Understanding the hydrological surface condition changes, climate change and their combined impacts on flood runoff are critical for comprehending the hydrology under environmental changes and for solving future flood management challenges. This study was designed to examine the relative contributions of the hydrological surface condition changes and climate change in the flood runoff of a 45,421-km² watershed in the Loess Plateau region. Statistical analytical methods, including Kendall’s trend test, the Theisen median trend analysis, and cumulative anomaly method, were used to detect trends in the relationship between the climatic variables, the normalized difference vegetation index (NDVI), land use/cover change (LUCC) data, and observed flood runoff. A grid-cell distributed rainfall–runoff model was used to detect the quantitative hydrologic responses to the climatic variability and land-use change. We found that climatic variables were not statistically significantly different (p > 0.05) over the study period. From 1985 to 2013, the cropland area continued to decrease, while the forest land, pastures, and residential areas increased in the Jinghe River Basin. Affected by LUCC and climate change, the peak discharges and flood volumes decreased by 8–22% and 5–67%, respectively. This study can provide a reference for future land-use planning and flood runoff control policy formulation and for revision in the study area. Full article

Remote sensing image scene classification has become more and more popular in recent years. As we all know, it is very difficult and time-consuming to obtain a large number of manually labeled remote sensing images. Therefore, few-shot scene classification of remote sensing images has become an urgent and important research task. Fortunately, the recently proposed deep nearest neighbor neural network (DN4) has made a breakthrough in few-shot classification. However, due to the complex background in remote sensing images, DN4 is easily affected by irrelevant local features, so DN4 cannot be directly applied in remote sensing images. For this reason, a deep nearest neighbor neural network based on attention mechanism (DN4AM) is proposed to solve the few-shot scene classification task of remote sensing images in this paper. Scene class-related attention maps are used in our method to reduce interference from scene-semantic irrelevant objects to improve the classification accuracy. Three remote sensing image datasets are used to verify the performance of our method. Compared with several state-of-the-art methods, including MatchingNet, RelationNet, MAML, Meta-SGD and DN4, our method achieves promising results in the few-shot scene classification of remote sensing images. Full article

This paper proposes a method for efficient and accurate removal of grating lobes in automotive Synthetic Aperture Radar (SAR) images. Grating lobes can indeed be mistaken as real targets, inducing in this way false alarms in the target detection procedure. Grating lobes are present whenever SAR focusing is performed using data acquired on a non-continuous basis. This kind of acquisition is typical in the automotive scenario, where regulations do not allow for a continuous operation of the radar. Radar pulses are thus transmitted and received in bursts, leading to a spectrum of the signal containing gaps. We start by deriving a suitable reference frame in which SAR images are focused. It will be shown that working in this coordinate system is particularly convenient since it allows for a signal spectrum that is space-invariant and with spectral gaps described by a simple one-dimensional function. After an inter-burst calibration step, we exploit these spectral characteristics of the signal by implementing a compressive sensing algorithm aimed at removing grating lobes. The proposed approach is validated using real data acquired by an eight-channel automotive radar operating in burst mode at 77 GHz. Results demonstrate the practical possibility to process a synthetic aperture length as long as up to 2 m reaching in this way extremely fine angular resolutions. Full article

To reveal the high-resolution atmospheric and statistical characteristics of haze events within the boundary layer (BL) in different months, this study conducted a combined detection experiment using a wind-profiling radar, a microwave radiometer, and an ambient particulate monitor on 1230 haze events occurring at Xianyang Airport from 2016 to 2021. First, the boundary layer heights (BLHs) of the haze events were calculated using the atmospheric refractive index structure constant, wind direction and speed, and these were verified against reanalysis data from ERA-Interim. Spatial–temporal evolution and statistical characteristics of temperature, and relative humidity and horizontal wind during haze events, were then analyzed. Finally, the relationships between the BLH and AQI (air quality index) and ${\mathrm{PM}}_{2.5}$ during the haze events were analyzed. The results indicate that the average BLHs during haze events at Xianyang Airport were generally lower than 1000 m. Moreover, the average BLHs in December and January were distributed in the range of 200–600 m, and lower than that in June and July, in a range of 500–1100 m. Furthermore, the maximum value of the average BLH appears at 13:00–15:00. When the temperature was low in the morning, the stratification difference was small and the sensible heat flux between ground and air was still weak, leading to a low BLH value. Meanwhile, when the air quality was poor, the relative humidity was relatively large, and the corresponding AQI and ${\mathrm{PM}}_{2.5}$ were very large. Subsequently, when the temperature gradually increased with time, the heat flux and the average BLH also gradually increased. Moreover, the relative humidity within the BL decreased, and the corresponding AQI and ${\mathrm{PM}}_{2.5}$ also gradually decreased, with the corresponding air quality improving accordingly. The results obtained herein provide a key reference for the preparedness of haze events. Full article

Dams are essential structures in the growth of a region due to their ability to store large amounts of water and manage it for different social activities, mainly for human consumption. The study of the structural behavior of dams during their useful life is a fundamental factor for their safety. In terms of structural monitoring, classic terrestrial techniques are usually costly and require much time. Interferometric synthetic aperture radar (InSAR) technology through the persistent scatterer interferometry (PSI) technique has been widely applied to measure millimeter displacements of a dam crest. In this context, this paper presents an investigation about the structural monitoring of the crest of the Sanalona dam in Mexico, applying two geodetic satellite techniques and mathematical modeling to extract the risk of the dam–reservoir system. The applicability of the InSAR technique for monitoring radial displacements in dams is evaluated and compared with both GPS systems and an analytical model based on the finite element method (FEM). The radial displacements of the Sanalona dam follow a seasonal pattern derived from the reservoir level, reaching maximum radial magnitudes close to 13 mm in November when the rainy season ends. GPS recorded and FEM simulated maximum displacements of 7.3 and 6.7 mm, respectively. InSAR derived radial displacements, and the reservoir water level presented a high similarity with a correlation index equal to 0.8. In addition, it was found that the Sanalona dam presents the greatest deformation in the central zone of the crest. On the other hand, based on the reliability analysis, the probability of failure values lower than 8.3 × ${10}^{-2}$ was obtained when the reservoir level was maximum, which means that the radial displacements did not exceed the limit states of the dam–reservoir system in the evaluated period. Finally, the extracted values of the probability of failure demonstrated that the Sanalona dam does not represent a considerable risk to society. Full article

Nowadays, both BDS-3 and Galileo can provide global positioning and navigation services. This contribution carried out a comprehensive analysis and validation of positioning performance in terms of positioning accuracy (RMS) and convergence time, which are derived from BDS-3 and Galileo precise point positioning (PPP) solutions at a global scale. Meanwhile, the comparison with GPS was demonstrated. The performance and geographical distribution of RMS and convergence time for each satellite system were analyzed. GPS outperforms the other two systems on a global scale. Galileo and BDS-3, on the other hand, only perform moderately well in certain latitude zones. The combination of dual systems related to each single system is analyzed. For the dual-system combinations, the combination of systems presents a definite advantage over Galileo and BDS-3, and this advantage is more pronounced for the kinematic PPP. For GPS, the combination with Galileo and BDS-3 has little improvement in positioning performance. For the dual-system combination based on Galileo and BDS-3, the RMS and convergence time can be improved by 50% compared with the single system. The influence of single-system kinematic PPP selection for precise products from different MGEX analysis centers on positioning performance was studied. Among the five precise products, grg products have the best positioning performance for GPS, while cod products have the best positioning performance for Galileo and BDS-3. The difference in RMS and convergence time between 2 cm and 15 min can be caused by different precise product selections. Full article

The Tarim River Basin (TRB) is situated in the hinterland of northwest China, which is an extremely arid and fragile ecological zone. In recent years, the region’s ecological civilization construction has been facing huge challenges that are exacerbated by climate change and human activities. In order to verify the current ecological status of TRB, this paper explores the spatial and temporal variation in ecosystem service value (ESV) and the impact mechanism based on LUCC data from 2000 to 2020, using the adjusted unit area value equivalent method, the elasticity index method and the geo-probe analysis method. The results show that: (1) the ESV of the TRB has fluctuated since 2000, increasing by CNY 14.02 billion, especially in the Hotan River region. Among the individual ecosystem services, the increase in regulatory services is the largest, rising to CNY 8.842 billion. The growth of ESV mostly occurred in the mountains and oases. (2) The rise in ESV is mainly due to the conversion of barren land to water and grassland; ESV loss is mainly affected by the conversion of water to cropland and barren land and grassland to cropland and barren land. (3) Human activity impact or intensity (HAI) is the key driving factor for the spatial stratified heterogeneity of ESV, followed by elevation (DEM). In the interaction analysis, HAI∩DEM interaction is the primary reason for ESV’s spatial differentiation. The study’s findings show that the combined effects of human activities, DEM, and hydrothermal conditions underlie the spatial stratified heterogeneity of ESV in the TRB. This conclusion provides a scientific basis for future ecological civilization construction planning. Full article

The evolution of forest landscape patterns can reveal the landscape stability of forest dynamics undergoing complex ecological processes. Analysis of forest landscape dynamics in regions under ecological restoration can evaluate the impact of large-scale afforestation on habitat quality and provide a scientific basis for achieving sustainable eco-environment development. In this study, a method for assessing forest landscape stability by characterizing changes in forest landscape patterns was proposed. Toeplitz inverse covariance-based clustering (TICC) was used to automatically identify landscape pattern evolution by investigating the synergistic changes of two landscape indices—forest cover area (CA) and patch density (PD)—and to extract the short-term processes—degradation, restoration, and stable—that took place between 1987 and 2021. Four long-term evolution modes, no change, increase, decrease, and wave, based on the temporal distribution of short-term change processes, were also defined to assess landscape stability. Our results showed that (i) the forest’s short-term change processes have various forms. The restoration subsequence was the largest and accounted for 46% of the total subsequence and existed in 75% of the landscape units. The time distribution of these three change processes showed that more landscape units have begun to transition into a stable state. (ii) The long-term change modes showed an aggregation distribution law and indicated that 57% of the landscape units were stable and 6.7% were unstable. Therefore, our study can provide a new perspective for the dynamic analysis of landscape patterns and offer insights for formulating better ecological restoration strategies. Full article

Constraining the age of formation and repeated movements along fault arrays in superimposed rift basins helps us to better unravel the kinematic history as well as the role of inherited structures in basin evolution. The Inner Moray Firth Basin (IMFB, western North Sea) overlies rocks of the Caledonian basement, the pre-existing Devonian–Carboniferous Orcadian Basin, and a regionally developed Permo–Triassic North Sea basin system. IMFB rifting occurred mainly in the Upper Jurassic–Lower Cretaceous. The rift basin then experienced further regional tilting, uplift and fault reactivation during the Cenozoic. The Devonian successions exposed onshore along the northwestern coast of IMFB and the southeastern onshore exposures of the Orcadian Basin at Sarclet preserve a variety of fault orientations and structures. Their timing and relationship to the structural development of the wider Orcadian and IMFB are poorly understood. In this study, drone airborne optical images are used to create high-resolution 3D digital outcrops. Analyses of these images are then coupled with detailed field observations and U-Pb geochronology of syn-faulting mineralised veins in order to constrain the orientations and absolute timing of fault populations and decipher the kinematic history of the area. In addition, the findings help to better identify deformation structures associated with earlier basin-forming events. This holistic approach helped identify and characterise multiple deformation events, including the Late Carboniferous inversion of Devonian rifting structures, Permian minor fracturing, Late Jurassic–Early Cretaceous rifting and Cenozoic reactivation and local inversion. We were also able to isolate characteristic structures, fault kinematics, fault rock developments and associated mineralisation types related to these events Full article

Retrieving cloud vertical structures with satellite remote-sensing measurements is highly desirable and technically challenging. In this paper, the conditional adversarial neural network (CGAN) for retrieving the equivalent cloud radar reflectivity at 94 GHz of the Cloud Profile Radar (CPR) onboard CloudSat is extended and evaluated comprehensively for different cloud types and geographical regions. The CGAN-based retrieval model was extended with additional data samples and improved with a new normalization adjustment. The model was trained with the labeled datasets of the moderate-resolution imaging spectroradiometer (MODIS) cloud top pressure, cloud water path, cloud optical thickness, and effective particle radius data, and the CloudSat/CPR reflectivity from 2010 to 2017 over the global oceans. The test dataset, containing 24,427 cloud samples, was statistically analyzed to assess the performance of the model for eight cloud types and three latitude zones with multiple verification metrics. The results show that the CGAN model possesses good reliability for retrieving clouds with reflectivity > −25 dBZ. The model performed the best for deep convective systems, followed by nimbostratus, altostratus, and cumulus, but presented a very limited ability for stratus, cirrus, and altocumulus. The model performs better in the low and middle latitudes than in the high latitudes. This work demonstrated that the CGAN model can be used to retrieve vertical structures of deep convective clouds and nimbostratus with great confidence in the mid- and lower latitude region, laying the ground for retrieving reliable 3D cloud structures of the deep convective systems including convective storms and hurricanes from MODIS cloud products and used for predicting these storms. Full article

The response of vegetation spring phenology to climate warming has received extensive attention. However, there are few studies on the response of vegetation spring phenology to extreme climate events. In this study, we determined the start of the growing season (SOS) for three vegetation types in temperate China from 1982 to 2015 using the Global Inventory Modeling and Mapping Study’s third-generation normalized difference vegetation index and estimated 25 extreme climate events. We analyzed the temporal trends of the SOS and extreme climate events and quantified the relationships between the SOS and extreme climate events using all-subsets regression methods. We found that the SOS was significantly advanced, with an average rate of 0.97 days per decade in China over the study period. Interestingly, we found that the SOS was mainly associated with temperature extremes rather than extreme precipitation events. The SOS was mainly influenced by the frost days (FD, r = 0.83) and mean daily minimum temperature (TMINMEAN, r = 0.34) for all three vegetation types. However, the dominant influencing factors were vegetation-type-specific. For mixed forests, the SOS was most influenced by TMINMEAN (r = 0.32), while for grasslands and barren or sparsely vegetated land, the SOS was most influenced by FD (r > 0.8). Our results show that spring phenology was substantially affected by extreme climate events but mainly by extreme temperature events rather than precipitation events, and that low temperature extremes likely drive spring phenology. Full article

Due to the advantages of being nondestructive, rapid, and convenient, the electromagnetic detection method has attracted growing interest in the field of shallow subsurface detection. With the rapid development of unmanned aerial vehicle (UAV) technology, the use of the UAV platform for measurement can not only improve work efficiency but also avoid the significant losses that may be caused by humans working in dangerous areas. Therefore, we propose a broadband frequency domain electromagnetic system AFEM-3 based on a UAV platform for shallow subsurface targets detection (within less than 2 m). The sensor head adopts a concentric planar coil structure with a high spatial resolution, and a bucking coil connected in reverse series with the transmitting coil is used to suppress the primary field at the receiving coil. We designed a transmitting module based on unipolar frequency multiplication sinusoidal pulse width modulation technology that can generate multi-frequency arbitrary combination transmitting waveforms with low total harmonic distortion. It can also be matched to a variety of different transmitter coils by using the same hardware circuit. In addition, the global navigation satellite system and inertial measurement unit are integrated on the sensor head. The measurement response value, position, and attitude information can be displayed in real-time through the host computer. Through the static experiment of a standard coil, we verified the consistency between the AFEM-3 system with the theory. The performance of the system was evaluated through field experiments. The experimental results show that the system can effectively detect multiple metal targets in shallow subsurface areas. For different metal targets, the AFEM-3 system can provide obvious frequency domain characteristics. Full article

Human engineering activities and climate warming induce permafrost degradation in the Da Xing’anling Mountains, which may affect the distribution of permafrost and the safety of infrastructure. This study uses the electrical resistivity tomography method, in combination with field surveys and ground temperature monitoring, to investigate the distribution and degradation characteristics of permafrost and influencing factors at a typical monitoring site (MDS304) near the China-Russia Crude Oil Pipeline (CRCOP). The results show that the isolated permafrost in this area is vulnerable to further degradation because of warm oil pipelines and thermal erosion of rivers and ponds. The isolated permafrost is degrading in three directions at the MDS304 site. Specifically, the boundary between permafrost and talik is on both sides of the CRCOP, and permafrost is distributed as islands along a cross-section with a length of about 58–60 m. At present, the vertical hydrothermal influence range of the CRCOP increased to about 10–12 m. The active layer thickness has increased at a rate of 2.0 m/a from about 2.4–6.8 m to 2.5–10.8 m from 2019 to 2021 along this cross-section. Permafrost degradation on the side of the CRCOP’s second line is more visible due to the river’s lateral thermal erosion, where the talik boundary has moved eastward about 12 m during 2018–2022 at a rate of 3.0 m/a. It is 2.25 times the westward moving speed of the talik boundary on one side of the CRCOP’s first line. In contrast, the talik boundary between the CRCOP’s first line and the G111 highway also moves westward by about 4 m in 2019–2022. Moreover, the maximum displacement of the CRCOP’s second line caused by the thawing of frozen soil has reached up to 1.78 m. The degradation of permafrost may threaten the long-term stability of the pipeline. Moreover, the research results can provide a useful reference for decision-makers to reduce the risk of pipeline freeze-thaw hazards. Full article

Semantic segmentation of remote sensing (RS) images, which is a fundamental research topic, classifies each pixel in an image. It plays an essential role in many downstream RS areas, such as land-cover mapping, road extraction, traffic monitoring, and so on. Recently, although deep-learning-based methods have shown their dominance in automatic semantic segmentation of RS imagery, the performance of these existing methods has relied heavily on large amounts of high-quality training data, which are usually hard to obtain in practice. Moreover, human-in-the-loop semantic segmentation of RS imagery cannot be completely replaced by automatic segmentation models, since automatic models are prone to error in some complex scenarios. To address these issues, in this paper, we propose an improved, smart, and interactive segmentation model, DRE-Net, for RS images. The proposed model facilitates humans’ performance of segmentation by simply clicking a mouse. Firstly, a dynamic radius-encoding (DRE) algorithm is designed to distinguish the purpose of each click, such as a click for the selection of a segmentation outline or for fine-tuning. Secondly, we propose an incremental training strategy to cause the proposed model not only to converge quickly, but also to obtain refined segmentation results. Finally, we conducted comprehensive experiments on the Potsdam and Vaihingen datasets and achieved 9.75% and 7.03% improvements in $No{C}_{95}$ compared to the state-of-the-art results, respectively. In addition, our DRE-Net can improve the convergence and generalization of a network with a fast inference speed. Full article

Compared with general object detection with horizontal bounding boxes in natural images, oriented object detection in remote sensing images is an active and challenging research topic as objects are usually displayed in arbitrary orientations. To model the variant orientations of oriented objects, general CNN-based methods usually adopt more parameters or well-designed modules, which are often complex and inefficient. To address this issue, the detector requires two key components to deal with: (i) generating oriented proposals in a light-weight network to achieve effective representation of arbitrarily oriented objects; (ii) extracting the rotation-invariant feature map in both spatial and orientation dimensions. In this paper, we propose a novel, lightweight rotated region proposal network to produce arbitrary-oriented proposals by sliding two vertexes only on adjacent sides and adopt a simple yet effective representation to describe oriented objects. This may decrease the complexity of modeling orientation information. Meanwhile, we adopt the rotation-equivariant backbone to generate the feature map with explicit orientation channel information and utilize the spatial and orientation modules to obtain completely rotation-invariant features in both dimensions. Without tricks, extensive experiments performed on three challenging datasets DOTA-v1.0, DOTA-v1.5 and HRSC2016 demonstrate that our proposed method can reach state-of-the-art accuracy while reducing the model size by 40% in comparison with the previous best method. Full article