[Significance] As a critical pathway to achieving Sustainable Development Goal (SDG) 2, “Zero Hunger,” and ensuring long-term ecological sustainability, the concept and practice of sustainable agriculture are undergoing a paradigm shift toward data-driven and system-oriented approaches. In recent years, Big Earth Data—comprising remote sensing, geospatial, meteorological, and agricultural Internet of Things (IoT) data—has emerged as a foundational driver for agricultural monitoring, decision support, and technological innovation in sustainable development. [Analysis] Given the interdisciplinary, multi-stakeholder, cross-regional, and evolving-goal nature of sustainable agriculture, this study begins by systematically reviewing the conceptual evolution of the term. It highlights the multidimensional implications and diverse practical pathways of sustainable agriculture, noting its growing role as a core component of global development strategies. On this basis, the paper proposes a new, data-oriented and operational interpretation of sustainable agriculture. The study then establishes an analytical framework—“data-Technology”—to clarify the pivotal role of Big Earth Data in supporting sustainable agriculture. It examines the evolution of core datasets and key technical methods across three periods: before 2015, 2015—2019, and 2020 to the present. The applications reviewed include agricultural resource monitoring, multi-scale crop condition assessment, and evaluations of agriculture's environmental impacts. The findings suggest that sustainable agriculture, enabled by Big Earth Data, is rapidly shifting from a paradigm of "observational analysis" to one of "intelligent decision-making." Furthermore, the study conducts a comparative assessment of China, the United States, and the European Union across four critical dimensions: data infrastructure; technological advancement and application; scientific research capacity; and policy support. While China has made significant progress in all four areas—with strengths in remote sensing capabilities, rapid technological rollout and demonstration, substantial research output, and clearly defined policy directives—it continues to face challenges in data ecosystem development, original algorithm innovation, commercialization of scientific outputs, and the alignment of standards and incentive mechanisms. Finally, in light of the current needs for sustainable agricultural development, this study systematically analyzes the major challenges facing Big Earth Data from four aspects: data acquisition capacity, intelligent processing methods, application promotion and services, and data governance and ethical security. In response, it proposes multi-level strategies covering standardization, model optimization, improvements to service systems, and protection of data rights, with the aim of providing a reference pathway for the efficient utilization and sustainable development of agricultural big data in the future. [Prospect] The article aims to analyze the data-driven transformation pathway of sustainable agriculture and provide a systematic reference for its green, inclusive, and intelligent development.

Accurately identifying the "production-living-ecological" spaces (PLES) is the premise of urban functional zone optimization, livable cities construction, and urban-rural development balancing. Existing research on identifying spatial distribution of PLES is insufficient in identifying and quantitatively characterizing integration space and interactive space. There is an urgent need to establish methods that consist of complete system and can accurately quantify spatial functions by integrating multi-source geographic data based on existing rural-urban regional systems. Thus, this present constructed three indices, including Spatial Function Strength index (SFS), Spatial Function Coverage index (SFC), and Spatial Function Interaction index (SFI). These indices were calculated using the Point of Interest (POI) data, land cover, and cell phone communication data using the road network community as the fundamental unit. The PLES in Haidian district, Beijing was identified by determining the priority of the three indices with the decision tree interpretation method. In addition, this study compared the spatial distribution and characteristics of functions at the global, class, and patch levels using landscape indices. The results include: (1) The functional zones at different regional systems were significantly different. Along the urban-rural gradient, the diversity spatial types and complexity of shapes, and the patch density increased, but the spatial aggregation in the same class decreased. (2) Individual ecological and living spaces showed concentrated distribution within the rural and urban regional systems, while the spatial characteristics of individual production space in different regional systems were quite different in terms of dominant industry type. Within the integrated spaces, the type and characteristics were different between rural and urban areas. For interactive spaces, they were mainly distributed in the transitioning spaces between rural and urban zones, characterized by "separation between living and working spaces". (3) At the patch level, landscape indices complemented and supported the spatial pattern for the system and classes of the PLES at a finer scale. In addition, landscape indices effectively identified special city functional regions with the largest spatial diversity and smallest spatial aggregation. The result of this study realized system interpretation and spatial identification of multi-functionality for PLES at the urban-rural regional systems based on innovated methods for integrating data in illustrating the advantages of multi-source geographic big data, which provided supports in terms of data and methods for territorial spatial planning and management.

With the advent of the remote sensing big data era, the approach to extracting remote sensing information has shifted from single-image processing to an integrated method that combines spatio-temporal-spectral fusion. In other words, this method emphasizes a "large-scale and fine-grained" processing strategy. At the large-scale level, the complexity of the geographical environment presents challenges for remote sensing imaging, such as "same spectrum, different objects" and "same object, different spectra". Proper zoning can effectively reduce the heterogeneity within regional units, thereby improving the accuracy of remote sensing image classification. At the fine-grained level, remote sensing imaging captures subtle changes in surface features, but the inherent "macro pattern, micro complexity" of geographical environments, lacking top-down global regulation, often leads to significant uncertainty and cognitive biases when relying solely on bottom-up classification from remote sensing data. To address this, the paper proposes a multi-scale remote sensing geographical zoning framework designed to resolve the differences in scale and representation between geographical patterns and remote sensing imaging across macro, meso, and micro levels. Through application examples at these three levels, the study demonstrates that appropriate zoning not only enhances the accuracy of remote sensing information extraction but also enriches the variety of extracted attributes, thereby improving the precision of industry-specific remote sensing applications.

[Objectives] Population big data, characterized by large sample sizes and high spatiotemporal completeness, has become an essential foundational dataset for research in demography, population geography, and spatial population studies. Despite its widespread application, effective calibration methods for population big data remain lacking. Quantitative research, in particular, requires accurate and reliable population big data. However, common mathematical models struggle to precisely and realistically describe the relationship between big data and the Seventh National Population Census. [Methods] This paper proposes a method to calibrate population big data quantities by using the legally authoritative Seventh National Population Census data as an anchor point, integrating statistical data with 2020 Baidu population big data at a spatial level. Based on the mathematical relationship between population big data and official statistics, an operations-research-based optimization model is constructed to obtain the globally optimal deviation values for calibration. Using resident population data from the Seventh National Population Census for Hunan Province as the anchor point, the method calibrates the 2020 Baidu resident population big data and conducts two validation procedures. [Results] The results show that the deviation ratio between the calibrated resident population big data for Hunan Province and the census data is -1.01% (an improvement of 25.87%), with city-level deviation ratios ranging from -2.05% to +0.92% and county-level ratios from -2.06% to +1.99%, without altering the pre-calibration deviation trends. Validation against National Bureau of Statistics data indicates that the deviation ratio for calculated total urban population ranges from -2.7% to 1.7%. Validation against village-reported data in the "Green Heart" region at the intersection of Changsha, Zhuzhou, and Xiangtan cities shows a deviation ratio of 0.47% between the calculated resident population and the village-reported figures. [Conclusions] The calibration and validation results fully demonstrate the effectiveness of the proposed method, offering a viable approach for estimating population figures in non-census years and generating spatially distributed population datasets based on big data. This method can be applied to calibrate population big data from any provider and can be extended beyond resident population figures to calibrate gender ratios, age structures, working populations, mobile populations, OD flow data, and population profiles in big data.

As part of the Belt and Road Initiative (BRI), China has actively participated in global port construction over the past decade. This study aims to evaluate the outcomes of these efforts and enrich the theoretical framework of BRI development. We constructed a port vitality index (PVI) using shipping big data. The PVI was calculated across four key dimensions (production, maritime, service, and radiation) using 17 fundamental indicators based on the port transportation chain model. This study evaluated the vitality of port development over the past decade. The results were as follows. (1) There was significant growth from 2013 to 2022 in the overall vitality index of ports invested in China, with Asia and Europe being the primary hubs of port vitality, and Africa emerging as a major center of growth. (2) There was higher vitality growth among ports where China was involved in both investment and operations, compared to ports where China's role was limited to construction. (3) Chinese port construction companies held global power by helping developing countries or regions lacking transportation capacity quickly establish international transportation capabilities. (4) Terminal operators and shipping companies invested in ports with strong underlying conditions, located on key shipping routes with minimal economic risks, to ensure effective investment outcomes. (5) Some small and medium-sized ports gained rapid production vitality through investment, yet lacked maritime vitality, as insufficient urban market growth hinders the increase in maritime vitality. The port vitality evaluation method proposed in this study provides insights for adjusting China's future port investment strategies and optimizing location choices for enterprise investments. It also enriches the theoretical framework of the BRI development and provides practical evidence of China's commitment to fostering shared prosperity with the Global South through port infrastructure development.

[Objectives] With the deepening of urbanization and intensified market competition, long working hours have become a pervasive social issue, posing challenges to both workers' physical and mental health and to urban sustainable development. Current studies on urban residents' work activities predominantly rely on questionnaire survey data, which suffer from limited sample sizes and a lack of in-depth exploration into long working hours in megacities. [Methods] This research utilized mobile signaling data from Beijing, collected between November and December 2019, to identify stay points using a threshold rule method. Residential and workplace locations were determined through a time-window approach, and users' working hours were extracted. The study then examined the spatial distribution patterns of long-working-hours employees (defined as those working over 40 hours per week) and investigated spatial characteristics across various gender and age groups. Finally, the study also explored the characteristics of long working hours in different employment clusters in Beijing. [Results] The findings reveal that 47.1% of Beijing's workforce engages in long working hours (weekly working hours ≥40 hours), with an average weekly working duration of 48.86 hours. Spatial analysis demonstrates a polycentric agglomeration pattern, concentrated in major employment hubs such as the CBD, Financial Street, Zhongguancun, and Yizhuang. Significant disparities exist across gender and age groups. Male employees work an average of 49.62 hours per week, 1.5 hours more than their female counterparts (48.12 hours). Among male age groups, those aged 20~29 have the longest average weekly working hours at 50.68 hours. In contrast, although women aged 30~39 constitute the largest proportion of the female workforce (22.13%), their average weekly working hours are the lowest, at 47.59 hours. The characteristics of overtime work in different employment clusters show a clear pattern: the CBD and Zhongguancun have a higher number of overtime workers, while Yizhuang stands out with the highest proportion at 58.0%. Wholesale and logistics hubs such as Xinfadi and Majuqiao exhibit the most intensive work schedules, with average weekly working hours exceeding 50 hours. [Conclusions] This study provides rich empirical evidence for understanding the phenomenon of long working hours in Beijing. The results offer data-driven support for optimizing labor time policies, contributing to urban sustainable development and social equity.

Recreational spaces play a pivotal role in enhancing the quality of national life, fostering physical and mental health, and promoting social interaction. While previous research has predominantly examined the supply side of urban recreational spaces, there is a noticeable gap in the characterization of these spaces based on societal perceptions. Using Guangzhou as a case study, this study harnesses big data sourced from Ma Feng Wo and Ctrip. Based on machine learning techniques, it discerns recreational emotions and synergizes Geographic Information System (GIS) spatial analysis with heat-emotion matching analysis. This approach facilitates a nuanced and precise examination of the structural and experiential characteristics of urban recreational spaces from the perspective of social perception. The objective of this study is to offer informed references for the strategic planning and management of urban recreational spaces. The findings indicate that: (1) The distribution of recreational spaces in Guangzhou exhibits a "core agglomeration, edge dispersion" pattern, characterized by a "one core, multiple centers" configuration. Notably, the central urban region and its proximate suburbs—specifically, the Panyu, Baiyun, and Huangpu Districts—show a marked concentration in both the number and popularity of recreational spaces. In contrast, peripheral urban zones primarily feature government residences, premium ecological recreational spaces, and hot spring resources. These areas—including the Huadu, Conghua, and Zengcheng urban areas, Conghua District's hot spring town, and Paitan town—stand out as secondary hubs in terms of recreational space concentration and popularity. Moreover, recreational spaces situated along waterfronts and in areas with dense road networks tend to follow a "point-axis" distribution model, with a staggering 90.45% of these spaces located within a 1 km radius of the road network. (2) The overall approval rate of urban recreational spaces in Guangzhou is significantly high, evidenced by an average positive emotion ratio of 86.99%. However, there is a marked polarization in terms of popularity, manifesting as a "core-edge" decline in spatial distribution. Recreational spaces that evoke predominantly positive emotions are primarily located in the city center and are proximate to the administrative seats of the Panyu, Baiyun, Huangpu, and Nansha Districts. These areas also record a low ratio of negative emotions. In contrast, neutral and negative emotions are more prevalent in the commercial streets and pedestrian zones of the central city, as well as the suburbs of the Conghua, Huadu, and Zengcheng Districts. A considerable emphasis on "cost performance" is observed across all emotional categories related to Guangzhou's urban recreational spaces, indicating a widespread concern among tourists/residents regarding the balance between the costs of these spaces and their service quality. (3) The congruence between popularity and emotional response in Guangzhou's urban recreational spaces is suboptimal. Spatially, this disconnect can be categorized into three distinct types: the "polarized" pattern in the central city, the "experience enhancement" pattern in the Huadu, Panyu, Baiyun, and Conghua Districts, and the "insufficient supply" pattern in the Zengcheng, Huangpu, and Nansha Districts. These findings provide valuable insights for metropolitan areas aiming to refine the layout and management of recreational spaces. By understanding social demands and experiences, cities can craft a more optimized group perception of urban recreational spaces, thereby enhancing tourists' satisfaction and elevating the well-being of residents.

[Objectives] The quality of the leisure environment is a critical factor influencing residents' leisure experiences and participation, and it is closely related to the vitality of urban areas and economic development. Therefore, exploring how environmental quality influences the vitality of leisure space is crucial for promoting urban development. [Methods] A human-centered approach is adopted to construct a research framework for exploring the relationship between leisure environment quality and leisure space vitality based on image-text fusion perception. Online review texts and street view images are used to comprehensively perceive the leisure environment quality of the city. Natural language processing and semantic segmentation techniques are used to assess the leisure environment quality, while mobile signaling data is utilized to quantitatively measure the vitality of leisure spaces through user trajectory semantic modeling. Finally, using an Optimal Parameter-based Geographical Detector (OPGD), an in-depth analysis is conducted on the impact mechanisms of individual leisure environment quality factors and their interactions with the vitality of leisure spaces at global and local spatial scales in Nanjing. [Results] The findings reveal that: (1) The spatial distribution of leisure space vitality exhibits a "single-core-multi-center" pattern. The vitality in the main urban area is concentrated around the Xinjiekou commercial district, while Jiangbei District forms a "three-point" pattern with interactions between the two ends and the center. In the Xianlin area, high-vitality zones are distributed around the university town, while in the Dongshan area, they are located along the Shuanglong Avenue corridor. (2) On a macro scale, the leisure space vitality of Nanjing is indirectly dominated by economic levels. On a local scale, the influence of 14 leisure environment quality factors on leisure space vitality demonstrates significant regional heterogeneity. However, in municipal and district-level core areas with high leisure space vitality, the effects of these environmental quality factors are all significant. (3) The formation mechanism of leisure space vitality in Nanjing is closely related to regional geographical location, population density and composition, and economic income levels. [Conclusions] The analysis of Nanjing indicates that the exploration of leisure environment quality through image-text fusion perception enhances the systematic and comprehensive understanding of the factors influencing leisure space vitality and its mechanisms. This provides a scientific basis for optimizing the quality of the urban leisure environment and enhancing the vitality of leisure space.

As the most basic unit of society, family is a key object for understanding urban society, activity, and space. However, the systematic exploration of interaction pattern between family activity and urban environment was not sufficient, and they were always in an imbalanced state, which raised a few concerns. Therefore, it is essential to introduce the concept of "human-environment relationship" into family research, exploring the relationship between family and urban environment, that is the study of "human-environment relationship" of urban families (here, we call it "family-environment relationship"). To this end, we firstly propose a brand-new identification method for family and its spatiotemporal behaviors based on mobile phone location big data. Subsequently, taking the case of Wuhan, we verify the effectiveness of this method by using China Unicom's mobile signal data, which proves that the family identification samples can be regarded as large-scale sampling of realistic families. Finally, based on the concept of "human-environment relationship", we attempt to build a theoretical framework for "family-environment relationship" at the urban micro scale, and summarize how family big data should be appropriately applied in this framework. Our study provides a theoretical framework and a series of methods for exploring the interaction pattern between family and urban environment, which contribute to accurately diagnose the imbalance phenomenon of family-environment relationship and effectively formulate "family oriented" environmental optimization strategies, thus formulate a strong push for improving the quality of family life, constructing a family friendly city, and promoting sustainability of urban society.

[Significance] Cities globally face increasingly frequent multi-hazard risks, driving them pursuing more sustainable and resilient urban transportation systems. This paper presents a comprehensive systematic literature review of the application of spatial-temporal data in transportation system resilience studies. It highlights the pivotal role of spatial-temporal big data in understanding and enhancing the resilience of urban transportation systems under various hazard scenarios. Spatial-temporal big data, characterized by high temporal resolution and fine spatial granularity, has been increasingly applied to the field of transportation system resilience, providing essential support for decision-makers. [Progress] This study reveals two significant findings: Firstly, quantitative analysis of transportation system resilience is one of the most widely applied uses of spatial-temporal big data. However, real-time monitoring and early warning explorations are relatively rare. Most studies remain at the modelling and numerical simulation stage, indicating a need for more empirical studies using multi-source spatial-temporal big data. Moreover, compared to English literature, Chinese transportation system resilience studies are primarily qualitative and lack empirical research, indicating divergent research emphases between domestic and international scholars. Secondly, high-quality, multi-source spatial-temporal big data could facilitate more comprehensive spatial analysis in transportation system resilience studies. Improved data quality allows for deeper exploration from a microscopic perspective, focusing on individual behaviors and aligning closely with real-world needs. The concept of resilience has evolved from its previous post-disaster focus to a comprehensive life-cycle perspective encompassing pre-, during-, and post-disaster phases, transforming the study framework for transportation system resilience. [Prospect] As spatial-temporal big data technology advances and new transportation modes emerge, more innovations and breakthroughs in transportation system resilience studies are expected. Future research should further explore and utilize the potential of spatial-temporal big data in this field, amplifying the policy ramifications of abrupt-onset occurrences. Increased emphasis should be placed on research conducted at the scale of urban agglomerations. Simultaneously, a nuanced examination from a microscopic perspective is imperative to dissect the underlying causes and mechanisms contributing to variations in resilience among distinct groups. Despite the significant progress in transportation system resilience studies, there are still challenges in data collection, processing, and analysis. As technology progresses, researchers should leverage advanced algorithms, platforms, and tools to enhance data processing capabilities and analytical precision, facilitating more complex and detailed studies on transportation system resilience. This will provide a scientific basis for planning and managing urban transportation systems, significantly contributing to the overall resilience and sustainable development of cities.

[Significance] Data resources have become pivotal in modern production, evolving in close synergy with advancements in artificial intelligence (AI) technologies, which continuously cultivate new, high-quality productive forces. Remote sensing data intelligence has naturally emerged as a result of the rapid expansion of remote sensing big data and AI. This integration significantly enhances the efficiency and accuracy of remote sensing data processing while bolstering the ability to address emergencies and adapt to complex environmental changes. Remote sensing data intelligence represents a transformative approach, leveraging state-of-the-art technological advancements and redefining traditional paradigms of remote sensing information engineering and its applications. [Analysis] This paper delves into the technological background and foundations that have facilitated the emergence of remote sensing data intelligence. The rapid development of technology has provided robust support for remote sensing data intelligence, primarily in three areas: the advent of the big data era in remote sensing, significant advancements in remote sensing data processing capabilities, and the flourishing research on remote sensing large models. Furthermore, a comprehensive technical framework is proposed, outlining the critical elements and methodologies required for implementing remote sensing data intelligence effectively. To demonstrate the practical applications of remote sensing data intelligence, the paper presents a case study on applying these techniques to extract ultra-high-resolution centralized and distributed photovoltaic information in China. [Results] By integrating large models with remote sensing data, the study demonstrates how remote sensing data intelligence enables precise identification and mapping of centralized and distributed photovoltaic installations, offering valuable insights for energy management and planning. The effectiveness of remote sensing data intelligence in addressing challenges associated with large-scale photovoltaic extraction underscores its potential for application in critical fields. [Prospect] Finally, the paper provides an outlook on areas requiring further study in remote sensing data intelligence. It emphasizes that high-quality data serves as the foundation for remote sensing data intelligence and highlights the importance of constructing AI-ready knowledge bases and recognizing the value of small datasets. Developing targeted and efficient algorithms is essential for achieving remote sensing intelligence, making the advancement of practical data intelligence methods an urgent research priority. Furthermore, promoting multi-level services for remote sensing data, information, and knowledge through data intelligence should be prioritized. This research provides a comprehensive technical framework and forward-looking insights for remote sensing data intelligence, offering valuable references for further exploration and implementation in critical fields.

[Objectives] Remote Sensing Intelligent Interpretation (RSII) often encounters challenges when applied for practical resource and environmental management, especially for complex scenes. To address this, we start from the explanation of why remote sensing interpretation is needed, and clarify that the mission of RSII is to achieve more rapid interpretation to build the digital twin earth with lower cost compared to manual interpretation. However, most RSII systems operate as a unidirectional process from remote sensing data to geoscience knowledge, lacking the feedback from knowledge to data. As a result, remote sensing information extracted from data often mismatch the knowledge of existing geoscience, creating a trust crisis between RSII researchers and geoscience researchers. And the crisis becomes more severe with the uncertainty of remote sensing information. [Analysis] We believe that an agreed upon representation model of geoscience knowledge between RSII researchers and geoscience researchers is necessary to alleviate the crisis. Based on this analysis, we propose a framework using geo-science zoning as the bridge to connect RSII researchers and geoscience researchers. In this framework, knowledge from geoscience could be transferred into the RSII system through geo-science zoning so that the interpretation results could be more coincided with geoscience knowledge. The framework mainly relies on (a) the scene complexity measurement, (b) the knowledge coupling of geographic regions to form the geological zoning method for remote sensing intelligent interpretation, and (c) the sampling specification of regional samples. The scene complexity measurement provides quantitative features for geoscience zoning and sampling weights assignment. Existing zoning data, such as ecological zoning data, geographic elements, and multisource remote sensing images are the main data inputs for geoscience zoning. The main principles for constructing zoning methods include (a) the geoscience elements type, (b) the scale of geoscience zoning, and (c) the process of information flow from data to knowledge. [Prospects] With these models, we can realize regional RSII guided by the knowledge. Preliminary experiments on complexity and optimization sampling, image segmentation scale optimization, cultivated land type fine classification, etc., reveal that this framework has great potential in improving the geoscience knowledge acquisition by RSII, enhancing the accuracy of the state-of-the-art RSII by 6%~10%, especially for the high-complexity nature scenes. However, the superiority of the framework may disappear if the scene for interpretation is simple, like the first level land use/cover classification, which is mainly caused by the inefficient samples after geoscience zoning. Therefore, more attention is needed in sampling when developing geoscience zoning framework.

[Objectives] The active discovery and scientific assessment of the ecological damage caused by mining disturbances in coal mining areas is a key focus in the research of "intelligent mining and green mines". However, traditional analysis methods, characterized by a reliance on monitoring, limited discovery capabilities, dependence on experts, and retrospective assessments, struggle to meet the regulatory authorities’ actual needs for intelligent identification and rapid early warning. This article aims to verify the adaptability of knowledge graph-based spatial reasoning methods for actively detecting and intelligently identifying ecological damage in coal mining areas, while exploring innovative approaches and technologies for ecological environment governance in the modern era. [Methods] By integrating multi-source monitoring data from "Space-Air-Ground-Human" systems and summarizing knowledge on the location, form, group distribution, distribution patterns, and spatiotemporal evolution of ecological units in coal mining areas, an indicator system for describing these units is designed. Additionally, intelligent identification and reasoning rules for ecological damage are constructed using knowledge graph technology. [Results] Coal mining subsidence is a typical land use/cover change phenomenon caused by coal resource extraction. Remote sensing technology is crucial for extracting subsidence and its variations, yet traditional methods often overestimate the affected area due to misclassification of natural water surfaces as mining-induced subsidence. To address this, new knowledge and spatial reasoning rules were introduced to accurately differentiate between mining subsidence areas and natural water surfaces. Using a coal mining area in Shanxi Province as a case study, spatial reasoning rules were applied to identify subsidence units accurately. Experimental results demonstrated that the proposed method improved the precision and intelligent recognition accuracy of mining disturbance units. Compared with traditional recognition approaches, the proposed method reduced false positives by 21.43%. [Conclusions] Knowledge graph technology proves highly adaptable for analyzing and evaluating ecological environments in coal mining areas. It offers technical support for the proactive discovery and accurate identification of ecological damage caused by mining disturbances. Furthermore, it provides new technological tools and ideas for building advanced ecological governance models.

[Significance] As a comprehensive observation of natural resource development and utilization, spatio-temporal big data on natural resources contains valuable knowledge about resource distribution, spatio-temporal process evolution, and interrelationships. [Progress] This paper examines spatio-temporal big data mining and knowledge services for natural resources, highlighting key data mining techniques and their critical applications in knowledge services. First, it introduces the core concepts, technical frameworks, and methodological processes of spatio-temporal clustering analysis, association mining, anomaly detection, predictive modeling, and geographic risk assessment, along with their applications in natural resource management and land-use decision-making. Second, a four-tier natural resource spatio-temporal knowledge service system is proposed, encompassing descriptive, diagnostic, predictive, and decision-making knowledge services, which provide essential support for applications such as resource status monitoring, land-use regulation, and disaster prevention and mitigation. Finally, the paper indicates that current natural resource management is transitioning from data aggregation and analysis to knowledge-driven intelligent services, forming an emerging research and application paradigm of big data, big analysis, big knowledge, and big services. [Prospect] Future efforts will focus on advancing collaborative data and knowledge mining technologies, addressing the standardization challenges in spatio-temporal knowledge bases and services, and exploring the potential of cutting-edge technologies such as generative large models in the natural resource domain to drive the information and intelligent transformation of natural resource management.