In recent years, with the continuous development and rapid iteration of emerging technologies such as mobile communication, big data, the Internet of Things (IoT), Artificial Intelligence (AI), digital twins, and autonomous driving, new smart cities have become a significant frontier in the field of Geographic Information Systems (GIS) applications. Digital twin cities represent a complex integrated technological system that underpins the development of next-generation smart cities. Intelligent, holistic mapping for digital twin cities relies on comprehensive urban sensing, and the interactive control of urban sensing facilities plays a pivotal role in achieving the seamless integration of the physical and digital aspects of digital twin cities, fostering the convergence of entities within the urban environment. Describing spatiotemporal entities of the real world through a spatiotemporal data model, as well as modeling the behavioral capabilities of these entities using spatiotemporal object behavior, represents not only an innovative extension of GIS spatiotemporal data models but also addresses the practical requirements of triadic fusion and interactive analysis of human, machine, and object components with the development of digital twin city. As a crucial facet of urban infrastructure, urban sensing facilities epitomize distinctive spatiotemporal entities. Current research into the interactive control of these facilities is predominantly concentrated within the domains of the IoT, Virtual Reality/Augmented Reality (VR/AR), and GIS. However, these domains often lack research pertaining to interactive control of urban sensing facilities within the GIS-based digital realm. To tackle these issues, a viable approach involves mapping the direct physical control processes of humans over objects in the Internet of Things domain to the realm of GIS. Specifically, this involves using a GIS spatiotemporal data model to abstractly represent urban sensing facilities in the real world as spatiotemporal entities. These entities are then expressed as spatiotemporal objects within a spatial information system. Subsequently, the changes or actions of these facility spatiotemporal entities are uniformly abstracted as the behavioral capabilities of these spatiotemporal facility objects. Ultimately, the interaction control of these sensing facilities by humans is transformed into a process where humans invoke the behavioral capabilities of facility spatiotemporal objects, resulting in specific outcomes. Based on the aforementioned idea, this study employs a multi-granular spatiotemporal object data model to construct behavior capabilities for urban sensing facilities. Building upon this foundation, a spatiotemporal object behavior-driven approach for interactive control of urban sensing facilities with virtual-reality integration is introduced. By constructing a "quintuple" model for interactive control of facility objects, this approach facilitates users in engaging in interactive control through a reciprocal linkage between virtual scenarios and physical facilities. This mechanism effectively translates the process of urban sensing facility interaction control based on direct communication commands into the digital world, providing theoretical and technical support for the intelligent and interactive analytical applications of sensing facilities within digital twin cities. Experimental results substantiate the effectiveness and feasibility of the proposed method for interactive control of urban sensing facilities.

This study aims to explore the complex spatiotemporal patterns of bicycle-sharing trips, reveal the influence of urban factors on the OD of bicycle-sharing trips, and improve the accuracy of OD prediction. Combining the theory of urban computing, urban factors such as the epidemic, months, weather conditions (minimum temperature, maximum temperature, and wind speed), and whether it is a weekday along with the length information of non-motorized lanes are selected to construct a bicycle-sharing demand prediction model (USTARN) that integrates urban computing and spatiotemporal attention residual network. USTARN first captures the spatiotemporal dependence of sharing bicycle flow through spatial area division and time series slicing, then combines the attention mechanism for deep residual learning, and finally adjusts the deep residual prediction results according to the urban factor prediction results to improve the model performance. Using the big data from bicycle orders and urban factor datasets in Shenzhen obtained from the government data open platform, this study visualizes the spatiotemporal distribution patterns of bicycle-sharing trips and analyzes their influencing factors using the Python development environment. The OD data set is divided into training set, verification set, and test set in a 7: 1:2 ratio, and the model training, model parameter adaptive adjustment, and model result comparison are carried out, respectively. The results show that the average error of the USTARN model for OD prediction of bike-sharing trips is 7.68%, which is 5.93%, 7.55%, and 6.07% lower than that of the STARN model without urban computing and the traditional CNN model, which is good at data feature extraction, and the BiLSTM model, which is good at dealing with bi-directional time-series data, respectively. The USTARN model fully reflects the influence of time, space, epidemic, weather, and other factors on the OD of bike-sharing trips. Our results have theoretical guiding significance for the accurate prediction of bike-sharing trip OD, which can provide a scientific basis for urban non-motorized roadway planning and have practical application value for the promotion of bike-sharing travel mode and solving the 'last mile' problem of residents travel.

The geographically and temporally weighted regression method based on weighted least squares estimation achieves optimal estimates under the assumption of Gauss-Markov independent identical distributions. However, these conditions cannot be always satisfied. If there are outliers or heavy-tailed distributions in the data, the least squares estimates may be significantly biased. On the other hand, quantile regression is less affected by outliers and is more robust than least squares regression, which can be applied in a broader range of applications under more relaxed conditions. More importantly, the least squares regression model only focuses on the mean of the response, while quantile regression explores the global distribution of the response variable (e.g., quantiles of the response variable) and can obtain richer information. In this paper, we propose the geographically and temporally weighted quantile regression model based on the local polynomial estimation. This model allows for different optimal bandwidths for different explanatory variables and use a two-step estimation method to obtain the estimates of the coefficients. To illustrate the superiority of the proposed method, we compare the proposed method with the geographically and temporally weighted least squares regression through numerical simulations. The simulation results show that the mean square error and the mean absolute error of the coefficient estimates for the proposed quantile regression model are both smaller than those of the least squares regression model. For example, at the 0.75 quantile, the mean square error and mean absolute error of the coefficient estimates based on the least squares regression are 10 times and 4 times those based on the quantile regression, respectively. This indicates that our proposed method is robust and can explore the global distribution of the response variable compared to the least squares regression model. Finally, to illustrate the practical ability of the method, we apply it to the data of Shanghai's commercial residential neighborhoods from 2017 to 2021 to investigate the effects of different factors on residential prices at different quantiles (e.g., high house prices, medium house prices, and low house prices). The results show that the explanatory variables have different effects on house prices at different quantiles. The spatial and temporal distributions of the coefficients of the variables differ significantly among high house prices, medium house prices, and low house prices, and the optimal bandwidths for different explanatory variables also differ. Compared to the MGTWR based on least squares regression, the quantile regression model proposed in this paper is more robust with the presence of outliers. After removing 1% of extreme values, the change in the mean absolute error of the fitting based on the quantile regression model is 1% smaller than that based on the least squares regression model. Additionally, the quantile regression model can explore the factors affecting the different price levels of the housing such as the high house prices, medium house prices, and low house prices.

Triple Collocation (TC) is a technique for assessing the uncertainties of three samples individually without knowledge of the true values. This method is based on the assumptions of linearity, orthogonality, and zero cross-correlation. In practical use, these three assumptions are often difficult to achieve, particularly the orthogonality and zero cross-correlation assumptions, which often encounter significant violations. Moreover, we are uncertain about the impact of these assumption violations on the errors of the method's results. In this study, we simulated multiple sets of synthetic samples with varying degrees of two assumption violations to investigate the impact of assumption violations on the accuracy of the TC method. The results of synthetic samples experiment indicate that, in general, when there is an increase in the violation of orthogonality or zero cross-correlation assumptions, the error of the method's results increases linearly or quadratically. However, under certain specific conditions of assumption violation, there is a sudden and spike-like increase in the error of TC method results. This phenomenon is referred to as "outliers". To understand the origin of the outliers, we derived the complete mathematical relationship between the violation of assumptions and the errors of the results. This relationship exhibits a fractional structure rather than a linear one, contributing to the emergence of outliers. From the perspective of the difference notation, this fractional structure results from rescaling coefficients. Continuing to analyze this mathematical relationship, we can draw two conclusions. Firstly, merely ensuring the approximate independence of the three samples does not necessarily lead to improved method results. When the structural relationships among the three samples meet certain conditions, outliers emerge. Additionally, previous attempts at method improvement have aimed at overall reducing the sensitivity of this method to assumptions, neglecting the presence of outliers. Considering these factors, the key to suppressing outliers lies in better designing these rescaling coefficients. The paper presents two possible improvement methods:(1) Ignoring the additive bias, so that the rescaling coefficients are not affected by the orthogonality or zero cross-correlation assumptions. (2) Limiting the upper and lower bounds of the rescaling coefficients. We achieved favorable results in suppressing outliers by constraining the absolute values of the rescaling coefficients between 0.25 and 4. Both improvement methods can suppress the occurrence of outliers. However, when the additive bias is significant, the first improvement method generates substantial extreme errors due to its inherent structure, which is insufficient to eliminate outliers. The second method performs effectively even in complex scenarios. Lastly, we conducted a simple estimation of the probability of outliers occurring in practical usage, which was approximately 3.2%. In addition, we used SMOS, SMAP, and AMSR2 soil moisture data to validate the phenomenon of outliers and compared the two improved methods. According to real data, some outliers appear as negative values and are removed because the calculated results cannot be negative. Therefore, A portion of the outlier does not cause a significant deviation in the calculation result; instead, they simply prevent the calculation of meaningful results. Therefore, when employing the TC method with fewer repetitions for calculations (e.g., with fewer than 500 repetitions), the influence of outliers can be disregarded.

In order to enhance the high-quality development, accelerate China-style modernization, and promote the dissemination of Chinese civilization, numerous traditional Chinese settlements and historical villages and towns with abundant historical cultural information have garnered unprecedented attention. It becomes imperative to mine the rich traditional knowledge and core cultural features from these traditional settlements to meet existing social needs. Cultural Landscape Genes of Traditional Settlements (CLGTS) are generally regarded as the basic units of the abundant historical-cultural information and traditional cultural characteristics within traditional settlements. CLGTS has attracted wide concerns from the scholars and has been extensively employed to deal with various issues on traditional settlements, such as preservation, tourism development plan, and sustainable development. However, there is still a lack of research on using spatial data mining methods and theories to reveal the core cultural features of traditional settlement landscapes. To address this issue, based on the principles of conceptual lattices, this study proposes the CLGTS-conceptual lattice. This lays a solid theoretical foundation for developing an algorithm capable of addressing the complex relationships of various CLGTS. In this study, we develop a novel conceptual lattice algorithm “Object-oriented Layered Conceptual Lattice (OOLCL)” that treats the traditional settlements as objects and defines the CLGTS as the properties of conceptual lattice. In order to evaluate the performance and efficiency of OOLCL, we carry out an experiment with 30 historical-cultural famous villages and towns in Hunan Province, which are well-preserved and popular among tourists due to the distinctive characteristics of traditional cultures. Additionally, we also compare OOLCL with the typical Bordat algorithm which is a commonly used method for constructing conceptual lattice. In our algorithm, when the properties (CLGTS ) outnumber objects (traditional settlements), OOLCL can reduce the redundant nodes and data significantly by conducting intersection calculation between objects and thus enhance the efficiency of conceptual lattice construction. According to the theoretical findings and experimental results in this study, OOLCL holds significant implications for advancing research on the key traditional cultural characteristics of traditional Chinese settlements by combining GIS and spatial data mining methods.

Landslides frequently occur in the mountainous areas of western China. Accurate mapping of landslide susceptibility is essential for geohazard management. Integrated models combining statistical methods and machine learning models have been widely applied to landslide susceptibility mapping. However, further optimization of their results is still worth investigation. This study proposes a comprehensive assessment method that couples statistical methods, machine learning models, and clustering algorithms. The effectiveness of the proposed method on improving the accuracy of landslide susceptibility mapping in Ningnan County is investigated. Firstly, the landslide influencing factors are selected from five aspects: geological environment, topography and geomorphology, meteorology and hydrology, vegetation and soil, and human engineering activities in the study area. Indicators are initially selected based on correlation analysis using the Pearson correlation coefficient method, and highly correlated factors are eliminated to establish the landslide susceptibility mapping index system. Next, the Information Value (IV), Certainty Factor (CF), and Frequency Ratio (FR) methods are combined with Random Forest (RF) model respectively to obtain three integrated models (IV-RF, CF-RF, and FR-RF). Then, the ISO clustering algorithm, Natural Breaks clustering, and Kmeans clustering algorithms are introduced to classify the results of the three integrated models, obtaining nine coupled assessment models (IV-RF-ISO, CF-RF-ISO, FR-RF-ISO, IV-RF-NBC, CF-RF-NBC, FR-RF-NBC, IV-RF- Kmeans, CF-RF- Kmeans, and FR-RF- Kmeans). Lastly, Area Under the Curve value (AUC), accuracy, F1 score, and Seed Cell Area Indexes (SCAI) are used to evaluate the accuracy of the models. The results demonstrate that all the integrated models outperform single models. The accuracy and F1 score of all integrated models both exceed 0.85, and their AUC values exceed 0.9. The integrated models effectively address the misclassification of non-landslide samples, which is especially prominent in single IV and CF models. Among the integrated models, the FR-RF model performs the best. The accuracy (0.911), F1 score (0.912), and AUC value (0.965) of FR-RF model improves by 0.095, 0.096, and 0.074, respectively, compared to the FR model. Compared with the natural break and Kmeans clustering methods, the coupled FR-RF-ISO model exhibits the optimal classification results, and the difference in SCAI values between its high and low susceptibility zones is more significant. The extremely high landslide susceptibility zones are primarily concentrated in the southern, eastern, and central parts of Ningnan County. The study demonstrates the high accuracy of the integrated assessment method that couples statistical methods, machine learning, and clustering algorithms, and provides insights for improving the accuracy of landslide susceptibility mapping.

Scientific Ocean Drilling is a large-scale and long-standing international collaborative project in Earth sciences. Over the past 50 years, the program has carried out more than 300 expeditions and acquired a large amount of scientific data. The data exhibit typical characteristics of big scientific data, such as complex sources, diverse storage formats, and varied data structures. Currently, earth science has entered the fourth paradigm of data-driven scientific discovery. Effective organization and management of data, as well as enhanced data integration and services, are important foundational requirements for utilizing scientific ocean drilling data for data-driven Earth science discoveries. Existing scientific ocean drilling databases were established at an early stage, featuring relatively simple data retrieval capabilities and a lack of integrated and diverse data-processing tools. This poses challenges to the unified management, effective integration, efficient scheduling, extensive sharing, and comprehensive utilization of data. In response to these problems, firstly, the issues and requirements of scientific ocean drilling data organization and modeling are thoroughly analyzed. The process of generating scientific ocean drilling data, considering the whole lifecycle, was analyzed for organizing multi-source heterogeneous data. Then, based on the information expression system with elements of time, place, character, object, event, phenomenon, and scene, dimensions of semantic, spatial location, geometric structure, attribute, interrelationship, evolution process, and mechanism of interpretation from the perspective of geography, a scientific ocean drilling data model was constructed, taking into account the entire lifecycle of drilling data. Building upon this foundation, a framework for the scientific ocean drilling data integration and service application was proposed, encompassing data management, data querying, and thematic mapping. To optimize storage space and improve query efficiency, the storage implementation based on object-relational database and Elasticsearch was completed, following the concept of data cold-hot separation. To meet diversified data acquisition needs, a data retrieval approach with elemental on-demand query and multi-modal result integration was proposed. To better visualize the data, a customizable and configurable thematic mapping method was implemented. Based on these methods, a verification platform with the aforementioned capabilities was developed. The scientific ocean drilling data of Exp349, Exp367, and Exp368 in the South China Sea were used as examples to validate the feasibility of the methods and the usability of the platform. The research findings provide methodological references for organizing scientific ocean drilling data and serve as a reference for the efficient management and application of big scientific data.

Textured 3D mesh models are digital virtual spaces that provide a true, three-dimensional representation of human production, living, and ecological spaces. They have been widely used as foundational data input in areas such as smart cities and visual exhibitions. The semantic interpretation of textured 3D models is the foundation for fully exploring the potential of these models to achieve automatic understanding and analysis of scenes. Existing interpretation methods suffer from issues such as incomplete interpretation of occluded objects and inaccurate interpretation of different object boundaries. To address these challenges, in this study, we propose a multiview-based classification method for textured 3D mesh models. A textured 3D mesh model is first segmented into ground surfaces and 3D objects by Cloth Simulation Filtering (CSF) method. The ground surface is projected to a 2D orthophoto and classified using object-based image analysis methods. The textured 3D objects are transformed into five 2D images through orthographic and multiview oblique projections. These 2D images are then classified using object-based image analysis methods. Furthermore, these 2D semantic maps are inverse-projected to the 3D mesh model, and a multiview voting strategy is proposed for fusing sematic information from different views to obtain the sematic 3D objects. Finally, the semantic terrain surface and 3D objects are merged together to obtain the semantic 3D mesh model. A textured 3D mesh model of Shenzhen University is used to verify the effectiveness of the proposed method. Besides, the proposed method is compared with two state-of-the-art methods. The results show that the proposed method effectively addresses the problems in interpreting occluded objects and distinguishing edges between different objects. It outperforms the competing methods, particularly in the areas of orthographic occlusion and where different ground objects are connected or adhered, and achieves the highest classification accuracy (overall accuracy is 96.69%, Kappa coefficient is 0.942). Future research endeavors could consider the introduction of hyper-facet as the basic unit for classification and multiview fusion. Besides, we used only five fixed views in this study, and adaptive multiview estimation strategy could be further investigated to enhance the accuracy and robustness of the method. This method makes full use of the multiview information of the textured 3D mesh models, which holds significant theoretical and practical values. It not only contributes valuable insights but also offers methodological support for advancing the development and utilization of textured 3D models, especially in the field of natural resources management using textured 3D mesh models.

The effect of "space-time compression" caused by "space flow" breaks the independent allocation of resources between cities and drives the formation of regionally integrated development pattern, and the organizational structure and operation mechanism of the urban network cannot be separated from the inter-city relationship. Based on Baidu migration big data from October 2021 to September 2022, this paper constructs the intercity population flow network for 366 cities in China. At the node level, a population flow surpassing index is proposed to measure urban centrality and explore the spatial clustering characteristics of urban centrality. At the network community level, the monthly intercity population flow pattern and characteristics of 366 cities are analyzed. The results show that: (1) The population flow surpassing index considering flow direction meets the actual needs of intercity population mobility evaluation for measuring urban centrality and can effectively characterize the centrality of cities in the intercity population flow network. Using Baidu Migration big data from January 2023 to April 2023 after the end of the epidemic for comparison, we found that the central impact on national central city is small due to the prevention and control of COVID-19 transmission; (2) Cities in the intercity population flow network exhibit "High-High (HH)" and "Low-Low (LL)" agglomeration characteristics according to their centrality. HH clustering areas are formed in the eastern coastal and central regions, while LL clustering areas are mainly located at the edge of the Qinghai Tibet Plateau, the edge of the three northeastern provinces, and some areas in Hainan Island; (3) The intercity population flow pattern shows different characteristics in different months due to the influence of holidays, COVID-19 transmission, etc., generally in accordance with the first law of geography, and exhibits provincial differentiation characteristics; (4) The finding of urban cohesive subgroups shows that the intercity population flow patterns of Chengdu-Chongqing Urban Agglomeration, Greater Bay Area, Central Plains Urban Agglomeration, Guanzhong Plain Urban Agglomeration, Yangtze River Delta Urban Agglomeration, and other urban clusters are relatively stable, characterized by cross-provincial population flow integration. The Shandong Peninsula Urban Agglomeration and the Beijing-Tianjin-Hebei Urban Agglomeration have close connection in intercity population flow patterns, characterized by cross-urban cluster intercity population flow. The intercity population flow pattern within Zhejiang Province is gradually enhanced, and the urban clusters in middle reaches of Yangtze River and the west bank of the Taiwan Strait haven’t yet formed a stable population flow pattern across provincial borders.

In the context of global warming and accelerated urbanization, urban thermal environment has received widespread attention. Understanding the spatiotemporal changes of urban thermal environment and the impact of urban spatial form on urban local climate is crucial for alleviating the urban heat island effect. ECOSTRESS can generate Land Surface Temperature (LST) with high temporal resolution at different times of day and night, providing an opportunity for dynamic evaluation of urban thermal environment from a fine spatiotemporal scale. This paper explores the spatiotemporal changes of surface urban heat island intensity (SUHII) within Beijing's sixth ring road based on ECOSTRESS LST data, as well as the SUHII differences of inter- and intra-LCZ at different times of day and night, to investigate the impact of different urban landscapes. The results show that: (1) The SUHII of the study area has spatiotemporal heterogeneity. At 6 am, the SUHII is the lowest; at 10 am, the SUHII reaches its maximum; and SUHII gradually decreases in the afternoon and begins to rise around 6 pm, reaching its nighttime maximum around 9 pm; (2) During the daytime, LCZs exhibit significant differences in source and sink. Built-up LCZs (excluding LCZ 9) and natural LCZs (LCZ E~F) are generally sources, LCZ 9 and LCZ B~C are generally sinks, and LCZ A and LCZ G exhibit diurnal variations in source and sink; (3) Intra-LCZ SUHII exhibits significant day/night and type differences. The intra-LCZ SUHII differences are the lowest around 6 am and reach maximum around 1 pm. In the built-up LCZs, the intra-LCZ SUHII differences in low rise buildings are generally larger than those in mid to high rise buildings. In the natural LCZs, the intra-LCZ SUHII differences in LCZ C and LCZ E are relatively low, while the intra-LCZ SUHII differences in LCZ D, LCZ F, and LCZ G are relatively large; (4) The LCZs exhibit different thermal characteristics and changes in their roles as source and sink during day and night. LCZ G exhibits diurnal changes in source and sink, water bodies exhibit significant source characteristics at nighttime. The use of ECOSTRESS LST data from 10 different times of the day in this paper overcome the overestimation and underestimation of SUHII using only a single fixed time LST data in the past studies. The comparison results of inter- and intra-LCZ SUHII differences obtained reduce the uncertainty of quantitative research on urban thermal environment, and provide theoretical basis and practical support for the day and night balance of urban thermal environment source and sink landscape design.

Due to differences in spectral and spatial scales, the fusion results of panchromatic and multispectral images often have spectral or spatial distortion. How to achieve alignment on both scales simultaneously is crucial for enhancing fusion performance. The traditional Smoothing Filter-based Intensity Modulation (SFIM) remote sensing image fusion method can ensure consistency on the spectral scale but is not precise enough in measuring spatial scale consistency. To address this issue, this paper proposes a spatial scale alignment method considering local variance mutual information and further improves the SFIM method with the constraint of average gradient consistency. This method first linearly fits each band of the multispectral images to generate an intensity image and applies Gaussian low-pass filtering to high-resolution panchromatic images. By iteratively calculating the mutual information between the local variance images of the two images, the optimal filtering estimation parameters are determined when the mutual information is maximized. Then, the Gaussian filter is used to convolve the high-resolution panchromatic image, obtaining a low-resolution panchromatic image that matches the spatial scale of the multispectral images. The detail image is obtained by processing the ratio between the high-resolution and low-resolution panchromatic images. Based on the average gradient of high-resolution panchromatic images, an adjustment coefficient is introduced to control the amount of detail injection. Finally, the fusion image is obtained by multiplying the detail image, modulation factor, and multispectral image. To validate the effectiveness of this method, fusion experiments are conducted on six sets of images from three different scenes: vegetation area, building area, and mixed area of the IKONOS and Quickbird datasets. For the IKONOS data, the three experimental groups of our method all rank second in terms of spectral retention index SAM, and the information content EN ranks first in two groups. For the Quickbird data, the proposed method performs best in terms of SAM, EN, and AG indices in all three sets of experiments, demonstrating good spectral preservation and information richness. The proposed method outperforms the AGSFIM method in terms of SAM, EN, and AG in four sets of experiments, though the AGSFIM method obtains the highest spatial information preservation index SCC. Compared with the GSA or SFIM methods with similar SCC values, the proposed method shows an average improvement of 13.39%, 39.52%, and 34.03% for the other three indicators in the six experiments. In terms of fusion scenes, the proposed method performs well in scenes where vegetation or mixed areas dominate, while scenes dominated by buildings show the advantages of the proposed method in spectral methods. The abundance and clarity of image information also satisfactory, especially in fusion scenes with a higher proportion of vegetation. Moreover, the proposed method also exhibits good visual appearance. There is minimal color difference between the fused true-color image and the original true image and acomparable image clarity against the panchromatic image. In terms of fusion scenes, the method in this paper demonstrates a clear advantage in spectral preservation for vegetation-dominated or mixed areas, with a relatively rich amount of image information; for scenes dominated by buildings, the fusion results also show good performance in terms of spectral richness, information content, and clarity.

Geostationary meteorological satellite products play a crucial role in various weather monitoring and climate change applications. Due to weather conditions such as cloud cover, precipitation, and atmospheric influence, the Land Surface Temperature (LST) data retrieved from geostationary meteorological satellites often exhibits missing values, which significantly limits the further applications of LST products. In the process of reconstructing missing values of LST using time series data, most of the existing studies focus on comparing the accuracy of different time dimensional reconstruction methods. There has been limited research on evaluating the impact of missing data on the effectiveness of different model reconstruction methods. Taking the Heihe River Basin as the case study area, this study adopts the INA08 Diurnal Temperature Cycle (DTC) model to analyze the impact of missing data on the reconstruction of summer LST data from the FY-4A (Fengyun 4A satellite) AGRI (Advanced Geostationary Orbit Radiation Imager). Then, a data replacement scheme is proposed for different missing time periods, and the LST reconstruction results are verified against field measurement data. Research results show that: (1) the absence of LST data during the second time period (local time of 13:00 to15:00) has a minimal impact on the reconstruction results, while the absence of LST data during the fifth time period (0:00 to 6:00 of the next day) has the most substantial impact on the reconstruction results; (2) with the increasing number of missing time periods, the available original AGRI LST data for fitting the INA08 model are reduced, leading to a decrease in the reconstruction accuracy; (3) the data replacement scheme with the simulation experiment provides valuable guidance for the reconstruction of FY-4A AGRI LST by using the INA08 model, and the reconstruction accuracy of missing data can be significantly improved by replacing certain missing time periods with other suitable time periods. In our study, different time periods are divided based on the daily temperature variation in the Heihe River Basin in the summer season. The time period division may vary depending on the geographical location and season. For a given region and season, appropriate time periods can be determined based on the local sunrise time, the time of maximum daily temperature, and the time of temperature attenuation, in conjunction with typical characteristics of temperature variation throughout the day.

Ozone concentrations tend to be heterogeneous across a city's space due to the mixed land use and diverse landscapes. Studying spatial patterns of urban ozone pollution contributes to the knowledge of the mechanism of pollution formation and also provides scientific reference for pollution prevention and control. Nevertheless, most previous research focused on the averaged value of ozone concentration from monitoring sites, which cannot describe the spatial characteristics of the entire region's concentration surface. Additionally, the classification method was seldom used to analyze pollutants' spatial patterns, and thus very few studies paid attention to the varied types of patterns and their temporal variations. In this study, based on the distributions of ozone’s daily maximum 8-h moving average estimated from satellite data, an approach of semi-supervised few-shot learning was proposed to classify ozone's spatial patterns in Beijing. The self-training method considered the difficulty of data labeling and can utilize information from a large number of unlabeled samples to augment the training set iteratively. Three kinds of normalized features were involved in classification to describe the spatial variations of concentrations. Totally, there were 40 training samples and 249 test samples for the year of 2020, and the overall classification accuracy was 81.12% with a kappa coefficient of 0.741 6. This demonstrated the effectiveness of the semi-supervised classification method despite the small size of training samples. The classification results showed that, among the eight patterns of ozone distributions in Beijing, three of them were major patterns, including Pattern 1: high concentrations in the south/east/southeast and low in the north/west/northwest, Pattern 2: high concentrations in the north/northwest and low in the south/southeast, and Pattern 6: low concentrations in the center. They dominated the warm season (from Mar. to Oct.), the cold season (from Nov. to Feb.), and the transition period, respectively. These temporal variations of ozone's spatial patterns indicated the influence from the seasonality of regional transport and photochemical reactions. When training samples were transferred to the year of 2019, the overall classification accuracy reached 80.97%, and the kappa coefficient was 0.745 6, suggesting the high potential of sample migration. And the results of 2019 further confirmed the previous findings. Thus, the proposed classification method for spatial patterns of urban ozone pollution can not only benefit the identification of regions with heavy pollution but also support the study on mechanisms of different pollution events.

Soil Organic Matter (SOM) content is an important indicator for measuring soil fertility and has a significant impact on food production. China's coastal saline-alkali lands are vast, and SOM exhibits significant spatial heterogeneity. The traditional method of “field collection and experimental measurement” is time-consuming and labor-intensive, making it difficult to quickly depict the spatial distribution characteristics of SOM content in saline-alkali areas. Therefore, efficiently and accurately using remote sensing technology to invert SOM content can provide insights into soil fertility, so as to help adjust and optimize agricultural production and management. This study focused on the bare soil (0~10 cm soil layer) in a typical coastal saline-alkali land of the Yellow River Delta and constructed remote sensing indices from the Unmanned Aerial Vehicle (UAV) remote sensing spectral and spatial texture information. The multi-linear stepwise regression model, partial least squares model, and random forest model were employed to quantitatively estimate SOM. We compared and analyzed the impact of cultivated and uncultivated soil on SOM content retrieval through UAV remote sensing methods, and also explored whether the integration of remote sensing information sensitive to soil salt content aids in SOM retrieval in saline-alkali areas. The results show that: (1) In the inversion of SOM content, the accuracy of random forest model (R² ranging from 0.83 to 0.95) was significantly higher than that of multiple linear stepwise regression (R² ranging from 0.26 to 0.69) and partial least squares model (R² ranging from 0.37 to 0.72); (2) Compared to uncultivated soil (R² ranging from 0.26 to 0.95), the inversion accuracy of organic matter in cultivated soil (R² ranging from 0.54 to 0.94) was significantly higher. That is to say, cultivation treatment enhanced the response of spectral indices to SOM content, thereby increasing the inversion accuracy of SOM content, which provides new insights for precise monitoring of SOM; (3) Integrating remote sensing texture information or soil salt content information (i.e., salt content and salt sensitivity) can significantly increase the inversion accuracy of SOM. This study provides theoretical and technical support for improving SOM content inversion in salinized coastal farmland at the field scale, ultimately contributing to the development of modern agriculture in salinized coastal areas.

High consequence areas within oilfields are critical zones for the safety management of petroleum transport pipelines. Accurately and efficiently capturing the spatial distribution of key features in high-consequence areas of oilfields is essential for the smooth operation of petroleum safety production and the scientific management of oilfield regions. However, there are still challenges in extraction tasks of the high-consequence areas of oilfields, such as diverse ground object shapes, small spectral differences, and complex types, and the extraction results often include misclassification, omissions, and road discontinuities. To address these challenges, we propose an SML_ResUnet model for land cover extraction in high-consequence areas of oil fields based on ResUnet architecture. This model integrates Strip Pooling (SP) units in the pooling stages and incorporates Mixed Pooling Modules (MPM) and Label Attention Modules (LAM) between the encoding and decoding processes. The SP units are designed to capture elongated and isolated features, excluding information from other irrelevant areas, while the MPM combines the advantages of standard pooling and strip pooling, effectively preserving feature information across different spatial positions. The label attention module introduces label information to optimize the attention probability maps generated within the attention module, further enhancing the extraction results. We applied the proposed model on a high-resolution dataset of a high-consequence area of an oilfield. The results of the ablation experiments indicated that the proposed SML_ResUnet network achieved the optimal extraction results. The metrics of Overall Accuracy (OA), Mean Intersection over Union (MIoU), and F1-score reached 97.24%, 84.23%, and 91.26%, respectively. Compared to the classical ResUnet model, improvements are observed in all evaluation metrics of the proposed model, with OA, MIoU, and F1-score increasing by 0.48%, 2.49%, and 1.55%, respectively. For a land cover extraction task within a high consequence area of an oilfield in Shandong Province, the OA of the extraction results averaged at 97.66%. We then extended the model in other high-consequence areas of oilfields in Shandong Province and achieved an Overall Accuracy (OA) of 95.63%. Our results meet the accuracy requirements for rapid acquisition of surface information in large-scale high-consequence areas of oilfields and demonstrate that the SML_ResUnet model is particularly suitable for large-scale land cover extraction tasks within oilfields characterized by diverse and complex terrain types.