Accurate estimation of snow depth on Antarctic sea ice is critical for understanding ice mass balance, surface thermodynamics, and satellite-altimetry-based sea ice thickness retrievals. This study introduces a dual-mode retrieval framework for deriving a snow depth product on Antarctic sea ice using the Microwave Radiation Imager (MWRI). The MWRI snow depth product outperforms existing passive-microwave products in accuracy, seasonal adaptability, and temporal consistency. Validation against multiple in situ datasets shows that MWRI snow depth achieves superior performance across most Antarctic regions, notably with an RMSE below 9.0 cm and a correlation coefficient above 0.70 in West Antarctica. During the melt season, validation with AWI snow buoys yielded an RMSE of 8.5 cm, demonstrating robustness under complex surface conditions. Time-series comparisons with ICESat-2 snow depth demonstrate that the MWRI snow depth effectively captures seasonal variability (r = 0.79), accurately reproducing the winter-to-spring snow-accumulation trend. Seasonally, snow depth rises in winter and early spring and diminishes during summer melt and compaction. Interannually, long-term snow-covered zones—particularly the Weddell and Amundsen Seas—remain relatively stable and thick, while marginal ice areas exhibit a clear thinning trend. The dataset is available at https://doi.org/10.57760/sciencedb.25039.

Sea ice leads are crucial for investigating the mechanisms of polar climate change and ensuring the safety of maritime navigation. Although deep learning techniques have been widely employed for ice leads recognition, they still demonstrate significant limitations. These limitations are particularly due to substantial classification errors in the boundary transition regions between ice and leads, as well as difficulties in effectively identifying narrow leads. To address these challenges, we propose a neural network model named BAFormer, which employs a two-stage decoder composed of a coarse-level decoder and a coarse-to-fine decoder to refine the classification of boundary regions. Furthermore, a boundary evaluation metric is incorporated to enhance the loss function during the training phase. We applied BAFormer to Sentinel-1 images to obtain high-resolution ice lead maps and conducted comparative experiments with ten classical segmentation models. Results demonstrate that the proposed BAFormer achieves state-of-the-art performance, delivering optimal boundary adherence with leads extraction accuracy reaching 99.21% and at least 18.20% improvement in Intersection over Union (IoU) metrics. We further integrate the generated results with Sentinel-2 images and MODIS ice surface temperature (IST) products for combined analysis, and verify that BAFormer maintains stable performance on multi-scale ice lead datasets.

Abstract. Melt ponds are a key component of the summer Arctic sea-ice surface because their formation and evolution strongly affect surface albedo, energy absorption, meltwater redistribution, and sea-ice mass balance. Most previous remote-sensing studies have treated melt ponds as a single surface class, limiting the characterization of heterogeneous pond states during late-summer melt and refreezing. In this study, we developed MP-Unet, a stage-aware semantic segmentation framework for identifying Open, Transitional, and Frozen Melt Ponds from high-resolution unmanned aerial vehicle imagery acquired during the 14th Chinese National Arctic Research Expedition. MP-Unet integrates residual blocks with channel attention, atrous spatial pyramid pooling, attention-gated skip connections, and an auxiliary binary segmentation head. The full model achieved an F1-score of 0.9440 and a mean intersection over union of 0.7466, with class-specific IoU values of 0.5897, 0.7544, and 0.6539 for Open, Transitional, and Frozen Melt Ponds, respectively. Stage-resolved mapping revealed marked spatial heterogeneity among the five observation sites, while Transitional Melt Ponds accounted for approximately 80.3 % of the total pond area in the pooled sample. Pond area–frequency distributions showed a general scale-dependent decline and a sparse large-area tail, although the strength of the fitted scaling relationship varied among sites. Object-level analysis further showed that Frozen Melt Ponds generally had more compact and regular shapes, whereas Open Melt Ponds exhibited broader circularity distributions extending toward lower values. DEM-assisted analysis indicated significant stage-dependent differences in local relative elevation: Transitional Melt Ponds occupied lower local topographic positions than Frozen Melt Ponds, despite the absence of significant differences in distance to the nearest ridge-like feature. These findings demonstrate that stage-aware classification provides information beyond conventional binary melt pond mapping by linking surface-state identification with pond morphology and local microtopographic position. The proposed framework offers a practical basis for fine-scale observations of Arctic sea-ice surface evolution and for the validation and improvement of satellite and numerical melt pond products.

The Qinghai–Tibet Plateau, a globally critical climate-sensitive alpine region, is facing severe meadow degradation, habitat fragmentation and biodiversity loss driven by climate change and anthropogenic disturbances. Most existing assessments lack comprehensive ecological indicators for quantifying climate-driven habitat responses of endangered mammals, use fragmented methods that cannot capture cascading landscape processes, and over-rely on single-species models, which restricts integrated evaluation of multi-species habitat dynamics under future climate scenarios. To address these gaps, we established an integrated framework coupling land-use simulation, habitat suitability and landscape connectivity modeling. We quantified composite habitat suitability as a core indicator, together with Morphological Spatial Pattern Analysis (MSPA)-derived ecological core area and ecological connectivity cost distance. Results show that warming does not necessarily cause a generalized decline in habitat for endangered alpine mammals. Instead, habitat outcomes emerge from non-linear interactions among warming intensity, precipitation regime shifts, topographic constraints, and scenario-specific land-use disturbance patterns. SSP2–4.5 yields the smallest and most fragmented ecological sources (55,653 km2); warm-wet SSP5–8.5 creates continuous high-elevation habitats (137,261.08 km2); SSP1–2.6 boosts core habitats by 258.8% to 210,554.08 km2. We identified six habitat response patterns for nine endangered mammals, validated the effectiveness of existing protected areas and detected dynamic conservation gaps. This study clarifies climate-driven habitat indicator responses and supports climate-adaptive conservation for endangered mammals on the Qinghai–Tibet Plateau.

Since the 1890s, buoy- and camp-based Lagrangian observations relying on ice floes have been indispensable for data acquisition in the difficult-to-access central Arctic Ocean in winter. Evaluating the potential observation duration, and how it changes in association with changes in the Arctic climate system, is crucial for planning ice camp or buoy deployment. Using a remote sensing sea ice motion product, we reconstructed sea ice drift trajectories for each annual cycle from 1979-1980 to 2022-2023 and identified ideal areas for ice camp or buoy deployment in the central Arctic Ocean. The results show that, based on the setup time of 1 October, areas centered at 82 degrees N and 160 degrees E, near north of the East Siberian and Laptev seas, with a size of 7.0 x 105 km2, could ensure Lagrangian observations for at least 9 months, with the drifting remaining in the ice zone and not entering the exclusive economic zones (EEZs) of Arctic coastal countries, with the probability of 75.0 %-90.9 % over 44 years. The potential deployment areas favored ice advection to the Transpolar Drift (TPD) region relative to the Beaufort Gyre (BG) region. Ice trajectory terminal points did not reveal an obvious long-term tendency, but they were regulated by large-scale atmospheric circulation patterns, especially those in the early drifting stage in autumn (OND). In particular, the autumn east-west surface air pressure gradient across the central Arctic and the Arctic dipole anomaly indices significantly influenced the terminal points of ice trajectories after 9 months, and their extreme positive phases were found to expand the ideal deployment areas. The rate of increase in near-surface air temperatures in autumn-spring along the trajectories was more pronounced in the TPD region than that in the BG region. The sea ice response to wind stress significantly intensified in recent Lagrangian observations, suggesting stronger ice dynamic processes as the sea ice thins. The geopolitical boundaries of EEZs have a significant impact on the sustainability of the Lagrangian observations, limiting them to a maximum of 10 months. Without this restriction, the potential Lagrangian observations in the BG and TPD regions would expand southward, with an increased duration by 20.5 and 5.0 d, respectively, compared to those with the EEZ restriction.

Coastal polynyas are critical polar marine systems that benefit from solar radiation in summer, triggering early phytoplankton blooms. However, phytoplankton dynamics within the coastal polynya in the Ross Sea remain understudied due to harsh polar conditions. This study delineated the coastal polynya using sea ice concentration (SIC) data and investigated spatio-temporal variations in chlorophyll-a (Chl) concentration from November 2003 to March 2025. Results showed that Chl formed a long belt along the Ross Ice Shelf, extending northward with decreasing concentration. Phytoplankton blooms initiated in November, expanded through December and January, and declined by February and March, with monthly average Chl ranging from 0.78 mg/m3 to 2.15 mg/m3. Seasonal Chl variations aligned with photosynthetically active radiation (PAR) and sea surface temperature (SST), but were inversely related to SIC and wind speed (Wspeed). A notable increase in Chl (0.03 mg/m3 yr−1) was observed after 2014, alongside increasing PAR (0.23 einstein/m2/d yr−1), which was identified as the dominant driver. Spatial patterns illustrated that high PAR (35–40 einstein/m2/d) and low SIC (0–5%) promoted bloom development. Moderate SST (−1.2°C to −0.6°C) and offshore winds (Wspeed: 5–7 m/s) influenced bloom intensity and extent, while wind direction shaped Chl dispersion. These findings enhance our understanding of phytoplankton dynamics and potential climate responses in this key polar region.

Objective The terrestrial ecosystem carbon monitoring satellite (CM-1) is China's first forestry-focused remote sensing satellite designed to measure the vertical structure of terrestrial ecosystem forests. Equipped with a 5-beam LiDAR, the satellite collects high-precision ground elevation data. However, laser altimetry data is affected by atmospheric conditions and complex terrain during transmission, making it unsuitable for direct use as elevation control points in its raw form. To address this challenge, we develop an automatic classification extraction method for laser elevation control points tailored to CM-1. This method leverages the characteristics of satellite data and employs multi-criteria constraints to ensure high elevation accuracy, providing critical support for generating regional digital surface model (DSM) using stereo images. Methods To ensure the extracted laser elevation control points meet accuracy requirements, we propose a multi-criteria constraint-based automatic classification method. First, the elevation difference between the laser point and an open digital elevation model (DEM) is calculated, and laser points with differences exceeding 30 m are flagged as gross errors. Coarse screening is then conducted to assess data validity. The maximum amplitude of the echo waveform is analyzed to identify and eliminate saturated data, while low signal-to-noise ratio (SNR) data is also eliminated. Subsequently, only laser data with a single waveform peak is retained to mitigate the influence of complex ground surfaces on elevation accuracy. Using the laser radar equation, the relationship between surface slope, received waveform pulse width, and elevation accuracy is analyzed. The pulse width is employed to estimate the elevation accuracy of laser points, which are then classified based on different accuracy levels. Results and Discussions High-precision airborne laser point cloud data from Shenyang and Pennsylvania are used to validate the accuracy of extracted laser elevation control points. In Shenyang, 1353 laser points are initially identified, of which 778 are retained after screening. The overall elevation accuracy improves from 2.410 m to 0.440 m (Table 3). In Pennsylvania, 23713 laser points are identified with 5226 retained, resulting in an accuracy improvement from 4.130 m to 0.747 m (Table 4). The influence of different screening parameters, including DEM elevation difference, saturation, SNR, and waveform peak count, is statistically analyzed in the two test areas (Table 5). A regional DSM test is conducted by integrating laser elevation control points with stereo images. The results demonstrate a significant improvement in DSM accuracy, with elevation errors reduced from 11.45 m to 2.27 m. Conclusions In this paper, we first analyze the quality of multi-beam laser data from the CM-1. Based on the characteristics of the laser data, a multi-criteria constraint method for extracting laser elevation control points is developed, enabling classification by elevation accuracy. Validation in Shenyang and Pennsylvania demonstrates significant improvements in elevation accuracy. The errors of the extracted laser elevation control points are reduced from (0.099 +/- 2.410) m and (0.945 +/- 4.130) m to (-0.007 +/- 0.440) m and (-0.086 +/- 0.607) m, respectively. The extracted points meet elevation control requirements for 1:50000 or larger scale stereo mapping. Moreover, integrating laser elevation control points with multi-angle block adjustment reduces the root mean square error (RMSE) of 10 m grid DSM, generated from +/- 19 degrees images, from 11.45 m to 2.27 m, meeting the elevation accuracy requirements for 1:50000 scale topographic mapping.

Polar regions play a crucial role in the global climate system, serving as indicators and amplifiers of climate change. Their unique geographical environment and climate processes have a significant impact on the Earth system. Laser altimetry technology, with its sub-meter or even centimeter-level measurement accuracy, has received much attention in polar research. In recent years, the number of satellites carrying laser altimetry payloads in China has gradually increased. However, there are few polar studies based on the altimetry data from Chinese satellites. This paper first verstrate that the laser data from GF-7 and ZY-3 03 satellites achieve accuracies better than 1 meter in polar regions, while the Terrestrial Carbon Monitoring Satellite exhibits an accuracy of approximately 1. 2 meters. Subsequently, laser altimetry data is employed to assist in constructing three-dimensional polar terrain from stereo imagery, with the resulting topographic products meeting the cartographic standards for 1:10,000 scale topographic maps, thereby validating the effectiveness of the composite surveying and mapping method in polar regions. Finally, multi-source laser altimetry data is integrated to calculate ice sheet surface elevation changes, revealing the application potential of domestic satellites in polar change monitoring. This study comprehensively evaluates the polar application capabilities of domestic satellite laser altimetry data from multiple perspectives, providing critical references for future large-scale polar research utilizing domestic satellite data.

With the rapid loss of Arctic sea ice in recent decades, the pack ice zone (PIZ) is gradually transitioning to the marginal ice zone (MIZ), but its impacts on heat exchanges within the atmosphere-sea ice-ocean system remain unquantified. This study identifies the transition region from PIZ to MIZ using a positive difference in MIZ occurrence frequency between 1979-2010 and 1992-2023, and compares changes in heat exchanges in this region to those over the pan Arctic Ocean. The transition from PIZ to MIZ in summer increases shortwave radiation absorption by the ice-ocean surface (0.7 W m(-2) yr(-1), P < 0.05), further increasing upper ocean heat content (0.04 x 10(8) J m(-2) yr(-1), P < 0.05); while that in winter enhances the exchanges of longwave radiation (about 0.8 W m(-2) yr(-1), P < 0.05) and surface upward latent and sensible heat fluxes (0.4 and 0.5 W m(-2) yr(-1), P < 0.05), partly leading to an increase in 2-m air temperature (about 0.2 K yr(-1), P < 0.05), more than twice the average of the pan Arctic Ocean, although the transition region occupies less than 25% of the pan Arctic Ocean. The amplification of heat exchanges is more pronounced in the transition region from PIZ to MIZ than in the MIZ defined by ice concentration between 15% and 80%. Results highlight that the transition process from PIZ to MIZ is more critical for the thermodynamic coupling of the Arctic atmosphere-sea ice-ocean system, compared to changes in the location and extent of MIZ.

Standard ocean colour algorithms exploiting only shorter visible wavelengths (less than 560 nm) perform poorly in the Arctic Ocean (AO) due to the interference from colored detrital material (CDM). The incorporation of longer wavelengths, which are less susceptible to interference from CDM, could prove beneficial in retrieving water properties, particularly in Arctic waters with high CDM content. Similarly, algorithms that exploit only the red region of the spectrum, such as fluorescence-based approaches, are also unsuitable for these waters. This is due to the difficulty in accurately describing the background elastic scattering signal. In this study, we propose an algorithm that accounts for elastic scattering and fluorescence of phytoplankton in the full visible spectral domain by coupling a tuned version of the Garver-Siegel-Maritorena (GSM) algorithm (GSMA) for the AO with an optimized fluorescence emission model. Our novel algorithm, FGSM, demonstrate comparable overall performance to an empirical algorithm derived for chlorophyll a concentration (Chl) estimates in the AO (AO.emp), with a mean absolute difference (MAD) of 1.83. In addition, FGSM outperforms both the GSMA and the fluorescence line height (FLH) algorithms, with an improvement in the MAD of Chl estimates up to 41 %. Assessments conducted using both in situ datasets and satellite data at the Lena River Delta, a region characterized by high productivity and the presence of coastal CDM, revealed that for eutrophic waters where Chl is generally high, FGSM significantly mitigate the underestimation of Chl by AO.emp and GSMA, and exhibit enhanced robustness to produce more retrievals than the other semi-analytical algorithms. FGSM also demonstrates superior performance compared to the other algorithms assessed in this study for waters with high suspended particulate matter (SPM). Further validations for Arctic waters, particularly turbid coastal waters, are still expected in the future.

Accurate estimates of Chlorophyll-a (Chl) concentration from satellite observations are critical for understanding large-scale phytoplankton variations, particularly in the context of climate change. However, existing operational Chl retrieval algorithms have been shown to perform poorly in the Southern Ocean (SO). To address this issue, this study proposed improved Chl algorithms tailored to the SO. To this end, three Chl satellite products (MODIS, OC-CCI, and GlobColour) were evaluated against high-precision (high-performance liquid chromatography-derived, HPLC), long-term (1997–2021), and spatially widespread (south of 40°S) in situ Chl observations. Subsequently, OC3M-based empirical algorithms were improved using remote sensing reflectance (Rrs) data. Among the original products, OC-CCI exhibited the best overall performance (R2 = 0.36, Slope = 0.36), followed by GlobColour-AVW (R2 = 0.27, Slope = 0.21), whereas Aqua-MODIS showed the worst agreement (R2 = 0.18, Slope = 0.18) with in situ observations. All three products systematically underestimated Chl concentrations, with average biases of 43% (Aqua-MODIS), 24% (OC-CCI), and 36% (GlobColour-AVW), particularly at high Chl concentrations (> 0.2 mg/m3 for Aqua-MODIS and GlobColour-AVW; > 0.3 mg/m3 for OC-CCI). The parameter-tuned algorithms significantly reduced these biases to 1% (OC-CCI), 3% (GlobColour-AVW), and a slight overestimation of 2% (Aqua-MODIS). All products showed marked improvements in performance, with R2 increasing to 0.68–0.91, slopes approaching 1.0 (0.62–0.92), and notable reductions in MAE (1.39–1.42) and RMSE (1.49–1.51). These results offer enhanced capabilities for Chl retrieval in the data-sparse and optically complex waters of the SO.

Antarctic sea ice is an important part of the Earth’s atmospheric system, and satellite remote sensing is an important technology for observing Antarctic sea ice. Whether Chinese Haiyang-2B (HY-2B) satellite altimeter data could be used to estimate sea ice freeboard and provide alternative Antarctic sea ice thickness information with a high precision and long time series, as other radar altimetry satellites can, needs further investigation. This paper proposed an algorithm to discriminate leads and then retrieve sea ice freeboard and thickness from HY-2B radar altimeter data. We first collected the Moderate-resolution Imaging Spectroradiometer ice surface temperature (IST) product from the National Aeronautics and Space Administration to extract leads from the Antarctic waters and verified their accuracy through Sentinel-1 Synthetic Aperture Radar images. Second, a surface classification decision tree was generated for HY-2B satellite altimeter measurements of the Antarctic waters to extract leads and calculate local sea surface heights. We then estimated the Antarctic sea ice freeboard and thickness based on local sea surface heights and the static equilibrium equation. Finally, the retrieved HY-2B Antarctic sea ice thickness was compared with the CryoSat-2 sea ice thickness and the Antarctic Sea Ice Processes and Climate (ASPeCt) ship-based observed sea ice thickness. The results indicate that our classification decision tree constructed for HY-2B satellite altimeter measurements was reasonable, and the root mean square error of the obtained sea ice thickness compared to the ship measurements was 0.62 m. The proposed sea ice thickness algorithm for the HY-2B radar satellite fills a gap in this application domain for the HY-series satellites and can be a complement to existing Antarctic sea ice thickness products; this algorithm could provide long-time-series and large-scale sea ice thickness data that contribute to research on global climate change.

The snow depth on sea ice is an extremely critical part of the cryosphere. Monitoring and understanding changes of snow depth on Antarctic sea ice is beneficial for research on sea ice and global climate change. The Microwave Radiation Imager (MWRI) sensor aboard the Chinese FengYun-3D (FY-3D) satellite has great potential for obtaining information of the spatial and temporal distribution of snow depth on the sea ice. By comparing in-situ snow depth measurements during the 35th Chinese Antarctic Research Expedition (CHINARE-35), we took advantage of the combination of multiple gradient ratio (GR (36V, 10V) and GR (36V, 18V)) derived from the measured brightness temperature of FY-3D MWRI to estimate the snow depth. This method could simultaneously introduce the advantages of high and low GR in the snow depth retrieval model and perform well in both deep and shallow snow layers. Based on this, we constructed a novel model to retrieve the FY-3D MWRI snow depth on Antarctic sea ice. The new model validated by the ship-based observational snow depth data from CHINARE-35 and the snow depth measured by snow buoys from the Alfred Wegener Institute (AWI) suggest that the model proposed in this study performs better than traditional models, with root mean square deviations (RMSDs) of 8.59 cm and 7.71 cm, respectively. A comparison with the snow depth measured from Operation IceBridge (OIB) project indicates that FY-3D MWRI snow depth was more accurate than the released snow depth product from the U.S. National Snow and Ice Data Center (NSIDC) and the National Tibetan Plateau Data Center (NTPDC). The spatial distribution of the snow depth from FY-3D MWRI agrees basically with that from ICESat-2; this demonstrates its reliability for estimating Antarctic snow depth, and thus has great potential for understanding snow depth variations on Antarctic sea ice in the context of global climate change.

Sea ice and its surface snow are crucial components of the energy cycle and mass balance between the atmosphere and ocean, serving as sensitive indicators of climate change. Observing and understanding changes in snow depth on Antarctic sea ice are essential for sea ice research and global climate change studies. This study explores the feasibility of retrieving snow depth on Antarctic sea ice using data from the Chinese marine satellite HY-2B. Using generic retrieval algorithms, snow depth on Antarctic sea ice was retrieved from HY-2B Scanning Microwave Radiometer (SMR) data, and compared with existing snow depth products derived from other microwave radiometer data. A comparison against ship-based snow depth measurements from the Chinese 35th Antarctic Scientific Expedition shows that snow depth derived from HY-2B SMR data using the Comiso03 retrieval algorithm exhibits the lowest RMSD, with a deviation of −1.9 cm compared to the Markus98 and Shen22 models. The snow depth derived using the Comiso03 model from HY-2B SMR shows agreement with the GCOM-W1 AMSR-2 snow depth product released by the National Snow and Ice Data Center (NSIDC). Differences between the two primarily occur during the sea ice ablation and in the Bellingshausen Sea, Amundsen Sea, and the southern Pacific Ocean. In 2019, the monthly average snow depth on Antarctic sea ice reached its maximum in January (36.2 cm) and decreased to its minimum in May (15.3 cm). Thicker snow cover was observed in the Weddell Sea, Ross Sea, and Bellingshausen and Amundsen seas, primarily due to the presence of multi-year ice, while thinner snow cover was found in the southern Indian Ocean and the southern Pacific Ocean. The derived snow depth product from HY-2B SMR data demonstrates high accuracy in retrieving snow depth on Antarctic sea ice, highlighting its potential as a reliable alternative for snow depth measurements. This product significantly contributes to observing and understanding changes in snow depth on Antarctic sea ice and its relationship with climate change.

Snow on the Antarctic sea ice is a crucial component of the cryosphere. In response to the dynamic and highly heterogeneous Antarctic snow during the sea ice melting season, this study employed a combined multi-source data and deep learning method to accurately retrieve snow depth on Antarctic sea ice. Initially, we integrate multiple datasets, including satellite remote sensing, geospatial information, and meteorological data. Subsequently, a Convolutional Neural Network (CNN) is utilized to construct a snow depth retrieval model (PSDCNN-5_7 model). Compared to snow depth measurements from Alfred Wegener Institute (AWI) snow buoys, the PSDCNN-5_7 model outperforms existing algorithms, exhibiting a deviation of only −3.38 cm. The uncertainty of the snow depth caused by the model input is only 1.64 cm. In West Antarctica, snow depth is more affected by snowfall (SF), 2-m air temperature (T2m), and sea ice velocity (SIV). Conversely, in East Antarctica, snow depth is primarily influenced by SIV. The proposed approach accurately retrieves snow depth on Antarctic sea ice and facilitates the derivation of long-term variations and trends in snow depth, contributing to a better understanding of the relationship between sea ice, snow, and climate change.

Chlorophyll a concentration (Chl) is a key variable for estimating primary production (PP) through ocean-color remote sensing (OCRS). Accurate Chl estimates are crucial for better understanding of the spatio-temporal trends in PP in recent decades as a consequence of climate change. However, a number of studies have reported that currently operational chlorophyll a algorithms perform poorly in the Arctic Ocean (AO), largely due to the interference of colored and detrital material (CDM) with the phytoplankton signal in the visible part of the spectrum. To determine how and to what extent CDM biases the estimation of Chl, we evaluated the performances of eight currently available ocean-color algorithms: OC4v6, OC3Mv6, OC3V, OC4L, OC4P, AO.emp, GSM01 and AO.GSM. Our results suggest that the empirical AO.emp algorithm performs the best overall, but, for waters with high CDM acdm(443) > 0.067 m−1), a common scenario in the Arctic, the two semi-analytical GSM models yield better performance. In addition, sensitivity analyses using a spectrally and vertically resolved Arctic primary-production model show that errors in Chl mostly propagate proportionally to PP estimates, with amplification of up to 7%. We also demonstrate that, the higher level of CDM in relation to Chl in the water column, the larger the bias in both Chl and PP estimates. Lastly, although the AO.GSM is the best overall performer among the algorithms tested, it tends to fail for a significant number of pixels (16.2% according to the present study), particularly for waters with high CDM. Our results therefore suggest the ongoing need to develop an algorithm that provides reasonable Chl estimates for a wide range of optically complex Arctic waters.

The characteristics of sea ice leads (SILs) in the Weddell Sea are an important basis for understanding the mechanism of the atmosphere–ocean system in the Southern Ocean. In this study, we derived the sea ice surface temperature (IST) of the Weddell Sea from MODIS thermal images and then generated a daily SIL map for 2015 and 2022 by utilizing the iterative threshold method on the optimised MOD35 cloud-masked IST. The results showed that SIL variations in the Weddell Sea presented remarkable seasonal characteristics. The trend of the SIL area exhibited an initial rise followed by a decline from January to December, characterised by lower values in spring and summer and higher values in fall and winter. SILs in the Weddell Sea were predominantly concentrated between 70~78°S and 60~30°W. The coastal spatial distribution density of the SILs exceeded that of offshore regions, peaking near the Antarctic Peninsula and then near Queen Maud Land. The SIL variation was mainly influenced by dynamical factors, and there were strong positive correlations between the wind field, ocean currents, and sea-ice motion.

The Arctic Ocean (AO) is the most river-influenced ocean. Located at the land-sea interface wherein phytoplankton blooms are common, Arctic coastal waterbodies are among the most affected regions by climate change. Given phytoplankton are critical for energy transfer supporting marine food webs, accurate estimation of chlorophyll a concentration (Chl), which is frequently used as a proxy of phytoplankton biomass, is critical for improving our knowledge of the Arctic marine ecosystem and its response to the ongoing climate change. Due to the unique and complex bio-optical properties of the AO, efforts are still needed to obtain more accurate Chl estimates, especially for coastal waters with high colored detrital material (CDM) content. In this study, we optimized the the Garver-Siegel-Maritorena (GSM) algorithm, using an Arctic bio-optical dataset comprised of seven wavelengths (the original GSM wavelengths plus 625 nm). Results suggested that our tuned algorithm, denoted GSMA, outperformed an alternative AO GSM algorithm denoted AO.GSM, but the accuracy of Chl estimates was only improved by 8%. In addition, GSMA showed appreciable robustness when assessed using a satellite image and two non-Arctic coastal datasets.