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Objectives: Geological hazard points and hidden danger points are the data basis for geological hazard evaluation, while the existing records of geological hazard points have poor timeliness and are incomplete. To solve this problem, the deformation information obtained by multi-temporal interferometric synthetic aperture radar (InSAR) was integrated into the geological hazard evaluation model. And we explore how to make better use of the deformation information. Methods: The greater the deformation level, the greater the possibility of geological hazards. This paper not only takes the deformation points obtained by multi-temporal InSAR as the geological hazard points/hidden danger points, but also integrates the deformation level information obtained by multi-temporal InSAR as an evaluation factor into the susceptibility evaluation model, making full use of the effective deformation information obtained by multi-temporal InSAR. And the evaluation model adopts the coupling model based on information value model and the analytic hierarchy process model to obtain the susceptibility evaluation and zoning of the geological hazards in Baiyin City, Gansu Province,China. Results: Through the verification of the existing geological disaster point data, it is found that the partitions in this paper are in good agreement with the existing geological hazard points distribution.In the designated extremely high-prone areas, there are nearly 8 geological disaster points within 10 km2 , while less than one in the extremely low-prone areas. Conclusion: The multi-temporal InSAR deformation information added to the geological hazard evaluation model greatly improves the timeliness and quantity of records of geological hazard points/hidden points. However, only one kind of synthetic aperture radar data cannot completely identify all geological hazard points/hidden danger points, due to the limitations of incidence angle and microwave wavelength. In the futher work, we will focus on the combination of multiple deformation monitoring technologies to jointly monitor surface deformation, such as multi-sensor and multi-track InSAR technology, airborne light laser detection and ranging and high-resolution optical remote sensing. [ABSTRACT FROM AUTHOR]

Objectives: Convolutional neural network (CNN) was widely used in landslide susceptibility assessment because of its powerful feature extraction capability. However, with the demand for scene diversification and high accuracy, the algorithm of CNN was constantly improved. The practice of improving accuracy by deepening the network hierarchy or combining other models tended to increase the number of model parameters and the amount of calculation greatly, which led to the difficulty of model training or the overfitting of results, thus limiting its practical application. We want to solve the above problems from the model structure. of feature maps, one-dimensional CNN（1D-CNN） and two-dimensional CNN（2D-CNN） were connected to maintain network depth, limit model parameters, and reduce computation. The parameters sharing of multidimensional convolution kernels was used to capture the deep coupling features of different dimensions and different landslide factors, so as to make full use of features and avoid overfitting. Taking Sedongpu gully in Xizang,China as the experimental area, this paper selected 11 kinds of landslide influencing factors to analyze the landslide susceptibility. Results: The results show that the multi-dimensional CNN coupling structure had the same training efficiency as the shallow 2D-CNN with fewer parameters due to the reduced computational effort. While compared with the deep 2D-CNN with the approximate number of parameters, the training time of the proposed method was significantly reduced and the training cost was lower. In addition, the coupled model had a stronger feature learning ability than the independent 1D-CNN and 2D-CNN, and therefore obtained higher model accuracy. Under each confusion matrix metric of the testing data, the coupled model received higher scores, and thus obtained more reliable landslide susceptibility assessment results. Conclusions: The multidimensional CNN coupling model proposed in this paper is a reliable method applicable to landslide susceptibility assessment. This study provides new theoretical guidance and technical support for further landslide hazard monitoring and prevention [ABSTRACT FROM AUTHOR]

Objectives: The conventional group sparse representation (GSR) model encounters challenges in precisely estimating the sparsity of individual image groups, because the influence of noise results in distortion in the restoration of original image group. Methods: This paper introduces a strategy denominated as the group sparse residual constraint. This strategy leverages non-local self-similarity priors to derive precise estimate of sparse coefficients for each original image group. Subsequently, the sparse coefficients of corresponding degraded image groups are constrained to approximate this estimate. Furthermore, the intricate nature of noise in area-array satellite images (e. g., Luojia3-01 satellite images), surpasses the simplicity of additive white Gaussian noise (AWGN). Consequently, conventional GSR model tailored for AWGN exhibits a reduction in efficacy when deployed in real image denoising scenarios. To address this, this paper introduces two distinct weight matrices into the data fidelity term and regularization, respectively, with the intent of characterizing the statistical features inherent in both images and noise. This paper adopts the alternating direction method of multipliers for optimizing the proposed image denoising task. Results: The effectiveness of the proposed model is validated by the experiments using simulated data and real images obtained from the Luojia3-01 satellite. In the experiments of simulated data, the proposed model outperforms other methods in removing AWGN and spatially variant noise. In the experiments of real images, the entropy of denoised images by the proposed model shows improvements of 2.03%, 1.18%, 1.26%, 1.24%, and 2.10%, compared to the block-matching and 3-D filtering method, multi-channel weighted nuclear norm minimization method, nonlocally centralized sparse representation method, low-rankness guided group sparse representation method, and trilateral weighted sparse coding method, respectively. Conclusions: The proposed method exhibits good performance in processing area-array satellite images with low signal-to-noise ratios and complex noise structures, significantly enhancing the quality of the image. By effectively reducing noise while preserving crucial details and edges, it provides a reliable solution for enhancing the usability and interpretability of area-array satellite images, particularly in challenging conditions. [ABSTRACT FROM AUTHOR]

Objectives: The dynamic and high spatial resolution data obtained by optical video satellites provide a new technical means for the dynamic observation of the earth. As a new generation of intelligent mapping remote sensing scientific test satellite, Luojia3-01 satellite was launched in early 2023, and it could acquire high-definition color video by gazing mode. However, the original video frame rate is low, only 6 frame per-second. To further improve the fluency and reduce the stuttering effect of visual perception, this paper carries out related research on intelligent video frame interpolation (VFI) of Luojia3-01 satellite video. Methods: First, a satellite video image stabilization method based on the perspective transformation model is proposed after analyzing the imaging errors during the gaze mode. Second, satellite video is greatly different from daily video, and there is no available satellite VFI dataset at present. This paper builds a VFI dataset termed as Luojia3_VFISet based on the stabilized satellite videos, which covers different scenes. Finally, based on the flow-agnostic video representation (FLAVR) VFI network without optical flow module, the FLAVR_Plus VFI network is proposed to further improve the interpolation effect for satellite video by in⁃ troducing the feature coding information of different scales from the encoder into the decoding process. Results: The experimental results show that the interpolation of FLAVR_Plus VFI network raises the peak signal to noise ratio (PSNR) and structural similarity (SSIM) to 35.544 6 dB and 0.917 9, respectively, and it enhances the quality of synthesized intermediate frames with hardly-observed artifacts under different scenarios. Compared with other methods, the proposed network can improve the PSNR by 0.5% to 7.2% and the SSIM by 0.5% to 8.7%, respectively. Conclusions: In this paper, the application of satellite VFI is studied by taking Luojia3-01 satellite as an example. The proposed Luojia3_VFISet dataset is conducive to the development of related research. The proposed FLAVR_Plus VFI network can effectively improve the fluency of the Loujia3-01 satellite video by generating interframes without artifacts and provide more interframe information for subsequent applications. [ABSTRACT FROM AUTHOR]

Objectives: Individual driving destination prediction has important value in location-based services such as personalized services recommendation and intelligent transportation. However, most of the existing deep learning methods construct travel features on the basis of densely-sampled trajectory points, which generate data redundancy but only achieve limited information gain, and in turn introduce negative impacts on model training. Road intersection sequence can simplify the expression of driving trajectories restricted on the roads, and reduce the model training cost. Meanwhile, transfer preference between intersections and current movement mode imply the spatiotemporal correlation between road intersections and destinations, and are capable to represent individual travel intention to some extent, but few researches have applied them to destination prediction. Methods: This paper proposes a novel individual driving destination prediction method that takes intersection transfer preference (ITP) and current movement mode (CMM) into account, named ITPCMM. First, it constructs input features in units of each intersection, and adopts graph attention network to learn the individual-level transfer coefficients between intersections in different time slots. Then, it uses long short-term memory (LSTM) to capture the long-term dependence of transfer preferences. Second, ITP-CMM constructs time cyclic encoding features and driving features to learn current move mode by using two-layered LSTM. Third, intersection transfer preferences and current move mode are fused and weighted through feature intersection and attention mechanisms respectively. And the stacked residual networks are introduced to output prediction results. Results and Conclusions: Based on the trajectory data of twelve private car drivers in Shenzhen City, China for the whole year of 2019, this paper has conducted a group of experiments to verify the feasibility of the proposed method ITP-CMM. We verify the effectiveness of the proposed method by carrying out ablation experiments and comparing the prediction accuracy and stability of ITP-CMM with four baselines, including hidden Markov model, LSTM, distance neighboring dependencies model and attentionaware LSTM for real-time driving destination prediction by considering location semantics and location importance trajectory points. Besides, we demonstrate the value of transfer preference in capturing the spatial correlation between intersections through visual analytics, and explore the impact of the number of transfer intersections on the prediction accuracy. [ABSTRACT FROM AUTHOR]

Objectives：In mid- to late- April 2024, an extreme heavy rainfall event occurred in Shaoguan City, Guangdong Province, inducing a large number of landslides in Jiangwan Town, Shaoguan. People lost connection with the outside world for nearly 36 hours, which aroused widespread social concern. Rapidly and accurately identifying the basic characteristics of landslides, development and distribution patterns and formation conditions is crucial for disaster emergency decision-making and risk elimination and disposal. Methods: Using the post-disaster optical remote sensing images and combining with deep learning model, the rainfall-induced landslides in Jiangwan Town, Shaoguan, were quickly and automatically identified. Results:After manually calibration, a total of 1 192 landslides were deciphered, with a total area of about 3.14 km². The scale of the landslides was dominated by small and medium-sized landslides, which were mainly distributed as an aggregated belt along the river in the northeast-southwest direction, with a significant characteristic of concentrated occurrence. Spatial statistical analysis showed that the landslides were mainly distributed on concave slopes with slopes of 10°-30° in the range of 200-300 m elevation. Further quantitative analysis of the geomorphic controlling factors of landslides using the random forest model and SHAP theory reveals that different topographic and geomorphic factors have different degrees of nonlinear effects on landslide formation, and that multiple factors such as elevation, slope, and catchment conditions are coupled to jointly control the formation of landslides. Conclusions: This paper highlights the great advantage of deep learning-based intelligent identification and analysis technology in the emergency investigation and formation conditions analysis of landslide disasters, which can provide important technical support for the rapid assessment of disaster losses and risk identification. [ABSTRACT FROM AUTHOR]

Objectives: Flood disaster is a very destructive natural disaster. The main reasons for its generation include heavy rainfall, storm surge and dam break. When flood disaster occurs in populated areas such as cities and towns, the flood will directly threaten the safety of life and property of residents. Also it will cause paralysis of land and underground transportation, interruption of water and electricity transportation. In the process of flood rescue, quick and accurate identification of flooded roads is conducive to planning appropriate personnel transfer and material transportation routes, and reducing subsequent losses caused by floods. Aiming at the problem that roads in the flood disaster scenario cannot be automatically and correctly identified, this paper proposes an end-to-end flooded road detection method based on deep learning. Methods: The proposed method uses a three-branch encoder-decoder structure and uses strip convolution, in which the efficient extraction of linear features is realized. And the coordination dual attention mechanism can effectively guide the network, and realize the identification of road areas. The method can effectively utilize the historical optical remote sensing images before the disaster and the real-time optical remote sensing during the disaster. The image is applied to detect the flooded and non-flooded roads in the disaster-stricken area, and a comparative experiment is carried out on the self-built dataset. Results: The experimental results show that the precison and recall rate are 0.838 1 and 0.666 8 on pre-disaster road, 0.796 6 and 0.607 4 on post-disaster road, 0.780 0 and 0.661 4 on affected road respectively. Conclusions: The proposed method has achieved the goal of automatically identifying the flooded roads in the flood-stricken area. The ability of detecting flooded roads and non-flooded roads can provide strong support for flood disaster rescue and reduce losses of life and property caused by flood disasters. [ABSTRACT FROM AUTHOR]

Objectives: The landslides disaster is one of the most important factors for the construction of major infrastructure in the western China. How to effectively and reliably carry out wide-area landslide susceptibility prediction has always been a frontier difficulty in domestic and foreign studies. However, the present data-driven methods for prediction of landslide susceptibility in large-scale and complex scenarios still face two major issues: (1) The difference between various landslide inducing environments in a wide range of scenarios, would cause the difficulty to applying a single model to account for multiple landslide phenomenon; (2) small sample problem: Complex environmental tasks require models with large capacity and strong representative power, but there is a lack of sufficient landslide samples in practice. Methods: In response to the above problems, this paper takes Qijiang and Fuling District of Chongqing City, China as an example, proposes a local prediction strategy, and introduces the idea of meta-training an intermediate representation suited to be generalized, that can be adapted to the landslide sensitivity prediction task corresponding to the current local area, with only a very small number of samples in the local area, and number of iterations. Thus, the two issues mentioned can be well settled. Results: The proposed method is different from traditional methods such as support vector machines, multilayer perceptrons, and random forests, which require a large number of samples and gradient iterations to train the supervised model. Instead, only a small sample is required to fine-tune the intermediate model, which still improves the global accuracy by 1%-5%, the precision by 1%-3%, the F1-score by 0.5%-6%, and the recall rate is close to the highest level of other methods. Conclusions: The meta-learning paradigm enables few-shot adaptation of landslide susceptibility prediction model with superior statistical performance. [ABSTRACT FROM AUTHOR]

Objectives In recent years, many deep learning-based classification models have been proposed. Because the size of histopathology images is extremely large, current methods usually divide it into many small patches of the same size and then build the classification model. The models first extract the features of patches and then use pooling methods etc. to integrate patch-level features into slide-level features for classification, in which the extraction of patch-level features and independent with the integration and classification process, making it impossible to adapt the feature extractor with the feedback of classification results. Therefore, they can not obtain the feature which is most useful for the whole slide image (WSI) classification. The objective of the paper is to solve the problems faced by above methods.Methods We propose an end-to-end attention pooling method based on a patch-scoring model. First, we build a patch scoring model to score each patch with the multiple instance learning methods, selecting patches based on the scores. Second, we utilize an attention-pooling module to integrate the features of selected patches to build a classification model. Besides, according to the character of the histopathology image, we propose to use a customized squared average function to normalize the attention weights to focus more on the high scored patches in positive WSI, making the classification results more interpretable.Results On the public available dataset CAMELYON16, the proposed method achieves F1 score at 0.644. And it achieves F1 score at 0.593 at the public available BACH dataset. When the new normalization function is used, the average attention weights difference between positive patches and negative patches is 0.017, which is much higher than when softmax function was used.Conclusions We can conclude from the experiments that the end-to-end attention pooling method is a better method for breast histopathology image classification. The proposed normalization function can make the attention module more interpretable. [ABSTRACT FROM AUTHOR]

Objects: Mountain peak extraction technology determines the accuracy of the hill position classification and the efficiency of automatic classification of micro-landform. Because of the limitations of elevation and contour lines, such as loss of local mountain peaks and incomplete removal of false mountain peaks, other landform factors could be used to express the distribution feature around the mountain peaks. Methods: Based on the uniform distribution feature of the aspect around the mountain peaks, this paper proposes an efficient mountain peak extraction model, in which the aspect is centered on the mountain peak and gradually increases clockwise. Moreover, according to the ridge line fitting method and the recursive thought of depth first search algorithm, the false mountain peaks in the extraction results are removed, while the real mountain peaks are retained. Considering the negative influences of the terrain fragmentation of the entity digital elevation model(DEM), this paper experiments and analyzes with simulated DEM and entity DEM respectively. Results:The results show that the method overcomes the uncertainty of subjective threshold based on closed contour lines. The mountain peak extraction accuracy of simulated DEM can achieve 100% due to its continuity and smoothness. Considering the terrain fragmentation of entity DEM will make it ill-posed, we improve it by adjusting the aspect distribution constraint condition and obtain average extraction accuracy of 96.1%. Conclusions: An efficient mountain peak extraction model is proposed based on the digital terrain analysis technology and the uniform distribution feature of the aspect around the mountain peaks, and the topographic feature points are extracted from the perspective of terrain geometry. Compared with the traditional mountain peak extraction methods, the proposed method is relatively accurate and simple, and can effectively reduce the false mountain. [ABSTRACT FROM AUTHOR]

Objectives: The traditional methods of identifying urban features use spatial and statistical algorithms to extract analysis indicators, but feature evaluation indicators are very subjective. Street view images contain visual information of the city and can be used to identify urban features. Methods: Taking Qingdao, China as an example, this paper proposes a multi-scale semantic segmentation model, named MS-DeepLabV3+, based on street view images. The proposed model adds full feature extraction channels in the encoding process to aggregate multi-scale features, and adds multi-scale feature extraction channels in the decoding process to effectively capture low-level features. And convolutional block attention module and efficient channel attention modules focusing on key features are introduced to improve the accuracy of semantic segmentation of street views. The mean intersection over union, accuracy and recall of the proposed model have been increased by 3.47%, 2.37% and 3.96%, respectively. We build a multi-dimensional feature vector of the city in six dimensions, including environment dimension, facility convenience dimension, economic affluence dimension, transportation dimension, urban safety dimension and urban synthesis dimension. Based on the semantic segmentation results of the street view images, the data are combined with the point-of-interest data and residential land use data. At the plot scale, we extract the feature vectors and calculate the values in six dimensions to characterize the urban features of each urban area in Qingdao. This paper uses the Grad-CAM method for interpretable analysis of semantic segmentation models and the feature attribution SHAP method to mine the intrinsic drivers of multi-dimensional features in cities. Results: Different urban areas have different feature vectors, and the feature vectors of different urban areas have the advantages in specific dimensions. Conclusions: The above analysis helps optimize the multi-dimensional features in urban space for the planning and construction of cities. [ABSTRACT FROM AUTHOR]

Objectives: In recent years, a great breakthrough has been made in the text generation image problem based on generative adversarial networks (GAN). It can generate corresponding images based on the semantic information of the text, and has great application value. However, the current generated image results usually lack specific texture details, and often have problems such as collapsed modes and lack of diversity. Methods: This paper proposes a determinant point process for generative adversarial networks(GAN-DPP) to improve the quality of the generated samples, and uses two baseline models, Stack-GAN++ and ControlGAN, to implement GAN-DPP. During the training, it uses determinantal point process kernel to model the diversity of real data and synthetic data and encourages the generator to generate diversity data similar to the real data through penalty loss. It improves the clarity and diversity of generated samples, and reduces problems such as mode collapse. No extra calculations were added during training. Results: This paper compares the generated results through indicators. For the inception score, a high value indicates that the image clarity and diversity have improved. On the Oxford-102 dataset, the score of GAN-DPP-S is increased by 3.1% compared with StackGAN++, and the score of GAN-DPPC is 3.4% higher than that of ControlGAN. For the CUB dataset, the score of GAN-DPP-S increased by 8.2%, and the score of GAN-DPP-C increased by 1.9%. For the Fréchet Inception Distance score, the lower the value, the better the quality of image generation. On the Oxford-102 dataset, the score of GANDPP-S is reduced by 11.1%, and the score of GAN-DPP-C is reduced by 11.2%. For the CUB dataset, the score of GAN-DPP-S is reduced by 6.4%, and the score of GAN-DPP-C is reduced by 3.1%. Conclusions: The qualitative and quantitative comparative experiments prove that the proposed GAN-DPP method improves the performance of the generative confrontation network model. The image texture details generated by the model are more abundant, and the diversity is significantly improved. [ABSTRACT FROM AUTHOR]

Objectives: The quality of network structure is the key to obtain excellent classification performance. Most of existing deep models are designed by the trial-and-error method. When the same model processes different hyperspectral images (HSIs), different network hyperparameters need to be set artificially to adapt to the data characteristics of different images, greatly increasing the difficulty of network design. Aiming at the problem of deep learning model design in hyperspectral image classification, this paper proposes an automatic network structure search method for HSI classification. Methods: In the proposed method, the differentiable structure search technology is first used to search the network structure on the source HSI data sets, then the deep network model is constructed by stacking cells, and finally the classification performance is evaluated on the target HSIs. It should be pointed out that, the proposed method only conducts one search on the source HSI data sets, and the obtained deep model can be applied to other target HSI classification tasks, which can effectively improve the utilization rate of the model. To improve the generalization ability and classification accuracy of the model, multi-source and multi-resolution HSIs are employed to construct the precollected data set, and the partial channel connection operation is introduced to further improve the search efficiency. Results: Three HSIs inculding Houston, Chikusei and Pavia Center are selected as source data. Four HSIs including University of Pavia, Indian Pines, Salinas and Houston 2018 are selected as the target HSIs. The source data is used to determine the network model and the target HSIs are used to evaluate the classification performance. Experimental results show that the proposed method can automatically search out a universal deep network model suitable for HSI classification task, and this model can achieve better classification performance than the conventional deep learning models. The overall classification accuracies of University of Pavia, Indian Pines, Salinas and Houston 2018 are 98.15%, 98.74%, 97.30% and 74.47%, respectively. Conclusions: Compared with other deep models, the model in this paper is automatically determined by differentiable architecture search algorithm according to the data characteristics of HSIs, effectively improving the classification accuracy of target HSIs while avoiding complex network design. Extensive experiments verify the applicability and effectiveness of the proposed method in HSI classification. [ABSTRACT FROM AUTHOR]

Objectives: Curve simplification is of importance in automated map generalization; nevertheless, the Douglas-Peucker (DP) algorithm popularly used in map generalization is not automatic, because a key parameter called distance tolerance (ε) must be given by experienced cartographers and needs to be input before execution of the algorithm. Methods: To solve the problem, this paper proposed a method to automatically calculate ε and by which the automation of the DP algorithm is achieved. The method consists of the following steps: (1) A formula is constructed by the Hausdorff distance for calculating the similarity degree (Ssim) between a curve at a larger scale and its simplified counterpart at a smaller scale. (2) 15 linear rivers are selected, and each of them is manually simplified to get their counterparts at seven different scales. The Ssim of each original river and each of its simplified counterpart at a smaller scale can be obtained using the formula constructed by the Hausdorff distance, and 15×7=105 coordinate pairs consisting of (S,Ssim) can be got, and a function between Ssim and S are constructed by the curve fitting using the coordinates. (3) In the meanwhile, the 15 rivers are simplified using a number of ε, and the Ssim of each original river and each of its simplified counterpart at a smaller scale can be calculated using the formula constructed by the Hausdorff distance. In this way, a number of coordinate pairs (ε,Ssim) are got, and a function between ε and Ssim is constructed by the curve fitting. (4) By the function between Ssim and S and that between ε and Ssim, a formula between ε and S can be deducted. Using the formula ε can be calculated automatically, because in a map generalization task S is usually known. After this step, automation of the DP algorithm is achieved. Results: The experiment results show that (1) The proposed DP algorithm can automatically simplify the rivers in a specific geographical area to get the results at different scales; and (2) the resulting river curves generated by the proposed DP algorithm have a high degree of similarity with the ones made by experienced cartographers. Their average similarity degree is 0.927. Conclusion: The proposed DP algorithm can simplify curve features on maps automatically, and the results are highly intelligent and credible. Although only river data is tested in this paper, the principle of the proposed method can be extended to other linear features on maps. Our future work will be on improving the accuracy of the proposed DP algorithm using more river data so that the algorithm can be used in practical map generalization engineering. [ABSTRACT FROM AUTHOR]

Objectives: Spaceborne global navigation satellite system-reflectometry （GNSS-R） has been demonstrated to be effective in retrieving global sea surface wind speed, and many missions have been launched in orbit or planned. “Jilin-1” Kuanfu-01B satellite, as a commercial remote sensing satellite of China, equipped a GNSS-R instrument to observe Earth's physical parameters. This paper aims to preliminarily analyze and demonstrate the effectiveness and feasibility of GNSS-R data from “Jilin-1” Kuanfu01B satellite for retrieving sea surface wind speed. Methods: Three basic observables sensitive to wind speed, namely the delay-Doppler map (DDM) peak signal-to-noise ratio (SNR), the leading-edge slope and trailing-edge slope of the normalized delay waveform, are extracted from the DDMs of “Jilin-1” Kuanfu-01B satellite, and are used to demonstrate and assess the potential in retrieving wind speed. Three basic observables have obvious inversely proportional relationships with wind speed; therefore, an empirical exponential function is used to develop the geographic model function (GMF) between the basic observables and wind speed. Furthermore, the root mean square error (RMSE) between the retrieved wind speeds and European Centre for Medium-Range Weather Forecasts (ECMWF) data are obtained. Results: The RMSE of the retrieved wind speed using DDM peak SNR, leading-edge slope and trailingedge slope are 3.22 m/s, 3.67 m/s and 3.49 m/s without data quality control and data calibration. When SNR is controlled to be larger than −6 dB, the RMSE of the retrieved wind speed using the DDM peak SNR reduces to be within 2 m/s, and the useful data are 78.11 % of that without data quality control. For leading-edge slope and trailing-edge slope, when SNR threshold is −3 dB, the RMSE of retrieved wind speed is also decreased to be within 2 m/s, and the useful data reduce to 53.74 %. Conclusions: The results show that the GNSS-R data from “Jilin-1” Kuanfu-01B satellite can be used to measure sea surface wind speed. The data used in the paper are uncalibrated, therefore, the retrieved performances are not optimal. It is a key work to calibrate the influence of transmitted power, transmission gain, and propagation path on the DDM. [ABSTRACT FROM AUTHOR]

As the most dynamic component of terrestrial water cycling, soil moisture plays a pivotal role in the formation, transformation, and consumption of surface water. Simultaneously, it serves as a crucial parameter influencing hydrological processes, vegetation status, and climatic conditions. Therefore, highprecision, high spatiotemporal resolution soil moisture data holds significant importance across various fields, including agriculture, forestry, and meteorology. Presently, traditional methods such as the timeconsuming and labor-intensive drying-weighing technique are inadequate for large-scale monitoring demands, highlighting the advantages of remote sensing methods. However, optical sensors are susceptible to cloud cover and vegetation obscuration, while microwave remote sensing technology faces challenges in balancing spatial and temporal resolutions. The space-borne global navigation satellite system-reflectometry (GNSS R) technology, characterized by short revisit cycles and pseudo-random sampling, presents a new opportunity for soil moisture retrieval. This paper begins by introducing the fundamental theory of soil moisture retrieval using GNSS-R and analyzes error sources during the retrieval process. This includes the scattering and attenuation effects of surface factors such as vegetation and surface roughness on reflected signals, as well as the impact of water bodies on reflectivity. Existing error correction methods and their limitations are summarized, and potential avenues for improvement are discussed. Subsequently, the research progress in recent years regarding space-borne GNSS-R soil moisture retrieval is analyzed from three perspectives: (1) The development status of space-borne GNSS-R constellation. (2)An overview of algorithms for space-borne GNSS-R soil moisture retrieval, including empirical models, semi-empirical models, and machine learning methods. (3) Advancements and directions in integrating GNSS-R with other data for soil moisture retrieval. In conclusion, based on the scattering mechanism of reflected signals, the selection of auxiliary data, and the utilization of incident angle information, this paper discusses the technical challenges faced in current soil moisture retrieval using space-borne GNSS-R technology and provides insights into future research directions. [ABSTRACT FROM AUTHOR]

Objectives: High-resolution agile optical satellites can continuously observe specific regions or targets, thereby realizing applications such as regional monitoring and target tracking. In order to obtain stable and high-quality video data, continuous observation data need to be geographically corrected and stabilized. This process requires a large amount of calculation, but the on-board computing and storage device cannot meet the demand. Therefore, this process can only be completed afterwards by on-ground system, resulting in a significant delay in information acquisition. Methods: This paper proposes an object-spaceconsistency-based real-time stabilization approach for Luojia3-01 satellite video data, which is based on data characteristics and computing capability of embedded on-board hardware. The proposed approach employs real-time geometric positioning to extract and correct the region of interest (ROI) in real-time during satellite imaging. Additionally, it employs the consistency of geographical information in real-time align adjacent image frames to ensure the relative accuracy between sequence frames. On the basis, it generates a sequence of video frames of ROI with geographical encoding in real-time through the construction of a parallel pipeline of algorithms. Results: The experiments are conducted by the actual data of Luojia3-01 satellite with the frame rate of 6 frame per second, and the proposed approach can realize the faster processing based on the embedded on-board processing hardware. Furthermore, the obtained video frame sequence with geographic encoding and the inter-frame stabilized accuracy is better than 0.328 pixels. Conclusions: The proposed approach can meet the requirements of on-board real-time service. [ABSTRACT FROM AUTHOR]

Objectives: A real-time and robust 3D dynamic object perception module is a key part of autonomous driving system. Methods: This paper fuses monocular camera and light detection and ranging (LiDAR) to detect 3D objects. First, we use convolutional neural network to detect 2D bounding boxes and generate 3D frustum region of interest (ROI) according to the geometric projection relation between camera and LiDAR. Then, we cluster the point cloud in the frustum ROI and fit the 3D bounding box of the objects. After detecting 3D objects, we reidentify the objects between adjacent frames by appearance features and Hungarian algorithm, and then propose a tracker management model based on a quad-state machine. Finally, a novel prediction model is proposed, which leverages lane lines to constrain vehicle trajectories. Results: The experimental results show that in the stage of target detection, the accuracy and recall of the proposed algorithm can reach 92.5% and 86.7%, respectively. The root mean square error of the proposed trajectory prediction algorithm is smaller than that of the existing algorithms on the simulation datasets including straight line, arc and spiral curves. The whole algorithm only takes approximately 25 ms, which meets the real-time requirements. Conclusions: The proposed algorithm is effective and efficient, and has a good performance in different lane lines. [ABSTRACT FROM AUTHOR]

Objectives: The side-scan sonar shipwreck detection method based on the YOLOv3 model has the problems of high miss-alarm rate of small targets, heavy model weight, and slow detection speed that fails to meet real-time requirements. Methods: This paper introduces the YOLOv5 algorithm and proposes an improved YOLOv5 model according to the characteristics of the side-scan sonar shipwreck dateset. We test YOLOv5a, YOLOv5b, YOLOv5c, YOLOv5d, YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x under the basic framework of YOLOv5 with eight different depth and width model structures. Then we choose the best structure by using genetic algorithm and K-means algorithm to optimize the detection frame, and to improve the loss function through complete intersection over union. Results: The results show that under the different range of intersection over union as 0.5 and 0.5-0.95,the average precisions of the improved YOLOv5a model are increased by about 0.3% and 0.6% than that of the original model,respectively. Compared with the YOLOv3 model, the average precisions of the improved YOLOv5a model are increased by 4.2% and 6.1%, respectively, and the detection speed reaches 426 frames per second which is almost doubled that of YOLOv3. Conclusions: The proposed method is more conducive to practical applications and engineering deployment. [ABSTRACT FROM AUTHOR]

Objectives: Facing the needs of digital twins for the whole life cycle of railway, the problems exist in independent expression, decentralized management and difficult in-depth integration of the continuously changing railway geographic and geologic data for a long time. Methods: This paper takes railway geographic and geologic elements as an inseparable whole, and proposes an integrated expression model of the three-domain association for railway geographic and geologic data, which includes ontology domain, change domain and state domain. First, it takes spatial-temporal change as the center and defines the change participants (elements), drivers (events) and presentation modes (processes). Then, it defines a multi-level spatial-temporal change association relationship between state level, entity level and process level. Finally, it constructs a knowledge graph of railway geologic data with multi-domain association, and finely portrays the semantic association relationship between three aspects of geological elements, events and processes. Results and Conclusions: The importance and potential of the model in typical tunnel construction applications are analyzed. [ABSTRACT FROM AUTHOR]

Objectives: Under the traditional satellite observation data processing operation mode, outlier detection methods in gravity gradiometry have the problems of insufficient accuracy, low efficiency, and so on. Methods: This paper constructs an intelligent outlier detection method, which combines variational autoencoder and gated recurrent unit by the significant characteristics of variational autoencoders in multimodal data integration analysis. First, on the basis of the origin and propertier of satellite gravity gradionmetry outliers, the gravity gradiometry dataset with outlier are simulated. Second, the network model captures the effective features of dataset by variational autoencoder, makes predictions on dataset by combining with gated recurrent unit, and automatically finds optimal convergence of the loss function by designing adaptive moment estimation as optimizer. Finally, the tested training model is applied to actual gravity observation data of civilian gravity satellites. Results: The accuracy of the proposed model in outlier detection reaches more than 98%, with a good detection effect on both discrete and regional gross errors. Conclusions: The trained network can quickly and accurately construct the sample features of the simulated gravity dataset，achieve fast and efficient gross error detection capabilities，and be effectively applied in data preprocessing of autonomous satellite gravity missions. [ABSTRACT FROM AUTHOR]

Objectives: Satellite on-orbit autonomous mission planning requires high-precision orbit prediction data to calculate the feasible imaging time windows and imaging posture requirements for the intended observation targets. This process involves integrating these factors with other constraint conditions to accomplish the mission planning. Methods: In the context of limited computational resources onboard satellites, this paper proposes a novel method to reduce resource consumption in orbit prediction process and meet the demands of autonomous mission planning. First, based on two-line element (TLE) and a simplified general perturbation model, the proposed method utilizes multiple sets of orbital data as input and introduces a new measurement metric. Then, it iteratively generates TLE by rationally setting data weights and progressively adjusting them to get the results of orbit prediction. Finally, the total error for orbit prediction and the initial value error of imaging parameters for simulating observation targets are calculated for quantitative evaluation. Results: Experimental results demonstrate that the proposed method requires only a small amount of input data to achieve a 72-hour orbit prediction with the accuracy better than 4 km and the average computation time of 12.76 s. Compared to high precision orbit propagator model, the proposed method provides higher prediction accuracy and faster execution speed in long-period orbit prediction. The calculated start imaging time error for simulated observation targets is less than 0.2 s, and the initial imaging posture errors in the three-axis directions are all better than 0.03°, which is lower than the attitude pointing accuracy requirement for the imaging process of camera onboard Luojia3-01 satellite. Conclusions: The proposed method offers high prediction accuracy and demands fewer temporal and spatial resources, making it of great significance for satellite on-orbit autonomous mission planning [ABSTRACT FROM AUTHOR]

Forecasting trends in sustainable sustainability levels in metropolitan areas serves as the foundation for developing development policies that are tailored to local conditions. Sustainability evaluation studies based on a multidimensional comprehensive index system fall short of highlighting the variability, systemic, and dynamic nature of metropolitan area development. To better understand the role of multidimensional scenarios in guiding the sustainable development of metropolitan areas, a greater emphasis on suburban spatial units and spatial heterogeneity characteristics is required. Methods: We simulate the trends of sustainable development levels of 16 municipal districts in Beijing, China from 2011 to 2030 by combining the system dynamics model and the shared socioeconomic pathways, and thus condense the metropolitan area's sustainable development model and compare the advantages and shortcomings of each development scenario under different models. Results: The findings show that: (1) Beijing's municipal districts perform differently in economic, social, and environmental subsystems, and there are three types of typical districts: Synergistic development, ecologically biased development, and economic constraint. (2) Shared socioeconomic pathway (SSP) 1 is the most desirable urban sustainable development scenario among the four scenarios of SSP1, SSP2 (BAU (business-as-usual)), SSP3, and SSP5. (3) In order to achieve the transition from the historical trajectory to the SSP1 scenario, each district type should pursue a distinct development strategy. To improve the quality of coordinated development, synergistic development type zones should use social security and employment expenditures to compensate for the shortcomings of social development. Ecologically biased zones should reduce energy consumption and increase environmental protection investment to maintain environmental advantages, and economic constraint type zones can maintain high gross domestic product growth rates in the short term to get out of the development dilemma. Conclusions: The findings of this paper highlight the complex cause-and-effect relationships between urban economic, social, and environmental systems and offer a framework for system evaluation and simulation technology for the sustainable development of metropolitan areas. This framework will serve as a source of regulations and a foundation for management of urban refinement. [ABSTRACT FROM AUTHOR]

Objectives: Disaster event evolutionary graph can comprehensively express the development process of disaster, information of sub-events and multiple event relationships, providing knowledge services for disaster analysis. The problems in the construction of disaster event evolutionary graph include the lack of Chinese corpus containing spatiotemporal relationship, imprecise event extraction due to fuzzy boundaries of Chinese vocabulary, and difficulty in recognizing implied event relationships. Methods: This paper proposes a method for constructing disaster event evolutionary graph based on spatiotemporal relationship. First, a vocabulary enhancement event extraction model based on naming regularity (VENR) is designed to discern entity name patterns, and determine event information boundaries and types. Second, a relation recognition model by fusing attention and gateddilated convolution (AGDC) is proposed to obtain multi-dimensional text features, thereby uncovering potential event relationships. Additionally, a Chinese disaster corpus with spatiotemporal relationship (CDCSTR) is established. Results: We experiment on CDCSTR and Chinese emergency corpus (CEC), the results show that the F1 scores of VENR are 88.59% and 78.49%, respectively on CDCSTR and CEC. Compared with the existing methods, AGDC improves the performance of four tasks on CDCSTR, especially excelling in spatial relationship recognition, achieving a performance lead of over 3.08%. Conclusions: The Chengdu-Leshan expressway tailgating accident event evolutionary graph is built for verification, enabling the effective expression of event evolution and spatiotemporal features in real disaster scene, consequently supporting emergency responses to disaster. [ABSTRACT FROM AUTHOR]

Objectives: Recognition of building group patterns is an important part of the building generalization. Efficient methods for the recognition of building group patterns can effectively improve the quality of automated map generalization. Traditional recognition methods mainly include geometric methods and traditional machine learning methods, which are limited by the complex rule definitions and huge feature engineering. The graph convolution neural network (GCN) can overcome the limitations of traditional methods to some extent and has been successfully applied to the pattern analysis of buildings. However, there are few methods to recognize multiple building group patterns using GCN, and the existing GCN methods are almost based on spectral graph convolution, which do not consider local spatial information fully. Methods: As the spatial GCN is more efficient than the spectral GCN, this paper introduces a spatial GCN method DGCNN to recognize three building group patterns, including linear pattern, grid pattern and irregular pattern. The first step is to cluster the buildings into different groups, then some indices are selected to construct the feature vector of each group according to the Gestalt principles. Second, Delaunay triangulation (DT) and minimum spanning tree (MST) are chosen to construct graph structures of building groups. Finally, graph structures are used as input for the DGCNN model, and building group patterns are obtained after training. Results: The experiment selects the urban areas in Shanghai and we compare the accuracies with traditional spectral GCN method. The results show that the accuracy for the DGCNN model in the train set can reach 97.06%, and 95% for the test set. And the accuracies for the spectral GCN model in the train set is 90.90%, and 86.05% in the test set. Conclusions: The proposed method overcomes the limitations of spectral GCN, and it does not need graph Fourier transform. It can improve the recognition accuracy significantly and faces less difficulty in explaining the process of feature extraction. Hence it is an effective method for the recognition of building group patterns. [ABSTRACT FROM AUTHOR]

Objectives: In recent years, the climate change in Xinjiang region, China is complex. It is of great significance to study the terrestrial water storage (TWS) and its load deformation for the accurate maintenance of the regional reference frame. Methods: The satellite gravity data GRACE (gravity recovery and climate experiment) was used to invert the changes of land water storage in Xinjiang region from 2010 to 2014, and the independent component analysis was used to decompose it into spatiotemporal models, and the spatial characteristics of multiple time scales were analyzed. On this basis, the correction on the first-order of gravity potential coefficient and load Love number is carried out, the correction of the non-tidal atmospheric and ocean model (GAC) from GRACE is introduced to obtain the vertical change of the crust caused by the migration of the land water load. The GRACE inversion results are corrected by scale factor method derived from WaterGAP global hydrology model (WGHM). The influence of deformation is analyzed by introducing monthly precipitation data from global precipitation climatology project. And the deformation displacements of the 13 continuously operating reference system stations in the survey area are quantitatively compared. The correlation between TWS load signal of each station and the annual vertical displacement of global navigation satellite system (GNSS) is analyzed. Results: The results show that TWS in Xinjiang region presents multi-time scale characteristics, with obvious annual variation and long-term trend. The annual change is particularly significant near the western Pamirs Plateau, and the trend term is reflected in the long-term decrease year by year. The signal is relatively strong in the western Urumqi and Tianshan Mountains. In general, the change of land water load causes the vertical displacement of the crust in the study area, and the time series shows a small fluctuation with an amplitude of -1.5-1.5 mm, which is consistent with the deformation results of WGHM model. In addition to the obvious time lag, the time series of TWS load deformation and precipitation change are consistent with the overall trend. After GAC correction, the consistency between GRACE results and GNSS vertical displacement was enhanced, and the correlation coefficient and weighted root mean square (WRMS) were significantly improved. The correlation coefficient of each station was 0.63-0.91, and WRMS was 16.18%-58.97%. Conclusions: The research method in this paper can reveal more detailed hydrological signal characteristics in Xinjiang region, and the research results can provide important technical support for accurate maintenance of regional reference frame. [ABSTRACT FROM AUTHOR]

Objectives: As an important grain-producing area and the most populous plain area in China, the groundwater resources in the North China Plain are seriously exploited for agricultural irrigation and domestic water use, and it is of great ecological significance and socio-economic value to accurately monitor the changes of groundwater storage in the region and continuously recharge and restore the groundwater. Methods: The gravity satellite data were used to invert the groundwater storage changes in the North China Plain, and compared and analyzed with the hydrological model, precipitation information, South-North water transfer project and groundwater well information, and proposed a method to integrate shallow groundwater storage changes and deep groundwater changes to obtain the contribution of total groundwater. Re⁃ sults: The results show that: (1) Both gravity satellite and global land data assimilation system model estimated water storage changes in the North China Plain exhibit obvious seasonal characteristics. (2) The correlation coefficients between the gravity satellite inversions and the shallow groundwater well estimations are consistent in terms of interannual variability, multi-year trend variability and seasonal variability, with correlation coefficient greater than 0.8. (3) Considering the contribution of deep groundwater, the estimated results of integrating shallow and deep groundwater well information and the gravity satellite inversions are in better agreement than those using only shallow groundwater, and indicate that shallow groundwater is the main variation of groundwater storage change. (4) From 2003 to 2017, groundwater in the North China Plain is in a long-term deficit state, with a gravity satellite estimated groundwater deficit rate of-1.3± 0.6 cm/a. From 2018 to 2020, the gravity satellite estimated groundwater deficit rate is −1.9±0.7 cm/a, which is almost consistent with the total groundwater deficit rate of -1.8±0.8 cm/a during the same period taking into account the deep groundwater; the groundwater trend in 2021 is increasing, and the growth rate of gravity satellite inversion is 12.7±1.8 cm/a, which is more consistent with the total ground-water well estimation in the same period. The correlation coefficient is 0.98, and the weighted fused total groundwater storage change is more consistent than that estimated by using only shallow groundwater. (5) Precipitation and North-South water transfer project have a direct impact on water storage changes in the North China Plain, making a significant contribution to the mitigation of groundwater storage deficits and groundwater restoration. Conclusions: The research method in this paper provides a new way to accurately observe groundwater storage changes in the North China Plain, and provides reliable verification of gravity satellite monitoring results. [ABSTRACT FROM AUTHOR]

Objectives: Global navigation satellite system (GNSS) interferometric reflectometry (GNSS-IR) is a new passive remote sensing technique for determining surface environment parameters, which places an important part in the inversion of earth's surface properties, such as soil moisture monitoring, snow parameter retrieval, and vegetation remote sensing, etc. GNSS-IR offers several benefits over the traditional soil moisture inversion approach, including all-weather capability, high temporal precision, and cheap cost. Methods: Considering the fact that the existing soil moisture inversion algorithms only utilize one single feature of GNSS reflected signal and from the perspective of increasing data availability, this paper proposes a GNSS-IR soil moisture inversion approach that integrates multi-type feature data by utilizing phase, amplitude, and frequency extracted GNSS signals reflected by soil. The main work is to effectively filter all available features extracted from the original GNSS signal-to-noise ratio observations. The feasibility and effect of the suggested method are compared and evaluated using three machine learning models, including least squares support vector machine (LSSVM), random forest (RF), and back propagation neural network (BPNN). Results: Comparing the inversion effects of above three models, BPNN model has the best inversion effect, followed by RF model, and LSSVM model is the worst. The results show that the correlation coefficients between the reference value and soil moisture inversed by the multi-feature fusion method LSSVM, RF, and BPNN models are 0.830, 0.953, and 0.980, respectively, and the corresponding root mean square errors are 0.045, 0.035 and 0.032 cm³ /cm³ Conclusions: Compared with the single feature inversion method, both the accuracy and correlation coefficient of soil moisture inversion increase significantly. The results demonstrated that the proposed method has higher inversion accuracy and reliability than the single feature inversion method. [ABSTRACT FROM AUTHOR]

Soil moisture is an important parameter to measure the material and energy exchange between soil and atmosphere. It is a key environmental factor in the fields of hydrology, meteorology, agriculture and so on. Remote sensing inversion and model simulation are two basic means to obtain global soil moisture data. Remote sensing inversion can obtain soil moisture data with relatively high resolution, but there are often time-space gaps while land surface model can simulate spatiotemporal continuous data, but the spatial resolution is often coarse. Both two data cannot meet the fine observation of soil moisture. Therefore, it is very important to develop an effective method to obtain high-resolution, spatiotemporal seamless soil moisture data. Methods: Focusing on their complementary advantages, we propose a global seamless soil moisture data generation method integrating microwave remote sensing and model simulation. Specifically, for the vacant area of 9 km soil moisture data of soil moisture active passive satellite, GLDAS Noah 0.25° model assimilation data is introduced to establish the spatiotemporal fusion model between them. Through this model, Noah data is downscaled to 9 km to fill the vacant area. As for some areas that have not been filled, Noah data is used for fitting, interpolation and filling, and the residual correction is further carried out based on Poisson equation method, and then the spatiotemporal seamless 9 km daily soil moisture data is generated. Results: We evaluated the experimental results by simulation experiment evaluation, site data evaluation and triple collocation evaluation. Experiments show that the global seamless soil moisture data produced in this paper performs well in all evaluation methods. Conclusions: This method can effectively combine the spatial resolution advantage of remote sensing observation with the spatiotemporal continuity advantage of model simulation, provide spatiotemporal seamless global soil moisture data, and better meet the needs of global scale water cycle monitoring and water resources management. [ABSTRACT FROM AUTHOR]

Aiming at the problem of image registration difficulties caused by the significant differences of multi-source remote sensing images due to the sensor type, temporal phase and illumination conditions, this paper proposes a multisource image registration method guided by first-order Gaussian steerable filters. Methods: First, based on the geometric reference information of the image, the overlapping area of the reference image and the image to be aligned in the image space is calculated, the overlapping area is uniformly partitioned with the reference image surface as the reference, the corresponding image block to be aligned is calculated by the rational function model and the digital elevation model, and the affine transformation model is established to geometrically correct the image block to be aligned, so as to realize the coarse registration between the local images. Second, for feature detection, anti-cluster features from accelerated segmentation test improved by the chunking uniformity checking strategy are constructed to obtain a large number of uniformly distributed feature points. And for feature description, a set of multi-scale, multi-directional first-order Gaussian steerable filters are constructed to convolve the image, and by pooling the convolution results to achieve feature dimensionality reduction, a multi-source consistent feature description is constructed. Finally, feature matching is performed based on the nearest-neighbor principle, and high-precision correspondences are obtained by eliminating mismatches. And bundle adjustment is further performed based on the rational function model to calibrate the rational polynomial coefficients of the images to be aligned and geometrically correct the images, so as to realize the fine registration of the images. Results and Conclusions: Experimental results using multiple pairs of satellite multisource images show that the accuracy of the proposed method is better than 1 pixel on multitemporal optical data, optical-infrared data, and 1.5 pixels on optical-synthetic aperture radar data, and the computational efficiency is more than doubled compared to the existing similar methods. [ABSTRACT FROM AUTHOR]

Objectives: High definition map (HDM) is the “sensor” for automated driving (AD), which integrates the real-time data collected by various sensors and the prior data collected in the early stage, and serves the application of AD, including providing the necessary prior data and the corresponding over-the-horizon perception, calibration positioning, dynamic planning and decision-making and control. However, there still exists a gap between the supply of HDM data and the demand for AD knowledge, including difficulties in data retrieval due to the large volume of data and challenges in semantic understanding and intelligent decision-making due to weak data correlation. Therefore, how to balance the data supply of HDM and the knowledge demand of AD is the main goal of this paper. Methods: Knowledge graph (KG) is a representation of knowledge in a graph structure to describe entities and their relationships, involving entity extraction and relationship extraction, so that it can make the AD with interpretable, understandable and inferential. This means that KG can serve as an alternative and explicit simulation of the human mind and map cognition that driverless vehicles are missing. Therefore, we introduce KG on the basis of HDM data, and propose a framework of HDM-KG-driving task, so as to support the transformation of HDM data to AD knowledge. The construction of HDM-KG adopts the top-down method, that is, the pattern layer is first followed by the data layer. As the conceptual hierarchy of KG, pattern layer defines the concept, attribute and relation of map ontology, and it can explicitly describe the indirect information and implicit correlation information of map domain from the perspective of traffic. The data layer is an instantiation of the pattern layer, a way to populate the pattern layer through instance matching. Results: To verify the validity of the proposed method, a virtual simulation dataset based on OpenStreetMap data is constructed and converted into two formats. The first is the OpenDRIVE format, which is used to build the KG of static data, including the static knowledge of roads, lanes, intersections, road signs and road markings. The other is the data format of CARLA AD-simulator, which is used for AD simulation and the construction of dynamic real-time KG, including the dynamic knowledge of self-driving cars and vehicles in front of them, pedestrians and other traffic participants. Through the application of the resulting HDM-KG in semantic query, knowledge reasoning and local decision planning, the results show that the HDM-KG explicitly expresses the semantic relationship, enhances the application of HDM in AD, and can further improve AD by connecting additional knowledge such as safety requirements, traffic rules or scene background. Conclusions: HDM-KG can structurally and explicitly express the traffic semantics contained in map objects and the semantic relations between objects, traffic rule constraints, traffic scenes and other knowledge, and realize the three-layer evolution of data-information-knowledge of data sources-HDM-KG, providing support for semantic relation query, logical reasoning and motion planning required by driving tasks. In addition, the introduction of KG eliminates the need for driving tasks to perceive and predict objects and semantic relationships from data collected by sensors, which are explicitly defined by HDM-KG, reducing computational latency and improving safety and reliability. The top-down method of constructing KG will cost more labor and time, and the subsequent research will carry out the bottom-up method to automatically fill the KG with multi-source data. [ABSTRACT FROM AUTHOR]

Objectives: To analyze spatial disparities in urban living service facilities based on supply and demand service levels, we not only detect the spatial distribution characteristics of facilities, but also gain indepth insights into the underlying causes of these differences. Methods: This paper specifically focuses on Jianghan district in Wuhan city and conducts a case study by using the food market as a representative facility. The analysis involves constructing spatial disparity in accessibility of services (SDAS) index to assess the disparities between the supply and demand of life service facilities. Subsequently, SDAS is optimized by an improved gravity model based on the road traffic network and income levels of residents. Results: The results show that most community units in Jianghan district exhibit a high level of disparity between supply and demand, with notable differences observed in Wangjiadun central business district. The service capacity of life service facilities around communities significantly influences SDAS value under various conditions. The multi-mode case provides a more comprehensive reflection of the spatial differences between the facility accessibility and the needs of community units, offering a nuanced view of the actual situation. Conclusions: The results contribute to shaping city structure and facilitating optimal allocation of life service facility resources. It can provide scientific basis for layout optimization of public facilities in urban area. [ABSTRACT FROM AUTHOR]

Objectives: With the rapid popularization of low-cost receivers, single-frequency global navigation satellite system (GNSS) is now widely used. The cycle slip rate of a single-frequency GNSS receiver is commonly high in a complex situation. Although geometry-free (GF) model can detect some large cycle slips, it is not sensitive to small cycle slips. The geometry-based (GB) model is efficient for cycle slip detection and repair, but its success rate of detection descends correspondingly with an excessive number of abnormal satellites. Methods: By combining the advantages of GF and GB models, this paper proposes a new cycle slip detection and repair method. The proposed method firstly detects and rejects the abnormal satellites with large cycle slips by GF model, then uses GB model to detect small cycle slips and repair large cycle slips. Results: The results show that the proposed cycle slip detection and repair method can enhance the success rate if many cycle slips occur simultaneously. The success rate can be improved by approximately 50% even when the abnormal satellites reach the half or more of total numbers simultaneously. Conclusions: Compared with the traditional GB model, the proposed GF+GB model can take advantage of each model to improve the performance when multi-cycles occur. [ABSTRACT FROM AUTHOR]

Objectives: Taylor series expansion is often used in the downward continuation of potential field, and its performance depends on the accuracy and reliability of vertical partial derivatives or radial partial derivatives (RPDs) of potential field parameters. Methods: In order to avoid the singularity on spherical boundary and the uncertainty to the computational results by using the closed analytic kernel function to solve the partial derivative, first, this paper considers the fact that all kinds of gravity observations behave as a type of limited spectrum bandwidth signal after being filtered, and proposes to express the kernel function of the Poisson integral for the external gravity anomaly by a spherical harmonic series expansion, which is truncated into a band-limited summation with the same spectrum range as the gravity observation. Then, we derive a set of band-limited formulas to calculate the high-order RPDs, which are modified and applied to the downward continuation of the gravity anomaly by Taylor series expansion. Results and Con⁃ clusions: The formulas are validated using the ultra-high-degree geopotential model EGM2008 by a twostage procedure. The numerical tests of the band-limited formulas and the Taylor series expansion downward continuation model show that the proposed band-limited formulas have good reliability and validity, and are superior to other models in terms of stability and accuracy. [ABSTRACT FROM AUTHOR]

An introduction about the application, evaluation and funding of various projects of the National Natural Science Foundation on Geodesy in 2023, is made in this paper. A statistical analysis is conducted from the perspective of the research direction and supporting institution of the applied projects, and a word cloud analysis is carried out for the keywords of the applied projects. A total of 534 project applications in geodesy were received in this year, with a growth of 3.1% over 2022, 116 of which were funded by the end of October 2023. The result of the keyword cloud analysis shows that the current hotspots in geodesy mainly focus on global navigation satellite system, interferometric synthetic aperture radar (InSAR), BeiDou satellite navigation system (BDS) and their related research. Satellite precision positioning, orbit determination, navigation and application, InSAR technology and its application, as well as study on material migration and climate change in the Tibet Plateau, constitute the three major research sections in geodesy. [ABSTRACT FROM AUTHOR]

Objectives: In order to improve the inversion accuracy of cylindrical oil tank height from satellite imagery, this paper introduces a height inversion model of oil tank using satellite imagery with the same name arc distance (SNAD). Methods: First, we define the concept of SNAD and clarify several main geometric elements and the physical meanings of oil tank on satellite image. Then, considering the isotropic characteristics of the cylindrical oil tank, we propose a height inversion model based on imaging simulation under multiple strategic conditions. Finally, we clarify each parameter in the inversion model and deduce the calculation method of key parameters. In order to verify the accuracy and applicability of the proposed model, we invert the height of two types of oil tanks in four high-resolution satellite images and compare with the conventional methods. Results: The proposed model can effectively realize the detection of SNAD and height inversion of cylindrical oil tank in multi-satellite images. The root mean square error of the proposed model ranges from 0.50 m to 1.00 m, with an average value of 0.78 m, while that of the conventional model ranges from 9.28 m to 9.59 m, with an average value of 9.44 m. The standard deviation of the proposed model ranges from 0.35 m to 0.63 m, with an average value of 0.46 m, while that of the conventional model ranges from 6.50 m to 6.69 m, with an average value of 6.60 m. Conclusions: The proposed model has the advantages of high inversion accuracy, excellent stability, strong universality, convenience and efficiency. It can provide a new solution to carry out oil tank height inversion based on high-resolution satellite image. [ABSTRACT FROM AUTHOR]

Objective: Deep convolutional neural network (DCNN) is widely used in automatic road extraction from high-resolution remote sensing images (HRSIs). However, the existing methods make it difficult to model the context relationship of pixels in the predicted results. To solve this problem, some studies have used fully connected conditional random field (FullCRF) to perform secondary optimization of semantic segmentation results combined with context information, but the discontinuity problem of road structure cannot be effectively improved. Methods: In order to improve the integrity of road structure, this paper proposes a short range conditional random filed (SRCRF) model combined with DCNN. SRCRF mainly includes a unary potential function based on road pre-segmentation, a binary potential function based on spectral spatial features, and a K-neighborhood mean field inference algorithm. First, the prior knowledge of road pre-segmentation results is obtained by using the powerful feature extraction capability of DCNN as the unary potential function of SRCRF. Then, the dependence of the binary potential function defined by the linear combination of Gaussian kernel functions on the surrounding nodes is modeled. The binary potential function enables the classification results to have local consistency,that is, adjacent pixels with similar spectral features have the same label. Finally, the K-neighborhood mean field inference algorithm based on the mean-field approximation inference algorithm optimizes the inference range to make full use of the spatial context information and spectral feature context information of the road, and then calculates the optimal label corresponding to each pixel based on the space and spectral feature to optimize the road accurately. The convolution method is adopted to control the inference range of SRCRF within the radius of K in order to improve the proportion of feature vectors. Results: The experimental results show that SRCRF not only alleviates the transition smoothness of FullCRF, but also alleviates the structural discontinuity and incompleteness in the road acquisition results of HRSIs. In Zimbawe-Roads dataset and Cheng-Roads dataset, F1-scores of SRCRF are increased by about 4.01% and 3.73% respectively compared with DCNN, and about 3.25% and 2.28% respectively compared with FullCRF. Conclusions: The proposed SRCRF combines the advantages of DCNN, optimizes the fully connected structure of traditional conditional random fields into the K-neighborhood structure, reduces the inference scope, and improves the proportion of feature vectors. Compared with FullCRF, SRCRF can make better use of image color features and spatial features to accurately optimize the road extraction results of deep learning output. The performance of SRCRF is improved compared with DCNN and FullCRF, and the time is shortened by one order of magnitude compared with FullCRF. In the future, we will further investigate the potential for learning Gaussian features and investigate more complex architecture of conditional random field to better capture global context information. Additionally, we are particularly interested in exploring the application potential of SRCRF in other fields, such as building extraction, vehicle extraction, lake extraction, etc. [ABSTRACT FROM AUTHOR]

Objectives: Lanzhou New Area is an important window for the development of northwest China. In recent years, the large‐scale construction of mountain excavation and city construction project has led to different degrees of land subsidence, which has seriously threatened the economic and social development of the new area. Methods: Taking Lanzhou New Area as the research object, this paper uses small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) to process 58 ascending Sentinel-1A image data covering the study area from May 2017 to February 2021. The distribution characteristics and spatio temporal evolution of land subsidence in the study area were obtained, and the SBAS-InSAR measurement results were verified by field investigation. Then the related factors affecting land subsidence were discussed. Results: The results show that land subsidence in Lanzhou New Area is mainly concentrated in mountain excavation and city construction area, with an area of about 33.5 km² and a maximum subsidence rate of-68 mm/a. In space, with the development of excavation and filling projects, the land subsidence develops to the southeast of the new district, and the land surface presents a long‐term continuous settlement process. Conclusions: Land subsidence is closely related to many factors such as mountain excavation and city construction project, thickness of excavation and filling, geology and road. Specifically, mountain excavation and city construction project control the spatial extent of land subsidence, and then the filling thickness control the magnitude of land subsidence. The nature of artificial fill is an intrinsic factor causing land subsidence, while road construction will also accelerate the process of land subsidence. The research results can provide scientific basis for the sustainable and safe development of the new district and urban planning and construction. [ABSTRACT FROM AUTHOR]

Objects: It is tried to understand the adjustment from an information theoretic viewpoint. Methods: Besides the concepts of covariance matrix, cofactor matrix and weight matrix, which are often introduced in “Surveying Adjustment” courses, we introduce the concept of Fisher information matrix (or simply information matrix) in this paper. Results: Several important properties of the information matrix are shown; the population and the sample information matrices are distinguished. The information matrix under the assumption of multivariate normal distribution is derived; the links among the population/sample information matrix, the population/sample covariance matrix and the cofactor/weight matrix are explained; and it is revealed that the weight matrix can be viewed as a normalized information matrix. Based on the information matrix, the concept of information vector is introduced; the adjustment in information domain is derived in which the information matrix and the information vector are calculated rather than the parameter vector and the covariance matrix calculated in standard adjustment. Though equivalent with the standard adjustment in terms of the results, the information-domain adjustment has simpler format and clearer structure; this provides a new perspective for understanding the traditional adjustment; more importantly the information-domain method has special merits in solving under-determined model and also in initializing the recursive adjustment method. Finally, the dynamic adjustment in information domain is derived for filtering a dynamic state space model; it is nothing but the information filter which is equivalent to the standard Kalman filter. Conclusions: Information theory provides new insights for comprehensively and thoroughly understanding the surveying adjustment theory. [ABSTRACT FROM AUTHOR]

Objectives: Co-seismic deformation monitoring is of great significance for the interpretation of co-seismic deformation characteristics and intuitive understanding of fault geometric characteristics. For large earthquakes with surface rupture, global navigation satellite system(GNSS) technique has a low spatial resolution, and interferometric synthetic aperture radar(InSAR) technology will cause phase decorrelation due to large deformation gradients, it is impossible to obtain specific deformation around the fault. Optical image correlation(OIC) and pixel offset tracking(POT) based on sub-pixel cross-correlation can solve these problems well. Methods: The main idea of the sub-pixel cross-correlation method is to use the pre-event image in the two images as a reference, and then compare the post-event image with it to evaluate the similarity between the two images, and then retrieve the displacement between the homonymous points. OIC can obtain east-west and north-south deformations, and POT can obtain range and azimuth deformations. In this paper, the deformation in each direction of the Kaikoura earthquake obtained from Sentinel-1 data and Sentinel-2 data is used to form different combinations, and the least square method is used to calculate the three-dimensional deformation. Results: The combination of OIC+POT_As_Des has the best constraint. The accuracy of the north-south deformation obtained by OIC+POT_Range is not as good as that of OIC+POT_As_Des. Compared with POT_As_Des, the north-south deformation precision obtained by it is higher, and the deformation performance on the right side of the Kekerengu fault and the left side of the Papatea fault is also better. In the northeast of the Kaikoura earthquake epicenter, very complex and huge surface deformation and multiple ruptures were detected in two close areas, and the vertical defoamation was mainly uplift. Conclusions: For Sentinel-1 and Sentinel-2, the combination of OIC+ POT_As_Des is most suitable for obtaining the three-dimensional deformation of the Kaikoura earthquake. This earthquake is a dextral strike-slip earthquake with reverse. [ABSTRACT FROM AUTHOR]

Objectives: Using battlefield text data for spatiotemporal topic analysis, we can obtain the spatial distribution pattern of geographical environment elements and their impact characteristics on battlefield activities from micro to macro and from scattered to gathering places, and mine the spatial distribution and development law of battlefield events, which further enriches the perception mode of battlefield environment and provides a new means of battlefield environment efficiency analysis. It is of great significance and value for in-depth understanding of battlefield environmental knowledge. Methods: The key technology to improve the quality of spatiotemporal topic discovery in geographical environment is to effectively construct entity composite relationship network and integrate multi-dimensional heterogeneous relationships for topic clustering. First, a spatiotemporal topic tensor model integrating multi-dimensional relationships is constructed, and the complete expression of topic relationship is given by using the Tucker decomposition of topic tensor model. Then, the feature vector space of multi-dimensional relational clustering is constructed as the objective function of topic classification, and the block value matrix decomposition technology is used for joint clustering calculation, and the core tensor matrix is used to solve the problem of data sparsity. Finally, the block value matrix obtained by multi-dimensional relational clustering is used to obtain the association value between geographical environment elements and spatiotemporal topics. Results: The results show that: (1) The geographical environment entities and entity relationships are correctly clustered into spatiotemporal topic structure. The accuracy rates in the training set were 88.4% and 86.9% respectively, and in the test set were 87.3% and 85.8% respectively. (2) The number of entities and tags clustered under different subject structures decreases gently with the reduction of subject scale. The statistical results show that the most subject tags are maneuver, attack and interception, and the most location tags are highlands, roads and villages. (3) Compared with latent Dirichlet allocation (LDA) algorithm, the multi-dimensional relationship joint clustering method can be seen that the number of entities and labels mined by this algorithm is generally higher than LDA algorithm, so it can be seen that the accurate and clear space-time topic structure can be obtained after integrating multi-dimensional relationships. (4) The block value matrix obtained by multi-dimensional relationship clustering reflects the internal characteristic relationship of the spatiotemporal topic structure of the geographical environment, indicating that the spatial-temporal theme has strong cohesion. Conclusions: This method can effectively improve the quality and efficiency of spatiotemporal topic discovery, making the obtained topics better show the cohesive correlation between geographical environment elements, geographical location and event topics, providing a basis for clearly reflecting the evolution process of spatiotemporal topics, and supporting for mastering the development trend of events. Since this paper only takes the co-occurrence frequency of entity words as the weight value in the construction of relationship matrix, there is a certain deviation in data analysis. In the future, we will combine the attention mechanism to dig deep into multi-source text data, improve the efficiency and accuracy of data analysis, and establish the temporal and spatial correlation between different hot spots on the basis of effectively discovering cohesive hot spots, for inferring the event change process and providing an important reference value for the dynamic deduction of battlefield environment. [ABSTRACT FROM AUTHOR]

Low spatial resolution is one of the main bottlenecks restricting the fine application of remote sensing parameters. Spatial downscaling is an effective way to improve their spatial resolution and application capabilities. Researchers have developed various downscaling methods for different remote sensing parameters. However, a unified and general method classification system has not yet been formed. Based on the in-depth commonality analysis of the downscaling of various parameters, this paper systematically summarizes the spatial downscaling methods from the perspective of data fusion, taking into account the required complementary information. Four types of downscaling methods are summarized: Multi-parameter fusion, spatiotemporal fusion, remote sensing-model fusion, and super-resolution reconstruction implicit fusion. The advantages, disadvantages, and applicable scenarios of various methods are analyzed, and the development trend of spatial downscaling method research is discussed. It can provide theoretical and technical support for improving the refined application ability of remote sensing data. [ABSTRACT FROM AUTHOR]

Objectives: When performing plantation surveys using laser point cloud data, there are missing points in the scanned point cloud data due to the occlusion and self-occlusion of trees during laser scanning, the felling of trees and other reasons. So, the locations of the missing trees are inaccurate, and the forest survey results have large errors. The key to solving this problem is to realize the filling of the missing tree point cloud. Methods: This paper defines a concept named degree-of-collinearity, and constructs a method based on degree-of-collinearity combined with straight line detection to fill in missing data. Results: For the experimental results of simulated data, the average accuracy of the proposed algorithm is 97.28%; for the experimental results of plantation data, the proposed algorithm detects the location of 9 missing trees, and the degree-of-collinearity rises from 0.219 3 to 0.270 5. Conclusions: The experimental results show that this method can realize the optimal inference of missing location, strengthen the collinear relationship of filled data and can also be applied to count the missing trees in the artificial forest. [ABSTRACT FROM AUTHOR]

Objectives: The high accuracy alignment requirements of particle accelerators present a great challenge to measurement techniques. Due to the advantages of large volume, high precision and three-dimensional measurement of laser tracker, it is necessary to carry out the research on the measurement accuracy by laser tracker in particle accelerators. Methods: First, this paper studied the measurement accuracy of laser tracker itself, introduced the expression and test method of the accuracy, derived the calculation formula and explained its specific meaning of rated accuracy, which provided a theoretical basis for the measurement application and data processing. Then, the linear control network was taken as a representative for the accuracy analysis of control network by laser tracker in particle accelerator, and a measurement activity was implemented in the linac of China Spallation Neutron Source facility, two groups were assigned to survey with different laser trackers during the same period. Results: By analyzing the adjustment precision of each group and the coordinate deviations between the two groups, the weakest transversal accuracy of 200 m linear control network was confirmed and better than 0.2 mm. Moreover, in order to make a quick evaluation of the accuracy of linear control network, a method was proposed to estimate the accuracy with the network simplified to traverse network, and the rule of the weakest point accuracy was acquired that was proportional to the square root of the third power of length. Conclusions: These studies can provide guidance for accuracy analysis and scheme design of alignment in particle accelerators. [ABSTRACT FROM AUTHOR]

Objective: Navigation context comprises observation environment and carrier activity. While global navigation satellite system (GNSS) can provide high-precision navigation, positioning, and timing services for users, users can also annotate the types of navigation scenarios they are in by distinguishing the quality and features of terminal observation values. Navigation context clustering is important for adaptive seamless navigation and location services, which can significantly promote context adaptability, configuration flexibility, and system robustness. Methods: In terms of current issues that the division of water/underwater context is simple and has insufficient granularity, the quality and characteristics of GNSS observation signals in different water depth layers are analyzed by collecting GNSS observations in different water regions. Results and Conclusions: The results show that the underwater navigation context has remarkable layering. Considering that the navigation context has inherent properties such as segmentation, merging, and connectivity, this paper subdivides the underwater navigation context into three layers: Submerged water (about less than 6.5 cm), shallow water (about greater than 6.5 cm and less than 8.5 cm), and deep water (about greater than 8.5 cm) through K-means clustering, and conducts navigation context classification experiments using various GNSS signal features. It reveals that the clustering accuracy rate is 90.4%, indicating the effectiveness of submerged-shallow-deep navigation context division. [ABSTRACT FROM AUTHOR]

Objectives: The spaceborne global navigation satellite system-reflectometry (GNSS-R) technology has been extensively employed in monitoring surface hydrology in recent years. However, research on the key factor of precipitation in the surface hydrological cycle, especially its spatiotemporal relationship with cyclone global navigation satellite system (CYGNSS) signals, remains limited. Therefore, this paper conducts a detailed and comprehensive analysis of the direct relationship between CYGNSS reflectivity and precipitation, taking into account multiple influencing factors and scale effects. Methods: This study utilizes monthly CYGNSS data and precipitation data from 2019 to 2020, taking Guangdong Province, China as the study area. It analyzes the spatiotemporal response relationship between CYGNSS signals and precipitation, while comprehensively considering the influence of different driving factors such as digital elevation model (DEM), slope, land cover type, soil type, and normalized difference vegetation index (NDVI) on the CYGNSS signal-precipitation response. The study also explores scale effects, and uses a factor detector model in the geographical detector to investigate the degree of influence of each driving factor. Results: From 2019 to 2020, the monthly average CYGNSS reflectivity and precipitation show significant seasonal differences and nearly synchronous variations, validating the potential of CYGNSS signals for precipitation monitoring. The positive correlation between CYGNSS signals and precipitation strengthens with increasing spatial scale. For example, at small scale (1 km), the positive correlation between CYGNSS signals and precipitation is 21.00% (significant at the 0.05 level), while at medium scale (3 km) and large scale (9 km), it reaches 26.84% and 40.36% respectively, highlighting the advantage of CYGNSS in monitoring precipitation at larger scales. Additionally, the study found that at different spatial scales, regions with lower DEM and slope (indicating relatively flat terrain), areas with land cover type of cropland, and regions with lower to moderate vegetation coverage exhibited a stronger positive correlation between CYGNSS signals and precipitation. However, the influence of soil type on the relationship between CYGNSS signals and precipitation response varied across different scales. The driving factors 􀆳 influence on the relationship between CYGNSS signals and precipitation is ranked as follows: DEM>NDVI>slope>land cover type>soil type, and their influence becomes stronger with increasing scale. Conclusions: This study elaborates on the response relationship between CYGNSS signals and regional precipitation, providing useful references for further utilizing GNSS-R technology to monitor precipitation and facilitating accurate assessment of surface hydrological changes. The results demonstrate that certain spatiotemporal response relationships exist between CYGNSS signals and precipitation across different scales, and various driving factors have certain influences on their response relationships. [ABSTRACT FROM AUTHOR]

Objectives: The seasonal subsidence and uplift of the surface elevation in permafrost area occur due to the annual melting and freezing of active layer, which has an important impact on the safety of engineering construction, the balance of ecological environment, and global climate change. Using global navigation satellite system-interferometric reflectometry (GNSS-IR) to monitor frozen soil deformation is a new technique. Aiming at the terrain influence caused by the non-daily-repeatable orbits of GLONASS and Galileo satellites, this paper proposes a new method to calculate the freeze-thaw deformation of permafrost using these GNSS data. Methods: By introducing the inclination angle of reflector surface, the effects of terrain changes are eliminated, and the seasonal freeze-thaw deformation closer to the actual situation is obtained. The GNSS signal-to-noise ratio data of 2018 and 2019 snow-free days at SG27 site in northern Alaska are used for experiments and compared with the results obtained by existing retrieval methods to verify the effectiveness of this method in monitoring permafrost deformation. Results: The results show that compared with the methods without considering the terrain effect, the surface elevation changes obtained by GLONASS and Galileo have smaller discreteness and smaller uncertainty, and the fitting consistency and determination coefficient R2 of the composite model have been improved. The total average standard deviations are reduced by about 28.9 % and 36.9 %, and the total average R2 are increased by about 0.23 and 0.24, respectively. The total average increases of daily data utilization rate of non-repetitive orbits are about 19.6 % and 22.8 % . Conclusions: This study provides a valuable reference for monitoring permafrost freeze-thaw deformation, and expands the application of GNSS-IR in GNSS system for permafrost freeze-thaw monitoring. [ABSTRACT FROM AUTHOR]

Objectives: Global navigation satellite system-reflectometry (GNSS-R ) is a new method for measuring soil moisture with rich signal sources, which utilizes GNSS reflected signals for detecting the physical parameters of the reflector with cheap cost and high spatial resolution. Methods: Considering the fact that in the traditional GNSS-R soil moisture retrieval, the data of land cover types is ignored or simpli fied, this paper makes detailed labels about the land cover types based on the definition of international geo sphere biosphere programme (IGBP) considering the similarities and differences between different land cover types, and uses back propagation (BP) neural network to establish soil moisture inversion models. The main work is to replace the IGBP number labels with refined feature labels. After the extraction, matching and quality control of cyclone global navigation satellite system (CYGNSS) and soil moisture active passive (SMAP) data, the dataset is randomly divided into training set and testing set. Then BP algorithm is used for training neural networks to establish soil moisture retrieval model. Results: The correlation coefficient between retrieved soil moisture and reference value is 0.85, and the root mean square error (RMSE) is 0.060 cm3 /cm3 . Meanwhile, the soil moisture obtained by CYGNSS and SMAP have good spatial and tem poral consistency, but the inversion performance varies on the different land cover types. On the barren or sparsely vegetated land type, the RMSE is smallest and on the evergreen ncedlcleaf forest land type, the RMSE is largest. Meanwhile on the shrublands, grasslands and croplands land types, the inversion effect is better than permanent wetlands, urban/built-up and cropland/natural vegetation mosaic land types. Com‐ pared with the existing IGBP numbering and labeling method, the accuracy has been improved by 7.7%. The precision improvement of deciduous broadleaf forest, closed shrublands and woody savannas types is significant. Conclusions: The proposed method of refined land type digitization can effectively retrieve soil moisture without relying on other auxiliary data such as vegetation opacity and surface roughness, and it is feasible and effective. [ABSTRACT FROM AUTHOR]

Objectives: Global navigation satellite system-interferometric reflectometry can be taken advantage of identifying soil moisture of land surface. Methods: Aiming at the problem of multi-GNSS combined soil moisture inversion, we use the phase extracted from the signal-to-noise ratio observation data of global positioning system (GPS), BeiDou satellite navigation system (BDS), GLONASS and Galileo system, and solve soil moisture inversion within consideration of satellite track clustering by using the empirical model. The estimation of multi-GNSS combination is obtained by weighted average method. Results: The inversion accuracy of BDS and Galileo is equivalent and superior to GPS and GLONASS. The root mean square error of the method in this paper is 0.041 4 cm3 /cm3, which is about 16.3% and 5.2% lower than that of single navigation satellite system and the optimal frequency band respectively. Conclusions: The results indicate that the multi-GNSS reflection signal estimation method based on track clustering can effectively monitor changes in soil moisture. [ABSTRACT FROM AUTHOR]

Global navigation satellite system-reflectometry (GNSS-R) is a kind of promising remote sensing technique, which utilizes the reflected signals of GNSS for geophysical parameters detection. Its applications on ocean surface are earlier and more mature. However, in recent years, with the development of spaceborne satellite exploration programs such as TechDemoSat-1, cyclone GNSS (CYGNSS) and Fengyun 3E,its advantages and potential in land remote sensing research have gradually emerged. The research work of the existing mechanism model is summarized. Meanwhile, this paper reviews the research status of spaceborne GNSS-R and other signal of opportunity reflection remote sensing (SoOP-R). GNSS-R's applications on soil moisture, vegetation, soil freeze-thaw monitoring are focused on, while the research status of the latest SoOP-R technology in root zone soil moisture and snow water equivalent is also summarized. In this way, we hope to promote the development of this technology in the detection of major climatic and meteorological parameters for the hydrological cycle to a certain extent. [ABSTRACT FROM AUTHOR]