Through over 70 years of development, the World Meteorological Organization (WMO) has established a global data-sharing network that covers 193 members. This study analyzed the WMO’s meteorological data-sharing system from two aspects: system architecture and composition, and management norms and standards. The meteorological data-sharing system comprises the global observing system (GOS), the global telecommunication system (GTS), the WMO information system (WIS), and the global data-processing and forecasting system (GDPFS). Specifically, the GOS coordinates and schedules observational facilities from land and marine stations, aircraft, environmental satellites, and other platforms. The GTS manages the real-time collection and distribution of meteorological information. The WIS is responsible for discovering, accessing, and managing data and products. The GDPFS provides various climate forecasting data to users. By formulating a unified data policy and establishing this meteorological data-sharing system, the WMO has enabled the global sharing of Earth system science data in multiple fields, such as weather, climate, hydrology, atmospheric composition, cryosphere, and oceans. This study summarizes the achievements of WMO’s meteorological data-sharing system and its alignment with China’s relevant data strategy requirements. It assists in enhancing the understanding of international meteorological data-sharing activities and facilitating the construction of a similar multi-departmental comprehensive Earth observation data-sharing system in China.

Remote sensing-based Earth observation and Earth system numerical simulation serve as two significant technical means in revealing the Earth’s environmental changes and predicting its future states. Hence, they assist in enhancing the capacity of human society to mitigate and adapt to global change and in ensuring the sustainable development of the natural environment and human society. The Coupled Model Intercomparison Project (CMIP) is a large-scale international collaboration project in the field of Earth system numerical simulation, aiming to coordinate various countries to complete the simulations of the Earth’s historical environment and the predictions of its future states. The Earth simulation data generated in the CMIP directly support the global climate change assessments of the Intergovernmental Panel on Climate Change (IPCC), United Nations, providing a solid scientific basis for global climate negotiations and governance. The CMIP Phase 6 (CMIP6) has generated up to 30 petabytes (PB) of Earth simulation data. The management and sharing of these data are achieved through the Earth System Grid Federation (ESGF). This study elucidates the CMIP organizational scheme, the ESGF system architecture, and the sharing and interoperability progress of Earth simulation data. It can provide a reference for planning, designing, and operating large-scale networks for sharing remote sensing science data.

Land cover classification systems constitute a significant aspect of land cover research. This study summarized nine major land cover classification systems. It presented these classification systems along with their relevant data products and analyzed the differences and connections between them. Moreover, this study discussed the relationship of their fineness with spatial resolution and coverage, as well as their semantic consistency. The results indicate that LCCS and Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) excel in fine-scale classification but face technical challenges and implementation difficulties in fine-scale classification based on high spatial resolution data. Current classification systems exhibit significant semantic inconsistencies in logical relationships, fine-scale classification, nomenclature, and code. Global land cover classification research shows the following development trends: the coexistence of globalization and regionalization, finer-scale classification, higher product accuracy, and more detailed temporal and spatial resolution. The semantic consistency of data products needs to be enhanced by strengthening the compatibility of classification systems and finding solutions to data product sharing and interoperability.

Monitoring wetland changes based on land cover mapping serves as a significant means for supervising contracting parties to the Convention on Wetlands of International Importance Especially as Waterfowl Habitat (also referred to as the Ramsar Convention) in fulfilling their obligations. However, substantial land cover mapping products available show significant differences in spatiotemporal characteristics, classification systems, and quality. This study conducted a consistency analysis of land cover mapping products for 40 wetlands of international importance in China from 2015 to 2019, aiming to provide a reference for selecting wetland mapping products and monitoring wetlands in Ramsar reserves. Using long time-series land cover mapping products CCI_LC, CGLS_LC, and MCD12Q1, this study preprocessed the data in terms of spatial and category consistency. Based on wetland classification areas, it conducted regression analysis and calculated the accuracy and uncertainty indicators of the mapping products. The results indicate that these products exhibited significant inconsistencies in wetland classification areas, with area differences averaging 6 to 10 times. Moreover, their wetland classification results were marked by low accuracy and high uncertainty. For most regions, the user accuracy (UA), producer accuracy (PA), and Kappa coefficient were below 0.1, and the standard deviation exceeded the mean. Overall, the three land cover mapping products fail to support credible monitoring of changes in wetlands of international importance.

At its 16th Plenary Session and Ministerial Summit, the Group on Earth Observations (GEO) proposed a new goal to build a “digital library for Earth observation applications”, highlighting the transition from “open data” to “open science”. It aims to achieve the management and sharing of knowledge resources, including data, algorithms, literature, and cases, thereby facilitating the comprehensive application and knowledge service provision of Earth observations in fields such as global change. Under this research background, this study systematically examined Earth observation data resources, including the conceptual system of Earth science variables, Earth observation satellites and payloads, observational and simulated data products, and open knowledge bases of academic literature. Based on the theories and techniques related to the Semantic Web and Knowledge Graph, this study established the Earth observation knowledge ontology with corresponding instances, involving Earth science variables, remote sensing satellites, observation payloads, observational and simulated datasets, journals, and academic literature. The knowledge representation results of this study will contribute to the representation, management, and integration of data and knowledge in the field of Earth observation applications. Moreover, they facilitate the discovery of potential associations between data and knowledge, enhancing the efficiency of scientific research and advancing scientific discovery.

The Arctic region is one of the most sensitive regions to global climate change in terms of response and feedback. Sea ice in the Arctic region affects the Arctic environment, ecosystems, and climate while also exerting profound influences on global ocean circulation, climate, and biodiversity. Hence, gaining a deep understanding of sea ice is critical for understanding the operational mechanisms of the Earth system, predicting climate change trends, conserving ecosystems, and advancing sustainable development. Through remote sensing observations and numerical simulations, substantial scientific data related to the historical distribution and future changes of Arctic sea ice have been acquired. These data are currently stored in large remote sensing science data centers and multiple Earth system simulation data centers involved in the Coupled Model Intercomparison Project (CMIP). However, a thorough comparative analysis of these distributed scientific data is challenged by the downloading of mass data. Based on the CMIP scientific data, this study demonstrated the difficulties encountered in data downloading. Accordingly, this study proposed a novel method and corresponding software solution for online collaborative analysis. Focusing on the sea ice data from remote sensing observations and numerical simulations, this study expounded the deployment and operation of the proposed method in multiple institutions. The proposed method can enrich the technical system for the findability, accessibility, interoperability, and reusability of the scientific data of sea ice. The demonstrated online collaborative analysis system can significantly enhance the analysis and utilization efficiency of sea ice data.

The simple and efficient water index method has been widely used to monitor and identify surface water along with its spatiotemporal variations. However, with the application of narrow-band multispectral sensors, this method faces a challenge in selecting optimal bands with similar features when the data source changes during large-scale water monitoring. Guided by the normalized difference water index (NDWI) and the modified NDWI (MNDWI), and based on the Google Earth Engine (GEE) platform, this study constructed water indices using the green bands and eight red bands from the Sentienl-2A/B multispectral sensor data. Employing Otsu's method, this study identified and extracted water bodies in six quadrats measuring 90 km × 90 km across different temporal and spatial ranges in China. The results indicate that the optimal band combination for water body extraction varied across different times and locations. Compared to the eight water indices constructed from the Sentienl-2A/B multispectral sensor data, the water index based on the combination of B3 and B11 bands, combined with Otsu's method, achieved optimal water identification and extraction results. These results were observed in summer in the lake regions of the Northeast China Plain and mountains, the eastern plains, the Inner Mongolian Plateau, the Yunnan-Guizhou Plateau, Xinjiang, and the Qinghai-Tibet Plateau. In both spring and summer, the water index based on the combination of B3 and B11 bands exhibited an overall accuracy (OA) exceeding 90% and a Kappa coefficient above 0.9, indicating its applicability across different time periods. Overall, the results of this study provide a reference for the design and development of sensors targeting water extraction and monitoring, and for feature band selection in water monitoring and extraction applications based on narrow-band remote sensing data.

Building extraction aims to separate building pixels from remote sensing images, which plays a crucial role in applications such as urban planning and urban dynamic monitoring. However, building extraction generally faces challenges, such as void, false positives, and false negatives. Given this, this paper proposed a densely nested network (DN-Net). The sub-networks in the DN-Net were integrated with the enhanced residual convolutional module (ERCM) to extract rough contours of buildings from remote sensing images. Furthermore, to accurately locate the buildings, a coordinate attention module (CAM) was incorporated, effectively avoiding false positives. To deal with the holes during building extraction, a cascade convolutional module (CCM) was used, allowing the extraction of richer details with convolution kernels of various sizes, thereby ensuring accurate building extraction. The DN-Net was tested with the WHU datasets to assess its accuracy. The results showed that the DN-Net exhibited an intersection over union (IoU) of 89.20% and a F1 score of 94.29% on the validation set and 89.85% and 94.65%, respectively, on the test set. The results confirm that the DN-Net can significantly improve the building extraction accuracy, with more complete and detailed boundaries of buildings being extracted, demonstrating an outstanding ability to extract buildings of varying sizes.

Given its all-day availability, all-weather adaptability, and high spatial resolution, the interferometric synthetic aperture radar (InSAR) technique has been widely applied in multiple fields, demonstrating strong adaptability and high practical value. Focusing on two typical landslide areas in Zhouqu County, Gansu Province, this study compared small baseline subset InSAR (SBAS-InSAR) monitoring results and Kalman filter prediction results with monitoring data from the global navigation satellite system (GNSS), confirming the reliability and accuracy of the SBAS-InSAR technique in monitoring landslide deformations. The results indicate that the SBAS-InSAR technique exhibited significant advantages in monitoring areas with deformations induced by geologic disasters, effectively overcoming the limitations of traditional monitoring means. This technique can provide critical technical support and scientific basis for early warning and management of geologic disasters in Zhouqu County and other areas prone to suffer these disasters.

Pansharpening of remote sensing images refers to the fusion of panchromatic images (PAN) and low-spatial-resolution hyperspectral (or multispectral) images (LR-HSI/LRMS) to produce high-spatial-resolution hyperspectral (or multispectral) images (HR-HSI/HRMS). Currently, deep learning-based pansharpening methods have increasingly matured. However, pansharpening still faces several challenges, including inadequate feature extraction, insufficient guidance for information fusion, and oversimplified single-stage architectures, resulting in HR-HSI imagery with compromised spatial and spectral fidelity. To address these issues, this paper proposed a two-stage pansharpening method for hyperspectral images based on feature enhancement and a Three-Stream Transformer architecture. In the first stage, preliminarily enhanced hyperspectral images (HSI) were generated using a feature enhancement module and a multi-scale fusion module. Specifically, the feature enhancement module strengthened spatial and spectral information across multiple scales, while the multi-scale fusion module integrated the enhanced HSI at different scales. In the second stage, the initially enhanced HSI, PAN, and images resulting from their fusion were treated as three separate feature streams using the self-attention mechanism of the Transformer. Then, these streams were transformed into the Q(Query), K(Key), and V(Value) matrices via linear layers, followed by multi-head attention computation, which effectively guides the extraction and fusion of spatial and spectral information. Furthermore, the enhanced HSI and an additional fusion module were leveraged to refine image quality, yielding HR-HSI results with richer spatial and spectral details. Validation experiments were conducted on three classic hyperspectral datasets. The results demonstrate that the proposed method outperforms both conventional and existing deep learning-based approaches in terms of quantitative evaluation metrics. Considering qualitative evaluation results, it also preserves spectral information of the HSI and spatial details of the PAN images, producing more realistic HR-HSI images.

Vegetation distribution serves as a crucial foundation for natural resource conservation and ecosystem health assessment. In mountainous regions, substantial terrain undulations and complex vegetation types complicate the mapping process. Moreover, the traditional remote sensing-based vegetation classification, whose mapping relies on 2D imagery, fails to depict the vertical structure and 3D spatial distribution of vegetation. To investigate the potential of realistic 3D models in fine-scale vegetation classification and mapping, this study proposed a realistic 3D model-based mapping approach for mountain vegetation by integrating optical images and light detection and ranging (LiDAR) data. Focusing on Neilingding Island in Guangdong, this study constructed a multi-source dataset using realistic 3D models, multispectral images, and LiDAR point clouds acquired by unmanned aerial vehicle (UAV)-based measurements, followed by data registration and feature extraction. Subsequently, the LightGBM algorithm was employed to achieve fine-scale vegetation classification and to assess the classification performance of multi-source data features. Finally, semantic 3D mesh models of vegetation were generated by projecting the 2D vegetation maps onto the 3D models. The results indicate that realistic 3D models can effectively distinguish vegetation types. Their combination with multispectral and LiDAR data provides a more comprehensive description of the topography and vegetation structures in mountainous areas. Compared to using a single data source, this approach achieves an increase in the overall accuracy (OA) of 2D classification by 4.28% to 11.29%. Concurrently, the OA of the 3D mapping based on realistic 3D models reached 92.06%, with a Kappa coefficient of 0.89. This approach can reflect the accurate, visualized, 3D distribution patterns of mountain vegetation and improve the accuracy of fine-scale vegetation information extraction. This study demonstrates the significant potential of 3D model-multisource data integration for natural resource monitoring and provides novel ideas and methods for fine-scale and 3D information extraction of regional vegetation.

The deep learning-based models currently available for detecting changes in remote sensing images face several challenges, including limited multi-granularity, poor classification performance of networks, high sensitivity to parameters, and great efforts in parameter adjustment. To address these challenges, this study proposed a deep forest-based model for detecting changes in remote sensing images. Initially, preliminary results were determined using a basic change detection method. Then, the results were optimized using the multi-granularity scanning characteristics and strong data classification of deep forest sub-networks. In this manner, the final change detection results were obtained. Verification experiments conducted on the LEVIR-CD and SYSU-CD datasets using various common change detection models indicated that the proposed deep forest-based model significantly outperformed other models in terms of precision, F1 score, and recall. Additionally, the proposed model exhibited strong adaptability on small datasets, as verified by loss function comparison, small-sample experiments, and ablation studies. This adaptability can reduce the complexity of parameter adjustment and address the issues that other deep learning sub-networks fail to be applicable to medium and small datasets.

The spatial density and interferometric phase quality of high-quality monitoring points serve as key indicators for deformation monitoring using the time-series interferometric synthetic aperture radar (InSAR) technique. To further enhance the deformation monitoring ability of the InSAR technique for non-urban areas, this study proposed a polarization time-series InSAR method that takes into account distributed scatterers (DSs) using dual-polarization images from Sentinel-1. Specifically, polarization processing of the intensity and phase information of time-series SAR data was conducted using various methods based on the characteristics of DSs and taking the dispersion of amplitude (DA) as an indicator for the phase quality assessment. Then, surface deformation monitoring was performed using the data before and after optimization. This study carried out experiments on Ningbo City in Zhejiang Province using 40 scenes of dual-polarization (VV-VH) images from Sentinel-1. The results indicate that the proposed method can significantly increase the density of monitoring points and the interferometric phase quality. Compared to single polarization, the proposed method increased the quantities of persistent scatterers (PSs) and DSs by about 20% and 57.5%, respectively. Furthermore, the interferometric phase quality was also significantly improved, with the average coherence increasing by more than 15%. The proposed method allows for a more detailed reflection of regional deformations.

Extracting information on various surface features from remote sensing images requires varying contextual data. To address this issue, this study proposed a new feature point matching method that integrated a large selective kernel-enhanced convolutional module. In this method, based on the ResNet34 network, a large selective kernel-enhanced convolutional module was embedded for dynamic feature extraction of different surface feature targets. Then, the initial dense matching was obtained using a sparse neighborhood consensus network. Meanwhile, geometric and motion consistency constraints were introduced to conduct the guided diffusion of matching points. Consequently, optimized matching results were achieved. This method yielded a PCK (α=0.05) accuracy of 0.89 on the Google Earth dataset, which increased by 7.22%, 5.95%, 2.30%, 4.71%, 7.22%, and 9.88%, respectively, compared to the SuperPoint, R2D2, NCNet, Sparse-NCNet, LoFTR, and Two-Stream networks. Additionally, it exhibited a high generalization ability on the Hpatches dataset. These results corroborate the effectiveness of the proposed method.

Benches, important surface features in open-pit coal mines, can reflect the production status in the mines. Extracting information about benches from remote sensing images can provide a significant basis for production monitoring in coal mines, as well as ecological protection and restoration. This study established the BenchSegNet deep learning model for extracting information on benches in open-pit coal mines from Sentinel-2 images. The results indicate that the BenchSegNet model inherited the strong generalization capability of SegFormer and the powerful detail extraction ability of U-Net, achieving an accuracy of 97.69%. Compared to the SegFormer model, the BenchSegNet model demonstrated increases of 6.19 percentage points, 4.09 percentage points, and 5.06 percentage points in precision, recall, and F1 score, respectively. Compared to two traditional convolutional neural network models, i.e., U-Net and ASPP-UNet, the BenchSegNet model exhibited increases of nearly 10 percentage points in the three metrics. In addition, compared to two traditional machine learning algorithms, i.e., random forest and support vector machine, the BenchSegNet model showed increases of approximately 15 percentage points in the three metrics. The comparisons verify that the BenchSegNet deep learning model delivers high accuracy. Given that the Sentinel-2 satellite is characterized by global coverage, short revisit time, and high spatial resolution, the combination of Sentinel-2 images and the BenchSegNet model can effectively monitor the change process of benches in open-pit coal mines.

The reservoir area of the Baihetan hydropower station (also referred to as the Baihetan reservoir area) suffers from frequent geologic hazards. However, there is a lack of monitoring studies on the central area and lower reaches of the hydropower station. Based on the ascending and descending synthetic aperture Radar (SAR) images from the Sentinel-1A satellite, this study performed deformation monitoring and landslide hazard identification in the Baishitan-Yezhutang section of the Baihetan reservoir area using the small baseline subset-interferometric synthetic aperture Radar (SBAS-InSAR) method supported by the generic atmospheric correction online service for InSAR (GACOS). Moreover, this study conducted cross-validation of deformation data from ascending and descending SAR images for low-slope zones. It investigated the spatial distribution of landslide hazards and the movement patterns of typical hazard sites in the study area. Finally, it examined the impacts of factors influencing geologic hazards on the distribution of these hazard sites. The results indicate that the deformation data from ascending and descending SAR images for low-slope zones can be used for cross-validation. Based on the deformation detection results from time-series InSAR and the optical images from Google Earth, 16 landslide hazards were identified, including 14 slow-moving landslides and two significant deformation hazards induced by human engineering activities. Integrating the data of ascending and descending SAR images validated the reliability of deformation results and also enhanced the effectiveness of landslide hazard identification. The analysis of the movement patterns at typical hazard sites indicates a correlation between deformation acceleration and seasonal rainfall. The statistical analysis of factors influencing geologic hazards in the study area reveals that the formation of hazard sites is driven by multiple factors, with varying dominant factors and degrees of influence across different hazards.

The Loess Plateau is recognized as a typical climate-sensitive and ecologically vulnerable region in China. Understanding the spatiotemporal characteristics and potential driving factors of vegetation dynamics in different dry/wet climate zones within the Loess Plateau holds critical significance for the conservation and management of regional ecosystems. Based on the kernel normalized difference vegetation indices (kNDVIs) of the Loess Plateau from 2000 to 2022, this study investigated the spatiotemporal patterns of vegetation dynamics in different dry/wet climate zones within the Loess Plateau using the coefficient of variation and trend analysis. Employing the optimal parameter-based geographical detector model, this study accurately and scientifically identified the driving factors and ranges of vegetation dynamics under the spatial scale and zoning effect, effectively addressing the challenge of spatial heterogeneity. The results indicate that the average kNDVI of the Loess Plateau presented a spatial distribution pattern characterized by low values in the northwest and high values in the southeast. In terms of vegetation dynamics, 91.57% of the Loess Plateau showed an upward trend, with the semi-arid climate zone accounting for the highest proportion (60.41%). Different driving factors in the Loess Plateau corresponded to varying optimal dispersion methods and optimal interval breakpoints. Under the optimal zoning effect, low temperature and high rainfall were identified as the primary conditions for vegetation growth. The different ranges and types of driving factors exerted different effects on the spatial distribution of vegetation dynamics. The optimal parameter-based geographical detector model demonstrates that rainfall and land use type constituted the principal driving factors of the Loess Plateau, accounting for 65.45% of the total explanatory power. The q value (0.69) of the interaction between the two driving factors was higher than the q values of interactions between other factors. This study provides a comprehensive insight into the response mechanisms of vegetation dynamics under natural and human factors, thereby guiding the sustainable development of regional ecosystems.

To quantify the mining disturbance of open-pit coal mines to surrounding ecosystems, this study investigated the Pingshuo mining area in Shanxi Province. Based on the pressure-state-response (PSR) model, seven types of assessment indicators were selected to construct the remote sensing ecological index of open-pit coal mining area (OMRSEI) through combination weighting. The validity of the OMRSEI was verified through correlation and comparative analyses. Moreover, the trend of ecological evolution in the study area for the next two years was predicted using the exponential smoothing method. The results indicate that the OMRSEI exhibited significant spatial correlation and validity, establishing it as an effective remote sensing indicator for ecological assessment in open-pit coal mining areas. The study area manifested an overall enhanced ecological quality from 2013 to 2023. Specifically, the Antaibao and Anjialing open-pit coal mines witnessed continuously improved ecological quality due to the progressive restoration of waste dumps. In contrast, the Dong open-pit coal mine displayed an ecological quality trend characterized by a first decline and then recovery. The average OMRSEI of the study area is predicted to continuously rise from 2025 to 2027, indicating sustained enhancement in ecological quality.

This study aims to investigate the spatiotemporal evolution and trade-off/synergy relationships of ecosystem services in the Xi'an section of the Qinling Mountains. To this end, it quantitatively assessed the spatiotemporal evolution patterns of four ecosystem services-water yield, soil conservation, carbon reserves, and food supply-from 2003 to 2023 based on the integrated valuation of ecosystem services and trade-offs (InVEST) model. By integrating Spearman's rank correlation coefficient and geographically weighted regression (GWR), this study identified and quantified trade-off/synergy relationships among ecosystem services. Finally, the impacts of changes in land use on ecosystem services were analyzed. The results showed that water yield and soil conservation generally showed a rapidly decreasing trend followed by a slow increase, while carbon reserves and food supply exhibited a slow decline. In addition, synergistic relationships were observed between water yield and soil conservation, between water yield and carbon reserves, and between carbon reserves and soil conservation. In contrast, trade-off relationships were identified between food supply and water production, soil conservation, and carbon reserves. In the study area, increases in forestland and grassland led to a diminution in water yield. The expansion of construction land and the loss of arable land resources directly triggered a reduction in carbon reserves, while an increase in forestland contributed to soil conservation. These findings can provide a scientific basis for the eco-environmental protection and sustainable development of the Qinling area.

Traditional methods for information extraction of small water bodies suffer from poor performance and low accuracy, failing to meet actual needs. Using the high-resolution images of the Erhai Lake basin from the Jilin-1 domestic satellite as the data source, this study proposed a deep learning-based semantic segmentation method using an improved DeepLabV3plus model. Replacing the ResNet-101 encoder with EfficientNet-B4, this study innovatively combined the BCE Loss and Dice Loss functions, identifying the optimal method for fine-scale information extraction of water bodies in the Erhai Lake Basin. The results indicate that compared to traditional methods, the improved DeepLabV3plus model performed better in the information extraction of water boundaries, enabling accurate identification of main water bodies, especially small streams. The improved DeepLabV3plus model exhibited higher precision (98.87%), recall (99.30%), and F1-Score (99.08%) than the normalized difference water index (NDWI) and object-oriented methods. Regarding comparison of details, the improved DeepLabV3plus model can effectively suppress the influence of building shadows, vegetation occlusion, and complex surface features, improving the information extraction effects of small water bodies and complex edge areas. In addition, ablation experiments show that the introduction of the combined loss functions and compound scaling strategy increased mIoU by 0.62% and 3.07%, respectively, significantly enhancing the model's segmentation accuracy and ability to extract multi-scale semantic information.

A wildland-urban interface (WUI) refers to the area where residential buildings meet or intermingle with natural vegetation such as forests. The delineation of the WUI plays an important role in fire risk management, forest resource development and utilization, climate change responses, and sustainable socio-economic development. Current methods for WUI delineation are primarily developed and refined based on the definition given in the Federal Register of the United States. Based on indicators such as building density, vegetation coverage, and the distance between buildings and vegetation, these methods can be categorized into three types: building density priority, fuel grade priority, and overlap between building-vegetation buffer zones. Initially, this study presented a summary and comparison of relevant literature on the three types of methods. Then, Wood Buffalo in Alberta, Canada, an area frequently affected by wildfires, was selected to compare the three methods using data on Canadian building footprints released by Microsoft, global land cover from GLC_FCS30-2020, and local historical fire points and fire scars. The results indicate that the building density priority method exhibited the highest coincidence rate with historical wildfire records. However, it overlooked low-density buildings that were also at risk of wildfire. The fuel grade priority method produced a larger delineation area, with a lower coincidence rate with historical wildfire records since it focused excessively on the vegetation around buildings while neglecting the buildings themselves. In contrast, overlap between building-vegetation buffer zones presented the lowest coincidence rate with historical wildfire records and the smallest delineation area. This occurred primarily due to the short distance setting of buffer zones. This study reveals the strengths and limitations of existing methods, contributing to more scientifically robust and rational WUI delineation in the future while also providing references for decision-making in fire risk management and emergency responses.

The black soil region in Northeast China is a major grain-producing area in China. To ensure the sustainable development of agriculture in the black soil region, the data from the third national land resource survey, remote sensing data, and the digital elevation model (DEM) can be integrated to explore the ecological protection assessment methods for cultivated land. This study investigated Nenjiang City, Heilongjiang Province, from the location conditions of cultivated land and surrounding ecological land use. It constructed five indicators, including the forest health index, the proportion of ecological land surrounding cultivated land, the distance to the nearest forest, the slope, and the topographic position. Notably, an improved forest health index was designed based on the remote sensing ecological index to comprehensively assess the ecological protection of cultivated land in Nenjiang City. The results indicate that the cultivated land in Nenjiang City was dominated by medium-low and medium ecological protection grades, covering 34.21% and 45.28% of the cultivated land area, respectively. In contrast, the high-grade cultivated land accounted for merely 2.11%, indicating considerable potential for improving the ecological protection grade of cultivated land. Among individual indicators, the proportion of ecological land around cultivated land and the forest health index exhibited low values, serving as the primary factors leading to an overall slightly low geological protection grade in the study area.

The East Qinling Mountains, located in the eastern Qinling orogen between the North China and Yangtze plates, boast the largest Mo-Au-W polymetallic metallogenic belt in China. Given that alteration played a key role in the mineralization process, its information extraction and distribution characteristics can provide critical insights for analyzing the mineralization mechanisms. To explore a more efficient method for extracting alteration information, this study investigated Dengfeng City in the East Qinling Mountains using data from the Sentinel-2A and Landsat-8 sensors. Data processing and analysis were conducted based on the Google Earth Engine (GEE) platform, and deep learning was applied to the extraction of alteration information. To improve the extraction efficiency, the information about vegetation, water bodies, and buildings was extracted first using the normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and normalized difference built-up index (NDBI), respectively. Subsequently, the interference information was masked by generating binary images using the threshold segmentation method. In combination with the spectral curves of typical hydroxyl minerals, the bands used to extract hydroxyl alteration information were determined. Then, the initial alteration information was extracted using the principal component analysis (PCA) method, and the pixels that overlapped spatially and exhibited concentrated information and high alteration levels were selected as labels to train the deep learning model. The potential information of remote sensing images was further extracted using the convolutional neural network (CNN) model that integrated multi-band data. Finally, in combination with the linear structure maps and mineralization anomalies of the target area, rock and soil samples were collected from the corresponding locations, and their main components were determined using X-ray fluorescence spectroscopy (XRF) and X-ray diffraction (XRD) analysis. In this manner, the reliability of the alteration information extracted was verified. The results indicate that compared to the PCA method alone, the CNN model can extract more comprehensive and clearer hydroxyl alteration information that was more easily graded. The samples collected at the field sampling points all contained minerals with hydroxyl alteration, such as muscovite, biotite, and chlorite. The laboratory XRF and XRD analysis results were consistent with the hydroxyl alteration information extracted using the CNN model. This verifies the reliability and efficiency of the interpretations of hydroxyl alteration information extracted using the deep learning-based CNN model. The results of this study can provide a theoretical and technical basis for remote sensing prospecting in the East Qinling Mountains.

The South China hilly and mountainous belt is one of the “three regions and four belts” involved in China's major ecosystem conservation and restoration program. This belt hosts the largest and most well-preserved middle subtropical forest ecosystem at the same latitudes globally, playing a crucial role in ensuring the ecological security in South and Southwest China. Based on the Google Earth Engine (GEE) platform, this study conducted preliminary monitoring of disturbances in this belt using the LandTrendr algorithm and the Jeffries-Matusita (JM) distance. It further applied the random forest algorithm to relevant disturbance outputs, enabling the monitoring and analysis of forest disturbances in this belt from 1985 to 2022. The results indicate that the total forest disturbance area in this belt reached 38 564.62 km² during the study period. Specifically, the disturbance areas of four ecological restoration projects decreased in the following order: Wuyi Mountains forests (12 040.27 km²), Nanling Mountains forests (11 820.79 km²), Hunan and Guangxi karst areas (8 228.97 km²), and mining areas (6 474.59 km²). Based on the 38-year forest loss dataset, this study analyzed the spatiotemporal variations in forest disturbances within this belt, revealing significant spatiotemporal forest disturbances. Spatially, forest disturbances were characterized by distinct geographic clustering. Temporally, the forest loss areas under four ecological restoration projects experienced several stages of change. Despite similar critical transition points and interannual variation patterns, differences in forest resources, climate, and economic conditions led to variations in the areas and trends of forest loss. Besides, the implementation of forestry policies somewhat influenced the forest loss trend. Overall, this study provides a scientific basis and decision-making reference for the management of forest ecosystems within this belt.

Siling Co, the largest lake in Xizang Autonomous Region, has expanded significantly in the past few years, threatening surrounding pastoral activities, infrastructures, and even the ecological environment. This study systematically reconstructed the time series of changes in the lake area, water level, and water volume of Siling Co from 1995 to 2023 using optical images from satellites Landsat and GF, as well as altimeter data from satellites ERS-2, ICEsat, Cryosat-2, and ICEsat-2. Through Mann-Kendall trend analysis, the study determined the stages of the lake area changes and revealed the key characteristics of various stages. Furthermore, it also made a preliminary judgement on the inundation trend and its impacts. The results indicate that from 1995 to 2023, Siling Co experienced an increase in water area of 676.75 km² (with an annual average of 24.17 km²/a), a water level rise of approximately 13.32 m (with an annual average of 0.48 m/a), and a water volume growth of 28.45 Gt (with an annual average of 1.02 Gt/a). The changes in Siling Co from 1995 to 2023 can be divided into four stages: the fluctuating growth stage from 1995 to 2000, the rapid expansion stage from 2000 to 2011, the relatively stable stage from 2011 to 2017, and the re-expansion stage from 2017 to 2023. The inundated areas during the fluctuating growth and rapid expansion stages were primarily concentrated in the northern and southern parts of the lake. During the relatively stable stage, no significant expansion was observed in the inundated areas. In the re-expansion stage, the inundated areas were distributed in the eastern part of the lake. The continuous rise in the water level of Siling Co led to an annually increasing risk of surrounding inundation. Currently, the areas exposed to a high inundation risk are primarily concentrated along the south bank of the lake, which should be the focus in future monitoring and research.

Ningbo, located in Zhejiang Province along the eastern coast of China, features diverse landforms and a complex geological environment. It is prone to geological hazards such as landslides, rockfalls, and debris flows, particularly during the flood season. Therefore, it is of great importance to conduct surface deformation monitoring in Ningbo for geological hazard prevention and control. This study integrated multiple remote sensing methods, including interferometric synthetic aperture radar (InSAR), high-resolution optical imagery, and unmanned aerial vehicle-based light detection and ranging (LiDAR). On this basis, landslide hazard monitoring was conducted in Ningbo using comprehensive remote sensing, obtaining the distribution of potential geological hazard sites, from which detailed locations and morphological information of typical high-risk slopes were extracted. Moreover, this study employed a combined-multi-temporal InSAR (CMT-InSAR) method, which integrated permanent and distributed scatterers to form a network. This method effectively increased the density of high-coherence points under vegetated hilly conditions, enhancing the coverage and accuracy of deformation monitoring. As indicated by the experimental results, Ningbo exhibited an overall stable land surface. However, local coastal areas showed significant surface deformation due to activities such as land reclamation, with a subsidence rate exceeding -20 mm/a. In mountainous areas, high-risk sites were primarily concentrated in the Fenghua District, Ninghai County, Yuyao City, and Xiangshan County, with some areas featuring annual average surface deformation rates ranging from -20 to -7 mm/a. The deformation inversion results aligned with field survey observations. This study proposes a high-precision, multi-level, and long-term approach for the early identification and monitoring of geological hazards in mountainous and hilly areas.

With the continuous advancement of urbanization, the local thermal environments and microclimates of cities have undergone varying degrees of change, leading to the surface urban heat island (SUHI) effect. Based on the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) data and local climate zones (LCZs), this study investigated the diurnal variations of the SUHI effect in Tianjin City, the contributions of various LCZs to the SUHI effect during daytime and nighttime, and the SUHI gradient attenuation differences. The results indicate that the central urban area of Tianjin exhibited significant diurnal variations in the SUHI effect, reaching a minimum value of 0.14 at 3:00 and a maximum value of 3.56 at 13:00, with an average diurnal-nocturnal difference of 1.59. On a daily scale, the contributions of various LCZs to the SUHI effect displayed notable intra-class and inter-class differences. Generally, LCZ1 (compact high-rise buildings) and LCZ2 (compact mid-rise buildings) showed thermal difference indices (TDIs) of 2.10 and 2.13, respectively, serving as the primary heat sources. In contrast, LCZA (dense trees) and LCZG (water bodies) yielded TDIs of 0.89 and 0.85, respectively, serving as the primary cold sources. Notably, the roles of LCZ7 (lightweight low-rise buildings), LCZA, and LCZG as cold/heat sources changed significantly during daytime and nighttime. A pronounced SUHI gradient effect was observed in the central urban area of Tianjin, with the SUHI intensity negatively correlated with the distance from the urban center, building height, and building density. The Moran’s I of the SUHI effect was 0.70 during daytime and 0.84 during nighttime, indicating that the SUHI effect exhibited stronger spatial aggregation and gradient effect during nighttime. Overall, by analyzing the diurnal dynamic changes of the SUHI effect and the contributions of various LCZs to the SUHI effect, this study reduces the errors associated with previous analyses that rely solely on fixed-time images. It provides a novel insight into understanding urban planning and sustainable development policies. Moreover, this study can be referenced for alleviating the SUHI effect and improving the livability and sustainable development of cities.