The operation mode of a single strip provides incomplete side-scan sonar image in a specific environment and range, resulting in the overlapping area between adjacent strips often with imperfect detection information or inaccurate target contour. In this paper, a sum-modified Laplacian energy filtering (SMLF) and improved dual-channel pulse coupled neural network (IDPCNN) are proposed for image fusion of side-scan sonar in the domain of nonsubsampled contourlet transform (NSCT). Among them, SMLF energy is applied to extract the fusion coefficients of the low frequency sub-band, which combines the characteristics of energy information, human visual contrast, and guided filtering to eliminate the pseudo contour effect of block flow. In addition, the IDPCNN model, which utilizes the average gradient, soft limit function, and novel sum-modified Laplacian (NSML) to adaptively represent the corresponding excitation parameters, is applied to improve the depth and activity of pulse ignition, so as to quickly and accurately select the image coefficients of the high frequency sub-band. The experimental results show that the proposed method displays fine geomorphic information and clear target contour in the overlapping area of adjacent strips. The objective index values are generally optimal, which reflect the information of image edge, clarity, and overall similarity.

Satellite altimeters have been used to monitor Arctic sea ice (ASI) thickness for several decades, but whether the different altimeter missions (such as radar and laser altimeters) are in agreement with each other and suitable for long-term research needs to be investigated. To analyze the spatiotemporal characteristics of ASI, continuous long-term first-year ice, and multi-year ice of ASI freeboard, thickness, and volume from 2002 to 2021 using the gridded nadirization method from Envisat, CryoSat-2, and ICESat-2, altimeter data are comprehensively constructed and assessed. The influences of sea surface temperature (SST) and sea surface wind field (SSW) on ASI are also discussed. The freeboard/thickness and extent/area of ASI all varied seasonally and reached their maximum and minimum in April and October, March and September, respectively. From 2002 to 2021, the freeboard, thickness, extent, and area of ASI all consistently showed downward trends, and sea ice volume decreased by 5437 km³/month. SST in the Arctic rose by 0.003 degrees C/month, and the sea ice changes lagged behind this temperature variation by one month between 2002 and 2021. The meridional winds blowing from the central Arctic region along the eastern coast of Greenland to the North Atlantic each month are consistent with changes in the freeboard and thickness of ASI. SST and SSW are two of the most critical factors driving sea ice changes. This study provides new data and technical support for monitoring ASI and exploring its response mechanisms to climate change.

A vital region for China’s water resource storage and ecological balance maintenance, the Yangtze River Basin is strategically significant for maintaining regional water security and promoting long-term social and economic development. Precipitation is the main driver of the hydrological cycle. In order to address current problems with the basin’s ecological environment and water supplies, comprehensive analyses of multi-source precipitation data are necessary. They provide an essential scientific basis for evaluating the sustainability of water resources in the Yangtze River Basin in the context of climate change. Most existing precipitation fusion studies utilize only a limited number of datasets and do not fully consider the independence among different data sources, which leads to less-than-ideal fusion accuracy and assessment metrics. This paper employs the Triple Collocation (TC) method to evaluate and fuse multiple precipitation datasets over a 19-year period from 2003 to 2021, with the aim of enhancing precipitation accuracy in the Yangtze River Basin. The Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation data were found to have the highest accuracy among seven datasets, with a Correlation Coefficient (CC), Relative Bias (Rbias), and Root Mean Square Error (RMSE) of 0.907, −0.027, and 25.930 mm, respectively. The “MSWEP–PERSIANN–NOAH (MPN)” fusion was shown to be the best using the Multiplicative Triple Collocation (MTC) method in conjunction with cross-error analysis. Compared to MSWEP alone, it improved CC by 0.8% and decreased RMSE by 3.8%, with matching spatial-grid CC and RMSE improvements of 1.2% and 1.8%, respectively. Further spatiotemporal analysis of the fused data increase detection capabilities for short-term flood and waterlogging occurrences and provide better knowledge of basin water-resource status.