Sea ice concentration (SIC) has been monitored with passive microwave (PM) observations for decades. Various techniques have been developed for its improvement. While techniques such as weather filters are commonly used, the necessity of combing radiative transfer model (RTM)-based atmospheric correction and dynamic tie points (DTP) remains an open question, particularly for near-90 GHz algorithm. This study investigates their respective influence on SIC retrieval using the FY-3D Microwave Radiation Imager (MWRI) data in 2019. The original and atmospherically corrected Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) algorithm (ASI and ASI2, respectively) are used in combination with fixed tie points (FTP) and DTP, resulting in four sets of ice concentration retrievals, namely ASI-FTP, ASI-DTP, ASI2-FTP, and ASI2-DTP. They are inter-compared with three PM-based ice concentration products and evaluated with a synthetic aperture radar (SAR)-based ice/water classification product and 20 clear-sky Moderate Resolution Imaging Spectroradiometer (MODIS) images from February to July 2019. The ASI2-based ice concentrations are overall higher and perform better, with the root mean square error (RMSE) and bias reduced by 5.4%–7.4% and 7.2%–8.0%, respectively. In comparison, the use of DTP has varying performances depending on the tie points extraction procedure. Good tie points work similarly to the atmospheric correction in mitigating SIC underestimations. The combined use of both varies substantially with seasons. During summer, it well captures the seasonal variability of tie points and effectively mitigates the atmospheric influence, thus significantly improving the retrievals. This highlights the necessity of combining both techniques for near-90 GHz algorithm, especially for summer.

This study exploits the unique capabilities of the FY-3E WindRAD scatterometer, the first spaceborne dual-frequency (Ku- and C-band) and dual-polarization (hhandvv) rotating fan-beam scanning measurements, to investigate the backscatter characteristics of open water (OW), first-year ice (FYI), and multi-year ice (MYI) under different seasonal, wavelength, and polarization conditions throughout 2022 in the Arctic. Four types of feature parameters were defined for systematic analysis based on WindRAD swath data. It is concluded that the mean backscatter coefficient σp,λand the wavelength gradient ratioGRpare key indicators for distinguishing between FYI and MYI, with the Ku-band exhibiting superior performance outside the melt season due to enhanced volume scattering from desalinated ice and bubble structures. During melting, however, both ice types become indistinguishable as meltwater increases dielectric loss and reduces penetration depth. Furthermore, the standard deviation of the backscatter coefficient Δσp,λand the polarization ratio γλprove highly effective in separating sea ice from OW with the C-band showing particular advantage owing to a wider incidence angle range and stronger angular sensitivity of Bragg scattering over water. The γλapproaches 1 for both FYI and MYI due to depolarizing rough surfaces, whereas OW exhibits lower values dominated by Bragg scattering. This study provides a systematic observational basis for exploring the benefits of dual-frequency joint detection in enhancing sea ice monitoring capabilities, providing vital support for the development and refinement of algorithms for FY-3E WindRAD operational sea ice products.

Arctic sea ice leads, which are narrow linear openings between sea ice, are critical to polar climate and ocean–atmosphere interactions. However, detecting multi-width leads accurately under the case of cloud interference remains challenging for thermal infrared (TIR) data. In this article, we propose an improved U-Net-based model–CTMLU-Net–which is designed to extract multi-width leads, including internal bright leads, from FY-3D MERSI-II 250-m TIR imagery under varied cloudy conditions. The model incorporates a dynamic fast Fourier transform (FFT) module to distinguish leads from clouds in the frequency domain and introduces cloud categories into the training labels to improve the lead detection accuracy. A dual-loss module and multi-window brightness temperature anomaly (BTA) inputs further enhance the sensitivity to different lead widths. Under cloudy conditions, CTMLU-Net achieved a precision of 86.82% and a recall of 71.73%, outperforming the traditional BTA method by 39.26% and 23.95%, respectively. For multi-width leads, it achieved a precision of 92.10% and a recall of 87.73%, and for bright leads, the corresponding scores were 90.59% and 84.75%, respectively. Compared to the original U-Net model, the FFT module improved the precision and recall by 4.80% and 17.66% in cloudy conditions, respectively. Compared to single-loss setting, the dual-loss module further enhanced the multi-width lead detection precision and recall by 23.64% and 9.92%. CTMLU-Net was also applied to 7045 scenes of MERSI-II TIR images to generate monthly Arctic lead frequency maps from November 2019 to April 2020. The spatiotemporal patterns of the leads can capture dynamic events, such as the collapse of the Beaufort High in spring 2020. Overall, CTMLU-Net offers a robust and accurate solution for multi-width sea ice lead detection under cloudy conditions.

Leads are linear fractures formed by the deformation of the ice cover and are crucial areas for Arctic heat exchange between the ocean and the atmosphere. Accurately identifying leads is essential for studying climate change, polar ecosystems, and shipping navigation. Recent research on leads detection methods developed using Synthetic Aperture Radar (SAR) tend to ignore the developmental stage of the leads and the distribution context of leads (first-year ice or multiyear ice), which hinders the generalizability of these methods. In this paper, we propose a Stacking Approach for Lead Classification (SALC) integrating five specialized learners for five typical lead conditions. Ablation experiments reveal the effectiveness of each component of SALC: 1) the optimal preprocessing method is to input the incidence angle as a feature and apply the advanced thermal noise removal. In addition, the effect of inputting the incidence angle is significantly stronger than incidence angle correction for original images; 2) the SALC performs best when the data features include polarisation, texture, and upscaling features; 3) XGBoost is the most suitable meta classifier as compared to SVM and RF. The results show that SALC outperforms both single learners and traditional classifiers across various conditions and quality criteria. In addition, SALC can tolerate at least 15% of erroneous samples.

Moulins play a pivotal role in delivering surface meltwater and significantly impacting the mass balance of the Greenland ice sheet (GrIS). Unlike crevasses, moulins are difficult to detect from satellite remote sensing imagery due to their significantly small size. Recently, unmanned aerial vehicle (UAV)-based remote sensing has become a prevalent tool for acquiring ultrahigh-resolution (UHR) imagery that facilitates the detailed extraction of small-scale surface features. Nevertheless, distinguishing among various ice surface features formed by ice stress and strain, such as crevasses, desiccated streams, and moulins, remains challenging due to their subtle differences in UAV images. This study proposes a hydrology knowledge-based framework for automatic detection of moulins using UHR (0.06 m) UAV images. By integrating a deep learning (DL) network for identifying supraglacial rivers with terrain data for recognizing significant depressions, this framework introduces multiple geometric and topological constraints to effectively enhance the detection accuracy. Applied to the Sermeq Avannarleq region, the framework achieves a recall of 0.795 and a precision of 0.729 for moulin detection. In contrast to methods relying solely on elevation changes to detect moulins, our approach exhibits a notable improvement of over 20% in$F1$-score accuracy. This enhancement further contributes to increased reliability in stream network modeling when considering the presence of moulins. We also find that this framework exhibits a certain degree of transferability for imagery at a 2-m resolution. These results show that our framework can effectively extract moulin and has the potential to be applied to large-scale moulin surveys using high-resolution (<2 m) satellite images.

Leads are almost linear fractures within the ice pack, which are commonly observed in polar regions. In wintertime, leads promote energy flux from the underlying ocean to the atmosphere. Synthetic aperture radar (SAR) can monitor leads at a finer spatial resolution than other spaceborne datasets, regardless of solar illumination and atmospheric conditions. However, the SAR-based lead detection methods proposed to date are restricted to some specific areas, instead of the entire Arctic. In this article, we present a generalized deep learning-based approach for automatic sea ice lead detection (SILDET) in the Arctic wintertime using Sentinel-1 SAR images. The validation results show that SILDET has the capability of detecting open and frozen leads at different stages of development. Compared with the visual interpretation of Sentinel-1 images, the overall detection accuracy is 97.80% and the Kappa coefficient is 0.88. The lead map of a regional study obtained from SILDET was compared to that from a previous SAR-based lead detection method and a lead dataset based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The lead map was also validated using Sentinel-2 images. The result shows that SILDET can provide a more detailed distribution of leads and a better estimation of lead width and area. SILDET was applied to present the Arctic-wide lead distribution from January to April 2023 with a spatial resolution of 40 m. The Arctic-wide lead width distribution follows a power law with an average exponent of 1.65. The SILDET approach can be expected to provide long-term high-resolution lead distribution records.

The gradient ratios (GRs), defined as the normalized difference between measured vertically (V) or horizontally (H) polarized brightness temperatures (TBs) at two frequencies, have been commonly used to retrieve snow depth on Arctic sea ice from passive microwave radiometer data. In this study, the influences of snow density on the relationship between GR of 6.9 and 18.7 GHz vertically polarized TBs (i.e., GRV(19/7)) and snow depth were investigated through observed data and simulation. The former was based on regression analysis between GRV(19/7) observations from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the altimetric snow depth estimates derived by differencing freeboard heights from ICESat-2 and CryoSat-2 while the latter was based on model simulations from the Microwave Emission Model for Layered Snowpacks (MEMLS). An improved snow depth retrieval algorithm is proposed based on a multilinear regression model with GRV(19/7) from AMSR2 and snow density from the NASA Eulerian Snow On Sea Ice Model (NESOSIM) as predictors, and then validated using three airborne snow depth datasets. The validation results show an overall good accuracy of the improved algorithm with the correlation coefficient (r) ranging from 0.60 to 0.72 and the root mean square error (RMSE) values varying between 6.18 cm and 7.53 cm. The improved algorithm enables daily snow depth estimation on sea ice over the entire Arctic Ocean during the full winter season (October to April). More importantly, it successfully captures the seasonal variation of snow depth which is expected to increase throughout the winter season due to snow accumulation.

Depth hoar (DH) forms when there is a strong temperature gradient in the snowpack. In the Antarctic ice sheet (AIS), DH mostly forms during summer insolation. The seasonal regularity provides an age marker for each internal snow/firn layer, which is necessary for estimating surface mass balance (SMB) using ice-penetrating radar (IPR) and ice core. However, little is known about DH inside the AIS because ice drill and snow pit observations are inefficient and sparse. The deployment of the Operation IceBridge (OIB) airborne snow radar has significantly enhanced the field observation dataset. So far, the spatial distribution of DH remains unclear due to the lack of DH extraction over the AIS from OIB snow radar. Based on instance segmentation SOLOv2 and self-attention mechanism Swin Transformer, a DH layer automatic extraction algorithm ST-SOLOv2 is proposed, with AP50 of 0.9 and F1-score of 0.83, outperforms other commonly used instance segmentation networks (SOLOv2, Mask R-CNN, BlendMask, YOLACT, and CondInst). After the proposed preprocessing pipeline, we conduct the ST-SOLOv2 to locate each DH near the surface. Our results suggest DH number increases with elevation and decreases with slope angle from coast to inland. And DH number decreases with surface melting days as melting/freezing cycles obscure snow layering. We present a method for efficiently monitoring the DH distribution that can be used in further studies of snow radar SMB estimation.

Monitoring Arctic sea ice drift is essential for understanding climate change. Currently, large-scale observed sea ice drift datasets primarily rely on single-sensor remote sensing data, which suffer from low spatial resolution or poor spatial continuity. To address these limitations, this study proposes a sea ice drift retrieval framework based on multi-sensor data, utilizing the complementary sea ice drift information derived from passive microwave radiometers and medium-resolution optical sensors. The proposed framework employs the maximum cross-correlation (MCC) based pattern-matching method to obtain sea ice drift fields from coarse-resolution Fengyun-3D (FY-3D) Microwave Radiation Imager (MWRI) data, and an A-KAZE-based feature-tracking method to extract sea ice motion vectors from FY-3D Medium-Resolution Spectral Imager II (MERSI-II) data. Finally, the sea ice drift vectors from different sensors are merged using the Co-Kriging algorithm to obtain the final sea ice drift result. The effectiveness of the proposed framework was assessed by comparing displacements from 166 buoys with the retrieved vectors derived from FY-3D single-sensor and multi-sensor data, as well as an existing sea ice drift product (Ifremer-AMSR2) collected in the Beaufort Sea, the East Siberian Sea, and the Fram Strait. The results demonstrate the proposed framework’s ability to retrieve fine-resolution (i.e., 1 km) and spatially continuous sea ice drift fields in areas where vectors from fine-resolution data can be obtained. The overall mean absolute errors (MAEs) of the merged sea ice motion vectors are 0.76 km/day for speed and 4.53° for angle, exhibiting superior drift accuracy to Ifremer-AMSR2 in areas covered by MERSI-II vectors.

Integrated retrieval using the optimal estimation (OE) method iteratively finds a set of geographical parameters that best match the observations. However, this method becomes more challenging over the ice surface due to the highly sensitive parameters such as sea ice concentration (SIC) and multiyear ice concentration (MYIC). In this study, a new time constraint that captures the distinct temporal characteristics of SIC and MYIC is incorporated into the OE method. The integrated retrievals, using both the original and time-constraint OE method (referred to as OE and OE-Z, respectively), were conducted based on FengYun-3D (FY-3D) microwave radiation imager (MWRI) data. Compared to other radiometer-based SIC and MYIC products, OE-Z outperforms OE, with the correlations increasing from 0.91 to 0.96 for SIC and from 0.41 to 0.49 for MYIC. The time constraint in OE-Z effectively mitigates the anomalous retrievals in SIC and MYIC, resulting in smoother and more reasonable time series than OE. Improvements in SIC and MYIC lead to enhanced simulation of surface microwave emission, thus improving the retrieval of atmospheric parameters. In comparison with the MOSAiC total water vapor (TWV) measurements, the RMSE in OE-Z reduces from 1.72 to 1.66 kg/m2, and the correlation increases from 0.46 to 0.50. The simulated brightness temperature (TB) biases in OE-Z reduce from 0.71 to 0.31 K at 36 GHz and from −8.95 to −7.72 K at 89 GHz. This emphasizes the importance of imposing suitable constraints on highly sensitive parameters in integrated retrieval.

The challenges of utilizing the 250-m resolution thermal infrared (TIR) data obtained from the Medium Resolution Spectral Imager-II (MERSI-II) onboard the Chinese Fengyun-3D (FY-3D) satellite are bowtie effect and nonuniform brightness stripe noise. While previous solutions have addressed these issues separately, this article introduced a more integrated two-step image quality enhancement strategy for MERSI-II TIR images. It considered the interactions between the two issues and overcame the excessive or inadequate destriping in existing models due to the ideal stripe-type assumption. Specifically, for the bowtie effect, a rigorous geometric model suitable for MERSI-II was constructed by considering the Earth’s curvature and adjusting the preset image width. For the nonuniform brightness stripe noise, a novel adaptive multiscale frequential (AMSF) algorithm was developed. The multiscale spectral detection effectively captured the anomaly frequency, and the adaptive threshold dynamically adjusted the detection range, profiting in preserving details. The proposed strategy was validated on MERSI-II TIR images, outperforming existing methods in quantitative and qualitative assessments with higher efficiency on both bowtie effect and stripe noise removal. Further experiments conducted on Moderate Resolution Imaging Spectroradiometer (MODIS) data demonstrated the AMSF algorithm’s applicability to different data. In addition, the 250-m MERSI-II FY-3D data can help us understand the rapid variations of Arctic sea ice leads, which are key features within the sea ice. Using the quality-enhanced images to extract the sea ice leads in winter Arctic Baffin Bay improved the overall accuracy from 0.88 to 0.95, thereby providing more accurate and reliable sea ice lead data.

The neutral form drag coefficient is an important parameter when estimating surface turbulent fluxes over Arctic sea ice. The form drag caused by surface features ($C_{\text {dn},\text {fr}}$) dominates the total drag in the winter, but long-term pan-Arctic records of$C_{\text {dn},\text {fr}}$are still lacking for Arctic sea ice. In this study, we first developed an improved surface feature detection algorithm and characterized the surface features (including height and spacing) over Arctic sea ice during the late winter of 2009–2019 using the full-scan laser altimeter data obtained in the Operation IceBridge mission.$C_{\text {dn},\text {fr}}$was then estimated using an existing parameterization scheme. This was followed by applying a satellite-derived backscatter coefficient (${\sigma }_{\text {vv}}^{o}$) to$C_{\text {dn},\text {fr}}$regression model to extrapolate, for the first time,$C_{\text {dn},\text {fr}}$to the pan-Arctic scale for the entire winter season over two decades (from 1999 to 2021). We found that the surface features have a larger height and smaller spacing over multiyear ice (1.15 ± 0.21 and 142 ± 49 m) than over first-year ice (0.90 ± 0.16 and 241 ± 129 m). The monthly mean$C_{\text {dn},\text {fr}}$increases through the winter from$0.2\times 10^{-3}$in November to 0.4–$0.5\times 10^{-3}$in April. The central Arctic has the largest$C_{\text {dn},\text {fr}}$(up to$2\times 10^{-3}$) but experienced a drop of ~50% in the period from 2001/2002 to 2008/2009. The interannual fluctuations in$C_{\text {dn},\text {fr}}$are strongly linked to the variability of sea ice thickness and deformation, and the latter has become increasingly important for$C_{\text {dn},\text {fr}}$since 2009.

The Antarctic blue ice can be classified into wind- and melt-induced based on their origins. They play a different role in the development of surface water systems, the surface energy balance and the infrastructure. Currently, visible light remote sensing is the most effective method for mapping blue ice. However, optical imagery faces difficulties in classifying blue ice accurately, and it is also greatly influenced by weather conditions. Synthetic Aperture Radar (SAR) images have the potential to map blue ice under all weather conditions, but it is difficult to distinguish blue ice from other similar weak microwave reflecting surfaces. In this study, by employing a segmentation method based on band ratios of the Sentinel-2 images, we delineated the geographical distribution of blue ice area (BIA) in the Lambert Glacier Basin. Taking advantage of the disparity in coherence levels between melt-induced and wind-induced blue ice, we performed blue ice classification in the Lambert Glacier Basin using Sentinel-1 images. The proposed method achieves an overall accuracy of 0.91 and F1-score of 0.91 and provides blue ice types with a spatial resolution of 10 m. The total area of blue ice in the Lambert Basin was estimated to be approximately 1.986 × 104km2. Among them, the area of melt-induced blue ice was approximately 1.276 × 104km2, while the wind-induced blue ice covered around 0.710 × 104km2. Melt-induced BIA was predominantly distributed in low-altitude coastal areas and downstream of glaciers, exhibiting higher surface temperatures compared to wind-induced BIA. Wind-induced BIA, on the other hand, was mainly found near nunataks and exposed rocks, displaying higher albedo than melt-induced BIA.

Ground-based interferometric radar (GBIR) is an innovative tool for monitoring land surface subsidence and urban infrastructure deformation caused by rapid urbanization. However, the interferograms of GBIR are often contaminated by severe atmospheric effects, especially in coastal areas. In this study, we use independent component analysis (ICA) to extract atmospheric effects for the interferograms of GBIR. Analysis of the performance of ICA and traditional surface fitting methods have been carried out. The results suggest that the average improved rate of ICA is 93.05%, which is 69.33% higher than that of surface fitting.

Snow on sea ice plays an important role in the polar climate system and accurate snow depth (SD) information on sea ice is necessary for satellite estimates of sea ice thickness from both radar and laser altimeters. In this study, three independent SD products have been generated from the Advanced Microwave Scanning Radiometer 2 (AMSR2) data using different algorithms which are trained separately based on three different reference data sets, including the Ice Mass Balance Buoy (IMB) measured snow depth, the Operation IceBridge (OIB) airborne SD measurements and the monthly altimetric snow depth (ASD) product derived from CryoSat-2 (CS2) and ICESat-2 (IS2). An in-situ validation based on SD measurements from OIB and IMB and a triple collocation (TC) evaluation are both conducted to assess the accuracy of these SD products. Furthermore, a merging scheme based on error variance estimates obtained from TC analysis has been tested for merging these three SD products into a single data set. Results indicate that TC can provide information about the error characteristics of each product which is complementary to in-situ validation. Meanwhile, the merged SD product is superior to its input parent products and achieves an overall good accuracy with the correlation (r) and root mean square error (RMSE) values being 0.70 and 6.06 cm when validating using OIB data, and 0.75 and 9.12 cm when validating using IMB data. This study demonstrates the great potential of the TC method in validating and merging snow depth estimates on sea ice.

Passive microwave (PM) and synthetic aperture radar (SAR) observations are essential tools for providing long time series of sea-ice cover information, including sea-ice concentration (SIC) and sea-ice extent (SIE). Large uncertainties have been revealed in PM SIC/SIE products in the marginal ice zone (MIZ) and during the melting season, where fusion with SAR data could be effective for improving accuracy due to its high spatial resolution and ability to preserve detailed ice distributions. A comprehensive comparison of PM and SAR ice cover products is needed for better data fusion. This study evaluates one of the PM SIE products, the advanced microwave scanning radiometer 2 (AMSR2) SIE product retrieved with the arctic radiation and turbulence interaction study (ARTIST) sea ice (ASI) algorithm, using a neural-network-based SAR SIE product throughout the year 2019. First, we present key results of three assessment parameters, including the overall accuracy (OA), error-of-ice (EI), and ice edge location distance (LD), and then estimate the optimal SIC segmentation threshold for AMSR2 ASI SIE. Based on OA and EI, the annual average SIC threshold of 12.24%, winter average of 9.25%, and summer average of 16.43% are obtained and regarded as optimal by excluding cases with large uncertainties. Second, the AMSR2 ASI SIE product is found to perform better in identifying thin ice and melt ponds, while the SAR NN SIE product has better detection of brash ice and frazil ice. We introduce a parameter of sea-ice fragmentation fraction (IFF) to analyze the primary impact factors behind the different performances. It is found that the ratio of LD to IFF could distinguish the aforementioned different ice conditions, thus providing hints for combining the complementary advantages of the two SIE products during data fusion.

The objective of this letter is to extend the commonly used gradient ratio (GR) method for Arctic sea ice snow depth estimation by combining brightness temperatures from the Soil Moisture Active Passive (SMAP) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). The L band (1.4 GHz) channel from SMAP together with higher frequencies (i.e., 6.9, 10.7, 18.7, and 36.5 GHz) from AMSR2 were used to calculate GRs, which were then used to derive empirical snow depth retrieval algorithms based on 5 years of Operation IceBridge (OIB) snow depth measurements acquired on Arctic sea ice during springtime. Results show that the gradient ratio GR(1/19) at vertical polarization is suitable for snow depth estimation over both first-year ice (FYI) and multi-year ice (MYI) and could achieve the best performance with correlationrand root mean square distance (RMSD) values being -0.80 and 5.95 cm, respectively. More importantly, there exists a one-to-one relationship between snow depth and GR(1/19) independent of sea ice types, which is an advantage of the GR(1/19) over the previous GR(7/19).

Lead is an important feature of the Arctic ice cover, with possible contents of thin ice /or open water. In this letter, we present an algorithm for lead detection based on brightness temperature observations from a single thermal infrared channel of MERSI-II onboard the Chinese FY-3D satellite. Lead contents is classified into open water and thin ice with support information from Sentinel-1 SAR data. Results are evaluated based on visual interpretation of MERSI-II TIR (Thermal infrared) and Sentinel-2 NIR (Near Infrared) data. The accuracy is found to be 85.6% for lead detection and 67% and 52% for thin ice and open water within the lead, respectively.

Time Series SAR interferometry (InSAR) (TS-InSAR) has been widely applied to monitor the crustal deformation with centimeter- to millimeter-level accuracy. Phase unwrapping (PU) errors have proven to be one of the main sources of bias that hinder achieving such high accuracy. In this article, a new time series PU approach is developed to improve the unwrapping accuracy. The rationale behind the proposed method is to first improve the sparse unwrapping by mitigating the phase gradients in a 2-D network and then correcting the unwrapping errors in time, based on the triplet phase closure. Rather than the commonly used Delaunay network, we employ the all-pairs-shortest-path (APSP) algorithm from graph theory to maximize the temporal coherence of all edges and to approach the phase continuity assumption in the 2-D spatial domain. Next, we formulate the PU error correction in the 1-D temporal domain as compressed sensing (CS) problem, according to the sparsity of the remaining phase ambiguity cycles. We finally estimate phase ambiguity cycles by means of integer linear programming (ILP). The comprehensive comparisons using synthetic and real Sentinel-1 data covering Lost Hills, California, confirm the validity of the proposed 2-D + 1-D unwrapping approach and its superior performance compared to previous methods.

Surface snowmelt is a vital environmental parameter that affects energy exchanges between polar ice sheets and the atmosphere. Due to the difficulties of continuous in-situ measurements, passive microwave remote sensing technology has become a major method for obtaining ice sheet surface snowmelt states over large areas. Feng Yun-3 (FY-3) series satellites, the second generation of Chinese polar-orbiting meteorological satellite missions, have great potential for providing long-term polar ice sheet surface snowmelt state products. In this study, we establish a monthly inter-calibration model to synergize brightness temperatures from the Microwave Radiation Imager (MWRI) aboard different FY-3 satellites. Based on the calibrated continuous brightness temperature record, an improved snowmelt algorithm is proposed by using an adaptive thresholding method, which does not rely on in-situ observation data. After inter-calibration, the consistency of the melt extent obtained by different sensors is considerably better than before, with the bias decreasing from 85 pixels to 3 pixels in the Greenland Ice Sheet (GrIS) and from 16 pixels to 6 pixels in the Antarctic Ice Sheet (AIS). Evaluation of the snowmelt result is conducted with the automatic weather station (AWS) air temperature, and a promising accuracy is found with an overall accuracy above 92% in the AIS and approximately 86% in the GrIS. This study provides new possibilities for a long-term continuous snowmelt product by connecting FY-3B, FY-3C, FY-3D, and its successors FY-3F and FY-3G. The inter-calibration coefficients and FY-3 crossing times are available at https://doi.org/10.6084/m9.figshare.20657712.v1.

As a result of global warming, multiyear ice (MYI) is being replaced by first-year ice (FYI) in the Arctic. Microwave scatterometers in the Ku-band and C-band can provide daily observations of sea ice type. However, their comparative capabilities in mapping ice type have not been thoroughly evaluated. We present a systematic intercomparison of the backscatter signature in VV polarization (${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$) and the sea ice classification from three scatterometer systems using the same ice classification approach. The systems are the Ku-band quick scatterometer (QSCAT) and the newly launched Chinese rotating fan-beam scatterometer (RFSCAT) and the C-band advanced scatterometer (ASCAT). Three freezing seasons are used, i.e., 2007/08 and 2008/09 for the QSCAT/ASCAT comparison and 2019/20 for the RFSCAT/ASCAT comparison. With reference to ASCAT,${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$bias between QSCAT and RFSCAT results from their different incidence angles. A continuous declining trend of${\sigma }_{\mathrm {vv}}^{\mathrm {o}}$from MYI and FYI is observed during winter, with a greater difference between MYI and FYI in the Ku-band. The MYI and FYI extent derived from QSCAT/RFSCAT is highly consistent with that derived from ASCAT, with a difference less than 7% and 3% for MYI and FYI, respectively. The overall accuracy (OA) is around 77% and 80% for the RFSCAT results and ASCAT results, respectively, compared with Sentinel-1 SAR images. The classification results show high consistency (81%–89%) with ice charts from the Canadian Ice Service. The incorporation of${\mathrm {Tb}}_{36\mathrm {h}}$from AMSR-E/AMSR2 improves the OA of the classification when using ASCAT or RFSCAT by 7%–11%.

Temporal and spatial variation of sea ice type in the Arctic is an indicator of regional and global change. Arctic sea ice can be classified into two major categories: multiyear ice (MYI) and first-year ice. In this paper, classification method based on machine learning is established and applied to produce daily sea ice classification data set during the winter (November-April) from 2002 to 2017 using active microwave data from QuikSCAT and Advanced Scatterometer as well as passive microwave data from Advanced Microwave Scanning Radiometer for EOS, Special Sensor Microwave Imager/Sounder, and Advanced Microwave Scanning Radiometer 2 radiometer. First, the open water area is flagged out using brightness temperature (Tb) from the passive microwave sensor. Then, K-means algorithm is applied to identify the clusters of the two ice types in the Tb/backscatter parameter space and finally assign pixels to each class. Two optimization methods based on the movement of MYI and marginal ice zone are used to correct the misclassification of MYI. The results have shown a decrease of MYI in winter from 2002 to 2017, especially in 2008 and 2013 with a remarkable recovery in 2014. The classifications are consistent with results by visual interpretation from synthetic aperture radar images in the Canadian Arctic Archipelago with overall classification accuracy over 93%. Comparison with classifications from previous studies and products shows that our method could reflect more differences in MYI declining trend interannually and less anomalous fluctuations in certain years.

We developed a new method to monitor the ice sheet melting periods with passive microwave measurements. Our method combined the original brightness temperature time series with those simulated by time series simulation software package TIMESAT in order to obtain a time series with similar behaviour, but less noise. A generalized Gaussian model was used to classify the pixels to wet and dry snow. Based on the steep rise and drop of the new time series, we detected the onset and end of the Antarctic ice sheet melting from 1988 to 2008. The results indicated that the whole Antarctic experienced a deceasing melting during the 20 years. The Antarctic Peninsula region exhibited a long and stable melt occurrence, in comparison with which the Ross Ice Shelf has the nearly shortest melt duration and the largest variability in melt extent.

To provide high-quality data for time-series change detection of lake water level, an automatic and robust algorithm for lake water footprint (LWF) identification is developed. Based on the Ice, Cloud, and Land Elevation Satellite GLA14 data file, six parameters were taken as features of an algorithm for LWF identification, and they are elevation difference between adjacent footprints, waveform width, number of peaks, reflectivity, kurtosis, and skewness of laser echoes. The sensitivity of each parameter was discussed, and elevation difference between adjacent footprints was proved to be most effective. The algorithm was described as a combination of these six parameters, and the thresholds of each parameter were set through statistics of LWF covering Peiku Co in Tibet, China, from 2003 to 2009. The performance of this classification algorithm was evaluated by the user's accuracy and producer's accuracy. Greater than 94% is achieved for all four tested lakes with 97% being the best result of producer's accuracy, and the user's accuracy ranges from 97.9% to 90% for these four lakes.