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To enhance the performance of visual SLAM in underwater environments, this paper presents an enhanced front-end method based on visual feature enhancement. The method comprises three modules aimed at optimizing and improving the matching capability of visual features from different perspectives. Firstly, to address issues related to insufficient underwater illumination and uneven distribution of artificial light sources, a brightness-consistency recovery method is proposed. This method employs an adaptive histogram equalization algorithm to balance the brightness of images. Secondly, a method for denoising underwater suspended particulates is introduced to filter out noise from images. After image-level processing, a combined underwater acousto–optic feature-association method is proposed, which associates acoustic features from sonar with visual features, thereby providing distance information for visual features. Finally, utilizing the AFRL dataset, the improved system incorporating the proposed enhancement methods is evaluated for its performance against the OKVIS framework. The system achieves a better trajectory estimation accuracy compared to OKVIS and demonstrates robustness in underwater environments.

Intermittent rivers and ephemeral streams are crucial for the water cycle and ecosystem services, yet they are often neglected by managers and researchers, especially in headwater areas. This oversight has caused a lack of comprehensive basemaps for these vital river systems. In headwater regions, water bodies are typically sparse and disconnected, with narrow and less distinct channels. Therefore, we propose a novel hybrid method that integrates topographic data and remote sensing imagery to delineate river networks. Our method reestablishes connectivity among sparsely distributed water bodies through topographic pairs, enhances less distinct channel features using the gamma function, and converts topographic and water indices data into a weighted graph to determine optimal channels with the A* algorithm. The topographic and water indices data are derived from the Multi-Error-Removed Improved-Terrain DEM (MERIT DEM) and an average composite of the Modified Normalized Difference Water Index (MNDWI), respectively. In the upper Lancang-Mekong River basin, our method outperformed five publicly available DEM datasets, achieving over 91% positional accuracy within a 30 m buffer. This hybrid method enhances positional accuracy and effectively connects sparse water bodies in headwater areas, offering promising applications for delineating intermittent rivers and ephemeral streams and providing baseline information for these river systems.

With the aim of solving the problem of identifying complex underwater acoustic targets using a single signal feature in the Time–Frequency (TF) feature, this paper designs a method that recognizes the underwater targets based on the Delay-Doppler joint feature. First, this method uses symplectic finite Fourier transform (SFFT) to extract the Delay-Doppler features of underwater acoustic signals, analyzes the Time–Frequency features at the same time, and combines the Delay-Doppler (DD) feature and Time–Frequency feature to form a joint feature (TF-DD). This paper uses three types of convolutional neural networks to verify that TF-DD can effectively improve the accuracy of target recognition. Secondly, this paper designs an object recognition model (TF-DD-CNN) based on joint features as input, which simplifies the neural network’s overall structure and improves the model’s training efficiency. This research employs ship-radiated noise to validate the efficacy of TF-DD-CNN for target identification. The results demonstrate that the combined characteristic and the TF-DD-CNN model introduced in this study can proficiently detect ships, and the model notably enhances the precision of detection.

In this study, a tracking and pointing control system with a dual-FSM (fast steering mirror) two-dimensional flexible turntable composite axis is proposed. It is applied to the target-tracking accuracy control in a GI LiDAR (ghost imaging LiDAR) system. Ghost imaging is a multi-measurement imaging method; the dual-FSM GI LiDAR tracking and pointing imaging control system proposed in this study mainly solves the problems of the high-resolution remote sensing imaging of high-speed moving targets and various nonlinear disturbances when this technology is transformed into practical applications. Addressing the detrimental effects of nonlinear disturbances originating from internal flexible mechanisms and assorted external environmental factors on motion control’s velocity, stability, and tracking accuracy, a nonlinear active disturbance rejection control (NLADRC) method based on artificial neural networks is advanced. Additionally, to overcome the limitations imposed by receiving aperture constraints in GI LiDAR systems, a novel optical path design for the dual-FSM GI LiDAR tracking and imaging system is put forth. The implementation of the described methodologies culminated in the development of a dual-FSM GI LiDAR tracking and imaging system, which, upon thorough experimental validation, demonstrated significant improvements. Notably, it achieved an improvement in the coarse tracking accuracy from 193.29 μrad (3σ) to 87.21 μrad (3σ) and enhanced the tracking accuracy from 10.1 μrad (σ) to 1.5 μrad (σ) under specified operational parameters. Furthermore, the method notably diminished the overshoot during the target capture process from 28.85% to 12.8%, concurrently facilitating clear recognition of the target contour. This research contributes significantly to the advancement of GI LiDAR technology for practical application, showcasing the potential of the proposed control and design strategies in enhancing system performance in the face of complex disturbances.

NDVI data are crucial for agricultural and environmental research. The Fengyun-3 (FY-3) series satellites are recognized as primary sources for retrieving NDVI products on a global scale. To apply FY-3 NDVI data for long-term studies, such as climate change, this study conducted a thorough evaluation to detect the potentials of the FY-3B and FY-3D satellites for generating a long time series NDVI dataset. For this purpose, the spatiotemporal consistency between the FY-3B and FY-3D satellites was evaluated, and their performances were compared. Then, a grey relational analysis (GRA) method was applied to detect the factors influencing the consistency among the different satellites, and a gradient boosting regression (GBR) model was constructed to create a long-term FY-3 NDVI product. The results indicate an overall high consistency between the FY-3B and FY-3D NDVIs, suggesting that they could be used as complementary datasets for generating a long-term NDVI dataset. The correlations between the FY-3D NDVI and the MODIS NDVI, as well as the leaf area index (LAI) measurements, were both higher than those of FY-3B, which indicates a better performance of FY-3D in retrieving NDVI data. The grey correlation degrees between the NDVI differences and four parameters, which were land cover (LC), DEM, latitude (LAT) and longitude (LON), were calculated, revealing that the LC was the most related to the NDVI differences. Finally, a GBR model with FY-3B NDVI, LC, DEM, LAT and LON as the input variables and FY-3D NDVI as the target variable was established and achieved a robust performance. The R values between the GBR-estimated NDVI and FY-3D NDVI reached 0.947, 0.867 and 0.829 in the training, testing and validation datasets, respectively, indicating the feasibility of the established model for generating long time series NDVI data by combining data from the FY-3B and FY-3D satellites.

The census and management of hazard-bearing entities, along with the integrity of data quality, form crucial foundations for disaster risk assessment and zoning. By addressing the challenge of feature confusion, prevalent in single remotely sensed image recognition methods, this paper introduces a novel method, Spatially Constrained Deep Learning (SCDL), that combines deep learning with spatial constraint strategies for the extraction of disaster-bearing bodies, focusing on dams as a typical example. The methodology involves the creation of a dam dataset using a database of dams, followed by the training of YOLOv5, Varifocal Net, Faster R-CNN, and Cascade R-CNN models. These models are trained separately, and highly confidential dam location information is extracted through parameter thresholding. Furthermore, three spatial constraint strategies are employed to mitigate the impact of other factors, particularly confusing features, in the background region. To assess the method’s applicability and efficiency, Qinghai Province serves as the experimental area, with dam images from the Google Earth Pro database used as validation samples. The experimental results demonstrate that the recognition accuracy of SCDL reaches 94.73%, effectively addressing interference from background factors. Notably, the proposed method identifies six dams not recorded in the GOODD database, while also detecting six dams in the database that were previously unrecorded. Additionally, four dams misdirected in the database are corrected, contributing to the enhancement and supplementation of the global dam geo-reference database and providing robust support for disaster risk assessment. In conclusion, leveraging open geographic data products, the comprehensive framework presented in this paper, encompassing deep learning target detection technology and spatial constraint strategies, enables more efficient and accurate intelligent retrieval of disaster-bearing bodies, specifically dams. The findings offer valuable insights and inspiration for future advancements in related fields.

Current CNN-based methods for infrared and visible image fusion are limited by the low discrimination of extracted structural features, the adoption of uniform loss functions, and the lack of inter-modal feature interaction, which make it difficult to obtain optimal fusion results. To alleviate the above problems, a framework for multimodal feature learning fusion using a cross-attention Transformer is proposed. To extract rich structural features at different scales, residual U-Nets with mixed receptive fields are adopted to capture salient object information at various granularities. Then, a hybrid attention fusion strategy is employed to integrate the complementing information from the input images. Finally, adaptive loss functions are designed to achieve optimal fusion results for different modal features. The fusion framework proposed in this study is thoroughly evaluated using the TNO, FLIR, and LLVIP datasets, encompassing diverse scenes and varying illumination conditions. In the comparative experiments, HATF achieved competitive results on three datasets, with EN, SD, MI, and SSIM metrics reaching the best performance on the TNO dataset, surpassing the second-best method by 2.3%, 18.8%, 4.2%, and 2.2%, respectively. These results validate the effectiveness of the proposed method in terms of both robustness and image fusion quality compared to several popular methods.

Ship detection in remote sensing images plays an important role in maritime surveillance. Recently, convolution neural network (CNN)-based methods have achieved state-of-the-art performance in ship detection. Even so, there are still two problems that remain in remote sensing. One is that the different modal images observed by multiple satellite sensors and the existing dataset cannot satisfy network-training requirements. The other is the false alarms in detection, as the ship target is usually faint in real view remote sensing images and many false-alarm targets can be detected in ocean backgrounds. To solve these issues, we propose a double augmentation framework for ship detection in cross-modal remote sensing imagery. Our method can be divided into two main steps: the front augmentation in the training process and the back augmentation verification in the detection process; the front augmentation uses a modal recognition network to reduce the modal difference in training and in using the detection network. The back augmentation verification uses batch augmentation and results clustering to reduce the rate of false-alarm detections and improve detection accuracy. Real-satellite-sensing experiments have been conducted to demonstrate the effectiveness of our method, which shows promising performance in quantitative evaluation metrics.

Height estimation has long been a pivotal topic within measurement and remote sensing disciplines, with monocular height estimation offering wide-ranging data sources and convenient deployment. This paper addresses the existing challenges in monocular height estimation methods, namely the difficulty in simultaneously achieving high-quality instance-level height and edge reconstruction, along with high computational complexity. This paper presents a comprehensive solution for monocular height estimation in remote sensing, termed HeightFormer, combining multilevel interactions and image-adaptive classification–regression. It features the Multilevel Interaction Backbone (MIB) and Image-adaptive Classification–regression Height Generator (ICG). MIB supplements the fixed sample grid in the CNN of the conventional backbone network with tokens of different interaction ranges. It is complemented by a pixel-, patch-, and feature map-level hierarchical interaction mechanism, designed to relay spatial geometry information across different scales and introducing a global receptive field to enhance the quality of instance-level height estimation. The ICG dynamically generates height partition for each image and reframes the traditional regression task, using a refinement from coarse to fine classification–regression that significantly mitigates the innate ill-posedness issue and drastically improves edge sharpness. Finally, the study conducts experimental validations on the Vaihingen and Potsdam datasets, with results demonstrating that our proposed method surpasses existing techniques.

Integer Aperture Bootstrapping (IAB) is a crucial method for testing ambiguity acceptance in carrier-phase differential global navigation satellite system (CDGNSS) positioning. It has the advantage that integrity parameters, such as the failure rate, can be analytically calculated, which is essential in safety-of-life applications. Although the IAB methods have been extensively studied, their threshold-determining method is still not well explained, theoretically. In this study, a new method, named Analytical Integer Aperture Bootstrapping (AIAB), is theoretically derived to determine the optimal IAB threshold. AIAB is novel in that: (1) a precise and easy-to-compute expression has been developed to describe the relationship between the IAB threshold and the failure rate, (2) an analytical function model has been derived from the expression to determine the IAB threshold; moreover, the function model is simplified, and (3) a data-constraint approach has been proposed to reduce the complexity of IAB. In the global CDGNSS simulations, AIAB is shown to outperform the existing IAB methods under both strong and weak models, particularly at low fix rates, which are 23% to 40% higher than the basic IAB method. The Monte Carlo simulation results show that AIAB can obtain almost theoretically the same performance as Optimal Integer Aperture (OIA).

Recently, hyperspectral image classification has made great progress with the development of convolutional neural networks. However, due to the challenges of distribution shifts and data redundancies, the classification accuracy is low. Some existing domain adaptation methods try to mitigate the distribution shifts by training source samples and some labeled target samples. However, in practice, labeled target domain samples are difficult or even impossible to obtain. To solve the above challenges, we propose a novel dual-attention deep discriminative domain generalization framework (DAD3GM) for cross-scene hyperspectral image classification without training the labeled target samples. In DAD3GM, we mainly design two blocks: dual-attention feature learning (DAFL) and deep discriminative feature learning (DDFL). DAFL is designed to extract spatial features by multi-scale self-attention and extract spectral features by multi-head external attention. DDFL is further designed to extract deep discriminative features by contrastive regularization and class discrimination regularization. The combination of DAFL and DDFL can effectively reduce the computational time and improve the generalization performance of DAD3GM. The proposed model achieves 84.25%, 83.53%, and 80.63% overall accuracy on the public Houston, Pavia, and GID benchmarks, respectively. Compared with some classical and state-of-the-art methods, the proposed model achieves optimal results, which reveals its effectiveness and feasibility.

The Han River Basin, a critical water conservation and ecological barrier in Hubei Province, is intricately associated with the United Nations Sustainable Development Goals (SDGs). Research results show that vegetation cover changes are affected by multiple factors, and understanding the influences of climate change and human activities on vegetation is imperative for achieving sustainable development in the basin. Through quantitative assessment of vegetation changes in diverse landform regions, implementing adaptive ecological construction and environmental protection will foster the sustainable development of ecological civilization in the Han River Basin. This study utilizes MODIS13Q1 data and employs diverse analytical methods to investigate the characteristics of vegetation change and the interrelationships between climate change, meteorological factors, and vegetation cover in various geomorphological areas of the Han River Basin from 2000 to 2020. The results showed that (1) throughout the entire study period, the NDVI of the six types of geomorphological divisions in the Han River Basin exhibited a fluctuating upward trend, with the changes in the low-altitude hilly geomorphic regions being particularly noteworthy. (2) Within the study area, approximately 92.67% of vegetation coverage displayed an increasing trend, while 7.33% showed degradation, predominantly in plains and platforms. Notably, the area of continuous improvement (31.16%) outweighed the area of continuous degradation (3.05%), with low and middle-relief mountain areas demonstrating the most robust growth and sustainability. (3) Human agriculture activities and urbanization processes have emerged as the primary driving force behind vegetation changes in the Han River Basin. The responses of vegetation to climate change and human activities exhibited significant variations across diverse geomorphological regions. In areas characterized by vegetation improvement, the contribution rate of human activities to NDVI changes in different vegetation types surpassed 70%, with plain areas displaying the highest contribution rate at a remarkable 90%. In contrast, the plain and platform regions of the vegetation degradation area were significantly influenced by climate change. In future watershed ecological environment management, it is essential to not only recognize the dominant role of human activities in promoting the growth of mountain vegetation NDVI but also address the impact of climate change on the degradation of vegetation NDVI in plains and platforms. A comprehensive understanding of these factors is crucial for devising effective strategies to ensure sustainable development and ecological balance in the Han River Basin.

A few spiking neural network (SNN)-based classifiers have been proposed for hyperspectral images (HSI) classification to alleviate the higher computational energy cost problem. Nevertheless, due to the lack of ability to distinguish boundaries, the existing SNN-based HSI classification methods are very prone to falling into the Hughes phenomenon. The confusion of the classifier at the class boundary is particularly obvious. To remedy these issues, we propose a boundary-aware deformable spiking residual neural network (BDSNN) for HSI classification. A deformable convolution neural network plays the most important role in realizing the boundary-awareness of the proposed model. To the best of our knowledge, this is the first attempt to combine the deformable convolutional mechanism and the SNN-based model. Additionally, spike-element-wise ResNet is used as a fundamental framework for going deeper. A temporal channel joint attention mechanism is introduced to filter out which channels and times are critical. We evaluate the proposed model on four benchmark hyperspectral data sets—the IP, PU, SV, and HU data sets. The experimental results demonstrate that the proposed model can obtain a comparable classification accuracy with state-of-the-art methods in terms of overall accuracy (OA), average accuracy (AA), and statistical kappa (κ) coefficient. The ablation study results prove the effectiveness of the introduction of the deformable convolutional mechanism for BDSNN’s boundary-aware characteristic.

A drought results from the combined action of several factors. The continuous progress of remote sensing technology and the rapid development of artificial intelligence technology have enabled the use of multisource remote sensing data and data-driven machine learning (ML) methods to mine drought features from different perspectives. This method improves the generalization ability and accuracy of drought monitoring and prediction models. The present study focused on drought monitoring in southwest China, where drought disasters occur frequently and with a high intensity, especially in areas with limited meteorological station coverage. Several drought indices were calculated based on multisource satellite remote sensing data and weather station observation data. Remote sensing data from multiple sources were combined to build a reconstructed land surface temperature (LST) and drought monitoring method using the two different ML methods of random forest (RF) and eXtreme Gradient Boosting (XGBoost 1.5.1), respectively. A 5-fold cross-validation (CV) method was used for the model’s hyperparameter optimization and accuracy evaluation. The performance of the model was also assessed and validated using several accuracy assessment indicators. The model monitored the results of the spatial and temporal distributions of the drought, drought grades, and influence scope of the drought. These results from the model were compared against historical drought situations and those based on the standardized precipitation evapotranspiration index (SPEI) and the meteorological drought composite index (MCI) values estimated using weather station observation data in southwest China. The results show that the average score of the 5-fold CV for the RF and XGBoost was 0.955 and 0.931, respectively. The root-mean-square error (RMSE) of the LST values reconstructed using the RF model on the training and test sets was 1.172 and 2.236, the mean absolute error (MAE) was 0.847 and 1.719, and the explained variance score (EVS) was 0.901 and 0.858, respectively. Furthermore, the correlation coefficients (CCs) were all greater than 0.9. The RMSE of the monitoring values using the XGBoost model on the training and test sets was 0.135 and 0.435, the MAE was 0.095 and 0.328, the EVS was 0.976 and 0.782, and the CC was 0.982 and 0.868, respectively. The consistency rate between the drought grades identified using SPEI1 (the SPEI values of the 1-month scale) based on the observed data from the 144 meteorological stations and the monitoring values from the XGBoost model was more than 85%. The overall consistency rate between the drought grades identified using the monitoring and MCI values was 67.88%. The aforementioned two different ML methods achieved a high comprehensive performance, accuracy, and applicability. The constructed model can improve the level of dynamic drought monitoring and prediction for regions with complex terrain and topography and formative factors of climate as well as where weather stations are sparsely distributed.

Scientific progress in the context of seismic precursors reveals a systematic mechanism, namely lithosphere–atmosphere–ionosphere coupling (LAIC), to elaborate the underlying physical processes related to earthquake preparation phases. In this study, a comprehensive analysis was conducted for two earthquakes that occurred on the sea coast through tidal force fluctuation to investigate ocean–lithosphere–atmosphere–ionosphere coupling (OLAIC), based on oceanic parameters (i.e., sea potential temperature and seawater salinity), air temperature and electron density profiles. The interrupted enhancement and diffusion process of thermal anomalies indicate that the intensity of seismic anomalies in the atmosphere is affected by the extent of land near the epicenter. By observing the evolution of the ocean interior, we found that the deep water was lifted and formed upwelling, which then diffused along the direction of plate boundaries with an “intensification-peak-weakening” trend under the action of the accelerated subduction of tectonic plates. Furthermore, the analysis shows that the seismic anomalies have two propagation paths: (i) along active faults, with the surface temperature rising as the initial performance, then the air pressure gradient being generated, and finally the ionosphere being disturbed; (ii) along plate boundaries, upwelling, which is the initial manifestation, leading to changes in the parameters of the upper ocean. The results presented in this study can contribute to understanding the intrinsic characteristics of OLAIC.

The Qilian Mountains (QMs) act as the “water tower” of the Hexi Corridors, playing an important role in the regional ecosystem security and economic development. Therefore, it is of great significance to understand the spatiotemporal characteristics of precipitation in the QMs. This study evaluated the performance of 21 models of phase 6 of the Coupled Model Intercomparison Project (CMIP6) from 1959 to 1988 based on ERA5 and in situ datasets. In addition, the precipitation changing trend from 2015 to 2100 was projected according to four shared socioeconomic pathways (SSPs): namely, SSP126, SSP245, SSP370, and SSP585. The results have shown the following: (1) all CMIP6 models could reflect the same precipitation changing trend, based on the observed datasets (−2.01 mm·10a−1), which was slightly lower than that of ERA5 (2.82 mm·10a−1). Multi-mode ensemble averaging (MME) showed that the projected precipitation-change trend of the four scenarios was 5.73, 9.15, 12.23, and 16.14 mm·10a−1, respectively. (2) The MME and ERA5 showed the same precipitation spatial pattern. Also, during the period 1959–1988, the MME in spring, summer, autumn and winter was 130.07, 224.62, 95.96, and 29.07 mm, respectively, and that of ERA5 was 98.57, 280.77, 96.85, and 22.64 mm, respectively. The largest precipitation difference in summer was because of strong convection and variable circulation. (3) From 2015 to 2100, the snow-to-rain ratio was between 0.1 and 1.1, and the snow-to-rain ratio climate tendency rate was concentrated in the range of −10~0.1 mm·10a−1. Both of these passed the significance test (p < 0.05). The projected rainfall of all four SSPs all showed an increasing trend with values of 6.20, 11.31, 5.64, and 20.41 mm·10a−1, respectively. The snowfall of the four SSPs all showed a decreasing trend with values of 0.42, 2.18, 3.34, and 4.17 mm·10a−1, respectively.

With China’s fruit tree industry becoming the largest in the world, accurately understanding the spatial distribution of fruit tree growing areas is crucial for promoting socio-economic development and rural revitalization. Remote sensing offers unprecedented opportunities for fruit tree monitoring. However, previous research has mainly focused on UAV and near-ground remote sensing, with limited accuracy in obtaining fruit tree distribution information through satellite remote sensing. In this study, we utilized the Google Earth Engine (GEE) remote sensing cloud platform and integrated data from Sentinel-1, Sentinel-2, and SRTM sources. We constructed a feature space by extracting original band features, vegetation index features, polarization features, terrain features, and texture features. The sequential forward selection (SFS) algorithm was employed for feature optimization, and a combined machine learning and object-oriented classification model was used to accurately extract fruit tree crop distributions by comparing key temporal phases of fruit trees. The results revealed that the backscatter coefficient features from Sentinel-1 had the highest contribution to the classification, followed by the original band features and vegetation index features from Sentinel-2, while the terrain features had a relatively smaller contribution. The highest classification accuracy for jujube plantation areas was observed in November (99.1% for user accuracy and 96.6% for producer accuracy), whereas the lowest accuracy was found for pear tree plantation areas in the same month (93.4% for user accuracy and 89.0% for producer accuracy). Among the four different classification methods, the combined random forest and object-oriented (RF + OO) model exhibited the highest accuracy (OA = 0.94, Kappa = 0.92), while the support vector machine (SVM) classification method had the lowest accuracy (OA = 0.52, Kappa = 0.31). The total fruit tree plantation area in Aksu City in 2022 was estimated to be 64,000 hectares, with walnut, jujube, pear, and apple trees accounting for 42.5%, 20.6%, 19.3%, and 17.5% of the total fruit tree area, respectively (27,200 hectares, 13,200 hectares, 12,400 hectares, and 11,200 hectares, respectively). The SFS feature optimization and RF + OO-combined classification model algorithm selected in this study effectively mapped the fruit tree planting areas, enabling the estimation of fruit tree planting areas based on remote sensing satellite image data. This approach facilitates accurate fruit tree industry and real-time crop monitoring and provides valuable support for fruit tree planting management by the relevant departments.

Gully erosion is considered to be a highly destructive form of soil erosion, often leading to the occurrence of natural calamities like landslides and mudslides. Remote sensing images have been extensively utilized in gully erosion research, and the suitability of extracting gully morphology parameters in various topographic regions needs to be clarified. Based on field measurements, this paper focuses on two widely used high-resolution remote sensing images: Unmanned Aerial Vehicle (UAV) and Google Earth (GE) imagery. It systematically examines the accuracy of gully morphological characteristic extraction using remote sensing in two regions with different terrain characteristics. The results show the following: (1) Compared to interpreting wide gullies with unclear shoulder lines, centimeter-level UAV imagery is more suitable for interpreting narrow gullies with clear shoulder lines. Conversely, the interpretability of sub-meter-level GE imagery is exactly the opposite. (2) The error in interpreting gully head points (GHPs) based on UAV images is less than 1 m, while the errors in gully length (GL), width (GW), perimeter (GP) and area (GA) are all below 3%, and these errors are hardly affected by gully morphology. (3) The error of GHPs based on GE images is concentrated within the range of 1–3 m. Meanwhile, the errors associated with GL, GP and GA are less than 10%. Conversely, the error of GW exceeds 11%. Furthermore, the aforementioned errors tend to increase as the gully width decreases and the complexity of the gully shoulder line increases. These findings shed light on the suitability of two commonly used remote sensing images for gully morphology extraction and provide valuable guidance for image selection in future research endeavors in this field.

Monitoring mine activities can help management track the status of mineral resource exploration and mine rehabilitation. It is crucial to the sustainable development of the mining industry and the protection of the geological environment in mining areas. To monitor the mining activities of shallow surface outcrops in the arid and semi-arid regions of northwest China, this paper proposes a remote sensing monitoring approach of mining activities based on deep learning and integrated interferometric synthetic aperture radar technique. This approach uses the DeepLabV3-ResNet model to identify and extract the spatial location of the mine patches and then uses object-oriented analysis and spatial analysis methods to optimize the mine patch boundaries. SBAS-InSAR technique is used to obtain the time-series deformation information of the mine patches and is combined with the multi-temporal optical imagery to analyze the mining activities in the study area. The proposed approach has a recognition accuracy of 95.80% for the identification and extraction of mine patches, with an F1-score of 0.727 at the pixel level, and the average area similarity for all patches is 0.78 at the object-oriented level. The proposed approach possesses the capability to analyze mining activities, indicating promising prospects for engineering applications. It provides a reference for monitoring mining activities using multisource satellite remote sensing.

For very-high-resolution (VHR) remote sensing images with complex objects and rich textural information, multi-difference image fusion has been proven as an effective method to improve the performance of change detection. However, errors are superimposed during this process and a single spectral feature cannot fully utilize the correlation between pixels, resulting in low robustness. To overcome these problems and optimize the performance of multi-difference image fusion in change detection, we propose a novel multi-difference image fusion change detection method based on a visual attention model (VA-MDCD). First, we construct difference images using change vector analysis (CVA) and spectral gradient difference (SGD). Second, we use the visual attention model to calculate multiple color, intensity and orientation features of the difference images to obtain the difference saliency images. Third, we use the wavelet transform fusion algorithm to fuse two saliency images. Finally, we execute the OTSU threshold segmentation algorithm (OTSU) to obtain the final change detection map. To validate the effectiveness of VA-MDCD on VHR images, two datasets of Jilin 1 and Beijing 2 are selected for experiments. Compared with classical methods, the proposed method has a better performance with fewer missed alarms (MA) and false alarms (FA), which proves that the method has a strong robustness and generalization ability. The F-measure of the two datasets is 0.6671 and 0.7313, respectively. In addition, the results of ablation experiments confirm that the three feature extraction modules of the model all play a positive role.

The purpose of this study is to employ a remote sensing reconnaissance survey based on optimal segmentation parameters and an object-oriented random forest approach to the identification of possible terrestrial impact craters from the global 30-m resolution SRTM DEM. A dataset consisting of 94 confirmed and well-preserved terrestrial impact craters, 104 volcanic calderas, and 124 valleys were extracted from real-world surface features. For craters with different sizes, eight optimal scale parameters from 80 to 3000 have been identified using multi-resolution segmentation, where the scale parameters have a positive correlation (R2 = 0.78) with the diameters of craters. The object-oriented random forest approach classified the tested impact craters, volcanic calderas, and valleys with an overall accuracy of 88.4% and a Kappa coefficient of 0.8. The investigated terrestrial impact craters, in general, have relatively lower rim circularity, higher length-to-width ratio, and lower relief, slope, and elevation than volcanic calderas. The topographic characteristics can be explained by geological processes associated with the formation and post-deformation of impact craters. The excavation and ejection by initial impact and rebound of excavated materials contribute to low elevation. The post-impact deformation, including inward collapse and slump of unstable rims, weathering, erosion, and sediment deposition, further reduces elevation and relief and modifies shapes resulting in lower circularity and higher length-to-width ratio. Due to the resolution limitation of the source DEM data and the number of real-world samples, the model has only been validated for craters of 0.88 to 100 km in diameter, which can be generalized to explore undiscovered terrestrial impact craters using cloud computing with global datasets provided by platforms such as Google Earth Engine and Microsoft Planetary Computer.

How to recover geometric transformations is one of the most challenging issues in image registration. To alleviate the effect of large geometric distortion in multimodal remote sensing image registration, a scale and rotate transform prediction net is proposed in this paper. First, to reduce the scale between the reference and sensed images, the image scale regression module is constructed via CNN feature extraction and FFT correlation, and the scale of sensed image can be recovered roughly. Second, the rotation estimate module is developed for predicting the rotation angles between the reference and the scale-recovered images. Finally, to obtain the accurate registration results, LoFTR is employed to match the geometric-recovered images. The proposed registration network was evaluated on GoogleEarth, HRMS, VIS-NIR and UAV datasets with contrast differences and geometric distortions. The experimental results show that the number of correct matches of our model reached 74.6%, and the RMSE of the registration results achieved 1.236, which is superior to the related methods.

With the rapid development of the Internet of Things (IoT)-based near-Earth remote sensing technology, the problem of network intrusion for near-Earth remote sensing systems has become more complex and large-scale. Therefore, seeking an intelligent, automated, and robust network intrusion detection method is essential. Many researchers have researched network intrusion detection methods, such as traditional feature-based and machine learning methods. In recent years, network intrusion detection methods based on graph neural networks (GNNs) have been proposed. However, there are still some practical issues with these methods. For example, they have not taken into consideration the characteristics of near-Earth remote sensing systems, the state of the nodes, and the temporal features. Therefore, this article analyzes the factors of existing near-Earth remote sensing systems and proposes a spatio-temporal graph attention network (N-STGAT) that considers the state of nodes and applies them to the network intrusion detection of near-Earth remote sensing systems. Finally, the proposed method in this article is validated using the latest flow-based datasets NF-BoT-IoT-v2 and NF-ToN-IoT-v2. The results demonstrate that the binary classification accuracy for network intrusion detection exceeds 99%, while the multi-classification accuracy exceeds 93%. These findings provide substantial evidence that the proposed method outperforms existing intrusion detection techniques.

Pulse noise (such as glacier fracturing and offshore pile driving), commonly seen in the marine environment, seriously affects the performance of Direction-of-Arrival (DOA) estimation methods in sonar systems. To address this issue, this paper proposes a high robustness underwater target estimation method based on variational sparse Bayesian inference by studying and analyzing the sparse prior assumption characteristics of signals. This method models pulse noise to build an observation signal, completes the derivation of the conditional distribution of the observed variables and the prior distribution of the sparse signals, and combines Variational Bayes (VB) theory to approximate the posterior distribution, thereby obtaining the recovered signal of the sparse signals and reducing the impact of pulse noise on the estimation system. Our simulation results showed that the proposed method achieved higher estimation accuracy than traditional methods in both single and multiple snapshot scenarios and has practical potential.

Monitoring the coastline dynamic can provide the basis for the balance of sediment erosion and deposition. The evaluation of coastal stability is beneficial to decision makers for the rational development and ecological conservation of coastal resources. The present study first collected 61 scenes of remote sensing images and extracted the multi-temporal coastlines from the years 1990–2020 in Jiangsu Province, China using an improved waterline method. Given the characteristics of gentle slopes of our study area, we modified the coastlines using actual tidal level data to avoid the influence from different tidal regimes. Finally, the coastal stability analysis was conducted on the central coast of Jiangsu, which experiences frequent changes in erosion and siltation. The results showed that the coastline has changed significantly; the natural coastline decreased by 116 km, while the artificial coastline increased by 108 km. the area of tidal flats decreased by 1152 km2, and the average width of the tidal flats decreased from 8.83 km to 3.55 km. In general, the coastline advanced seawards for many years, mainly due to sediment siltation and tidal flat reclamation, with annual average rates of siltation and reclamation of 9.67 km/a and 40.75 km/a, respectively. The node of siltation and erosion migrated 1.8 km southwards, moving from the Sheyang Estuary to the Doulong Port. The coastal stability gradually decreased from north to south, by values of 88.5 km (40%) for stable coast and 63.97 km (28.9%) for extremely unstable coast. The most unstable coast came from frequent reclamation areas. The method in this study is expected to provide a reference for evaluating the stability of typical muddy coasts, and our results can provide a basis for the sustainable development, utilization, and protection of coastal areas.

The accurate inversion of actual evapotranspiration (ETa) at a regional scale is crucial for understanding water circulation, climate change, and drought monitoring. In this study, we produced a 1 km monthly ETa dataset for Turpan and Hami, two typical arid cities in northwest China, using multi-source remote sensing data, reanalysis information, and the ETMonitor model from 1980 to 2021. We analyzed the spatiotemporal variation of ETa using various statistical approaches and discussed the impact of climate and land use and cover changes (LUCC) on ETa. The results show the following: (1) the estimation results correlate well with ETa products on monthly scales (coefficient of determination (R2) > 0.85, root mean square error (RMSE) < 15 mm/month) with high reliability. (2) The ETa values were spatially distributed similarly to precipitation and LUCC, with the multi-year (1980–2021) average of 66.31 mm and a slightly fluctuating downward trend (−0.19 mm/a). (3) During the 42-year period, 63.16% of the study area exhibited an insignificant decrease in ETa, while 86.85% experienced pronounced fluctuations (coefficient of variation (CV) > 0.20), and 78.83% will show an upward trend in the future. (4) ETa was significantly positively correlated with precipitation (94.17%) and insignificantly positively correlated with temperature (55.81%). The impact of human activities showed an insignificant decreasing trend (85.41%). Additionally, the intensity of ETa varied considerably among land types, with the largest for cropland (424.12 mm/a). The results of the study have implications for promoting the rational allocation of regional water resources and improving water use efficiency in arid zones.

Device-free wireless localization based on Wi-Fi channel state information (CSI) is an emerging technique that could estimate users’ indoor locations without invading their privacy or requiring special equipment. It deduces the position of a person by analyzing the influence on the CSI of Wi-Fi signals. When pedestrians block the signals between the transceivers, the non-line-of-sight (NLOS) transmission occurs. It should be noted that NLOS has been a significant factor restricting the device-free positioning accuracy due to signal reduction and abnormalities during multipath propagation. For this problem, we analyzed the NLOS effect in an indoor environment and found that the position error in the LOS condition is different from the NLOS condition. Then, two empirical models, namely, a CSI passive positioning model and a CSI NLOS/LOS detection model, have been derived empirically with extensive study, which can obtain better robustness identified results in the case of NLOS and LOS conditions. An algorithm called SVM-NB (Support Vector Machine-Naive Bayes) is proposed to integrate the SVM NLOS detection model with the Naive Bayes fingerprint method to narrow the matching area and improve position accuracy. The NLOS identification precision is better than 97%. The proposed method achieves localization accuracy of 0.82 and 0.73 m in laboratory and corridor scenes, respectively. Compared to the Bayes method, our tests showed that the positioning accuracy of the NLOS condition is improved by 28.7% and that of the LOS condition by 26.2%.

Soil erosion triggered by water and wind pose a great threat to the sustainable development of Pakistan. In this study, a combination of geographic information systems (GISs) and machine learning approaches were used to predict soil water erosion rates. The Revised Wind Erosion Equation (RWEQ) model was used to evaluate soil wind erosion, map erosion factors, and analyze the soil erosion rates for each land use type. Finally, the maps of soil water and wind erosion were spatially integrated to identify erosion risk regions and recommend land use management in Pakistan. According to our estimates, the Potohar Plateau and its surrounding regions were mostly impacted by water erosion and have a soil erosion rate of 2500–5000 t·km−2·a−1; on the other hand, wind erosion predominated the Kharan Desert and the Thar Desert, with a soil erosion rate exceeding 15,000 t·km−2·a−1. The Sulaiman and Kirthar Mountain Ranges were susceptible to wind–water compound erosion, which was more than 8000 t·km−2·a−1. This study offers new perspectives on the geographic pattern of individual and integrated water–wind erosion threats in Pakistan and provides high-precision data and a scientific foundation for designing rational soil and water conservation practices.

Drought poses a serious threat to agricultural production and food security in the context of global climate change. Few studies have explored the response mechanism and lag time of agricultural drought to meteorological drought from the perspective of cultivated land types. This paper analyzes the spatiotemporal evolution patterns and hysteresis relationship of meteorological and agricultural droughts in the middle and lower reaches of the Yangtze River in China. Here, the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products and surface temperature products were selected to calculate the Temperature Vegetation Dryness Index (TVDI) from 2010 to 2015. Furthermore, we obtained the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI) for the same period. Based on these indices, we analyzed the correlation and the hysteresis relationship between agricultural and meteorological drought in rainfed and irrigated arable land. The results showed that, (1) compared with SPEI, the high spatial resolution PDSI data were deemed more suitable for the subsequent accurate and scientific analysis of the relationship between meteorological and agricultural droughts. (2) When meteorological drought occurs, irrigated arable land is the first to experience agricultural drought, and then alleviates when the drought is most severe in rainfed arable land, indicating that irrigated arable land is more sensitive to drought events when exposed to the same degree of drought risk. However, rainfed arable land is actually more susceptible to agricultural drought due to the intervention of irrigation measures. (3) According to the cross-wavelet transform analysis, agricultural droughts significantly lag behind meteorological droughts by about 33 days during the development process of drought events. (4) The spatial distribution of the correlation coefficient between the PDSI and TVDI shows that the area with negative correlations of rainfed croplands and the area with positive correlations of irrigated croplands account for 77.55% and 68.04% of cropland areas, respectively. This study clarifies and distinguishes the details of the meteorological-to-agricultural drought relationship in rainfed and irrigated arable land, noting that an accurate lag time can provide useful guidance for drought monitoring management and irrigation project planning in the middle and lower reaches of the Yangtze River.

As drought vulnerability assessment is fundamental to risk management, it is urgent to develop scientific and reasonable assessment models to determine such vulnerability. A vulnerability curve is the key to risk assessment of various disasters, connecting analysis of hazard and risk. To date, the research on vulnerability curves of earthquakes, floods and typhoons is relatively mature. However, there are few studies on the drought vulnerability curve, and its application value needs to be further confirmed and popularized. In this study, on the basis of collecting historical disaster data from 52 drought events in China from 2009 to 2013, three drought remote sensing indexes were selected as disaster-causing factors; the affected population was selected to reflect the overall disaster situation, and five typical regional drought vulnerability curves were constructed. The results showed that (1) in general, according to the statistics of probability distribution, most of the normalized difference vegetation index (NDVI) and the temperature vegetation drought index (TVDI) variance ratios were concentrated between 0 and ~0.15, and most of the enhanced vegetation index (EVI) variance ratios were concentrated between 0.15 and ~0.6. From a regional perspective, the NDVI and EVI variance ratio values of the northwest inland perennial arid area (NW), the southwest mountainous area with successive years of drought (SW), and the Hunan Hubei Jiangxi area with sudden change from drought to waterlogging (HJ) regions were close and significantly higher than the TVDI variance ratio values. (2) Most of the losses (drought at-risk populations, DRP) were concentrated in 0~0.3, with a cumulative proportion of about 90.19%. At the significance level, DRP obeys the Weibull distribution through hypothesis testing, and the parameters are optimal. (3) The drought vulnerability curve conformed to the distribution rule of the logistic curve, and the line shape was the growth of the loss rate from 0 to 1. It was found that the arid and ecologically fragile area in the farming pastoral ecotone (AP) region was always a high-risk area with high vulnerability, which should be the focus of drought risk prevention and reduction. The study reduces the difficulty of developing the vulnerability curve, indicating that the method can be widely used to other regions in the future. Furthermore, the research results are of great significance to the accurate drought risk early warning or whether to implement the national drought disaster emergency rescue response.

NDVI data have been widely used to detect and monitor vegetation status at regional, continental, and global scales. FY-3D MERSI-II NDVI (FNDVI) is a critical operational product used in many studies monitoring ecosystems and agriculture and assessing climate change and its risks, including drought and fire. MERSI-II and MODIS have very similar spectral response functions in the red and near-infrared channels, making MERSI/NDVI an effective replacement for MODIS/NDVI (MNDVI). Therefore, it is critical to conduct a thorough evaluation of the product’s quality. In this study, the consistency characteristics of two normalized difference vegetation index (NDVI) products, FY-3D MERSI-II NDVI and MODIS NDVI, were compared and validated at national and regional scales in China from 2020 to 2021. To assess the consistency of these two NDVI datasets, the correlation coefficient, root-mean-square error, and mean bias error were used. The findings revealed that the spatial distribution patterns of FNDVI and MNDVI were highly consistent across the country at the monthly time scale. The correlation coefficients were greater than 0.9475 for the two years 2020–2021, while the average deviation was between 0.02 and 0.05, and the root-mean-square error was 0.11. Based on the difference in the time consistency between FNDVI and MNDVI, the changes in the monthly NDVI values of the two types of satellites are generally consistent across the country. Among the three typical experimental areas, the relative deviation of the regional time series for products was the highest in Xinjiang. The relative average deviation of FNDVI in other regions was low, and its change trend was consistent with that of MODIS.

Floating-algae detection plays an important role in marine-pollution monitoring. The surveillance cameras on ships and shores provide a powerful way of monitoring floating macroalgae. However, the previous methods cannot effectively solve the challenging problem of detecting Ulva prolifera and Sargassum, due to many factors, such as strong interference with the marine environment and the drastic change of scale. Recently, the instance-segmentation methods based on deep learning have been successfully applied to many image-recognition tasks. In this paper, a novel instance-segmentation network named AlgaeFiner is proposed for high-quality floating-algae detection using RGB images from surveillance cameras. For improving the robustness of the model in complex ocean scenes, the CA-ResNet is firstly proposed by integrating coordinate attention into the ResNet structure to model both the channel- and position-dependencies. Meanwhile, the Ms-BiFPN is proposed by embedding the multi-scale module into the architecture of BiFPN to strengthen the ability of feature fusion at different levels. To improve the quality of floating-algae segmentation, the Mask Transfiner network is introduced into the AlgaeFiner to obtain the high-quality segmentation results. Experimental results demonstrate that the AlgaeFiner can achieve better performance on floating-algae segmentation than other state-of-the-art instance-segmentation methods, and has high application-value in the field of floating-macroalgae monitoring.

The interferometric synthetic aperture radar (InSAR) technique based on time–frequency (TF) analysis has great potential for mapping the forest canopy height model (CHM) at regional and global scales, as it benefits from the additional InSAR observations provided by the sublook decomposition. Meanwhile, due to the wider swath and higher spatial resolution of single-polarization data, InSAR has a higher observation efficiency in comparison with PolInSAR. However, the accuracy of the CHM inversion obtained by the TF-InSAR method is attenuated by its inaccurate coherent scattering modeling and uncertain parameter calculation. Hence, a new approach for CHM estimation based on single-baseline InSAR data and sublook decomposition is proposed in this study. With its derivation of the coherent scattering modeling based on the scattering matrix of sublook observations, a time–frequency based random volume over ground (TF-RVoG) model is proposed to describe the relationship between the sublook coherence and the forest biophysical parameters. Then, a modified three-stage method based on the TF-RVoG model is used for CHM retrieval. Finally, the two-dimensional (2-D) ambiguous error of pure volume coherence caused by residual ground scattering and temporal decorrelation is alleviated in the complex unit circle. The performance of the proposed method was tested with airborne L-band E-SAR data at the Krycklan test site in Northern Sweden. Results show that the modified three-stage method provides a root-mean-square error (RMSE) of 5.61 m using InSAR and 14.3% improvement over the PolInSAR technique with respect to the classical three-stage inversion result. An inversion accuracy of RMSE = 2.54 m is obtained when the spatial heterogeneity of CHM is considered using the proposed method, demonstrating a noticeable improvement of 32.8% compared with results from the existing method which introduces the fixed temporal decorrelation factor.

Accurately detecting landslides over a large area with complex background objects is a challenging task. Research in the area suffers from three drawbacks in general. First, the models are mostly modified from typical networks, and are not designed specifically for landslide detection. Second, the images used to construct and evaluate models of landslide detection are limited to one spatial resolution, which struggles to meet the requirements of such relevant applications as emergency response. Third, assessments are primarily carried out by using the training data on different parts of the same study area. This makes it difficult to objectively evaluate the transferability of the model, because ground objects in the same area are distributed with similar spectral characteristics. To respond to the challenges above, this study proposes DeenNet, specifically designed for landslide detection. Different from the widely used encoder–decoder networks, DeenNet maintains multi-scale landslide features by decoding the input feature maps to a large scale before encoding a module. The decoding operation is conducted by deconvolution of the input feature maps, while encoding is conducted by convolution. Our model is trained on two earthquake-triggered landslide datasets, constructed using images with different spatial resolutions from different sensor platforms. Two other landslide datasets of different study areas with different spatial resolutions were used to evaluate the trained model. The experimental results demonstrated an at least 6.17% F1-measure improvement by DeenNet compared with three widely used typical encoder–decoder-based networks. The decoder–encoder network structure of DeenNet proves to be effective in maintaining landslide features, regardless of the size of the landslides in different evaluation images. It further validated the capacity of DeenNet in maintaining landslide features, which provides a strong applicability in the context of applications.

High spatial and temporal resolution products of near-surface air temperature (T2m) over the Greenland Ice Sheet (GrIS) are required as baseline information in a variety of research disciplines. Due to the sparse network of in situ data on the GrIS, remote sensing data and machine learning methods provide great advantages, due to their capacity and accessibility. The Land Surface Temperature (LST) at 780 m resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and T2m observation from 25 Automatic Weather Stations (AWSs) are used to establish a relationship over the GrIS by comparing multiple machine learning approaches. Four machine learning methods—neural network (NN), gaussian process regression (GPR), support vector machine (SVM), and random forest (RF)—are used to reconstruct the T2m at daily and monthly scales. We develop a reliable T2m reconstruction model based on key meteorological parameters, such as albedo, wind speed, and specific humidity. The reconstructions daily and monthly products are generated on a 780 m × 780 m spatial grid spanning from 2007 to 2019. When compared with in situ observations, the NN method presents the highest accuracy, with R of 0.96, RMSE of 2.67 °C, and BIAS of −0.36 °C. Similar to the regional climate model (RACMO2.3p2), the reconstructed T2m can better reflect the spatial pattern in term of latitude, longitude, and altitude effects.

This study investigated an asbestos mine restoration project using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data. The distribution of an abandoned asbestos mine (AAM) and treatment area were analyzed before and after the remediation based on the spectral indices for detecting naturally occurring asbestos (NOA) indicators and encapsulation. The spectral indices were developed for NOA, host rock, and encapsulation by logistic regression models using spectral bands extracted from the random forest algorithm. The detection models mostly used VNIR spectra rather than SWIR and were statistically significant. The overall accuracy of the detection models was approximately 84%. Notably, the detection accuracy of non-treated and treated areas was increased to about 96%, excluding the host rock index. The NOA index detected asbestos in the mine area as well as those in outcrops outside of the mine. It has been confirmed that the NOA index can be efficiently applied to all cases of asbestos occurrence. The remote sensing data revealed that the mine area was increased by ~5% by the remediation, and the treatment activity reduced asbestos exposure by ~32%. Moreover, the integrative visualization between the detection results and 3D high-resolution images provided an intuitive and realistic understanding of the reclamation project.

A long-series mass balance (MB) of glaciers can be used to study glacier–climate relationships. Using a distributed simplified energy balance model (SEBM) and an enhanced temperature-index model (ETIM), the MB of Laohugou Glacier No. 12 (LHG12) was reconstructed from 1980 to 2020, driven by a calibrated ERA5 reanalysis dataset. The simulation of SEBM performs better than that of ETIM. The results showed that the annual MB of LGH12 is a fluctuating trend of declining from 1980 to 2020, with annual means of −0.39 ± 0.28 m w.e. a−1 and cumulative value of −16 ± 4 m w.e. During 1980–1990, the annual MB fluctuated in a small range, while after 1990, LHG12 accelerated melting owing to rising air temperature, with annual means of −0.48 m w.e. a−1, three times as large as that of 1980–1990. The largest mass loss occurred during 2001–2010 at an average rate of −0.57 m w.e. a−1. The average equilibrium line altitude (ELA) was 4976 m a.s.l., and since 1980, the ELA has been increasing at a rate of 37.5 m/10 a. LHG12 is most sensitive to air temperature, and the MB sensitivity reaches −0.51 m w.e. a−1 with air temperature increase of 1 °C. The sensitivity of MB to incoming shortwave radiation (+10%) simulated by SEBM is −0.30 m w.e. a−1, three times larger than that simulated by ETIM. This is mainly because the two models have different conditions for controlling melting. Melting is controlled only by air temperature for ETIM, while for SEBM, it is controlled by air temperature and incoming shortwave radiation.

Smart noise jamming forms active jamming by intercepting, modulating, and forwarding radar signals into the radar receiver, which seriously affects the radar range recovery performance. In this paper, we propose a novel waveform design approach and an efficient range recovery method for high-resolution radar in the jamming scenario. Firstly, we propose an intra-pulse frequency-coded frequency-modulated continuous waveform (IPFC-FMCW), which contains multiple FMCW chips with different widths and frequencies, to combat the smart noise jamming. After the jamming suppression, the proposed waveform has a low sidelobe level, which is different from traditional FMCW signals for which the observations are periodically missing, resulting in high sidelobe levels. Then, to improve the range recovery performance of the waveform after jamming suppression, we optimize the range profile by designing the transmit waveform and then solve it by a simulated annealing algorithm. Next, based on the designed waveform, we derive the echo model after jamming suppression and propose a gridless compressed sensing (CS) method to recover the range of the targets. Compared with the existing waveforms and methods, the proposed waveform and the processing method achieve better range recovery performance in the jamming scenario. Numerical simulations are utilized to demonstrate the range recovery effectiveness of the proposed waveform and method in smart noise jamming.

Data stream partitioning is a fundamental and important mechanism for distributed systems. However, use of an inappropriate partition scheme may generate a data skew problem, which can influence the execution efficiency of many application tasks. Processing of skewed partitions usually takes a longer time, need more computational resources to complete the task and can even become a performance bottleneck. To solve such data skew issues, this paper proposes a novel partition method to divide on demand the image tiles uniformly into partitions. The partitioning problem is then transformed into a uniform and compact clustering problem whereby the image tiles are regarded as image pixels without spectrum and texture information. First, the equal area conversion principle was used to select the seed points of the partitions and then the image tiles were aggregated in an image layout, thus achieving an initial partition scheme. Second, the image tiles of the initial partition were finely adjusted in the vertical and horizontal directions in separate steps to achieve a uniform distribution among the partitions. Two traditional partition methods were adopted to evaluate the efficiency of the proposed method in terms of the image segmentation testing, data shuffle testing, and image clipping testing. The results demonstrated that the proposed partition method solved the data skew problem observed in the hash partition method. In addition, this method is designed specifically for processing of image tiles and makes the related processing operations for large-scale images faster and more efficient.

An analysis of the relationship between changes in Arctic stratospheric ozone (ASO) and precipitation in eastern North America (38°–54°N, 65°–87°W; PENA) was performed using observational and reanalysis data coupled with the Whole Atmosphere Community Climate Model version 4 (WACCM4). We found that March ASO exhibits a strong correlation with PENA in April, indicating that the one-month leading ASO exerts a potentially strong impact on April PENA. Changes in tropospheric circulation over the North Pacific and North America can be influenced by ASO anomalies via stratosphere–troposphere interactions. Increased ASO typically results in the transport of drier, colder air from northwest to eastern North America and suppresses local convective activity by enhancing regional downwelling. These conditions lead to a decrease in regional atmospheric water vapor content (1000–600 hPa). Abnormally high ASO may therefore suppress precipitation, whereas abnormally low ASO serves to enhance precipitation, and the finding is supported by WACCM4 simulations incorporating these ASO anomaly signals. We also present an ASO-based statistical linear model for predicting April PENA. Results confirm that the linear model reproduces April PENA for both training and testing periods, based on March ASO, demonstrating the reliability and stability of this linear model. This study verifies that ASO is a viable predictor for projecting April PENA and thus improving forecasts of regional seasonal precipitation.

In this paper, a method for extracting the on-the-fly (OTF) GNSS tide level was designed by combining variational modal decomposition (VMD) and a wavelet thresholding (WT) method to improve the extraction accuracy of the OTF GNSS tide level. First, the energy difference ratio method was used to determine the number of layers for the VMD. Subsequently, the VMD performed a second decomposition of the IMF1 obtained from the first VMD to achieve an efficient separation of signal and noise. The normalized cross-correlation coefficient (NCC) was applied to determine the number of layers for the WT method. Finally, experimental results showed that the VMD–WT method outperformed the other seven filtering methods in three metrics: maximum error, the root-mean-square error (RMSE), and error distribution. Therefore, the VMD–WT method was able to extract extremely accurate on-the-fly GNSS tide level and additionally obtain more accurate bathymetry data after tidal correction of the bathymetry data.

GNSS/INS can provide position, attitude, and velocity (PAV) information for shipborne platforms. However, if the ship has a long-term linear motion or a stationary state, and is under the combined actions of sea surface swells, there will be a situation of sideslip and drift; if the ship is traveling slowly or shaking violently, the attitude calculation will not be completed. In the above situation, the traditional single-antenna GNSS/INS measurement mode is not suitable, and the attitude observability is poor; the heading angle attitude information, especially, will gradually diverge. Unreliable information will directly lead to a significant increase in underwater positioning errors. In this paper, a multi-antenna GNSS/INS combination algorithm is developed and used to provide high-precision PAV information, and is thereby able to obtain high-precision underwater positioning results. The experimental results show that the method has improved the acquisition of position and velocity in the horizontal direction and the accuracy of navigation attitude measurement. In particular, the attitude measurement accuracy in the 3 degrees of freedom (DoF) are improved by 10.1% (roll), 8.6% (pitch), and 29.3% (yaw).

The Yellow River Basin is essential to China’s economic and social development and ecological security. In order to assess the temporal and spatial distribution of cloud water in the Yellow River Basin, we analyzed the distribution characteristics of water vapor content and cloud water content using ERA5 monthly averaged data from 1980 to 2019. The results showed that the high-value area of the annual average atmospheric water vapor content distribution was concentrated above the North China Plain in the eastern part of the basin, and the value was mostly between 21 and 24 mm. The low-value areas were mainly centered above the high mountain areas in the western part of the basin, and the value mostly fell between 3 and 6 mm. The seasonal distribution characteristics of the annual average water vapor content were relatively consistent with the annual average distribution characteristics. The high-value cloud ice water content area was in the northeastern part of the Qinghai-Tibet Plateau (4.5 g·m−2), while the low-value area appeared on the Loess Plateau (2 g·m−2). The high-value area of cloud liquid water content was on the north side of the West Qinling Mountains (12 g·m−2). The low-value area appeared on the Loess Plateau and the northern part of the Qinghai-Tibet Plateau (3 g·m−2). The cloud water content was higher in the eastern region than in the western region in the overall spatial distribution, and the content of cloud liquid water was higher than that of cloud ice water. The average annual atmospheric water vapor content was increasing, and the annual average content of cloud ice water and cloud liquid water was declining. The change in the total amount and spatial distribution of cloud water was not obvious in the Yellow River Basin.

With the gradual improvement of Galileo and the opening of BDS-3 services, water vapor tomography based on multi-GNSS can be effectively carried out to reconstruct three-dimensional water vapor distribution. In this paper, experiments in Hong Kong were conducted to analyze and assess the performances of GPS, BDS, GLONASS, and Galileo and their combinations in water vapor tomography. Numerical results show that the number of available signal rays varies widely in the four satellite systems, and the value can be increased by the combination of satellite systems; the combinations also increase the number of voxels crossed by signal rays, but this value is not directly related to the number of available signal rays; the number and distribution of the voxels with sufficient signal rays, which most closely related to the structure of the tomographic model, show no obvious differences in the four satellite systems and their combinations. Comparative results of slant water vapor (SWV) estimated by GNSS data and water vapor density derived from radiosonde data reveal that the differences in the water vapor tomography of the four satellite systems are small, and their combinations have limited improvement in the tomographic results.

Gully erosion susceptibility (GES) maps are essential for managing land resources and erosion control. Choosing the optimal horizontal resolution in GES mapping is a challenge. In this study, the optimal resolution for GES mapping in a complex loess hilly area on the Chinese Loess Plateau was tested using two machine learning algorithms. Unmanned aerial vehicle (UAV) images with a 9 cm resolution and GNSS RTK field-measured data were employed as base datasets, and 11 factors were used in the machine learning models. A series of horizontal resolutions, from 0.5–30 m, was used to determine which was the optimal level and how the resolution influenced the GES mapping. The results showed that the optimal resolution for GES mapping was 2.5–5 m in the loess hilly area, for both the random forest (RF) and extreme gradient-boosting (XGBoost) machine learning algorithms employed in this study. High resolutions overestimated the probability of gully erosion in stable regions, and it became difficult to identify gully and non-gully regions at too-coarse resolutions. The variable importance for GES mapping changed with the resolution and varied among variables. Slope gradient, land use, and contributing area were, in general, the three most critical factors. Land use remained an important factor at all the tested resolution levels. The importance of the slope gradient was underestimated at coarse resolutions (10–30 m), and the importance of the contributing area was underestimated at resolutions that were comparatively fine (0.5–1 m). This study provides an essential reference for selecting the optimal resolution for gully mapping, and thus, offers support for approaches attempting to map gullies using UAV.

It is hard for current time series reconstruction methods to achieve the balance of high-precision time series reconstruction and explanation of the model mechanism. The goal of this paper is to improve the reconstruction accuracy with a well-explained time series model. Thus, we developed a function-based model, the CCTM (Continuous Change Tracker Model) model, that can achieve high precision in time series reconstruction by tracking the time series variation rate. The goal of this paper is to provide a new solution for high-precision time series reconstruction and related applications. To test the reconstruction effects, the model was applied to four types of datasets: normalized difference vegetation index (NDVI), gross primary productivity (GPP), leaf area index (LAI), and MODIS surface reflectance (MSR). Several new observations are as follows. First, the CCTM model is well explained and based on the second-order derivative theorem, which divides the yearly time series into four variation types including uniform variations, decelerated variations, accelerated variations, and short-periodical variations, and each variation type is represented by a designed function. Second, the CCTM model provides much better reconstruction results than the Harmonic model on the NDVI, GPP, MSR, and LAI datasets for the seasonal segment reconstruction. The combined use of the Savitzky–Golay filter and the CCTM model is better than the combinations of the Savitzky–Golay filter with other models. Third, the Harmonic model has the best trend-fitting ability on the yearly time series dataset, with the highest R-Square and the lowest RMSE among the four function fitting models. However, with seasonal piecewise fitting, the four models all achieved high accuracy, and the CCTM performs the best. Fourth, the CCTM model should also be applied to time series image compression, two compression patterns with 24 coefficients and 6 coefficients respectively are proposed. The daily MSR dataset can achieve a compression ratio of 15 by using the 6-coefficients method. Finally, the CCTM model also has the potential to be applied to change detection, trend analysis, and phenology and seasonal characteristics extractions.

Superpixel segmentation algorithms are widely used in the image processing field. The size of the large-scale images usually exceeds the memory of a single machine given that the size of image data has increased rapidly in recent years. This leads to big challenges for implementing sequential superpixel segmentation methods, although these algorithms have good scalability. Additionally, segmentation of large-scale images over a distributed cluster is a feasible solution. Nevertheless, it is challenging to transplant sequential superpixel algorithms directly to a distributed environment, as usually there are incomplete object problems in the border area of image tiles. To overcome the incomplete object problems, one approach is to build a distributed strategy based on a sequential SLIC superpixel segmentation algorithm over a distributed cluster organized by Apache Spark. In our research, the decomposed image tiles were divided into two categories—even tiles and odd tiles. The even tiles were first segmented by the SLIC algorithm, then the cluster centers and buffer sizes of even tiles were extracted and switched to odd tiles. During the shuffle stage, the odd tiles acquired pixels from adjacent even tiles according to the buffer sizes, and then the buffered odd tiles were segmented by the SLIC algorithm with the help of the shared cluster centers. The superpixels with shared cluster centers were generated in even tiles and remained in order to enlarge the odd tiles rather than redundant computing of specific areas to modify incomplete superpixels well. Specifically, this strategy employs the shared variables to transmit intermediate results and the shuffle operations were carried out among approximately half of the entire image tiles, which reduces the communications further. The distributed strategy was evaluated in terms of the accuracy and execution efficiency, which revealed that the proposed strategy could not only get better F-measure values but is also implemented faster relative to the repeat calculation strategy, especially for limited calculation resources. Therefore, the proposed strategy is more suitable for superpixel segmentation algorithms. In addition, this research accumulates experience for expanding the abundant sequential algorithms to the distributed environment and provides more solutions for large-scale image processing demands.

Satellite platform jitter is a non-negligible factor that affects the image quality of optical cameras. Considering the limitations of traditional platform jitter detection methods that are based on attitude sensors and remote sensing images, this paper proposed a jitter detection method using sequence CMOS images captured by rolling shutter for high-resolution remote sensing satellite. Through the three main steps of dense matching, relative jitter error analysis, and absolute jitter error modeling using sequence CMOS images, the periodic jitter error on the imaging focal plane of the spaceborne camera was able to be measured accurately. The experiments using three datasets with different jitter frequencies simulated from real remote sensing data were conducted. The experimental results showed that the jitter detection method using sequence CMOS images proposed in this paper can accurately recover the frequency, amplitude, and initial phase information of satellite jitter at 100 Hz, 10 Hz, and 2 Hz. Additionally, the detection accuracy reached 0.02 pixels, which can provide a reliable data basis for remote sensing image jitter error compensation.

Most natural forests are mixed forests, a mixed broadleaf-conifer forest is essentially a heterogeneously mixed pixel in remote sensing images. Satellite missions rely on modeling to acquire regional or global vegetation parameter products. However, these retrieval models often assume homogeneous conditions at the pixel level, resulting in a decrease in the inversion accuracy, which is an issue for heterogeneous forests. Therefore, information on the canopy composition of a mixed forest is the basis for accurately retrieving vegetation parameters using remote sensing. Medium and high spatial resolution multispectral time-series data are important sources for canopy conifer-broadleaf ratio estimation because these data have a high frequency and wide coverage. This paper highlights a successful method for estimating the conifer-broadleaf ratio in a mixed forest with diverse tree species and complex canopy structures. Experiments were conducted in the Purple Mountain, Nanjing, Jiangsu Province of China, where we collected leaf area index (LAI) time-series and forest sample plot inventory data. Based on the Invertible Forest Reflectance Model (INFORM), we simulated the normalized difference vegetation index (NDVI) time-series of different conifer-broadleaf ratios. A time-series similarity analysis was performed to determine the typical separable conifer-broadleaf ratios. Fifteen Gaofen-1 (GF-1) satellite images of 2015 were acquired. The conifer-broadleaf ratio estimation was based on the GF-1 NDVI time-series and semi-supervised k-means cluster method, which yielded a high overall accuracy of 83.75%. This study demonstrates the feasibility of accurately estimating separable conifer-broadleaf ratios using field measurement data and GF-1 time series in mixed broadleaf-conifer forests.

The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected over open water (OW), first-year ice (FYI), and multi-year ice (MYI). A total of eight features extracted from GNSS-R observables collected in five months are applied to classify OW, FYI, and MYI using the ML classifiers of random forest (RF) and support vector machine (SVM) in a two-step strategy. Firstly, randomly selected 30% of samples of the whole dataset are used as a training set to build classifiers for discriminating OW from sea ice. The performance is evaluated using the remaining 70% of samples through validating with the sea ice type from the Special Sensor Microwave Imager Sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). The overall accuracy of RF and SVM classifiers are 98.83% and 98.60% respectively for distinguishing OW from sea ice. Then, samples of sea ice, including FYI and MYI, are randomly split into training and test dataset. The features of the training set are used as input variables to train the FYI-MYI classifiers, which achieve an overall accuracy of 84.82% and 71.71% respectively by RF and SVM classifiers. Finally, the features in every month are used as training and testing set in turn to cross-validate the performance of the proposed classifier. The results indicate the strong sensitivity of GNSS signals to sea ice types and the great potential of ML classifiers for GNSS-R applications.