Accurate crop mapping is crucial for ensuring food security. Recently, many studies have developed diverse crop mapping models based on deep learning. However, these models generally rely on a large amount of labeled crop samples to investigate the intricate relationship between the crop types of the samples and the corresponding remote sensing features. Moreover, their efficacy is often compromised when applied to other areas owing to the disparities between source and target data. To address this issue, a new multi-modal deep adaptation crop classification network (MDACCN) was proposed in this study. Specifically, MDACCN synergistically exploits time series optical and SAR images using a middle fusion strategy to achieve good classification capacity. Additionally, local maximum mean discrepancy (LMMD) is embedded into the model to measure and decrease domain discrepancies between source and target domains. As a result, a well-trained model in a source domain can still maintain satisfactory accuracy when applied to a target domain. In the training process, MDACCN incorporates the labeled samples from a source domain and unlabeled samples from a target domain. When it comes to the inference process, only unlabeled samples of the target domain are required. To assess the validity of the proposed model, Arkansas State in the United States was chosen as the source domain, and Heilongjiang Province in China was selected as the target domain. Supervised deep learning and traditional machine learning models were chosen as comparison models. The results indicated that the MDACCN achieved inspiring performance in the target domain, surpassing other models with overall accuracy, Kappa, and a macro-averaged F1 score of 0.878, 0.810, and 0.746, respectively. In addition, the crop-type maps produced by the MDACCN exhibited greater consistency with the reference maps. Moreover, the integration of optical and SAR features exhibited a substantial improvement of the model in the target domain compared with using single-modal features. This study indicated the considerable potential of combining multi-modal remote sensing data and an unsupervised domain adaptive approach to provide reliable crop distribution information in areas where labeled samples are missing.

针对国内立体空间不动产确权登记的日益迫切需求,本文结合广州市权籍调查成果入库审查工作实际,提出面向二、三维一体化的不动产权籍数据库设计.数据库以三维产权体为基本管理单元,使用三维界址点、空间方位、投影坐标系等属性字段约束,在满足三维宗地、自然幢、房屋空间特征描述的同时,充分兼容存量二维权籍数据.通过构建三维权籍管理系统,验证该数据库设计在三维不动产单元参数化建模和可视化展示应用方面的有效性,对三维不动产确权登记发展具有重要意义.

Global warming caused by massive carbon emissions poses a serious threat to the environment and human beings. Dynamic monitoring of the spatio-temporal evolution of carbon emissions is an essential way to achieve carbon reduction goals, especially in coastal areas like Guangdong Province faced with dual challenges of massive carbon emissions and rigorous reduction targets. To reveal the spatio-temporal characteristics of carbon emissions and provide a basis for further fine-grained carbon emission reduction strategies, we analyzed spatio-temporal distribution characteristics of carbon emissions in Guangdong Province at the county-level scale from 2000 to 2020. Based on the land-use data obtained from the Data Center for Resources and Environmental Sciences of the Chinese Academy of Sciences and energy consumption data from the Statistical Yearbook, the carbon emission of each county is estimated, and the spatio-temporal evolutionary features of them are explored by a series of methods of the exploratory spatio-temporal data analysis (ESTDA). The shift of spatial distribution characteristics and gravity center is further revealed with the standard deviational ellipse and gravity center migration analysis.The results reveal that the net carbon emissions increased from 6179.229 × 104 tons to 20765.723 × 104 tons during 2000–2020. The net carbon emissions of county-level administrative regions show a significant positive spatial correlation, and the spatial convergence shows a trend of first decreasing and then increasing. The spatio-temporal pattern of carbon emissions has an obvious path dependence and locked spatial features, and shows a gradually strengthening trend. The spatial distribution of carbon emissions formed a stable “northeast-southwest” pattern, and the gravity center of carbon emissions in the past 20 years has been distributed between 113.208°–113.357°E and 22.760°–22.878°N, tending slightly shifting to the northeast. The results of the study can help understand the spatio-temporal evolution of carbon emissions in Guangdong, and provide a reference for formulating effective carbon reduction policies.

Polycyclic aromatic hydrocarbons (PAHs) are traditionally considered to enter the Arctic Ocean through long-range transport. Arctic warming, especially sea ice retreat, will certainly increase the contribution from local source (such as river input and ice melting). However, this hypothesis remains poorly constrained for lack of quantitative evidence. Here PAHs in surface seawater (67°N-89°N, 152°E-177°E) and sea ice (82°N-89°N) were collected in the western Arctic in 2010. Dissolved concentrations of 15 PAHs (Σ15PAHs) in surface layer ice (26.2 to 49.8 ng/L) were one order of magnitude higher than the underlying seawater. The content of dissolved Σ15PAHs was significantly higher in the marginal ice zone than those in the Chukchi Sea shelf, and the dissolved Σ15PAHs concentration differed by nearly an order of magnitude in two closely adjacent sections in the basin area, which both showed high fraction of river water and sea ice meltwater. This pattern could be explained by the different local inputs from Eurasia and North America. This scenario was further visualized by ice back trajectories capturing significantly higher PAH signals from the Eurasian margin than those from North America and stable oxygen isotopic data finding a positive correlation of PAH levels with the fractions of river runoff and ice-melting water coming from the Eurasia. The PAHs budget of the Arctic Ocean was also dominated by local sources (river and ice melting) as inputs (76 %) and volatilization as outputs (47 %). This study reveals the importance of Eurasian local inputs in supplying PAHs to the central Arctic Ocean. Those processes, which have not been well recognized for PAHs previously, are expected to increase and will undermine global efforts to reduce exposure by remobilizing PAHs stored in permafrost and ice.

Arctic sea ice kinematics and deformation play significant roles in heat and momentum exchange between the atmosphere and ocean, and at the same time they have profound impacts on biological processes and biogeochemical cycles. However, the mechanisms regulating their changes on seasonal scales and their spatial variability remain poorly understood. Using position data recorded by 32 buoys in the Pacific sector of the Arctic Ocean (PAO), we characterized the spatiotemporal variations in ice kinematics and deformation for autumn–winter 2018/19, during the transition from a melting sea ice regime to a nearly consolidated ice pack. In autumn, the response of the sea ice drift to wind and inertial forcing was stronger in the southern and western PAO compared to the northern and eastern PAO. These spatial heterogeneities gradually weakened from autumn to winter, in line with the seasonal increases in ice concentration and thickness. Correspondingly, ice deformation became much more localized as the sea ice mechanical strength increased, with the area proportion occupied by the strongest (15 %) ice deformation decreasing by about 50 % from autumn to winter. During the freezing season, ice deformation rate in the northern PAO was about 2.5 times higher than in the western PAO and probably related to the higher spatial heterogeneity of oceanic and atmospheric forcing in the north. North–south and east–west gradients in sea ice kinematics and deformation within the PAO, as observed especially during autumn in this study, are likely to become more pronounced in the future as a result of a longer melt season, especially in the western and southern parts.

1984-2019年,中国共有7艘考察船38次远赴南北两极开展科学考察活动,其中南极29航次,北极9航次,累计航程160万km。重点分析了"雪龙"号破冰船的航行特征参数,并结合GPS、机舱、罗经等多源数据,建立了破冰船航行状态分类识别模型,进而研发了破冰船的经停港口和停靠极地站点的识别模型。运用该模型对以往极地考察航次中考察船的经停港口和停靠极地站点进行识别和统计,累计经停了14个国家的32个港口,累计停靠了11个国家的18个极地考察站。探究了考察航线的空间分布规律和考察船的往返路线选择情况,并总结了南极考察船穿越西风带的航迹规律。最后利用分析结果,总结出中国南极科考4个阶段的特征,为未来南极科考提供参考建议。

The rapid development of social media data, including geotagged photos, has benefited the research of tourism geography; additionally, tourists’ increasing demand for personalized travel has encouraged more researchers to pay attention to tourism recommendation models. However, few studies have comprehensively considered the content and contextual information that may influence the recommendation accuracy, especially tourist attractions’ visual content due to redundant and noisy geotagged photos; therefore, we propose a tourist attraction recommendation model for Flickr-geotagged photos which fuses spatial, temporal, and visual embeddings (STVE). After spatial clustering and extracting visual embeddings of tourist attractions’ representative images, the spatial and temporal embeddings are modeled with the Word2Vec negative sampling strategy, and the visual embeddings are fused with Matrix Factorization and Bayesian Personalized Ranking. The combination of these two parts comprises our proposed STVE model. The experimental results demonstrate that our STVE model outperforms other baseline models. We also analyzed the parameter sensitivity and component performance to prove the performance superiority of our model.

Measurements from 30 and 31 ice drifters during the seasonal transition from mid-August to late September of 2014 and 2016, respectively, were used to characterize sea ice motion and deformation in the western Arctic Ocean. The dispersion distance for the ice deformation in summer was markedly reduced because the mechanical behavior of sea ice in summer is closer to free drift and more granular compared to that in winter. For unconsolidated sea ice in late summer the logarithmic relationship between deformation and spatial scale is 2.4-3.1 times that for ice under freezing conditions. For 2007 to 2016 late summer (August - September) sea ice compactness in the Arctic was the highest (lowest) in 2014 (2016). Along the trajectories of the 2016 (2014) ice camp, the average 10-m wind speed in August-September was larger (smaller) than that averaged in 1979-2016 by 20% (13%). Relative to the great Arctic cyclone in August 2012, cyclones in summer 2016 had comparable sizes and intensity but with longer persistence. Lower ice compactness coupled with stronger cyclonic activity in late summer 2016 led to increased ice speed and enhanced deformation compared to 2014. Sea ice during late summer 2016 was closer to free drift, resulting in greater homogeneity of the drift field, a larger ice-wind speed ratio, as well as weaker multifractality, localization, and space-time coupling of the deformation field compared to late summer 2014. The localization of ice deformation in late summer 2014 was comparable to that obtained in the freezing season because of the high ice compactness. The enhanced ice dynamics in summer 2016 promoted ice melt and area loss via the positive albedo feedback.

Extracting representative images of tourist attractions from geotagged photos is beneficial to many fields in tourist management, such as applications in touristic information systems. This task usually begins with clustering to extract tourist attractions from raw coordinates in geotagged photos. However, most existing cluster methods are limited in the accuracy and granularity of the places of interest, as well as in detecting distinct tags, due to its primary consideration of spatial relationships. After clustering, the challenge still exists for the task of extracting representative images within the geotagged base image data, because of the existence of noisy photos occupied by a large area proportion of humans and unrelated objects. In this paper, we propose a framework containing an improved cluster method and multiple neural network models to extract representative images of tourist attractions. We first propose a novel time- and user-constrained density-joinable cluster method (TU-DJ-Cluster), specific to photos with similar geotags to detect place-relevant tags. Then we merge and extend the clusters according to the similarity between pairs of tag embeddings, as trained from Word2Vec. Based on the clustering result, we filter noise images with Multilayer Perceptron and a single-shot multibox detector model, and further select representative images with the deep ranking model. We select Beijing as the study area. The quantitative and qualitative analysis, as well as the questionnaire results obtained from real-life tourists, demonstrate the effectiveness of this framework.

The accuracy of sea-ice motion products provided by the National Snow and Ice Data Center (NSIDC) and the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) was validated with data collected by ice drifters that were deployed in the western Arctic Ocean in 2014 and 2016. Data from both NSIDC and OSI-SAF products exhibited statistically significant (p< 0.001) correlation with drifter data. The OSI-SAF product tended to overestimate ice speed, while underestimation was demonstrated for the NSIDC product, especially for the melt season and the marginal ice zone. Monthly Lagrangian trajectories of ice floes were reconstructed using the products. Larger spatial variability in the deviation between NSIDC and drifter trajectories was observed than that of OSI-SAF, and seasonal variability in the deviation for NSIDC was observed. Furthermore, trajectories reconstructed using the NSIDC product were sensitive to variations in sea-ice concentration. The feasibility of using remote-sensing products to characterize sea-ice deformation was assessed by evaluating the distance between two arbitrary positions as estimated by the products. Compared with the OSI-SAF product, relative errors are lower (<11.6%), and spatial-temporal resolutions are higher in the NSIDC product, which makes it more suitable for estimating sea-ice deformation.

Data collected by two buoy arrays that operated during the ice seasons of 2014/2015 and 2016/2017 were used to characterize annual cycles of ice motion and deformation in the western Arctic Ocean. An anomalously strong and weak Beaufort Gyre in 2014/2015 and 2016/2017 induced generally anticyclonic and cyclonic sea ice drift during 2014/2015 and 2016/2017, respectively. Cyclonic ice motion resulted in higher contributions of ice divergence to total ice deformation in 2016/2017 than in 2014/2015. In 2014, the autumn ice concentration and multiyear ice coverage were higher than in 2016; consequently, the response of ice motion to wind forcing was weak, and less ice deformation was observed in autumn 2014. During the autumn‐winter transition, the ice‐wind speed ratio, ice deformation rate and its spatial and temporal scaling exponents, and localization of ice deformation decreased markedly in both 2014/2015 and 2016/2017 as a result of freeze‐up and consolidation of ice floes. Such dynamic behavior was maintained through to spring with the further thickening of ice cover. Ice deformation increased due to weakened ice strength as summer approached. The amplitude of the annual cycle of ice deformation rate in the western Arctic Ocean in 2014/2015 and especially in 2016/2017 was larger than that observed during the Surface Heat Budget of the Arctic Ocean (SHEBA) program in 1997/1998. We attribute this phenomenon to ice loss during the recent summers, especially of thick multiyear ice.

To track sea ice motion, four ice‐tethered buoys were deployed at 84.6°N and 144.3°W, 87.3°N and 172.3°W, 81.1°N and 157.4°W, and 82.8°N and 166.5°W in summers of 2008, 2010, 2014, and 2016, respectively. In addition, the remote sensed ice motion product provided by National Snow and Ice Data Center was used to reconstruct backward and forward ice drifting trajectories from the buoy deployment sites during 1979–2016. Sea ice in the central Arctic Ocean in late summer is trending to have travelled from lower latitudes, and to be advected to the region more involved in the Transpolar Drift Stream (TDS) during 1979–2016. The strengthened TDS has played a crucial role in Arctic sea ice loss from a dynamic perspective. The trajectory of ice is found to be significantly related to atmosphere circulation indices. The Central Arctic Index (CAI), defined as the difference in sea level pressure between 84°N, 90°W and 84°N, 90°E, can explain 34–40% of the meridional displacement along the backward trajectories, and it can explain 27–40% of the zonal displacement along the forward trajectories. The winter Beaufort High (BH) anomaly can explain 18–27% of the zonal displacement. Under high positive CAI values or high negative winter BH anomalies, floes from the central Arctic tended to be advected out of the Arctic Ocean through Fram Strait or other marginal gateways. Conversely, under high negative CAI values or high positive winter BH anomalies, ice tended to become trapped within a region close to the North Pole or it drifted into the Beaufort Gyre region. The long‐term trend and spatial change in Arctic surface air temperature were more remarkable during the freezing season than the melt season because most energy from the lower troposphere is used to melt sea ice and warm the upper ocean during summer.

Amplification of climate warming in the Arctic is causing a dramatic retreat of sea ice, which means the Arctic sea routes are becoming increasingly accessible. This study used a satellite-derived sea ice motion product to quantify the kinematic features of sea ice in the Arctic outflow region which specially referred to the Fram Strait and to the north of the Northeast Passage（NEP）. An observed trend of increased southward sea ice displacement from the central Arctic to the Fram Strait indicated enhancement of the Transpolar Drift Stream（TDS）. In the regions to the north of the NEP, the long-term trend of northward sea ice speed in the Kara sector was +0.04 cm/s per year in spring. A significant statistical relationship was found between the NEP open period and the northward speed of the sea ice to the north of the NEP. The offshore advection of sea ice could account for the opening of sea routes by 33% and 15% in the Kara and Laptev sectors, respectively. The difference in sea level pressure across the TDS,i.e., the Central Arctic Index（CAI）, presented more significant correlation than for the Arctic atmospheric Dipole Anomaly index with the open period of the NEP, and the CAI could explain the southward displacement of sea ice toward the Fram Strait by more than 45%. The impact from the summer positive CAI reinforces the thinning and mechanical weakening of the sea ice in the NEP region, which improves the navigability of the NEP.

锚泊安全对于穿梭于南北两极、出入冰区等恶劣航行条件的“雪龙”号破冰船至关重要.从“雪龙”号第27次南极考察航迹数据入手,分析其锚泊过程中的GPS轨迹特征,基于决策树原理设计并实现了锚泊状态识别算法.利用最小二乘原理对锚泊轨迹点进行拟合,得到破冰船锚泊偏荡周期中的逐个锚位,分析发现了2011-02-14“雪龙”号停靠中山站锚地期间发生的走锚现象,结合气象数据进一步分析了走锚现象的成因,验证了利用拟合锚位监控锚泊状态的可行性.

TheXUE LONG is the only icebreaker currently deployed in Chinese polar research. The icebreaking status is an important feature of vessels sailing in polar regions. Based on GPS data of theXUE LONG, we designed an algorithm to recognize its crash icebreaking status. In the analysis of data obtained during the entirety of the Chinese 28th Antarctic Expedition, the recognition rate was >86.1% and the accuracy of rec-ognition was >98.0%. Using the developed algorithm to analyze the data obtained during the Chinese 27th Antarctic Expedition produced a recognition rate of 85.0%, and accuracy of recognition up to 93.5%. The GPS data of these two voyages verified the applicability of the algorithm, and the differences between the statistical results showed the different icebreaking states.

随着开源网络地图服务OpenStreetMap (OSM)数据精度的提高和数据覆盖范围的扩大,其应用已逐渐渗透到各个领域,国内外专家学者对OSM的研究也不断地深入.为探究OSM在极地科考领域的应用实现,同时促进我国极地信息化事业的快速发展,从应用角度出发,论证了OSM在补充谷歌地图实现“雪龙在线”网络信息平台功能的可行性.实现了本地OSM全球地图数据库的建设,并在实现极地网络地图服务(WMS)功能基础之上提出多源空间数据的融合与可视化表达的技术路线,为极地海冰检测与动态变化研究提供了有利的支持,为极地主管部门决策的制定与实施提供了可靠的保障.