Existing digital fingerprinting algorithms for vector geographic data predominantly utilize symmetric digital methods. In these algorithms, both merchant and buyer possess data containing fingerprints, which poses a significant challenge in determining responsibility for data leakage when illegal copies are found. If illegal copies are discovered, it becomes difficult to ascertain whether the merchant or the buyer is at fault. Moreover, there is a risk of merchants falsely accusing legitimate buyers or illegal buyers evading accusation. This scenario can lead to disputes and undermine trust in the data distribution process. To address this issue, this paper proposes an asymmetric fingerprint protocol for vector geographic data based on homomorphic encryption. The protocol begins with the buyer generating a unique fingerprint, which is then encrypted using the buyer's public key and sent to the merchant. Next, the merchant selects a portion of the vector geographic data and encrypts it using the buyer's public key. The Paillier homomorphic encryption scheme, known for its additive homomorphic properties, is employed here, allowing the merchant to embed the encrypted buyer's fingerprint directly into the encrypted geographic data. Simultaneously, the merchant embeds their own fingerprint into the unencrypted portion of the data. This dual-fingerprint embedding ensures that the data contains both the buyer’s and the merchant's fingerprints while maintaining the confidentiality of the buyer's fingerprint. After embedding both fingerprints, the merchant sends the resultant encrypted data back to the buyer, who then decrypts the data using a private key to obtain the plaintext version containing both fingerprints. This process ensures that the buyer receives the data with both fingerprints embedded, while the merchant is unable to view or misuse the buyer's fingerprint. The use of homomorphic encryption in this protocol offers several advantages. First, it prevents the merchant from obtaining the buyer's fingerprint in plaintext form, thereby eliminating the risk of the merchant embedding the buyer's fingerprint into other datasets to frame the buyer. Second, the protocol allows the merchant to verify the presence of the buyer's fingerprint in the data without revealing the fingerprint itself, facilitating infringement tracking. Moreover, the protocol is designed to withstand various types of attacks. Experimental results indicate that the fingerprint sequence can still be accurately extracted even after multiple attacks, demonstrating the robustness of the system. In the event of a dispute, the arbitration process is designed to be secure and unbiased. The protocol ensures that neither the buyer nor the merchant needs to disclose sensitive key information to a third party. Instead, the third party can conduct the arbitration without accessing the underlying private information, preventing any collusion that could harm the interests of either party. This approach ensures fair resolution while maintaining the confidentiality and integrity of the involved parties.

In the context of the rapid development of urbanization, the reasonable selection of locations for public service facilities is critical for delivering efficient services and enhancing the quality of urban residents' lives. However, prevailing approaches for allocation of public service facilities often fall short of meeting the demands on their performance and efficiency in complex and large-scale real-world scenarios. To address these issues, this article proposed a novel Graph-Deep-Reinforcement-Learning Facility Location Allocation Model (GDRL-FLAM), coupling a Facility Location Allocation Graph Attention Network (FLA-GAT) with a Deep Reinforcement Learning (DRL) algorithm. This proposed model tackled the location allocation problem for public service facilities based on graph representation and the REINFORCE algorithm. To assess the performance and efficiency of the proposed model, this study conducted experiments based on randomly generated datasets with 20, 50, and 100 points. The experimental results indicated that: (1) For the tests with 20, 50, and 100 points, the GDRL-FLAM model exhibited a significant improvement ranging from 11.79% to 14.49% compared to the Genetic Algorithm (GA) which is one of the commonly used heuristic algorithms for addressing location allocation problems. For the tests with 150 and 200 points, the improvement ranged from 1.52% to 9.35%. Moreover, with the increase in the size of the training set, the model also demonstrated enhanced generalizability on large-scale datasets; (2) The GDRL-FLAM model showed strong transfer learning ability to obtain the location allocation strategies in simple scenarios and adapt them to more complex scenarios; (3) In the case study of Singapore, the GDRL-FLAM model outperformed GA significantly, achieving obvious improvements ranging from 1.01% to 10.75%; (4) In all these abovementioned tests and experiments, the GDRL-FLAM model showed substantial improvement in efficiency compared to GA. In short, this study demonstrated the potential of the proposed GDRL-FLAM model in addressing the location allocation issues for public service facilities, due to its generalization and transfer learning abilities. The proposed GDRL-FLAM could also be adapted to solve other spatial optimization problems. Finally, the article discussed the limitations of the model and outlined potential directions for future research.

Due to the imbalanced regional development, data scarcity exists in some regions, which to some extent restricts the progress of spatial prediction research. The introduction of cross-area knowledge transfer offers a valuable method for mitigating the impact of data scarcity in areas with limited samples and for conducting spatial prediction. With technological advancements, spatial prediction methods based on transfer learning and the Third Law of Geography have become mainstream in the fields of computer science and geography. Transfer learning techniques leverage knowledge from a source domain with abundant data to solve related tasks in a target domain with limited data. Meanwhile, the proposal and application of the Third Law of Geography show that by comparing the similarity of geographical environmental variables between sampled regions and unsampled regions (rather than relying solely on traditional spatial distance or quantitative relationships), it is possible to predict target information in unsampled regions using a small amount of sample data. This provides a theoretical basis and methodological reference for selecting the source domain and target domain in cross-regional knowledge transfer. This paper conducts a literature review of cross-regional spatial prediction research based on these two major methods since 2018, focusing on the following key tasks: (1) Comparing and analyzing the basic principles of spatial prediction based on geographical similarity and transfer learning, and identifying differences in their technical procedures; (2) Summarizing the differences in similarity representation indicators and measurement methods between the two approaches; (3) Examining differences in commonly used auxiliary data, spatial analysis units, modeling methods, and evaluation indicators between the two prediction methods; (4) Discussing the challenges and limitations faced by these cross-regional knowledge transfer methods. The study shows that while the technical principles of both methods are basically consistent, they have specific limitations regarding their scope of application, similarity representation and measurement, relevant auxiliary variables, and parameter selection. The research offers useful insights for optimizing and improving these methods, integrating them effectively, innovating cross-regional prediction approaches, and expanding their application fields.

The timely identification of potential criminal travel routes of key surveillance individuals is a crucial research focus for public security early warning systems. Current studies often concentrate on the travel patterns and destination preferences of criminals, but there is a lack of research from the criminals’ perspective, considering the built environment and road network structure to analyze their criminal travel routes. To address this gap, a spatiotemporal analysis approach is proposed, considering criminals’ cognition of concealment. Based on the principles of criminal psychology and rational choice theory, this paper categorizes the travel patterns of criminals under the cognition of concealment into two principles: "the Principle of Minimal Exposure Risk" and "the Principle of Minimal Travel Cost". Firstly, the urban perceptual elements within a criminal's perceptual range, including safety and risk perception elements that bring exposure risks, are calculated and introduced into the choice degree model. The "Criminal Choice Degree of Roads" is proposed to measure the optimal local roads within a criminal's perceptual range. Next, using the "Length of the Route Already Traveled" as the cost function and the "Criminal Choice Degree of Roads" within the perceptual range as the heuristic function, an improved heuristic algorithm is employed to calculate the overall optimal criminal travel route. Finally, from the perspective of crime prevention and control, experiments are conducted to analyze the distribution of urban road choice degrees and the criminal travel routes of key personnel. By comparing the routes obtained by this method with those derived from existing methods and actual criminal travel routes, it is shown that the routes calculated by this proposed method are more reasonable. They are more likely to be chosen by criminals for concealment and have relatively short travel distances, without long-term exposure in public areas. The routes, in terms of distance, travel time, and the urban perceptual elements they pass through, are closer to the actual travel behaviors of criminals, verifying the rationality of this method. The research conclusion provides decision support for public security early warning efforts, emphasizing the importance of balancing travel distance and exposure risk when monitoring key personnel, and the need to allocate resources based on the distribution of urban perceptual elements and road networks to enable timely and accurate crime prevention and interception.

Tree shade is an important resources for mitigating the effects of extreme heat in urban areas. Quantifying the extent of tree shade resources can assist in the prediction and risk assessment of high temperatures in cities. Among the existing methods for estimating tree shade resources, the measured method is time-consuming and ineffective, while the image identification method is difficult to accurately respond to the spatial and temporal changes of tree shade. In this paper, a method was proposed for simulating and quantifying tree shade based on a three-dimensional(3D) scene. We simulated the urban street scene by employing 3D reconstruction technology, distinguished different geographic entity models, utilising the sun's geometric position parameter and construct the corresponding lighting environment, and the shade in 3D scene was simulated according to the principle of linear propagation of light and shadow. The formation of tree shade is determined through the use of a ray intersection algorithm, which allows for the differentiation of sun rays within a 3D model of the shading situation. This process enables the generation and classification of tree shade, which can then be distinguished from shadows cast by their features. The attributes of tree shade (e.g., shade area and shade coverage duration facilitates)can be quantified and visualized in the 3D scene for intuitive representation. A comparison and verification of the shadows taken by the Unmanned Aerial Vehicle(UAV). The results of relative error range from 3.35% to 13.27%, with an average relative error of 9.29%. This method is potential for the estimation of shade tree resources. In addition, a case of shade resources of trees in an urban street scene was simulated and quantified, taking into account their spatial orientation, species and life cycle. The method enables the simulation of the spatial and temporal distribution of shadow resources for real and virtual scenarios (both future and planned) at any given moment. It can be classified and counted, thereby providing the potential service for urban planning and management, as well as fundamental data for the analysis of the cooling effects of urban trees.

Precipitation merging technology integrates muliple precipitation datasets to obtain more accurate and reliable precipitation information. However, these data sources have inherent systematic biases and precipitation exhibits spatiotemporal heterogeneity. To address these issues, this paper proposed a two-stage precipitation merging method combining bias correction and precipitation spatiotemporal fusion. In the first stage, the biases in precipitation products are corrected by the Experience Cumulative Distribution Function matching method (ECDF). In the second stage, a Dynamic Constrained Linear Regression model (DCLR) is used to determine spatiotemporal weights, followed by weighted averaging of the the bias-corrected precipitation products. The proposed method is termed as ECDF_DCLR. In addition, the Dynamic Bayesian Model Average (DBMA) and Simple Model Average (SMA) are used in the second stage along with ECDF, forming the contrasting methods ECDF_DBMA and ECDF_SMA, to verify the effectiveness of ECDF_DCLR. ECDF_DCLR, ECDF_DBMA and ECDF_SMA were applied to integrate satellite precipitation product IMERG and reanalysis precipitation product ERA5-Land in Southwest China from 2005 to 2017, using precipitation data from ground meteorological stations as the reference for evaluation. Results show that: (1) ECDF can effectively reduce the systematic bias in IMERG and ERA5-Land while improving their accuracy, with the absolute values of RB decreasing by 95.5% and 99.6%, and KGE increasing by 12.7% and 41.5%, respectively. ECDF also enhances the precipitation event detection capability of ERA5-Land (The CSI of ERA5-Land increases by 7.8%), but has a minimal impact on IMERG (The CSI of IMERG remains unchanged at 0.53). Additionally, it is necessary to perform bias correction before fusion, as the KGEs and CSIs of precipitation products generated by combining bias correction and spatiotemporal fusion are, on average, 11.5% and 3.1% higher than those generated by spatiotemporal fusion alone, respectively. (2) DCLR, SMA, and DBMA all effectively integrate precipitation products. Among the three methods, DCLR has the best accuracy of precipitation fusion. There is little difference between them in improving the detection ability of precipitation events. At different time scales, spatial scale, and different altitude grades, the KGEs and CSIs of three fusion precipitation products are mostly greater than or close to the KGE and CSI of the best data source. Among fusion precipitation products, the precipitation product fused by DCLR has the highest KGE. While the differences in CSIs between fusion precipitation products do no exceed 0.01. Compared to Geographically Weighted Regression and Kriging with External Drift, the most metrics of ECDF_DCLR perform better, with KGE and CSI at least 4.3% and 1.8% higher than the former, respectively. In short, the precipitation merging method combined with ECDF and DCLR can provide more accurate precipitation data for Southwest China and offer new insights into multi-source precipitation data merging research.

Scientific knowledge of the spatio-temporal evolution processes and formation mechanisms of territorial space in countries along the Central Asia-West Asia Economic Corridor holds significant scientific value and practical importance for supporting the current "Going Global" strategy and the "Belt and Road" initiative. Based on the dominant functions of the territories, the Central Asia-West Asia Economic Corridor is divided into three major types of territorial space: urban and rural construction, agricultural production, and ecological protection. A long-term analysis base map of territorial space from 2002 to 2022 was constructed by integrating multi-source spatio-temporal data. The spatio-temporal cube model was employed to depict the spatio-temporal evolution processes and typical patterns, while the integrated spatial transformation intensity model analyzed the characteristics of spatial structural transformation across three dimensions: scale, location, and intensity. The VIVI-SHAP framework of an interpretable machine learning model was used to analyze the evolution mechanisms, focusing on the importance of driving factors, interaction intensity, and non-linear dependencies. The results show that: (1) Approximately 6.14% of the territorial space in countries along the corridor underwent structural transformation over the past 20 years. The proportion of urban and rural construction space, though small, increased steadily by 0.17%, while agricultural production space decreased by 19.04% overall, with significant structural changes within the ecological protection space. (2) The dynamic interchange between green and other ecological spaces within the ecological protection space is predominant, with a systematic tendency for green ecological space to convert into agricultural production space, while the main source of urban and rural construction space expansion was green ecological space, accounting for 56.36% of the total converted area. 3) The territorial spatial pattern of the corridor is shaped by multiple processes of territorial space transformation, each with different magnitudes, intensities, and driving mechanisms. Natural geographic factors and transportation location factors played decisive roles, while the global influence of population growth and socio-economic development on territorial space structural transformation was less pronounced. This study provides new perspectives and methods to reveal the patterns and mechanisms of changes in land spatial types in the Central Asia-West Asia region. It further provides data support for decision-making departments to formulate reasonable land spatial planning, and demonstrates its application value in achieving greater spatial comprehensive benefits and promoting coordinated regional economic development.

Under the combined influence of global climate warming and human activities, permafrost within the Qinghai-Tibet Engineering Corridor (QTEC) has significantly degraded, posing threats to human safety, the ecological environment, and the secure operation of permafrost engineering facilities. Consequently, it is urgent to assess the risks of permafrost thaw settlement along QTEC. Traditional permafrost settlement assessment indices are mostly static, neglecting dynamic factors. To address this, an Analytic Hierarchy Process (AHP)-based multi-factor assessment index (I_m) was proposed in this paper, which integrates ground dynamic deformation data and three geo-hazard indices: the allowable bearing capacity index, the risk zone index, and the settlement index. The SBAS-InSAR technique can overcome atmospheric delay and spatiotemporal decorrelation issues. The allowable bearing capacity index considers MAGT (Mean Annual Ground Temperature) and soil type. The risk zone index incorporates factors such as bare rock, soil properties, ALT (Active Layer Thickness), and VIC (Volumetric Ice Content). The thaw settlement index is based on VIC and ∆ALT, with ∆ALT derived using the Stefan formula. The allowable bearing capacity index is calculated using a formula based on MAGT and soil type. The risk zone index is determined through hazard zone assessment. The VIC in the thaw settlement index is calculated using MAGT, soil type, NDVI, and slope, while the ∆ALT is obtained through the Stefan formula. The evaluation results of the three different geological hazard indices were calculated and analyzed individually, and then compared to the multi-factor analysis results to verify the reliability of the proposed method. The correlation between the geological hazard index and ground deformation was also explored. The ground deformation rate was derived using time-series interferometric SAR (InSAR), ranging from -60 mm/year to 43 mm/year, with an average surface deformation rate of -7 mm/year across the entire study area. The I_m results show that the permafrost regions along QTEC are predominantly low-risk, accounting for nearly 60% of the area. High-risk areas make up roughly 22%, with the most concentrated high-risk regions located between Chumaerhe and Fenghuoshan. By combining static geological hazard indices with dynamic deformation information, this method provides a more accurate assessment of permafrost thaw settlement risk for the Qinghai-Tibet Railway project. A comparison with existing research validates the effectiveness of the proposed method, particularly in the Tanggula and Chumaerhe regions. These findings offer valuable guidelines for permafrost engineering design and construction in other permafrost regions.

Enhancing and sustaining urban competitiveness is contingent upon the presence of urban vitality. Urban planners and managers face increasing pressure to find more accurate and logical ways to manage urban development due to the growing challenges associated with municipal government. This study focuses on the central region of Nanjing. A detailed framework for evaluating urban vitality is proposed from three perspectives, human activities, network interactions, and the physical environment. This framework uses foundational road networks and building footprints from the World Map, Baidu heatmap, Dianping restaurant data, social media check-in data, Baidu Map POI, and innovation data. To create a comprehensive vitality evaluation framework, nine urban vitality indicators were reduced in dimensionality using the real-coded accelerated genetic algorithm based on the Projection Pursuit Model (RAGA-PPM). An analysis was also conducted on the differences with EWM and the spatial distribution patterns of both unidimensional and comprehensive vitality in Nanjing. The conclusions can be divided into three parts. First, the spatial distribution pattern of vitality in Nanjing's central urban area is successfully reflected by the comprehensive evaluation technique based on multi-source big data. The validity of the proposed evaluation system was confirmed by analyzing vitality cluster sample locations. Second, similar spatial features may be seen in Nanjing's unidimensional vitality, revealing a monocentric urban structure, with high-value areas gradually decreasing outward from the Xinjiekou commercial district. Commercial districts and metro stations are the focal points of population activity vitality, with each district exhibiting strong central values and secondary vitality clusters. Urban vitality values decline, with the Xinjiekou commercial area and Nanjing South Station serving as hubs of network interaction vitality. Urban vitality ratings decrease concentrically, with the Xinjiekou commercial area and Nanjing South Station serving as the hubs of network interaction vitality. Physical building vitality is geographically scattered, with high and relatively high values spread across most areas. Third, unidimensional vitality is not unfamiliar to comprehensive vitality. Additional viable centers for vitality were identified, with each district having a vitality hub. Xuanwu, Gulou, Jianye, and Qinhuai districts, which comprise the old city, form the core of Nanjing's vibrancy and serve as significant hubs. Liuhe and Yuhuatai districts have the lowest vitality, while Xuanwu and Qinhuai districts show the highest vitality. Most districts with above-average comprehensive vitality scores are located near transportation hubs, university areas, industrial parks, pedestrian streets, and commercial centers. According to the study, urban designers may benefit from a more thorough and multifaceted understanding of urban vitality patterns.

Lanzhou is an important node in the development of the western region and the Silk Road, holding a key strategic position in the development of modern economy, culture, and transportation. However, due to its unique geographical location and distinctive loess topography, land subsidence has been a persistent issue that threatens the city's ecological environment and the safety of its urban infrastructure. Therefore, it is very important to accurately identify subsidence and evaluate the impact of driving factors to prevent and control land subsidence in loess areas. This paper proposes combining SBAS-InSAR technology with Time Series Principal Component Analysis (TPCA) to deeply analyze land subsidence and its driving factors in Lanzhou. By examining subsidence information from both temporal and spatial perspectives, the study quantitatively analyze the influence of various driving factors. Firstly, SBAS-InSAR technology is used to obtain time series deformation data of land surface in Lanzhou City. Then, TPCA is applied to extract the principal components that represent a significant proportion of the deformation data. Finally, the driving factors of land subsidence in Lanzhou city are quantitatively analyzed by combining the signals of each principal component. The results show that the land subsidence of Lanzhou city is concentrated in the northwestern part of Honggu District, the southern part of Qilihe District, the Northern part of Chengguan District，and the Xicha Town of the new district, with the maximum subsidence rate reaching 45.87 mm/year. In the Qilihe District and the new district, where the subsidence is severe, the first four principal components explain more than 90% of the data characteristics, with the first Principal Component（PC1）alone containing over 75% of the original data information. The correlation coefficient between the change in the PC1 characteristic vector in the characteristic area and the change in water levels in different monitoring wells is above 0.6, indicating that groundwater change is the main influencing factor of land subsidence in Lanzhou City. The method proposed in this paper has practical value in analyzing land subsidence in Lanzhou, providing a reference for managing land subsidence in loess areas.

Urban construction land is categorized into different types in the overall planning of territorial space. Research on the relationship between spatial environment elements and Land Surface Temperature (LST) across various land types can provide a planning and management basis for ecological city construction. This paper focuses on the main urban area of Fuzhou and references the land classification system for territorial spatial planning published by the Ministry of Natural Resources in November 2023. Based on the urban road network and high-resolution remote sensing images, different types of construction land in Fuzhou, including residential land, public management and public service land, commercial service land, mixed commercial and residential land, industrial land, and green land and open space land, were identified. Landsat-8 remote sensing data and building vector data were used to calculate LST during summer and winter, as well as to gather spatial environment factor information (NDVI, NDISI, BCR, BH, BSD, SVF) for different land types. Finally, after analyzing the seasonal LST characteristics and spatial environment factors of various land types, spatial autocorrelation analysis and spatial autoregression model were used to explore the spatial relationship between these factors and seasonal LST. The results show that: (1) In summer, the average LST values for various land types are ranked as follows: Industrial land (40.38 ℃) > Public administration and public service land (38.28 ℃) > Commercial service land (38.25 ℃) > mixed commercial and residential land (37.87 ℃) > residential land (37.52 ℃) > green land and open space land (35.71 ℃). In winter, the ranking is: industrial land (18.32 ℃) > public administration and public service land (17.99 ℃) > commercial service land (17.93 ℃) > residential land (16.92 ℃) > mixed commercial and residential land (16.15 ℃) > green and open space land (15.79 ℃). (2) The correlation and influence of spatial environment elements on seasonal LST vary across different urban construction land types. In both summer and winter, BH and BSD are negatively correlated with urban land use LST, while NDISI, BCR, and SVF show positive correlations with LST. However, NDVI is negatively correlated with the LST of residential land and public administration and public service land during summer, with spatial error coefficients of -3.653 and -2.496, respectively, but shows a positive correlation in winter, with spatial error coefficients of 3.767 and 2.507, respectively.

The ship target detection methods using Synthetic Aperture Radar (SAR) images have a wide range of practical applications in many fields such as surveillance on the sea surface, trade to and from the sea surface, and emergency rescue on the sea surface, etc. With the demand for the development of autonomous processing in satellite orbits, the real-time in-orbit detection and localization of ships from SAR images have put forward higher requirements. Therefore, this paper proposes a lightweight SAR image ship detection algorithm in a complex background for the current problems of limited satellite hardware resources, diverse and differentiated feature scales of different ship targets in Synthetic Aperture Radar (SAR) images, and easy to be interfered by noise. First of all, the FasterNet network model combined with the attention mechanism is used to extract different high and low level features of the target. Second, in order to solve the problem of scale inconsistency between different targets, this paper constructs a Feature Enhancement Module (FEM) that can not only increase the network sensory field at the same time but also improve the ability of network target detection. Then, a multi-scale feature fusion structure combined with feature enhancement is constructed in this paper, which can enhance and fuse the multi-scale features extracted by the backbone feature extraction network, and can also strengthen the connection between the features of different layers of the network while obtaining the multi-scale contextual information of the target, and carry out the detection of the SAR image ship in the three feature maps output from the multi-scale feature fusion structure combined with feature enhancement. Experiments are conducted to compare the proposed method with some other mainstream target detection algorithms on SSDD, HRSID, and merged SSDD and HRSID datasets. The results show that the average accuracy of the proposed methods on three datasets in this paper is 98.6%, 92.3% and 93.0%, respectively. The recall of the method in this paper is 95.10%, 85.10% and 86.8%, respectively, for three datasets. The model size and parameter number of the proposed method in this paper are only 8.8 MB and 4.2 M, respectively. The proposed method significantly outperforms other algorithms in terms of recall and average accuracy ratio. Moreover, the method in this paper also has great advantages in terms of checking accuracy and detection rate, which is favorable to be migrated to other practical applications.

Scientific and accurate monitoring of mangroves is the basis and premise for protecting marine coastal wetland ecosystems. Multi-source remote sensing data can be used to classify mangrove species effectively, but challenges remain in applying optical and SAR image features along with their time-varying information. In this paper, based on Sentinel-1/2 image data, we propose a mangrove species classification framework using Multi-source Features-coupled and Ensemble Learning algorithm (MFEL). The framework analyzs the classification advantages of spectral index features, SAR polarization features, and their temporal harmonic spectral features in feature selection and coupling. It then stacks the Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models to construct an Ensemble Learning model for mangrove species classification. Comparing the RF classification model and XGBoost models based on feature optimization, we evaluated the classification accuracy and feature application differences of the MFEL classification method. Zhanjiang Mangrove Forest National Nature Reserve was selected as the experimental area. The results show that: ① compared to using only spectral index features, classification accuracy improves by 6% and 8% with the addition of SAR polarization features or temporal harmonic spectral features, respectively. Adding both SAR polarization features and temporal harmonic spectral features simultaneously improves classification accuracy by 12%, making it more effective for mangrove species classification. ② The MFEL method achieves the highest classification accuracy, with an overall accuracy of 88.03% and a Kappa coefficient of 0.86. When the MFEL model trained on samples from the experimental area was applied to other areas, the classification accuracies were 83.94% and 82.77%, respectively. ③ This study verifies the potential application of SAR polarization features and time-sequence harmonic spectral features in mangrove species classification, significantly improving the accuracy for five mangrove species, with accuracies ranging from 76% to 91%. The study results provide valuable insights for expanding the use of medium-resolution remote sensing satellite imagery in monitoring mangrove species.

High-precision, high-spatiotemporal-resolution precipitation datasets are essential for accurate regional hydrological simulations and effective water resource management. The hinterland of the Tibetan Plateau, characterized by elevated altitudes and significant uncertainty in the spatiotemporal distribution of precipitation, suffers from a severe scarcity of gauge stations. The accuracy of satellite precipitation products in such complex terrains remains suboptimal, particularly in capturing orographic precipitation at small scales, rendering them inadequate for fine-scale studies. To address these challenges, this study proposes a downscaling correction algorithm that combines physical mechanisms with statistical models. Initially, the wind effect index was calculated at small scales to distinguish between the windward side, leeward side, and isolation valley, thereby parameterizing the orographic precipitation effect. This index was then corrected using boundary layer height. This approach downscaled IMERG daily precipitation products from 2003 to 2022 to a 1-km resolution, significantly mitigating the correction error typically introduced by the discrepancy in spatial scale between satellite pixels and ground stations. Subsequently, a random forest model was employed to utilize high-resolution cloud attributes and station data. Cross-validation was performed to select the optimal model, which was then used to correct the downscaled precipitation data across four distinct seasons. The results can be divided into three parts. First, the proposed spatial downscaling methodology not only enhances the resolution of satellite precipitation data but also substantially improves the accuracy of the product, as evidenced by an increase in the correlation coefficient (R) from 0.62 to 0.64 and a reduction in the root mean square error (RMSE) from 2.40 mm/d to 2.22 mm/d. Second, the downscaling precipitation results at the test site location (R=0.68, RMSE=2.11 mm/d) and the final correction results (R=0.71, RMSE=2.01 mm/d) demonstrate a significant enhancement in the accuracy of hinterland precipitation across varying temporal and spatial scales. Notably, the seasonal correction algorithm outperforms the non-seasonal correction algorithm, with remarkable improvements during winter, elevating the correlation coefficient from near zero to 0.28~0.41. Third, the impact of cloud and topography on precipitation is significant, with different factors influencing precipitation to varying degrees across seasons. Overall, the cloud water path has the highest contribution, while elevation, as a constant variable, has the least importance. This model provides a reference for precipitation correction in other regions with complex terrains and scarce precipitation data, offering a scientific basis for the study of water resources in the hinterland of the Tibetan Plateau and other regions with similar climate and environmental conditions.