of Satellite Synthetic Aperture Radar Interferometry Applications in Permafrost Regions: Current status, challenges, and trends

With climate change and the increment of human activities, global permafrost is undergoing degradation, threatening the stability of engineering and the ecological environment of the permafrost region. With the advantages of all-day, all-weather, wide-coverage, and high-accuracy monitoring, synthetic aperture radar interferometry (InSAR) is becoming a substantial tool for permafrost monitoring, and many studies with InSAR in permafrost regions have been conducted in the recent 20 years. In this article, the basic principles of time–series InSAR are introduced first. Then, the development and applications of InSAR in permafrost, such as the coherence analysis of permafrost ground surface, the deformation of permafrost and infrastructure, and active layer thickness (ALT) retrieval, are given. Next, the existing problems, including temporal decorrelation, atmospheric delay, deformation models, and the effect of soil moisture and phase unwrapping (PU), are discussed.

Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice

In order to satisfy the demand of key sea ice parameters, including melt pond depth Hp and underlying ice thickness Hi, in studies of Arctic sea ice change in summer, four algorithms of retrieving Hp and Hi were compared and validated by using optical data of melt ponds from field observations. The Malinka18 algorithm stood out as the most accurate algorithm for the retrieval of Hp. For the retrieval of Hi, Malinka18 and Zhang21 algorithms could also provide reasonable results and both can be applied under clear and overcast sky conditions, while retrievals under clear sky conditions are more accurate. The retrieval results of Hi for Lu18 agreed better with field measurements for thin ice (Hi &lt; 1 m) than that for thick ice, but those results of Hp were not satisfactory. The König20 algorithm was only suitable for clear sky conditions, and underestimated Hp, while showing a good agreement with Hp &lt; 0.15 m. For Arctic applications, Malinka18 and Zhang21 algorithms provided a basis and reference for the satellite optical data such as WoeldView2 to retrieve Hp and Hi. Malimka18 also showed the ability to retrieve Hi, except for the Lu18 algorithm if pond color captured by helicopters and unmanned aerial vehicles were available. This study identifies the optimal algorithm for retrieval of Hp and Hi under different conditions, which have the potential to provide necessary data for numerical simulations of Arctic sea ice changes in summer.

Thaw Settlement Monitoring and Active Layer Thickness Retrieval Using Time Series COSMO-SkyMed Imagery in Iqaluit Airport

Thaw consolidation of degrading permafrost is a serious hazard to the safety and operation of infrastructure. Monitoring thermal changes in the active layer (AL), the proportion of the soil above permafrost that thaws and freezes periodically, is critical to understanding the conditions of the top layer above the permafrost and regulating the construction, operation, and maintenance of facilities. However, this is a very challenging task using ground-based methods such as ground-penetrating radar (GPR) or temperature sensors. This study explores the integration of interferometric measurements from high-resolution X-band Synthetic Aperture Radar (SAR) images and volumetric water content (VWC) data from SoilGrids to quantify detailed spatial variations in active layer thickness (ALT) in Iqaluit, the territorial capital of Nunavut in Canada. A total of 21 SAR images from COSMO Sky-Med (CSK) were first analyzed using the freely connected network interferometric synthetic aperture radar (FCNInSAR) method to map spatial and temporal variations in ground surface subsidence in the study area. Subsequently, we built an ALT retrieval model by introducing the thaw settlement coefficient, which takes soil properties and saturation state into account. The subsidence measurements from InSAR were then integrated with VWC extracted from the SoilGrids database to estimate changes in ALT. For validation, we conducted a comparison between estimated ALTs and in situ measurements in the airport sector. The InSAR survey identifies several sites of ground deformation at Iqaluit, subsiding at rates exceeding 80 mm/year. The subsidence rate changes along the runway coincide with frost cracks and ice-wedge furrows. The obtained ALTs, ranging from 0 to 5 m, vary significantly in different sediments. Maximum ALTs are found for rock areas, while shallow ALTs are distributed in the till blanket (Tb), the intertidal (Mi) sediments, and the alluvial flood plain (Afp) sediment units. The intersection of taxiway and runway has an AL thicker than other parts in the glaciomarine deltaic (GMd) sediments. Our study suggests that combining high-resolution SAR imagery with VWC data can provide more comprehensive ALT knowledge for hazard prevention and infrastructure operation in the permafrost zone.

Estimating thin ice thickness around Svalbard using MODIS satellite imagery

ABSTRACT Information about the state of the Arctic sea ice is becoming increasingly important. This paper describes an approach for automatic retrieval of daily thin sea ice thickness maps around Svalbard. The algorithm uses thermal satellite imagery from MODIS to estimate the surface temperature of the ice and further uses a thermal model of the ice surface to estimate the thickness of the sea ice. The approach is usable for thin sea ice, up to ca. 50 cm thick, during cold weather (freezing) conditions and without cloud cover present. The algorithm is compared with helicopter-borne electromagnetic ice thickness measurements. The comparison yields increasing root-mean-square deviation (RMSD) for thicker ice. The lowest RMSD found is 8.7 cm for ice thickness in the range 10 cm < hi ≤ 20 cm. The highest RMSD found is 25.2 cm for ice thickness in the range 30 cm < hi ≤ 40 cm. The bias shows no such trend, and the overall bias is found to be −5.5 cm. The results show that this is a promising approach, allowing monitoring of thin sea ice thickness at relatively higher spatial resolution.

A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations

Abstract. This paper presents a three-dimensional variational (3DVAR) data assimilation (DA) system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, developed for the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. For computational efficiency, 32 model variables in the MOSAIC_4bin scheme are lumped into 20 aerosol state variables that are representative of mass concentrations in the DA system. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was employed, which was obtained by simplifying the optical module in WRF-Chem. The tangent linear (TL) and adjoint (AD) operators were then established and passed the TL/AD sensitivity test. The Himawari-8 derived AOT data were assimilated to validate the system and investigate the effects of assimilation on both AOT and PM2.5 simulations. Two comparative experiments were performed with a cycle of 24 h from 23 to 29 November 2018, during which a heavy air pollution event occurred in northern China. The DA performances of the model simulation were evaluated against independent aerosol observations, including the Aerosol Robotic Network (AERONET) AOT and surface PM2.5 measurements. The results show that Himawari-8 AOT assimilation can significantly improve model AOT analyses and forecasts. Generally, the control experiments without assimilation seriously underestimated AOTs compared with observed values and were therefore unable to describe real aerosol pollution. The analysis fields closer to observations improved AOT simulations, indicating that the system successfully assimilated AOT observations into the model. In terms of statistical metrics, assimilating Himawari-8 AOTs only limitedly improved PM2.5 analyses in the inner simulation domain (D02); however, the positive effect can last for over 24 h. Assimilation effectively enlarged the underestimated PM2.5 concentrations to be closer to the real distribution in northern China, which is of great value for studying heavy air pollution events.

Eastern Arctic Sea Ice Sensing: First Results from the RADARSAT Constellation Mission Data

Sea ice monitoring plays a vital role in secure navigation and offshore activities. Synthetic aperture radar (SAR) has been widely used as an effective tool for sea ice remote sensing (e.g., ice type classification, concentration and thickness retrieval) for decades because it can collect data by day and night and in almost all weather conditions. The RADARSAT Constellation Mission (RCM) is a new Canadian SAR mission providing several new services and data, with higher spatial coverage and temporal resolution than previous Radarsat missions. As a very deep convolutional neural network, Normalizer-Free ResNet (NFNet) was proposed by DeepMind in early 2021 and achieved a new state-of-the-art accuracy on the ImageNet dataset. In this paper, the RCM data are utilized for sea ice detection and classification using NFNet for the first time. HH, HV and the cross-polarization ratio are extracted from the dual-polarized RCM data with a medium resolution (50 m) for an NFNet-F0 model. Experimental results from Eastern Arctic show that destriping in the HV channel is necessary to improve the quality of sea ice classification. A two-level random forest (RF) classification model is also applied as a conventional technique for comparisons with NFNet. The sea ice concentration estimated based on the classification result from each region was validated with the corresponding polygon of the Canadian weekly regional ice chart. The overall classification accuracy confirms the superior capacity of the NFNet model over the RF model for sea ice monitoring and the sea ice sensing capacity of RCM.

An Improved Algorithm for the Retrieval of the Antarctic Sea Ice Freeboard and Thickness from ICESat-2 Altimeter Data

ICESat-2 altimeter data could be used to estimate sea ice freeboard and thickness values with a higher measuring accuracy than that achievable with data provided by previous altimeter satellites. This study developed an improved algorithm considering variable lead proportions based on the lowest point method to derive the sea surface height for the retrieval of Antarctic sea ice freeboard and thickness values from ICESat-2 ATL-07 data. We first collocated ICESat-2 tracks to corresponding Sentinel-1 SAR images and calculated lead (seawater) proportions along each track to estimate the sea surface height in the Antarctic Ocean. Then, the Antarctic sea ice freeboard and thickness were estimated based on a local sea surface height reference and a static equilibrium equation. Finally, we assessed the accuracy of our improved algorithm and ICESat-2 data product in the retrieval of the Antarctic sea ice thickness by comparing the calculated values to ship-based observational sea ice thickness values acquired during the 35th Chinese Antarctic Research Expedition (CHINARE-35). The results indicate that the Antarctic sea ice freeboard estimated with the improved lowest point method was slightly larger than that estimated with the ICESat-2 data product algorithm. The root mean squared error (RMSE) of the improved lowest point method was 35 cm with the CHINARE-35 measured sea ice thickness, which was smaller than that determined with the ICESat-2 data product algorithm (65 cm). Our improved algorithm could provide more accurate data on the Antarctic sea ice freeboard and thickness, thus supporting Antarctic sea ice monitoring and the evaluation of its change under global effects.

A Suitable Retrieval Algorithm of Arctic Snow Depths with AMSR-2 and Its Application to Sea Ice Thicknesses of Cryosat-2 Data

Arctic sea ice and snow affect the energy balance of the global climate system through the radiation budget. Accurate determination of the snow cover over Arctic sea ice is significant for the retrieval of the sea ice thickness (SIT). In this study, we developed a new snow depth retrieval method over Arctic sea ice with a long short-term memory (LSTM) deep learning algorithm based on Operation IceBridge (OIB) snow depth data and brightness temperature data of AMSR-2 passive microwave radiometers. We compared climatology products (modified W99 and AWI), altimeter products (Kwok) and microwave radiometer products (Bremen, Neural Network and LSTM). The climatology products and altimeter products are completely independent of the OIB data used for training, while microwave radiometer products are not completely independent of the OIB data. We also compared the SITs retrieved from the above different snow depth products based on Cryosat-2 radar altimeter data. First, the snow depth spatial patterns for all products are in broad agreement, but the temporal evolution patterns are distinct. Snow products of microwave radiometers, such as Bremen, Neural Network and LSTM snow depth products, show thicker snow in early winter with respect to the climatology snow depth products and the altimeter snow depth product, especially in the multiyear ice (MYI) region. In addition, the differences in all snow depth products are relatively large in the early winter and relatively small in spring. Compared with the OIB and IceBird observation data (April 2019), the snow depth retrieved by the LSTM algorithm is better than that retrieved by the other algorithms in terms of accuracy, with a correlation of 0.55 (0.90), a root mean square error (RMSE) of 0.06 m (0.05 m) and a mean absolute error (MAE) of 0.05 m (0.04 m). The spatial pattern and seasonal variation of the SITs retrieved from different snow depths are basically consistent. The total sea ice decreases first and then thickens as the seasons change. Compared with the OIB SIT in April 2019, the SIT retrieved by the LSTM snow depth is superior to that retrieved by the other SIT products in terms of accuracy, with the highest correlation of 0.46, the lowest RMSE of 0.59 m and the lowest MAE of 0.44 m. In general, it is promising to retrieve Arctic snow depth using the LSTM algorithm, but the retrieval of snow depth over MYI still needs to be verified with more measured data, especially in early winter.

Retrieval and parameterisation of sea-ice bulk density from airborne multi-sensor measurements

Abstract. Knowledge of sea-ice thickness and volume depends on freeboard observations from satellite altimeters and in turn on information of snow mass and sea-ice density required for the freeboard-to-thickness conversion. These parameters, especially sea-ice density, are usually based on climatologies constructed from in situ observations made in the 1980s and earlier while contemporary and representative measurements are lacking. Our aim with this paper is to derive updated sea-ice bulk density estimates suitable for the present Arctic sea-ice cover and a range of ice types to reduce uncertainties in sea-ice thickness remote sensing. Our sea-ice density measurements are based on over 3000 km of high-resolution collocated airborne sea-ice and snow thickness and freeboard measurements in the western Arctic Ocean in 2017 and 2019. Sea-ice bulk density is derived assuming isostatic equilibrium for different ice types. Our results show higher average bulk densities for both first-year ice (FYI) and especially multi-year ice (MYI) compared to previous studies. In addition, we find a small difference between deformed and possibly unconsolidated FYI and younger MYI. We find a negative-exponential relationship between sea-ice bulk density and sea-ice freeboard and apply this parameterisation to one winter of monthly gridded CryoSat-2 sea-ice freeboard data. We discuss the suitability and the impact of the derived FYI and MYI bulk densities for sea-ice thickness retrievals and the uncertainty related to the indirect method of measuring sea-ice bulk density. The results suggest that retrieval algorithms be adapted to changes in sea-ice density and highlight the need of future studies to evaluate the impact of density parameterisation on the full sea-ice thickness data record.

Studies of Sea-Ice Thickness and Salinity Retrieval Using 0.5–2 GHz Microwave Radiometry

Arctic sea-ice thickness and salinity retrievals are simulated to explore the performance of nadir-observing microwave radiometry operating with up to 16 frequency channels in the 0.5–2-GHz frequency range. A radiative transfer model is used to create lookup tables of the circularly polarized thermal emissions of first-year (FY) and multiyear (MY) sea ice, and the performance of two distinct retrieval methods is examined. The first method retrieves only sea-ice thicknesses, while the second retrieves both ice thickness and ice salinity. Retrieval errors are simulated for both FY and MY sea ice as a function of ice thickness, salinity, and temperature to investigate the impact of radiometric uncertainty, the frequency channels used, and any errors in ancillary information. To gain further insight into Arctic scale retrieval performance, a simulated brightness temperature dataset is produced for Arctic sea ice for the period October 2020–March 2021 using sea-ice thicknesses from the SMOS-CryoSat-2 algorithm. Compared to existing sea-ice thickness retrievals obtained from 1.4-GHz microwave radiometers, the results demonstrate that 0.5–2-GHz radiometry can achieve higher sensitivity to a sea-ice thickness within the range 0.5–1.5 m for FY sea ice and enable the retrieval of multiple sea-ice parameters (thickness and salinity) simultaneously.

Satellite Aerosol Retrieval Using Scene Simulation and Deep Belief Network

Aerosol satellite remote sensing retrieval is of great importance in the study of climatic and environmental effects. However, due to varied factors affecting the signals reaching satellite sensors, accurate aerosol retrieval remains challenging. Focusing on limitations in the availability of aerosol data from real scenes, we simulated real scenes to obtain sample data to achieve aerosol retrieval using a deep belief network (DBN). The 6S atmospheric radiative transfer model was used to simulate various possible parameters of earth-atmosphere, sun, and sensor in real scenes. A large amount of simulated data was generated and used as sample datasets of DBN training to obtain the aerosol inversion model. Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to perform aerosol optical thickness (AOT) retrieval experiments. Global-scale aerosol retrieval experiments were conducted based on the following three representative regions: 1) Beijing-Tianjin-Hebei region in China; 2) Midwestern and Southern United States; and 3) Central and Western Europe. The retrieval results were verified using Aerosol Robotic Network (AERONET) datasets in comparison with MCD19A2 aerosol products. Five indicators, including mean absolute error (MAE) and within expected error (f $_{=EE}$ ), were used for the evaluation. The evaluation indicators of the proposed method, in which MAEs were 0.0626, 0.0366, and 0.0487, and f $_{=EE}$ 's were 86.28%, 85.21%, and 80.71%, performed better than MCD19A2 in three typical regions. The most significant advantage of the proposed method is that high-precision retrieval of spatially continuous AOT can be achieved using single-temporal satellite imagery data, which is not possible realizing in current aerosol retrieval methods.