Deep learning, especially convolutional neural network (CNN) techniques, has been shown to have superior performance in ship classification, as have small-target recognition studies in safety inspections of hydraulic structures such as ports and dams. High-resolution synthetic aperture radar (SAR)-based maritime ship classification plays an increasingly important role in marine surveillance, marine rescue, and maritime ship management. To improve ship classification accuracy and training efficiency, we proposed a CNN-based ship classification method. Firstly, the image characteristics of different ship structures and the materials of ship SAR images were analyzed. We then constructed a ship SAR image dataset and performed preprocessing operations such as averaging. Combined with a classic neural network structure, we created a new convolutional module, namely, the Inception-Residual Controller (IRC) module. A convolutional neural network was built based on the IRC module to extract image features and establish a ship classification model. Finally, we conducted simulation experiments for ship classification and analyzed the experimental results for comparison. The experimental results showed that the average accuracy of ship classification of the model in this paper reached 98.71%, which was approximately 3% more accurate than the traditional network model and approximately 1% more accurate compared with other recently improved models. The new module also performed well in evaluation metrics, such as the recall rate, with accurate classifications. The model could satisfactorily describe different ship types. Therefore, it could be applied to marine ship classification management with the possibility of being extended to hydraulic building target recognition tasks.

Flash floods in the Eastern Mediterranean (EM) region are considered among the most destructive natural hazards, which pose a significant challenge to model due to their high complexity. Machine learning (ML) methods have made a significant contribution to the advancement of flash flood prediction systems by providing cost-effective solutions with improved performance, enabling the modeling of the complex mathematical expressions underlying physical processes of flash floods. Thus, the development of ML methods for flash flood prediction holds the potential to mitigate risks, inform policy recommendations, minimize loss of human life, and reduce property damage caused by flash floods. Here, we present a novel approach for improving flash flood predictions in the EM region using Support Vector Machines (SVMs) with a combination of precipitable water vapor (PWV) data, derived from ground-based global navigation satellite system (GNSS) receivers, along with surface pressure measurements, and nearby lightning occurrence data to predict flash floods in an arid region of the EM. The SVM model was trained on historical data from 2004 to 2019 and was used to forecast the likelihood of flash floods in the region. The study found that integrating nearby lightning data with the other variables significantly improved the accuracy of flash flood prediction compared to using only PWV and surface pressure measurements. The results of the SVM model were validated using observed flash flood events, and the model was found to have a high predictive accuracy with an area under the receiver operating characteristic curve of 0.93 for the test set. The study provides valuable insights into the potential of utilizing a combination of meteorological and lightning data for improving flash flood forecasting in the Eastern Mediterranean region.

One of the most valuable and nutritionally essential agricultural commodities worldwide is milk. The European Union and New Zealand are the second- and third-largest exporting regions of milk products and rely heavily on pasture-based production systems. They are comparable to the Australian systems investigated in this study. With projections of herd decline, increased milk yield must be obtained from a combination of animal genetics and feed efficiencies. Accurate pasture biomass estimation across all seasons will improve feed efficiency and increase the productivity of dairy farms; however, the existing time-consuming and manual methods of pasture measurement limit improvements to utilisation. In this study, Sentinel-2 (S2) band and spectral index (SI) information were coupled with the broad season and management-derived datasets using a Random Forest (RF) machine learning (ML) framework to develop a perennial ryegrass (PRG) biomass prediction model accurate to +/−500 kg DM/ha, and that could predict pasture yield above 3000 kg DM/ha. Measurements of PRG biomass were taken from 11 working dairy farms across southeastern Australia over 2019–2021. Of the 68 possible variables investigated, multiple simulations identified 12 S2 bands and 9 SI, management and season as the most important variables, where Short-Wave Infrared (SWIR) bands were the most influential in predicting pasture biomass above 4000 kg DM/ha. Conditional Latin Hypercube Sampling (cLHS) was used to split the dataset into 80% and 20% for model calibration and internal validation in addition to an entirely independent validation dataset. The combined internal model validation showed R² = 0.90, LCCC = 0.72, RMSE = 439.49 kg DM/ha, NRMSE = 15.08, and the combined independent validation had R² = 0.88, LCCC = 0.68, RMSE = 457.05 kg DM/ha, NRMSE = 19.83. The key findings of this study indicated that the data obtained from the S2 bands and SI were appropriate for making accurate estimations of PRG biomass. Furthermore, including SWIR bands significantly improved the model. Finally, by utilising an RF ML model, a single ‘global’ model can automate PRG biomass prediction with high accuracy across extensive regions of all seasons and types of farm management.

Land use and land cover (LULC) changes resulting from rapid urbanization are the foremost causes of increases in land surface temperature (LST) in urban areas. Exploring the impact of LULC changes on the spatiotemporal patterns of LST under future climate change scenarios is critical for sustainable urban development. This study aimed to project the LST of Nanjing for 2025 and 2030 under different climate change scenarios using simulated LULC and land coverage indicators. Thermal infrared data from Landsat images were used to derive spatiotemporal patterns of LST in Nanjing from 1990 to 2020. The patch-generating land use simulation (PLUS) model was applied to simulate the LULC of Nanjing for 2025 and 2030 using historical LULC data and spatial driving factors. We simulated the corresponding land coverage indicators using simulated LULC data. We then generated LSTs for 2025 and 2030 under different climate change scenarios by applying regression relationships between LST and land coverage indicators. The results show that the LST of Nanjing has been increasing since 1990, with the mean LST increased from 23.44 °C in 1990 to 25.40 °C in 2020, and the mean LST estimated to reach 26.73 °C in 2030 (SSP585 scenario, integrated scenario of SSP5 and RCP5.8). There were significant differences in the LST under different climate scenarios, with increases in LST gradually decreasing under the SSP126 scenario (integrated scenario of SSP1 and RCP2.6). LST growth was similar to the historical trend under the SSP245 scenario (integrated scenario of SSP2 and RCP4.5), and an extreme increase in LST was observed under the SSP585 scenario. Our results suggest that the increase in impervious surface area is the main reason for the LST increase and urban heat island (UHI) effect. Overall, we proposed a method to project future LST considering land use change effects and provide reasonable LST scenarios for Nanjing, which may be useful for mitigating the UHI effect.

Vortex electromagnetic (EM) waves, with different orbital angular momentum (OAM) modes, have the ability to distinguish the azimuth of radar targets, and then the two-dimensional reconstruction of the targets can be achieved. However, the vortex EM wave imaging methods in published research have no ability to obtain the elevation of the targets, and thus, the three-dimensional spatial structure and richer feature information of the radar target cannot be obtained. Therefore, a three-dimensional imaging method of vortex EM waves with integer- and fractional-order OAM modes is proposed in this paper, which can realize a three-dimensional reconstruction of a radar target based on a uniform circular array (UCA) with two-step imaging. First, the vortex EM wave with integer- and fractional-order OAM modes is generated, and the echo model with different OAM mode types is established. Thereafter, the echo with integer order is processed to obtain the range-azimuth image by fast Fourier transform (FFT). Then, in order to realize the three-dimensional reconstruction, the echo with fractional order is processed by utilizing the butterfly operation and analyzing the characteristics of the fractional Bessel function. Moreover, the resolution and reconstruction precision of the azimuth and elevation are analyzed. Finally, the effectiveness of the proposed method is verified by simulation experiments.

Electromagnetic (EM) scattering of sea surface may exhibit sea-spikespikes when there exist breaking waves. As sea-spikes are usually mistaken for targets, the investigation of EM scattering characteristics and high-range resolution profiles (HRRPs) of three-dimensional (3D) sea surfaces with a plunging breaker is meaningful for target detection and recognition. To describe the basic features of a plunging breaker, this paper developed a feasible and wind-related plunging breaker model. Here, profiles of a plunging breaker in its life cycle are modeled according to the wind speed and time factor, and the small-scale roughness is considered. Then, the sea surface and plunging breaker are combined to obtain the composite model. Additionally, a hybrid algorithm based on the Capillary Wave Modification Facet Scattering Model (CWMFSM) and ray tracing technique is developed to calculate the EM scattering of 3D sea surface with a plunging breaker. Simulation results show that the sea-spike phenomenon is more likely to occur for the upwind and large incident angles. The amplitude of the backscattering electric field from the plunging breaker is much stronger than that of the sea surface. Furthermore, the HRRPs of 3D sea surface with a plunging breaker and target are computed. Sharp peaks from the plunging breaker that exhibit obvious target-like features are observed.

Using the Gravity Recovery and Climate Experiment (GRACE) satellite to monitor groundwater storage (GWS) anomalies (GWSAs) at the local scale is difficult due to the low spatial resolution of GRACE. Many attempts have been made to downscale GRACE-based GWSAs to a finer resolution using statistical downscaling approaches. However, the time-lag effect of GWSAs relative to environmental variables and optimal model parameters is always ignored, making it challenging to achieve good spatial downscaling, especially for arid regions with longer groundwater infiltration paths. In this paper, we present a novel spatial downscaling method for constructing GRACE-based 1 km-resolution GWSAs by using the back propagation neural network (BPNN) and considering the time-lag effect and the number of hidden neurons in the model. The method was validated in Alxa League, China. The results show that a good simulation performance was achieved by adopting varying lag times (from 0 to 4 months) for the environmental variables and 14 hidden neurons for all the networks, with a mean correlation coefficient (CC) of 0.81 and a mean root-mean-square error (RMSE) of 0.70 cm for each month from April 2002 to December 2020. The downscaled GWSAs were highly consistent with the original data in terms of long-term temporal variations (the decline rate of the GWSAs was about −0.40 ± 0.01 cm/year) and spatial distribution. This study provides a feasible approach for downscaling GRACE data to 1 km resolution in arid regions, thereby assisting with the sustainable management and conservation of groundwater resources at different scales.

The northeastern margin is a natural experimental field for studying crustal extrusion and expansion mechanisms. The accurate crustal deformation pattern is a key point in the analysis of regional deformation mechanisms and seismic hazard research and judgment. In this paper, the present-day GPS velocity field on the northeastern margin of the Tibetan Plateau was obtained from encrypted GPS observations around the Haiyuan–Liupanshan fault zone, combined with GPS observations on the northeastern margin of the Tibetan Plateau from 2010 to 2020. Firstly, we divided the study area into three relatively independent blocks: the ORDOS block, Alxa block, and Lanzhou block; secondly, the accurate fault distribution of the Haiyuan–Liupanshan fault zone was taken into account to obtain the optimal inversion model; finally, using the block and fault back-slip dislocation model, the inversion obtained the slip rate distribution, locking depth, and slip deficit rate of each fault. The results indicate that the Laohushan Fault and Haiyuan Fault are dominated by the left-lateral strike-slip, while the Liupanshan Fault is dominated by the thrust dip-slip, and the Guguan–Baoji Fault has both left-lateral strike-slip and thrust dip-slip components. The maximum locking depths of the Laohushan Fault, Haiyuan Fault, Liupanshan Fault, and Guguan–Baoji Fault are 5 km, 13 km, 15 km, and 10 km, respectively, and the locking of the Haiyuan Fault is strong in the middle section and weak in the eastern and western section. The Haiyuan Fault is still in the post-earthquake stress adjustment stage. The slip deficit rate decays from 3.6 mm/yr to 1.8 mm/yr from west to east along the fault zone. Combined with geological and historical seismic data, the results suggest that the mid-long-term seismic risk in the Liupanshan Fault is high.

Remote sensing is essential for monitoring fisheries. Optical sensors such as the day–night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) have been a crucial tool for detecting vessels fishing at night. It remains challenging to ensure stable detections under various conditions affected by the clouds and the moon. Here, we develop a machine learning based algorithm to generate automatic and consistent vessel detection. As DNB data are large and highly imbalanced, we design a two-step approach to train our model. We evaluate its performance using independent vessel position data acquired from on-ship radar. We find that our algorithm demonstrates comparable performance to the existing VIIRS boat detection algorithms, suggesting its possible application to greater temporal and spatial scales. By applying our algorithm to the East China Sea as a case study, we reveal a recent increase in fishing activity by vessels using bright lights. Our VIIRS boat detection results aim to provide objective information for better stock assessment and management of fisheries.

A new mean sea surface (MSS) was determined by focusing on the accuracy provided by exact-repeat altimetric missions (ERM) and the high spatial coverage of geodetic (or drifting) missions. The goal was to obtain a high-resolution MSS that would provide centimeter-level precision. Particular attention was paid to the homogeneity of the oceanic content of this MSS, and specific processing was also carried out, particularly on the data from the geodetic missions. For instance, CryoSat-2 and SARAL/AltiKa data sampled at high frequencies were enhanced using a dedicated filtering process and corrected from oceanic variability using the results of the objective analysis of sea-level anomalies provided by DUACS multi-missions gridded sea-level anomalies fields (MSLA). Particular attention was also paid to the Arctic area by combining traditional sea-surface height (SSH) with the sea levels estimated within fractures in the ice (leads). The MSS was determined using a local least-squares collocation technique, which provided an estimation of the calibrated error. Furthermore, our technique takes into account altimetric noises, ocean-variability-correlated noises, and along-track biases, which are determined independently for each observation. Moreover, variable cross-covariance models were fitted locally for a more precise determination of the shortest wavelengths, which were shorter than 30 km. The validations performed on this new MSS showed an improvement in the finest topographic structures, with amplitudes exceeding several cm, while also continuing to refine the correction of the oceanic variability. Overall, the analysis of the precision of this new CNES_CLS 2022 MSS revealed an improvement of 40% compared to the previous model, from 2015.