Snow is a core part of the cryosphere that plays an important role in climate regulation, hydrological cycles, and ecosystems. Therefore, deriving long-term and large-scale snow depth (SD) time series and monitoring their spatial and temporal variations are crucial. The high-resolution, all-weather SAR imagery provided by Sentinel-1 offers an important data source for monitoring SD. This study proposes a deep learning network called STCTNet, which combines the multi-channel characteristics of convolutional neural networks with the attention mechanism of Transformer to enhance the model ’ s ability to comprehensively process local and global features. The spatiotemporal distribution characteristics of SD stations are also considered to construct spatiotemporally weighted SD variable. This model was trained using data obtained from Colorado between 2017 and 2019, and results show that this model achieves an RMSE of 4.828 cm, MAE of 2.398 cm, and R2of 0.909. The trained model was then used to predict the average SD for February from 2017 to 2022 at a spatial resolution of 20 m. STCTNet obtained overall RMSE and MAE values of 8.7 cm and 4.6 cm, respectively, which show lower RMSE and MAE compared with those of AMSR2 (17.9 cm and 10 cm) and ERA5-Land datasets (18.5 cm and 13.1 cm). An analysis of SD changes from 2017 to 2022 shows that SD may be affected by a variety of climatic anomalies. The application of active microwave data and deep learning techniques in this study provides an approach that improves both the spatial resolution and estimation accuracy of SD.

The craters widely distributed on the lunar surface constitute important landforms for investigating the geological activities and evolutionary history of the Moon. Currently, deep learning models have made certain progress in detecting craters, but they still have limitations in identifying numerous small craters. To address these challenges, this article puts forward a CenterNet-based dense small crater detection model (DSCraterNet), which is designed to precisely detect craters within the permanently shadowed regions (PSRs) by leveraging the previously released enhanced PSR images. DSCraterNet achieves precise crater localization by predicting keypoint heatmap (location), size, and offset of craters. The model employs an encoder–decoder network architecture that combines various feature fusion and attention mechanisms. Feature fusion strategies include direct fusion between the encoder and decoder, multiscale encoder spatial feature fusion (MSF) structure, and multielement information fusion (MEF) structure. These fusion strategies balance the relationship between high-resolution features, strong semantic features, and multiscale spatial features according to craters’ characteristics. Furthermore, by reasonably configuring attention-based layers [like efficient multiscale attention (EMA)] at different stages, the utilization efficiency of robust features is improved. Ultimately, DSCraterNet successfully identified a large number of densely packed small craters in PSR, revealing the terrain features of the region. A series of thorough ablation and comparative experiments has confirmed that DSCraterNet surpasses existing crater detection models in terms of precision, recall, mean average precision (mAP), and$F1$-score metrics. Our code is available athttps://github.com/dl-zfq/DSCraterNet

Obtaining high-visibility images of the lunar polar permanently shadowed region (PSR) is quite important for internal landforms and material existence exploration. However, PSR images usually have poor quality due to a lack of sufficient illumination. Existing researches, that attempt to address this problem, face challenges caused by relying on virtual assumptions, manual processing, and paired data. To solve these problems, we aim to avoid using paired datasets and directly optimize PSR images, and accordingly propose a zero-shot parameter learning model (ZSPL-PSR) for PSR image enhancement. Our ZSPL-PSR, which enhances PSR images by estimating parameters to adjust image properties, consists of a parameter learning network and a parameter weight learning structure. Particularly, first, a parameter learning network that integrates robust information is constructed to separately estimate the midtone brightness parameters, shadow brightness parameters, and contrast parameters. Where these parameters are beneficial for iteratively improve the overall brightness, shadow brightness, and contrast of the image. Second, a parameter weight learning structure is exploited to coordinate the priority of different parameter maps. In addition, to highlight the terrain details in the enhanced PSR image, we use USM sharpening for postprocessing. The experimental results display the fully interpretable enhanced PSR maps of the lunar north and south poles and their sharpened versions, showcasing rich landforms in PSR. To validate the model performance, a benchmark PSR testing set has been constructed, and extensive comparisons conducted on it demonstrated that ZSPL-PSR exceeds other zero-shot learning methods significantly in image quality. Our code is available athttps://github.com/dl-zfq/ZSPL-PSR.

The distribution of snow in Greenland has important effects on the energy balance and climate change in the Arctic. The spatio-temporally continuous data of normalized difference snow index (NDSI) are key to understanding the mechanisms of snow occurrence and development as well as the patterns of snow distribution changes. However, frequent clouds in the Arctic region lead to a large number of missing pixels in the MODIS NDSI daily data. In order to generate the complete NDSI dataset, this paper proposes a spatio-temporal Extreme Gradient Boosting model. Various independent variables, including topographic, geometry-related, and surface attribute variables, were selected, and spatio-temporal variation information was employed to the reconstruction model. The results of simulation experiments show that the reconstructed data have R2= 0.953 and RMSE = 0.031. Moreover, the proposed model has better predictive power than the classical regression model.

Arctic sea ice albedo plays a crucial role in regional and global climate changes. A high-quality albedo product over Arctic sea ice surfaces with fine spatial and temporal resolution is scarce and therefore greatly required. This study proposed a new and operational algorithm named Multiband Reflectance Iteration (MBRI) to estimate broadband albedo of Arctic sea ice from Moderate Resolution Imaging Spectroradiometer (MODIS). This algorithm uses an iteration procedure with multiband spectral reflectance data to retrieve the sea-ice bidirectional reflectance distribution function (BRDF), which is built based on the combination of the asymptotic radiative transfer (ART) model and the three-component ocean water albedo (TCOWA) model. Then, the broadband albedo is calculated from single-angular/date observations of MODIS. A daily 500 m albedo product over Arctic sea-ice regions from 2000 to 2017 is generated. Validation with the in situ sea ice measurements from Tara Arctic Ocean expedition shows that the MBRI retrieved albedo has a correlation coefficient R of 0.79, bias of 0.005, and RMSE of 0.067. Moreover, compared with the automatic weather stations (AWS) measurements from Program for Monitoring of the Greenland Ice Sheet (PROMICE), the MBRI albedo achieves more satisfactory performance with an R value of 0.92, bias of 0.019, and RMSE of 0.062. Finally, MBRI albedo trend analysis reveals a decreasing trend for Arctic sea ice albedo throughout the past two decades, with a significant trend slope of -0.21% per year in the September sea ice zone.

Blockchain technology offers an intelligent amalgamation of distributed ledger, Peer-to-Peer (P2P), cryptography, and smart contracts to enable trustworthy applications without any third parties. Existing blockchain systems have successfully either resolved the scalability issue by advancing the distributed consensus protocols from the control plane, or complemented the security issue by updating the block structure and encryption algorithms from the data plane. Yet, we argue that the underlying P2P network plane remains as an important but unaddressed barrier for accelerating the overall blockchain system performance, which can be discussed from how fast and reliable the network is. In order to improve the blockchain network performance about enabling fast and reliable broadcast, we establish a trust-enhanced blockchain P2P topology which takes transmission rate and transmission reliability into consideration. Transmission rate reflects blockchain network speed to disseminate transactions and blocks, and transmission reliability reveals whether transmission rate changes drastically on unreliable network connection. This paper presents BlockP2P-EP, a novel trust-enhanced blockchain topology to accelerate transmission rate and meanwhile retain transmission reliability. BlockP2P-EP first operates the geographical proximity sensing clustering, which leverages K-Means algorithm for gathering proximity peer nodes into clusters. It follows by the hierarchical topological structure that ensures strong connectivity and small diameter based on node attribute classification. Then we propose establishing trust-enhanced network topology. On top of the trust-enhanced blockchain topology, BlockP2P-EP conducts the parallel spanning tree broadcast algorithm to enable fast data broadcast among nodes both intra- and inter- clusters. Finally, we adopt an effective node inactivation detection method to reduce network load. To verify the validity of BlockP2P-EP protocol, we carefully design and implement a blockchain network simulator. Evaluation results show that BlockP2P-EP can exhibit promising network performance in terms of transmission rate and transmission reliability compared to Bitcoin and Ethereum.

Modern bookcrossing leverages the mobile networks to help readers share books via convenient connection, and thus expedites the dissemination of information. However, the lack of traceability has significantly hindered the wide adoption of mobile bookcrossing, leading to loss of books. Meanwhile, the managerial inefficiency of current mobile bookcrossing systems becomes another obstacle for advancing the book circulation. To overcome these problems, we propose to leverage the promising blockchain technology with the merits of its immutability and cryptographic smart contract. In this paper, we present BookChain, a traceable and efficient blockchain-based innercampus book sharing system. BookChain stores the complete sharing data of an interested book permanently on blockchain, such that every reader can trace the borrowing history, which reduces the potential for loss of book. BookChain also introduces the use of smart contract to automate the circulation of books with minimal human intervention, resulting in the improvement of efficiency. The experimental results show the effectiveness and low-cost of the proposed system under high concurrent users.

Temporal tie points have been manually selected for sea ice concentration retrieval based on the linear combination relationship between the open water and the complete ice coverage pixel. In this paper, the multichannel information has been exploited using the constrained least-squares linear unmixing algorithm from AMSR-E multi-channels brightness temperature. Snow depth can be obtained from the innovative algorithm without considering the weather effect.