Yawen He

With global warming and the gradual melting of Arctic sea ice, the navigation duration of the Northeast Passage (NEP) is gradually increasing. The dynamic changes in sea ice concentration (SIC) during navigation time are a critical factor affecting the navigation of the passage. This study uses multiple linear regression and random forest to analyze the navigation windows of the NEP from 1979 to 2022 and examines the critical factors affecting the dynamic changes in the SIC. The results suggest that there are 25 years of navigable windows from 1979 to 2022. The average start date of navigable windows is approximately between late July and early August, while the end date is approximately early and mid-October, with considerable variation in the duration of navigable windows. The explanatory power of RF is significantly better than MLR, while LMG is better at identifying extreme events, and RF is more suitable for assessing the combined effects of all variables on the sea ice concentration. This study also found that the 2 m temperature is the main influencing factor, and the sea ice movement, sea level pressure and 10 m wind speed also play a role in a specific period. By integrating traditional statistical methods with machine learning techniques, this study reveals the dynamic changes of the SIC during the navigation period of the NEP and identifies its driving factors. This provides a scientific reference for the development and utilization of the Arctic Passage.

The dynamic changes of sea ice exhibit spatial clustering, and this clustering has characteristics extending from its origin, through its development, and to its dissipation. Current research on sea ice change primarily focuses on spatiotemporal variation trends and remote correlation analysis, and lacks an analysis of spatiotemporal evolution characteristics. This study utilized monthly sea ice concentration (SIC) data from the National Snow and Ice Data Center (NSIDC) for the period from 1979 to 2022, utilizing classical spatiotemporal clustering algorithms to analyze the clustering patterns and evolutionary characteristics of SIC anomalies in key Arctic regions. The results revealed that the central-western region of the Barents Sea was a critical area where SIC anomaly evolutionary behaviors were concentrated and persisted for longer durations. The relationship between the intensity and duration of SIC anomaly events was nonlinear. A positive correlation was observed for shorter durations, while a negative correlation was noted for longer durations. Anomalies predominantly occurred in December, with complex evolution happening in April and May of the following year, and concluded in July. Evolutionary state transitions mainly occurred in the Barents Sea. These transitions included shifts from the origin state in the northwestern margin to the dissipation state in the central-north Barents Sea, from the origin state in the central-north to the dissipation state in the central-south, and from the origin state in the northeastern to the dissipation state in the central-south Barents Sea and southeastern Kara Sea. Various evolutionary states were observed in the same area on the southwest edge of the Barents Sea. These findings provide insights into the evolutionary mechanism of sea ice anomalies.

The value of ecosystem regulation services (ERSV) is a crucial aspect of gross ecosystem product (GEP). Understanding and mastering the spatiotemporal evolution characteristics and driving factors of ERSV is essential for the efficient management of regional ecosystems. This study proposes an ERSV accounting model at the plot scale and uses the barycentric analysis method, the optimal parameters-based geographical detector model (OPGD), and the constraint line extraction method to analyze the spatiotemporal evolution characteristics, main driving factors, and constraint rules of ERSV in Yunyang District, Hubei Province in 2016, 2018, 2020, and 2021. The results show that (1) In the temporal dimension, the overall ERSV of the district increased by CNY 1.664 billion from 2016 to 2021, with an increase rate of 3.68%. The contribution values of climate regulation function and water retention function to ERSV was significant. (2) In the spatial dimension, the ERSV was high in the north and south and low in the middle, with high-value areas mainly located in woodland and wetland areas. The center of gravity of the ERSV increase shifted to the southwest by 12,455.42 m, while the center of gravity of the reduction shifted to the southwest by 3582.79 m from 2016 to 2021. (3) The interaction of any two driving factors had greater explanatory power for the spatial differentiation of ERSV than that of a single driving factor, and all of them showed nonlinear or double factor enhancement characteristics. The human active index (HAI) and construction land proportion (CLP) were the leading anthropogenic factors, while the land surface temperature (LST) and NDVI were the leading natural factors. (4) The ERSV could maintain a high and stable value output when the HAI was less than 0.3, CLP was less than 15%, LST was between 18 and 22 °C, and NDVI was greater than 0.5. These results can guide the practices of ecology, production, and life in Yunyang District and contribute to the high quality and sustainable development of the regional ecology and economy.

The 44 years (1979–2022) of satellite-derived sea ice extent in the Arctic and Antarctic reveals the details and new trends in the process of polar sea ice coverage changes. The speed of Arctic sea ice extent reduction and the interannual difference significantly increased after 2004. Trend analysis suggests that the Arctic Ocean may experience an ice-free period around 2060. The maximum anomaly of Arctic sea ice extent has gradually transitioned from September to October, indicating a trend of prolonged melting period. The center of gravity of sea ice in the Arctic Ocean is biased towards the Pacific side, and the spatial distribution pattern of sea ice is greatly influenced by the Atlantic warm current. The dynamism of the sea ice extent on the Atlantic side is significantly greater than in other regions. Since 2014, the Antarctic sea ice extent has shifted from slow growth to a rapid decreasing trend; the sea ice extent reached a historical minimum in 2022, decreasing by 2.02 × 106 km² compared to 2014. The Antarctic experiences seven months of ice growth each year and five months of ice melting period, the annual change patterns of sea ice extent in the Arctic and Antarctic are slightly different.

Despite economic progress and reducing poverty, poverty remains one of the world’s major problems. Cropland is fundamental for agricultural production and social development in rural areas. Previous research has theoretically discussed the relationship between cultivated land and poverty incidence from the perspective of spatial poverty. The linkage between arable land use and poverty incidence is ambiguous, and other regional conditions’ moderation effect has seldom been investigated. Seven geographical indicators and four interaction terms were used to explore the moderating effect of other factors between cultivated land and poverty incidence in Linyi based on geographical weighted regression. The results demonstrate-d that environmental factors moderated the relationship between cultivated land resources and the incidence of poverty. Specifically, we found spatial nonstationarity of moderation effect associated with the elevation difference. The moderation effect of road and accessibility with cultivated land presented different directions and strengths of the association in two datasets grouped by the median of elevation. Based on these findings, we expect this paper to provide valuable insights into analyzing the poverty of complexity of interaction with the environment.

Obtaining field sea ice data is crucial for the validation and improvement of satellite data and numerical models. During the 36th Chinese National Antarctic Research Expedition, seven field observations covering the whole area were conducted in Nella Fjord, and large amounts of in situ sea ice and snow thickness data were acquired. Then, the influences of snow cover, topography, and air temperature on the sea ice growth rate were examined. Based on the relationship between the sea ice growth rate and air temperature, an empirical model of sea ice thickness was developed. The results show that sea ice started to freeze in early March, and the peak growth rate was reached in mid-June, with a mean growth rate of 0.62 cm/d. Between mid-June and mid-October, the mean growth rate was 0.36–0.4 cm/d; between mid-October and mid-November, the mean growth rate decreased to 0.15 cm/d. After mid-November, ice growth stopped, and the ice thickness decreased. The growth rate and thickness of sea ice near the shore were generally greater than those closer to the center of the fjord, with differences of 0.25 cm/d in mean growth rate and 7.16 cm in mean thickness. Because of the large exchanges of heat between the atmosphere and the ocean, the sea ice thickness increased rapidly during the early stage of freeze-up. When the air temperature was between -12 and −15°C, the ice growth rate was between 0.36 and 0.4 cm/d. However, when the ice thickness reached 0.5 m, the sea ice growth rate became less sensitive to air temperature. In addition, we found that the proposed model has high simulation accuracy for the growth rate of sea ice. Based on the model, we estimated that the growth rates become zero at an air temperature of approximately −4°C. Our study highlights the inverse relationship between the sea ice growth rate and the snow thickness and the fact that the sea ice thickness is mainly affected by air temperature.

From the time that it first develops, a sea surface temperature anomaly (SSTA) will develop in space and time until it dissipates. Although many SST products are available, great challenges are still faced when attempting to directly explore the evolution of SSTAs. To address some of these problems, in this study, we developed a global SSTA dataset that included details of the spatial structure of SSTAs and their temporal evolution. This dataset is called GDPoSSTA. GDPoSSTA is comprised of three datasets and two relationship files and covers the period from January 1982 to December 2009. The three datasets are in SHP format and consist of a dataset of processed object-oriented SSTAs named DSPOSSTA, a dataset of sequenced object-oriented SSTA series named DSSOSSTA, and a dataset of variation object-oriented SSTA named DSVOSSTA. The two relationship files, which are in CSV format, store the evolving behavior of the SSTA sequence object and SSTA variation objects. Finally, geographic spatiotemporal statistics are derived for the DSPOSSTA and a comparison of applying TITAN to DSVOSSTA and DSPOSSTA is carried out which demonstrates the feasibility and applicability of GDPoSSTA. The GDPoSSTA dataset is available on ScienceDB platform (http://www.doi.org/10.11922/sciencedb.j00076.00090).

Climate warming has enabled the Arctic region to achieve seasonal navigation, and sea ice concentration is an important factor affecting the navigation of the Arctic waterways. This article uses the Arctic sea ice concentration data of the three highest temperatures in 2016, 2019, and 2020, combined with the Arctic summer sea level pressure, wind field, temperature, temperature anomaly, ice age, and sea ice movement data to analyze the spatial and temporal variation of sea ice and connectivity in the Northeast Passage (NEP) of the Arctic in Summer in three hot years, and summarizes the causes of sea ice anomalies. The results show that: (1) the summer Arctic sea ice extent in 2016, 2019 and 2020 were all lower than the multi-year average sea ice extent, and the summer sea ice extent in 2020 had the largest change trend; (2) in October of these three years, the sea ice was all negative anomalies, extending the opening time of the NEP; (3) when the sea ice concentration was 30% as the threshold, the navigation period of the NEP in 2016 was from mid-August to late October, 2019 was from the beginning of August to mid-October, 2020 was from the end of July to the end of October, and 2020 was the longest year since the opening of the NEP; (4) when the sea ice concentration was 10% as the threshold, the navigation period of the NEP in 2016 was from the end of August to the end of October, 2019 was from early August to mid-October, and 2020 was from the beginning of August to the end of October; (5) the key navigable areas of the NEP in the past three years were the central waters of the East Siberian Sea, the New Siberian Islands and the Vilkitsky Strait; (6) the navigation period of the NEP in 2016, 2019 and 2020 was longer. The main reasons were that the temperature of the NEP in the past 3 years was relatively high, the wind was weak, the sea ice movement had little effect, and the sea ice age in the key navigable areas was first year ice, which was easy to melt, which greatly promoted the opening of the NEP.

Automatic cloud detection in remote sensing images is of great significance. Deep-learning-based methods can achieve cloud detection with high accuracy; however, network training heavily relies on a large number of labels. Manually labelling pixel-wise level cloud and non-cloud annotations for many remote sensing images is laborious and requires expert-level knowledge. Different types of satellite images cannot share a set of training data, due to the difference in spectral range and spatial resolution between them. Hence, labelled samples in each upcoming satellite image are required to train a new deep-learning-based model. In order to overcome such a limitation, a novel cloud detection algorithm based on a spectral library and convolutional neural network (CD-SLCNN) was proposed in this paper. In this method, the residual learning and one-dimensional CNN (Res-1D-CNN) was used to accurately capture the spectral information of the pixels based on the prior spectral library, effectively preventing errors due to the uncertainties in thin clouds, broken clouds, and clear-sky pixels during remote sensing interpretation. Benefiting from data simulation, the method is suitable for the cloud detection of different types of multispectral data. A total of 62 Landsat-8 Operational Land Imagers (OLI), 25 Moderate Resolution Imaging Spectroradiometers (MODIS), and 20 Sentinel-2 satellite images acquired at different times and over different types of underlying surfaces, such as a high vegetation coverage, urban area, bare soil, water, and mountains, were used for cloud detection validation and quantitative analysis, and the cloud detection results were compared with the results from the function of the mask, MODIS cloud mask, support vector machine, and random forest. The comparison revealed that the CD-SLCNN method achieved the best performance, with a higher overall accuracy (95.6%, 95.36%, 94.27%) and mean intersection over union (77.82%, 77.94%, 77.23%) on the Landsat-8 OLI, MODIS, and Sentinel-2 data, respectively. The CD-SLCNN algorithm produced consistent results with a more accurate cloud contour on thick, thin, and broken clouds over a diverse underlying surface, and had a stable performance regarding bright surfaces, such as buildings, ice, and snow.

The ablation of Arctic sea ice makes seasonal navigation possible in the Arctic region, which accounted for the apparent influence of sea ice concentration in the navigation of the Arctic route. This paper uses Arctic sea ice concentration daily data from January 1, 2000, to December 31, 2019. We used a sea ice concentration threshold value of 40% to define the time window for navigating through the Arctic Northeast Passage (NEP). In addition, for the year when the navigation time of the NEP is relatively abnormal, we combined with wind field, temperature, temperature anomaly, sea ice age and sea ice movement data to analyze the sea ice conditions of the NEP and obtain the main factors affecting the navigation of the NEP. The results reveal the following: (1) The sea ice concentration of the NEP varies greatly seasonally. The best month for navigation is September. The opening time of the NEP varies from late July to early September, the end of navigation is concentrated in mid-October, and the navigation time is basically maintained at more than 30 days. (2) The NEP was not navigable in 2000, 2001, 2003 and 2004. The main factors are the high amount of multi-year ice, low temperature and the wind field blowing towards the Vilkitsky Strait and sea ice movement. The navigation time in 2012, 2015 and 2019 was longer, and the driving factors were the high temperature, weak wind and low amount of one-year ice. The navigation time in 2003, 2007 and 2013 was shorter, and the influencing factors were the strong wind field blowing towards the Vilkitsky Strait. (3) The key navigable areas of the NEP are the central part of the East Siberian Sea and the Vilkitsky Strait, and the Vilkitsky Strait has a greater impact on the NEP than the central part of the East Siberian Sea. The main reason for the high concentration of sea ice in the central part of the East Siberian Sea (2000 and 2001) was the large amount of multi-year ice. The main reason for the high concentration of sea ice in the Vilkitsky Strait (2000 to 2004 and 2007, 2013) was the strong offshore wind in summer, all of which were above 4 m s⁻¹, pushing the sea ice near the Vilkitsky Strait to accumulate in the strait, thus affecting the opening of the NEP.