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.

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.

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.

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).

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.

Incident solar radiation (Rs) over the Earth's surface plays an important role in determining the Earth's climate and environment. Generally, Rscan be obtained from direct measurements, remotely sensed data, or reanalysis and general circulation model (GCM) data. Each type of product has advantages and limitations: the surface direct measurements provide accurate but sparse spatial coverage, whereas other global products may have large uncertainties. Ground measurements have been normally used for validation and occasionally calibration, but transforming their “true values” spatially to improve the satellite products is still a new and challenging topic. In this paper, an improved thin-plate smoothing spline approach is presented to locally “calibrate” the Global LAnd Surface Satellite (GLASS) Rsproduct using the reconstructed Rsdata from surface meteorological measurements. The influence of surface elevation on Rsestimation was also considered in the proposed method. The point-based surface reconstructed Rswas used as the response variable, and the GLASS Rsproduct and the surface elevation data at the corresponding locations as explanatory variables to train the thin-plate spline model. We evaluated the performance of the approach using the cross-validation method at both daily and monthly time scales over China. We also validated the estimated Rsbased on the thin-plate spline method using independent ground measurements and independent satellite estimates of Rs. These validation results indicated that the thin-plate smoothing spline method can be effectively used for calibrating satellite-derived Rsproducts using ground measurements to achieve better accuracy.

The ocean environmental parameters of fishing ground have an important affection on the distribution and abundance of fishing ground. In this paper, a GIS-based method of retrieving the ocean environmental parameters of fishing ground is introduced. In case study, this method was used to study the relationships between Kuroshio path variations and CPUE of squid (Ommastrephes Bartrami) fishing ground in Northwest Pacific Ocean.