Although reanalysis data sequences have been widely used in tropical cyclone (TC) intensity forecasting, most existing methods still rely on single-frame or multi-frame satellite imagery, or use static environmental inputs, with relatively few efforts explicitly modeling the short-term temporal dynamics of environmental factors. In this research, we explore the applicability of sequence modeling for reanalysis factors in intensity estimation tasks by proposing a lightweight fusion framework that combines a Transformer encoder for 24-hour factor sequences with a Convolutional Neural Network (CNN) encoder for infrared satellite imagery. To combine environmental and structural cues, we apply a lightweight gating mechanism that re-weights temporal and spatial features before regression. In addition to standard meteorological inputs, we incorporate a smoothed lifecycle indicator that captures the storm’s developmental stage over time, enabling the model to better track temporal progression. By modeling temporal dynamics rather than relying on static inputs, our approach achieves a mean absolute error (MAE) of 3.48 m/s and a root mean squared error (RMSE) of 4.48 m/s on 183 western North Pacific typhoons from 1995 to 2003, representing an 8% reduction in RMSE compared with static-factor baselines.

The detection and recognition of ships hold significant practical implications for both military and civilian departments. Recent advancements in deep learning technology have led to notable progress in this field. However, the precise detection and recognition of ships remains a challenge, especially in multi-source remote sensing images, due to their different feature expression and resolution. Moreover, the effectiveness of the existing models in complex environments still needs to be further validated. Therefore, in this letter, we construct a new ship dataset which contains five distinct ship categories under a number of complex environments. Different from existing datasets that are based on unimodal data, the new dataset uses multi-modal remote sensing images with different resolutions. On this basis, this study introduces an adaptable and robust ship detection and recognition model, namely SIDE-YOLO. The model incorporates an super-resolution convolutional neural network (SRCNN) and side window (SRSW) based contour feature enhancement module, a SimAM feature attention Resblock (ResBlockSA), and a DConv-based cross-scale feature enhancement block (DCFB), to strengthen the ship edge features, adaptively improve the problem of limited sample in SAR images, and amalgamate multi-scale ship context information, respectively. Validation results demonstrate that the proposed model achieves an average precision (AP) rate [mean AP (mAP)] of 84.31%, surpassing state-of-the-art ship recognition models. Notably, for the civilian ship category, the model exhibits 2.17% and 2.63% higher recall and AP in complex scenes, respectively.

GNSS-R technology for the retrieval of sea surface wind speed (SW) has gradually matured, and many research results in terms of methodology and accuracy have been obtained. Multisource data fusion has been a major trend in remote sensing research in recent years. However, there are few fusion algorithms in SW retrieval, and most of them retrieve the SW of a single data source. Based on the principle of CYGNSS forward scattering and HY-2B microwave scatterometer (HSCAT-B) backscattering, this paper proposes a Fusion Model (FM) of SW based on CYGNSS and HSCAT-B. For CYGNSS SW inversion using the FM, there is no need to input HSCAT-B data, and the accuracy of CYGNSS SW inversion above 10 m/s is improved. Based on the true SW data of the European Center for Medium-Range Weather Forecasts (ECMWF), the root mean square error (RMSE) of SW inversion is improved from 2.517 m/s and 1.645 m/s with a single data source to 1.527 m/s with the FM. To further correct the outliers of the FM, the result fitting model is added after the FM. The experimental results show that the RMSE of the result fitting model is improved from 1.527 m/s for the FM to 1.489 m/s. Finally, CYGNSS L2 SW and the National Data Buoy Center (NDBC) data is used to verify the inversion results, the RMSE of the result fitting model is 1.688 m/s and 1.60 m/s, respectively. The results prove the feasibility of a fusion algorithm for SW using multisource data.

Surface meltwater critically impacts the Antarctic mass balance and global sea level rise. Quantifying the extent of surface meltwater in Antarctica on a large scale is a challenging task. Traditional methods, such as thresholding, have many limitations. We used a deep learning method, the U-Net with attention blocks, to automatically extract surface meltwater from Sentinel-2 images. We inserted attention mechanism blocks into U-Net to assign different weights to all pixels and channels to utilize the high resolution and multiple channels of Sentinel-2 images. The model was used to map surface water bodies in Sentinel-2 images, and the average accuracy reached 0.9969 on the test dataset. In East Antarctica, the Amery Ice Shelf (AIS) exhibits the largest surface meltwater area. Studying surface meltwater dynamics on the AIS is useful for understanding the East Antarctic mass balance and demonstrating the model performance. We analyzed the classification results for surface water bodies on the AIS from January 2017-2022. Spatially, 96% of surface water bodies are concentrated inland of the AIS from 70-73°S and account for 93% of the region 20 km from the coastline of the AIS. Temporally, the water body area varies considerably in different years, with a maximum in 2017 (932.54 km2) and a minimum in 2021 (58.34 km2). The spatial distribution of surface water body on the AIS is controlled by the firn air content, katabatic winds, bare rocks and blue ice. The interannual variability is associated with complex climate factors, including temperature, surface net solar radiation, snowfall, and snowmelt, among which temperature and snowfall show strong correlation.

Highway crack segmentation is a critical task for highway infrastructure monitoring and maintenance. While imagery from unmanned aerial vehicles (UAVs) is applied to the task of highway crack segmentation, it has great prospects in terms of speed and range. However, it is difficult to accurately identify road cracks from UAV remote sensing images, because the cracks are very narrow and small, often containing only a few pixels. To improve the segmentation of road cracks in UAV images, this study proposed an improved identification technique based on the U-Net architecture enhanced with a convolutional block attention module, an improved encoder, and the strategy of fusing long and short skip connections. A public road crack dataset was relabelled for network training and a UAV remote sensing road crack dataset containing 1157 images was used to verify the generalization ability of the enhanced network model. Results showed that the proposed method could effectively predict highway cracks in UAV images, with mean intersection over union (mIoU) of 77.47% and crack accuracy of 68.38%, which was better than the traditional U-Net model and some traditional semantic segmentation models. The proposed network is trained quickly by public dataset and can predict the road cracks on the new UAV images with high crack accuracy. This study provides an effective solution for the need to quickly grasp the damage status of roads over a wide area in the case of earthquake and other natural disasters. The highway crack segmentation benchmark dataset has been open sourced at:https://github.com/zhhongsh/UAV-Benchmark-Dataset-for-Highway-Crack-Segmentation.

Using Cyclone Global Navigation Satellite System (CYGNSS) L1 data with large amount and wide coverage, this paper establishes a sea surface wind direction retrieval model based on three machine learning algorithms. Wind direction will cause the asymmetry of Delay Doppler Map (DDM). Based on this, this paper extracts two angle characteristic parameters from DDM. Compared with CYGNSS full DDM, L1 compact DDM has a reduced dimension. Therefore, this paper expands more characteristic parameters, including L1 parameters and geophysical parameters such as wind speed, mean sea surface pressure (MSL), sea surface temperature (SST). Wind speed, direction, MSL and SST are from the European Centre for Medium-Range Weather Forecasts (ECMWF). After data preprocessing, the experimental data set is generated. Based on this data set, this paper establishes SVM, BP and CNN wind direction retrieval models and verifies their model performance and generalization performance. In addition, a filter that can optimize the accuracy of CNN model is constructed, and the retrieval effect under different wind direction intervals is further studied. The results show that the accuracy of CNN model for L1 data is higher than that of SVM and BP, and the retrieval error of global sea surface wind direction after filtering is less than 20°. The accuracy difference of different wind direction intervals also has a significant impact on the wind direction retrieval results.

This letter is dedicated to evaluating the potential use of reflected signals from the BeiDou Navigation Satellite System for retrieving the thickness of sea ice. Accurate phase altimetry results over sea ice would be helpful for detecting the sea ice thickness. Here, a new phase processing approach is proposed to estimate the adaptable phase altimetry result. During the data processing, fake wrap points in the residual interferometric phase are replaced by fit values; moreover, the phase altimetry result is shown to reach centimeter accuracy. A coastal experiment was performed from January 22 to February 19, 2016, in Dashentang, Tianjin, China. The sea ice thicknesses calculated from the phase altimetry results and Archimedes' principle range from 5 to 25 cm, which are consistent with the thicknesses of samples collected during the experimental period in Bohai Bay near Dashentang.

With the development of the Chinese BeiDou satellite navigation system, the applications of BeiDou reflected (BeiDou-R) signals would play a key role in Global Navigation Satellite System reflected signals. Different from other navigation systems, the BeiDou satellite navigation system has certain unique characteristics, and the Geostationary Earth Orbit (GEO) satellite is one of them. The aim of this letter is to prove the feasibility of coastal ocean phase altimetry using BeiDou GEO reflected signals. The coastal experiment was performed from October 18, 2014, to October 19, 2014, in Dayang Shan, Zhejiang, China. This is the first coastal ocean phase altimetry experiment using BeiDou GEO reflected signals. The phase altimetry results can invert the slope of ocean surface height variation (in time), and its highest accuracy can reach centimeter level.

The paper explored a method to obtain accurate lake surface heights using Global Navigation Satellite System (GNSS) Coarse/Acquisition Code (C/A)reflected from the ocean surface. The method is referred to as Global Navigation Satellite System-Reflection (GNSS-R) (C/A) code altimetry. It focuses on the extraction of the delay between the direct and reflected signal and inverting ocean altimetry with the delay and the geometric relationship. The ocean altimetry results are consistent with the height results of the differential positioning analysis of the data collected by a handheld GIS. The results show that we can achieve meter level height in seven minutes average.