最新刊期
- “新疆光伏电站分布及植被影响研究取得进展，基于深度学习模型的光伏电站识别准确率达98.64%，为光伏选址和生态评估提供数据支持。”
摘要：The Xinjiang Uygur Autonomous Region, endowed with abundant land and solar energy resources, has emerged as a national leader in installed photovoltaic (PV) capacity, driven by the growing demand for renewable energy and advancements in PV technology. Accurate and real-time identification of PV station distribution, along with quantitative analysis of their effects on the spatial aggregation of surrounding vegetation, provides crucial data and informed decision-making support for PV siting in Xinjiang.This study utilizes deep learning semantic segmentation models that integrate three architectures—UNet, PSPNet (Pyramid Scene Parsing Network), and DeepLabV3+—with eight backbone networks (ResNet-34, ResNet-50, ResNet-101, ResNet-152, MobileNetV2, DarkNet53, VGG16, and DenseNet121). The objective is to determine the optimal model for photovoltaic (PV) station detection and to map the spatial distribution of PV stations across Xinjiang. To assess the impact of PV station construction on vegetation spatial aggregation, Global Moran's I values were calculated as a time series within buffer zones divided into equal intervals, ranging from 30 m to 600 m around the PV stations.The results reveal that (1) The UNet-ResNet50 model demonstrates superior performance in photovoltaic station recognition, achieving an accuracy of 98.64% (an improvement of 0.09 percentage points), an F1 score of 95% (an improvement of 0.4 percentage points), and an Intersection over Union (IoU) of 90.47% (an improvement of 0.57 percentage points). The exceptional recognition capabilities are primarily attributable to the high accuracy of the photovoltaic sample set and the model's outstanding feature extraction and depth balancing abilities. (2) Utilizing Sentinel-2 remote sensing images and the UNet-ResNet50 model, the 2020 photovoltaic stations in Xinjiang were extracted and classified into vegetation-covered and bare land photovoltaic stations, with area proportions of 30% and 70%, respectively. (3) Within different buffer zones ranging from 30m to 210m from the photovoltaic station, the Global Moran's I of vegetation shows a significant downward trend from 2012 to 2020. In the buffer zones 210m to 600m from the photovoltaic station, the downward trend of the Global Moran's I of vegetation slows down significantly. The closer to the photovoltaic station, the greater the impact on the spatial aggregation of vegetation, and the more evident the downward trend in the time series.关键词：photovoltaic station;semantic segmentation model;vegetation spatial aggregation;Global Moran's Index.2|0|0更新时间：2025-05-15 - “记者从新疆阜康煤田火区报道，我国专家利用地基短波红外成像光谱仪，提出了检测煤火甲烷逃逸的新方法，为煤火自燃早期识别与预警提供新思路。”
摘要：Coal-rich regions such as Xinjiang, Ningxia, and Inner Mongolia frequently experience spontaneous combustion of coal seams, releasing significant quantities of methane, a high-impact greenhouse gas. The unorganized and diffuse nature of these emissions poses significant challenges for detection and quantification, often contributing to the so-called ‘missing carbon’ sector in greenhouse gas inventories. Due to the limitations of satellite resolution and the inadequate adaptability of existing methane detection technologies in rugged terrains, this study focuses on coal fire areas characterized by unorganized emissions. Using wide-field ground-based hyperspectral imagery, we developed a novel detection methodology for methane emissions from individual coal fire sources in diverse terrains. The goal is to analyze methane escape patterns and assess latent risks of underground coal fires, offering a new framework for early-warning systems.In this work, we investigated methane escape in two representative mountain coal fire areas located in Fukang, Xinjiang, using ground-based hyperspectral wide-field imagery collected in June 2023. Seven distinct algorithms were applied: First, we proposed a Modified Least Squares Image Enhancement (MLSIE) algorithm by applying the L2-norm least squares regression principle to methane-sensitive spectral windows (1.66 μm and 2.3 μm) in the SWIR range. Second, we developed the Methane Ratio Derivative Spectral Unmixing (RDSU) algorithm, represented by RCH4I1 and RCH4I3, incorporating the spectral ratio derivative unmixing technique and the spectral sensitivity of methane to suppress background interference from pseudo-coal fire regions, while enhancing methane’s spectral signature. Third, by integrating factors such as cloud shadow detection, mineral content, combustion characteristics, and building-related features, we developed two methane ratio indices: 1DSRCH4I3, which enhances the contrast between methane and artifacts, and 2DSRCH4I3, which mitigates artifact interference and highlights methane-enriched areas.This work presents a novel methodology for detecting methane emissions from high-temperature, panoramic coal fire sources with diverse geomorphic characteristics. The findings provide valuable technical support for the identification and assessment of underground coal fire risks. Furthermore, this work introduces a new approach to early-warning systems for coal fire disasters, using methane escape as a diagnostic indicator of potential fire formation. However, we recognize that the influence of black carbon aerosols, which may interfere with mixed spectral signals, was not addressed. Future research could explore the dynamic quantification of methane plumes by integrating hyperspectral image spectral enhancement techniques to improve detection accuracy.Objective:Method:ResultOur evaluation demonstrated the following (1) The proposed MLSIE, RDSU, and DSRCH4I algorithms significantly improved methane detection accuracy compared to the existing CH4I algorithm; (2) The 2DSRCH4I3, MLSIE(2.3μm) and RCH4I1 algorithms exhibited superior performance in complex terrains, while 2DSRCH4I and MLSIE(2.3μm) algorithms were also effective in relatively simple mountainous coal fire areas; (3) MLSIE(2.3μm) displayed robust generalization ability, 2DSRCH4I3 effectively minimized artifacts and false positives, leading to superior detection accuracy, and RCH4I1 demonstrated clear detection efficacy in coal fire areas with significant methane leakage; (4) Methane plumes were detected in two distinct forms: one coexisting with combustion flames and the other freely escaping into the surrounding atmosphere.Conclusion关键词：coal fire;methane;Hyperspectral imaging;SWIR;Unorganized emissions;Plume detection;Artifact suppression;Greenhouse gas;climate change4|0|0更新时间：2025-05-15 - “在遥感图像匹配领域，研究者提出了一种基于深度特征重构增强的光学和SAR图像鲁棒匹配方法，为解决复杂地物场景下的匹配问题提供解决方案。”
摘要：The automatic precise registration between optical and Synthetic Aperture Radar (SAR) imagery remains a significant challenge in remote sensing due to fundamental differences in their imaging mechanisms. The inherent modality gap manifests as substantial radiometric discrepancies (speckle noise vs. photometric consistency) and geometric distortions (side-looking geometry vs. nadir projection), posing critical obstacles for conventional feature matching approaches. While existing deep learning-based methods have made progress in extracting deep features, most architectures inadequately address two crucial aspects: multi-scale feature fusion across different imaging characteristics and cross-modal invariant feature representation, leading to compromised robustness in complex geographical scenarios. To address this, we propose a robust matching method based on deep feature reconstruction for optical and SAR images. Our method features a pseudo-Siamese network that integrates multi-scale deep features and image reconstruction. First, a multi-scale feature extraction architecture efficiently obtains multi-scale deep features at the pixel level. This architecture allows the network to capture detailed information from various scales, which is crucial for understanding the complex patterns present in remote sensing images. Second, a pseudo-SAR translation branch for optical images is designed to reconstruct images from deep features, enhancing the network's ability to learn robust common features. This branch mimics the characteristics of SAR images, enabling the network to find shared features between the two image types more effectively. Through this translation process, the network learns to focus on the essential elements that are common to both optical and SAR images, thereby improving the matching accuracy. Finally, a joint loss function based on multi-layer feature matching similarity and reconstructed image average error is constructed for robust matching. This loss function ensures that the network not only matches features accurately across different layers but also maintains a high degree of fidelity in reconstructing the original images. By combining these two aspects, the network can achieve a balance between feature similarity and image reconstruction quality, leading to more reliable matching results. Experiments on two remote sensing image datasets with different resolutions and diverse terrain scenes (urban, suburban, desert, mountain, water) show that our method outperforms several state-of-the-art matching methods in correct matching rate. The proposed method demonstrates superior performance in various environments, indicating its versatility and effectiveness in real-world applications. The ability to handle different resolutions and terrain types is particularly important for practical remote sensing tasks, where conditions can vary widely.This research advances cross-modal image analysis by providing: 1) A new paradigm combining feature learning with cross-domain translation 2) Practical solutions for SAR-optical registration in challenging environments.关键词：optical image;SAR image;image matching;deep learning4|0|0更新时间：2025-05-15 - “激光雷达技术在生态与地学领域取得突破，推动了点云配准、分类等算法发展，为相关研究提供新方向。”
摘要：LiDAR (Light Detection and Ranging) is one of the most innovative technologies in the field of remote sensing, capable of accurately reconstructing the three-dimensional (3D) structures of the objects being measured. Over the past few decades, LiDAR technology has advanced rapidly, significantly promoting research in the field of ecology and geosciences. This paper systematically reviews and explores the potential future developments in LiDAR hardware and algorithms, as well as their applications in ecology and geosciences.We first pointed out that, driven largely by the rapid advancements of autonomous driving technology, LiDAR hardware has demonstrated a trend towards diversification and enhanced precision. Types of near-ground LiDAR platforms have been particularly enriched, enabling efficient and high-resolution data acquisition at unprecedented spatial and temporal scales. Meanwhile, due to the progress of artificial intelligence technologies such as deep learning, Simultaneous Localization and Mapping (SLAM), and Large Language Model, LiDAR algorithms have also achieved significant development, leading to continuous innovations in point cloud registration, segmentation, classification, and the fusion of point clouds with multi-source data. Regarding LiDAR’s applications in ecology and geosciences, we detailed the applications of LiDAR in 11 research topics of ecology and geosciences: inland topographic mapping, ocean mapping, geological hazard monitoring, forest structure measurement, tree branching networks modeling, 3D radiative transfer and scene reconstruction, forest microclimate simulation, intelligent agriculture, biodiversity monitoring, urban and architectural studies, and planetary survey. Our comprehensive review underscores LiDAR’s versatility and its critical role in advancing both theoretical and applied ecological and geoscience research.Looking ahead, with the continuous advancement of hardware, algorithms, and LiDAR big data, LiDAR will continue to revolutionize research in ecology and geosciences and is poised to play a pivotal role in an even broader range of fields. For instance, combining LiDAR-derived 3D structural information with radiative transfer modeling, computational fluid dynamics, and plant physiology offers the potential to simulate essential biological processes such as photosynthesis, transpiration, and respiration. The advancement of multispectral and hyperspectral LiDAR systems is expected to tackle the challenge in species identification and vegetation trait quantification, opening possible new frontiers in biodiversity and functional ecology. At a broader scale, LiDAR is expected to support the implementation of “Realistic 3D China,” a comprehensive digital twin of the nation’s surface environment. In addition, LiDAR will be increasingly applied to underground remote sensing, power infrastructure inspection, and Earth system monitoring. In terms of big data, the establishment of LiDARNET (https://lidar.pku.edu.cn/[2025-04-14]), a national open-access platform for near-surface LiDAR data, represents a key milestone in enabling data standardization and large-scale collaboration. With the establishment of LiDARNET and other LiDAR data sharing platforms, the availability of high-resolution LiDAR datasets will be continuously augmented, providing critical foundations for the development of next-generation global vegetation dynamic models and enabling more accurate forecasting and management of ecosystem processes at multiple scales.In short, LiDAR is emerging as a pivotal technology in shaping the future of Earth observation, plant ecology, animal ecology, urban ecology, and a range disciplines of geosciences. With the ongoing advancements in hardware design, algorithm development, and high-resolution LiDAR big data, LiDAR is committed to drive transformative breakthroughs across more research fields.关键词：lidar;UAV;SLAM;deep learning;Large Language Model;forest;ocean;Planetary Survey29|16|0更新时间：2025-05-13 - “遥感科学与技术学科发展迅速，中国三代学者努力申建遥感一级学科，2022年获批一级交叉学科。”
摘要：Referring to the four-stages development of international remote sensing since 1960, which are defined as early explorations, application experiments, broad application, and comprehensive prosperity, we retrospected the chief events and important achievements in the history of remote sensing developing in China since 1970, which is coined as three periods as start learning, follow-up advancing, and making large-and-strong, respectively. The main gains from the past development of remote sensing discipline and the current situation of remote sensing talent cultivation in China are analyzed in this paper, based on the international developments in remote sensing science and technology. The continuous struggling of three generations of remote sensing scientists applying to the Academic Degrees Committee of the State Council for the construction of the first-level Remote Sensing discipline was summarized in brief, and the change of discipline connotation of Remote Sensing Science and Technology as well as its sub-disciplines changing was also elaborated. The Remote Sensing Science and Technology is finally approved by the Academic Degrees Committee of the State Council in 2022 as an interdisciplinary coded as 1404. According to the work of the Geomatics and Remote Sensing Disciplines Appraisal Group of the Academic Degrees Committee of the State Council, the connotation of Remote Sensing Science and Technology is described as detecting target properties, environmental parameters and their variation laws with non-contact manners by using of electromagnetics and other physical field or wave; its sub-disciplines include Remote Sensing Science (140401), Remote Detecting Technology (140402), Remote Sensing Infor-Engineering (140403), and Remote Sensing Application Technology (140404). We introduced in this paper the present situation for the interdisciplinary of Remote Sensing Science and Technology construction with Ph.D degree, including 7 self-set and 5 verify-approved, such as the main supporting discipline, the participating disciplines and the selected sub-disciplines. The construction institutions, supporting discipline and participating disciplines of 15 verification-approved and 2 self-set interdisciplinary Remote Sensing Science and Technology with master degree are also introduced. By sorting the frontier dynamics and developing directions of international remote sensing, the challenging issues on China remote sensing development in the future are presented, especially the knowledge structure building, curriculum system setting and cultivation mode innovation for high-level talents specialized in remote sensing science and technology. Furthermore, two suggestions are presented for reference, based on the large knowledge structure and verified conditions of the interdisciplinary construction institutions, in selecting and defining the sub-disciplines to be constructed and in designing the cultivation plan and curriculum system.关键词：remote sensing;development;interdisciplinary;Remote Sensing Science and Technology;discipline construction;talent cultivation1345|91|0更新时间：2025-05-13 - “农业遥感技术助力粮食体系数字转型，推动农业生产与生态服务功能协同优化，为可持续发展提供示范。”
摘要：Against the dual pressures of global population growth and evolving socio- natural environmental dynamics, agricultural systems face unprecedented challenges in ensuring food security, maintaining socio-economic stability, preserving ecological integrity, and achieving sustainable development. How agricultural remote sensing can drive the digital transformation of agri-food systems and to realize the “Four Betters” goals (Better Production, Better Nutrition, Better Environment, and Better Life) is a pressing issue that requires systematic planning. This review systematically examines the developments, current gaps, and future pathways of agricultural remote sensing from three perspectives: remote sensing-based agricultural monitoring, development and sharing of agricultural data products, and practical implementation of agricultural remote sensing technologies. Significant advancements in agricultural remote sensing have been achieved in innovative methodological development (e.g., automated self-learning algorithms) and multi-source data fusion for in-depth information extraction in agricultural production. However, critical gaps persist in three domains. First, the maturation and standardization of agricultural remote sensing techniques, For example, current deep learning algorithms demonstrate excellent performance in controlled experiments but face significant generalization challenges in real-world applications due to sample/lable data constraints and transferability to target spatial and temporal domain. In addition, the production and dissemination of high-quality, standardized large-scale and long-term agricultural data products remain critical challenges that require urgent attention. Currently, only major crops have regional or global scale remote sensing-derived data products, while crucial agricultural management parameters (e.g., irrigation, fertilization) lack standardized monitoring frameworks. The existing data products suffer from the temporal discontinuity, spatial inconsistency and the validation deficiencies (the point-based verification, not the field-boundary based precision). While crop identification, growth monitoring, and yield prediction dominate are the research focus, there are no adequate attention paid for monitoring the complex cropping systems (intercropping, relay cropping), non-grain conversion and field management practice and so on. What’s more, there is an even greater lack of development of standardized data products for crop growth monitoring and yield estimation. Besides, the transformation of agricultural remote sensing outputs into actionable decision-support systems remains a critical bottleneck in realizing the full potential of precision agriculture due to the data-to-decision latency, inadequate integration with farm management systems and the limited contextualization for diverse end-users. Moving forward, strategic priorities should focus on the following aspects. The integration of artificial intelligence, big data analytics, and domain-specific foundation models (e.g., Agri-GPT) represents a transformative leap for agricultural remote sensing, significantly enhancing model robustness, generalizability, and operational replicability. This technological integration is being institutionalized through comprehensive data platforms featuring standardized sharing protocols and nationally unified service frameworks, characterized by the development of agricultural-specific transformer models trained on multi-year, multi-sensor satellite data and high-resolution UAV data for mapping diverse crop varieties. These platforms incorporate open-source model hubs with pre-trained and domain adaptation tools for regional customization, enabling seamless deployment across different agricultural systems. These advancements collectively establish an intelligent agricultural monitoring infrastructure that supports both precision farming and national food security strategies, projected to improve annual income through improved input efficiency and reduced monitoring costs, and optimize synergies between agricultural productivity and ecosystem services and serve as a replicable model for cross-disciplinary remote sensing applications.关键词：Agriculture & food systems;agricultural remote sensing;development and standardization of data product;artificial intelligence;large models27|14|0更新时间：2025-05-13 - “据最新报道，中国科学家提出了下一代碳监测卫星TanSat-2的建设方案，建立了多尺度温室气体排放清单卫星校核技术体系，为构建高时空分辨率碳排放监测卫星体系提供技术保障。”
摘要：Global climate change has become a significant challenge facing human society, with greenhouse gas (GHG) emissions being one of its primary driving factors. To effectively assess global progress in greenhouse gas reduction, the Paris Agreement established the Global Stocktake (GST) mechanism, which requires independent verification of national GHG emission inventories. However, existing “bottom-up” emission inventories face significant uncertainties in compilation methods, data completeness, and verifiability, making it difficult to meet the need for high-precision, standardized carbon emission data for the GST. Therefore, it is urgent need to develop “top-down” satellite monitoring methods to enhance the transparency and scientific rigor of carbon emission inventory verification.This study focused on the requirement analysis and design for China’s next-generation carbon monitoring satellite (TanSat-2). Based on Observing System Simulation Experiments (OSSE), an evaluation framework for satellite program assessment was constructed, and a carbon satellite design proposal aimed at GST needs is presented. First, a satellite verification technology system for GHG emission inventories at multiple scales (“global-regional-hotspot”) was established, defining key technological requirements for inventory verification at different scales and proposing uncertainty constraints for the next-generation carbon satellite inventory verification. Second, a multi-component synergistic observation and anthropogenic emission source separation technology system was developed by integrating “greenhouse gases-pollutant gases-vegetation fluorescence”. The system was applied to evaluate the ability of satellite carbon monitoring to distinguish between ecosystem carbon cycles and anthropogenic carbon emissions, leading to the development of a scientific product indicator system. Additionally, we assessed the impact of various payload technical parameters (spectral resolution, signal-to-noise ratio, wavelength range, etc.) on the retrieval errors of each observation element. The technical indicators for GHG, pollutant gas, aerosol, and Solar-Induced Chlorophyll Fluorescence (SIF) detection payloads was qualified, aligned with the engineering capabilities and constraints of satellite platforms and payloads.Based on this, a design for the TanSat-2 platform was completed, incorporating multi-component, high-temporal, multi-scale, and high-precision capabilities. A mid-orbit elliptical frozen sun-synchronous orbit satellite scheme was proposed. The TanSat-2 was designed to equipped with three advanced effective payloads: (1) the Ultra-wide-field Carbon Pollution collaborative monitoring Instrument (UCPI), (2) the Hotspot Greenhouse gas Emission Tracker (HGET), (3) the Cloud Aerosol Polarization Imager (CAPI). With a coverage capacity of up to a thousand kilometers, UCPI was designed for monitoring carbon emissions at the national and regional scales. HGET was specially designed for detailed monitoring of major emission sources, capable of high spatial resolution for hotspot area investigation. And CAPI was designed to optimize GHG inversion accuracy and reduce the uncertainty of aerosol scattering in the remote sensing inversion of CO2, CH4, and other gases.关键词：TanSat-2;Global Stocktake;carbon emission;satellite retrieval;assimilation22|13|0更新时间：2025-05-13 - “气象卫星观测系统对天气预报等领域至关重要，专家提出业务连续性风险评估方法，为气象卫星发展提供解决方案。”
摘要：The meteorological satellite observation system integrates meteorological satellites in orbit to achieve global weather and atmosphere observation, which is crucial for application such as weather forecasting, climate change, meteorological disaster prevention and reduction etc. The continuous and stable operation of meteorological satellite observation system is particularly important for the effective application of meteorological satellite data, and its operation continuity risks need to be identified, evaluated, and effectively managed in a timely manner.This article proposes a risk assessment method and process for the operation continuity of meteorological satellite observation system based on a risk assessment model, and evaluates the operation continuity risk of the global meteorological satellite observation system. Firstly, based on the basic requirements of WMO for space-based observation systems, clarify the baseline of the global meteorological satellite observation system. Then, a risk assessment model has been established for the continuous operation of a remote sensing instrument based on its satellite launch time, design lifespan, and subsequent plans, as well as the orbit in which the satellite operates. The risk assessment model is a two-dimensional matrix based on the time dimension and the type dimension of space-borne remote sensing instruments. The process of conducting in orbit operation continuity risk assessment is to first clarify the in-orbit status of the satellite to be evaluated and its configured instruments, as well as the determined future plans. Then, the risk assessment model is introduced according to the instrument type. Based on the baseline of continuity operation, a risk assessment is conducted to identify the risks of continuous operation.Some operational continuity risks of the global meteorological satellite observation system have been identified, including the ultraviolet/visible/infrared detection instrument on geostationary satellites, global navigation satellite system occultation detectors on low-orbiting satellites, precipitation measurement radar and microwave imager on inclined-orbiting satellites, as well as the solar wind plasma, particle, and magnetic field observation instruments on L1 point satellites, all of which pose long-term operational continuity risks.The risk assessment results indicate that a global meteorological satellite observation system has been established currently, which basically meets the baseline of the global meteorological satellite observation system. However, there are still some operational continuity risks. The formulation of future plans for meteorological satellites should fully consider taking corresponding measures to reduce the risk of continuous operation of such instruments, in order to make the meteorological satellite observation system completer and more robust.Finally, suggestions are put forward for the continuous development of China’s future meteorological satellite program, including establishing a baseline for China’s meteorological satellite observation system, filling the gap in core meteorological element observation capabilities, and constructing a comprehensive observation system for the coordinated development of China’s comprehensive meteorological satellites and commercial small satellites.关键词：meteorological satellite;spaced-observation system;spaced-observation system baseline;operation continuity;risk assessment method8|4|0更新时间：2025-05-13 - “最新研究综述了植物多样性遥感监测技术进展，探讨了不同生态系统适用性，为大范围植物多样性监测提供新方向。”
摘要：Biodiversity is the foundation for providing ecosystem services and maintaining ecosystem functions, with plants playing a key role of biodiversity on Earth. Traditional plant diversity surveys provide localized point-scale snapshots of diversity, but are limited in capturing dynamic changes in plant diversity over large areas. With the development of new-generation platforms and high-resolution sensors, remote sensing technologies have significantly expanded the capacity to quantitatively monitor plant biodiversity across broader spatiotemporal scales. This review focuses on the three core dimensions of biodiversity—species, functional, and genetic diversity—exploring recent advancements in remote sensing technologies for plant diversity monitoring. Specifically, it addresses developments in plant species diversity hypotheses and algorithms, functional traits and functional diversity modeling, and the sensitivity of spectral characteristics to phylogenetic diversity. We further discuss the applicability of remote sensing methods for monitoring plant diversity across different ecosystems, including forest, grassland, wetland, and cropland. Finally, we summarize the current trends and future directions of remote sensing technology in plant biodiversity monitoring. Rapid and accurate monitoring of large-scale plant diversity is identified as a key challenge in plant diversity research. The future of this field lies in integrating multi-source remote sensing technologies across field, aerial, and space scales, developing Remotely-Sensed Essential Biodiversity Variables and constructing plant diversity upscaling models applicable to various ecosystems. Strengthening communication and collaboration between remote sensing experts and biodiversity researchers will further facilitate the integration of remote sensing technology with biodiversity issues, advancing scientific research in biodiversity monitoring and conservation across space and time.关键词：plant biodiversity;species diversity;functional diversity;phylogenetic diversity;remote sensing;forest;grassland;wetland;cropland7|7|0更新时间：2025-05-13 - “中国风云气象卫星技术取得突破，为全球气象观测和防灾减灾提供重要支撑。”
摘要：Presently, China has successfully launched a total of 21 FengYun meteorological satellites of two generations and four types, thereby becoming the only country in the world to operate four civil meteorological satellites in near-Earth orbit concurrently in the morning, afternoon, early morning, and inclined orbits. Furthermore, China has achieved the mode of having multiple satellites in orbit in geostationary orbit, operating in a coordinated manner, backing up each other, and enhanced observations as necessary. The impact of the FengYun satellites has been far-reaching, extending to various fields such as meteorological disaster prevention and mitigation, climate change response, ecological civilization construction, and governmental decision-making services. Notably, they have played a pivotal role in supporting the construction of the ‘One Belt, One Road’ initiative and the establishment of a community of shared destiny. This paper focuses on the scientific problems and development trends that need to be solved from observations to quantitative applications of FengYun satellite, and describes some innovations of key technologies for quantitative applications of FengYun satellite in four aspects, namely, navigation and positioning, high-precision calibration, geophysical parameter retrieval, and spectralized applications.Navigation and positioning technology forms the basis for the quantitative application of Fengyun meteorological satellite images, thereby resolving the issue of accurately identifying the observation target. The geometric positioning model is constructed using parameters such as satellite position and attitude, and the geometric model is corrected by using landmarks and sea-land boundary alignment to obtain the geographic coordinates of any pixel in the image in the geoidal coordinate system. This enables the high-precision positioning of the target of satellite observation. The analysis identified satellite orbit and attitude measurement errors, instrument installation errors, time errors, and scanning angle measurement errors as the primary factors contributing to deviations in image navigation and positioning.High-precision calibration of satellite remote sensing instruments is defined as the process of verifying and adjusting the observation data of remote sensing instruments to ensure optimal accuracy and stability. In the narrow sense, calibration entails the establishment of an accurate quantitative relationship between the input radiant energy and the instrument response voltage during the observation process. This enables the precise measurement of the radiant energy of the observation target through the variation of the instrument response during satellite observation to Earth. The calibration process encompasses various methods, including laboratory calibration, on-board calibration, active external calibration, and alternative calibration.Geophysical parameter retrieval is the study of the theory and method of using electromagnetic wave signals of surface features, clouds and atmosphere, acquired by remote sensing sensors, to convert them into geophysical parameters that are easy to understand and apply. In essence, it is an inverse solving process of the observed signals, i.e. starting from remote sensing observation, with the help of mathematical or physical models, linking the remote sensing information with the physical state parameters, and quantitatively extracting or extrapolating the parameter process of the physical state and movement of the ground surface, clouds, and the atmosphere. At present, the inversion methods mainly include physical inversion based on radiation transmission mechanism and data-driven statistical inversion based on two types of methods.Spectralized applications technology have been researched and applied to meet the needs of different users by development of SMART (Satellite Monitoring, Analysis and Remote sensing Tools), SWAP (Satellite Weather Application Platform), and ‘Fengyun Earth’ lightweight application platform to realise the ‘last kilometre’ of business application of Fengyun satellite data.关键词：FengYun satellites;observation;positioning;calibration;Retrieval;quantitative applications;ground segment40|100|0更新时间：2025-05-12 -
Satellite payloads for middle and upper atmospheric profile detection： An overview and perspective AI导读
“据最新报道，中高层大气廓线探测卫星技术取得重要进展，中国“天路一号”卫星搭载先进载荷，为气象预报和气候研究提供数据支持。”
摘要：The middle and upper atmosphere, as a crucial component of the Earth’s atmosphere, is influenced by both the lower atmosphere and the space environment. It exhibits significant multi-scale dynamic processes, chemical processes, and radiation transfer processes. Ground-based instrument, such as Lidar, optical interferometer, etc., can provide accurate middle and upper atmosphere profiles, but their observations are limited to specific locations. In contrast, satellite remote sensing offers global middle and upper atmospheric profile detection. To date, numerous satellite payloads have been launched to study the middle and upper atmosphere across multiple wavelengths and detection modes. This extensive satellite data has greatly enhanced our understanding of the physics and chemistry of the middle and upper atmosphere.This article provides an overview of the satellites dedicated to middle and upper atmospheric profile detection, and comprehensively summarizes the technical parameters of their payloads. Based on the observation mode, these payloads can be categorized into nadir-viewing observation, occultation observation and limb-viewing observation. Nadir-viewing observation targets the Earth’s surface directly, offering high spatial and temporal resolution, but it struggles to obtain accurate atmospheric profiles or delivers poor accuracy. Occultation observation studies atmospheric characteristics by monitoring changes in signals from celestial objects (such as the sun or satellites) as they pass through the Earth’s atmosphere. This method achieves high vertical resolution and precision in profiles but is limited in frequency of occultation event. Limb-viewing observation combines the advantages of both above methods by observing the atmosphere diagonally through the halo that surrounds the Earth. This approach not only provides atmospheric profile parameters with high vertical resolution but also offers better sampling rates and spatio-temporal resolution.This article reviews satellite payloads with nadir-viewing, occultation observation and limb-viewing observation for middle and upper atmosphere. By leveraging the benefits of limb-viewing observations, China has launched DAO-1, a pathfinder limb satellite dedicated to middle and upper atmosphere observation. The satellite is equipped with three advanced optical payloads: the Temperature and Ozone Profile monitoring Spectrometer, the Heterodyne atmospheric Density profile Imager, and the Doppler heterodyne Wind Imager. These instruments combine grating spectroscopy, Spatial Heterodyne Spectrometer technology, and Doppler Asymmetric Spatial Heterodyne techniques to enhance the accuracy and efficiency of temperature, composition, density, and wind fields profiles. This paper further explores the future development of satellite payloads for middle and upper atmospheric profile detection, providing insights for the planning of China’s subsequent satellite missions. The observation data will support the development of numerical forecasting models for the middle and upper atmosphere, and contribute to various applications, including weather forecasting and climate research.关键词：atmospheric detection;middle and upper atmosphere;satellite remote sensing;atmospheric profile;atmospheric temperature;atmospheric composition;atmospheric wind field13|8|0更新时间：2025-05-12 - “在“全球变化及应对”重点专项中，我国启动了“山地生态系统全球变化关键参数立体观测与高分辨率产品研制”项目，旨在提升山地生态系统监测能力，为全球变化和可持续发展研究提供科学数据和技术支撑。项目预期将研制出世界首套覆盖全球山地7种关键参数、25年、逐月、30米高分辨率数据集产品。”
摘要：Mountains serve as sentinels of global change, exhibiting an accelerated warming trend with increasing elevation. Global change directly impacts critical mountain surface processes including hydrological cycles, ecological dynamics, and biogeochemical fluxes, thereby threatening future water resource availability and ecosystem services in mountainous regions. While remote sensing Earth observation provides essential data sources for studying mountain ecosystem dynamics, most existing global change remote sensing datasets exhibit kilometer-level spatial resolutions that inadequately characterize the intense spatiotemporal heterogeneity of mountain surfaces. There is an urgent need for high spatiotemporal resolution, spatially seamless, and accuracy-reliable remote sensing products to enhance scientific understanding of mountain ecosystems’ responses and adaptations to global change. In recent years, the mountain remote sensing modeling and parameter retrieving algorithms have witnessed significant advances, where comprehensive field observation platforms for typical mountainous regions have been established. These developments have laid a solid foundation for conducting stereoscopic monitoring of global change-critical parameters in mountain ecosystems and subsequent high-resolution product development. Addressing the requirements for stereoscopic observation and high-resolution monitoring products in mountain ecosystems, the Ministry of Science and Technology of China funded the project named “the stereoscopic observation and high spatial resolution products development of key global change parameters for mountain ecosystem” (2020—2025) in the key programme of “Global Change and Response” under the National Key Research and Development Plan during the 13th Five-Year Plan period. This paper outlines the project’s background, key scientific challenges, research objectives, implementation framework, and expected outcomes. It systematically reviews progress achieved in establishing near-surface stereoscopic observation systems, spatiotemporal scaling expansion methods, remote sensing retrieval models, and product development for global change parameters in mountain ecosystems. This project will establish ground-aerial-satellite stereoscopic observation technology by combining ground-based, aerial, and satellite platforms across different topographic gradients and ecosystem types through terrain-specific experimental designs, and will develop the spatiotemporal scaling expansion models for complex terrains to create high-resolution reference “truth” products for Wanglang mountainous regions. The project will also develop terrain-adapted remote sensing retrieval models, and produce the world’s first set of global mountain remote sensing products featuring seven key parameters at 30m spatial resolution with monthly temporal resolution. The project is expected to propose a set of applicable technology for three-dimensional comprehensive observation of key parameters of global changes in mountain ecosystems, produce 14 kinds of key parameters and high-resolution reference “truth” products in typical mountain areas of Wanglang, and form a high-resolution product production capacity of 11 kinds of key parameters. The world’s first set of global mountain remote sensing products with 7 key parameters, 25 years (2000—2024), monthly, 30 meters high-resolution will be produced. The project is expected to promote China’s comprehensive monitoring capability of the global mountain ecosystem, and provide scientific data and technical support for research on global mountain change and the sustainable development of mountain society, especially mountain ecological environment protection, disaster prevention and reduction, agricultural production and water resources management.关键词：mountain;global change;stereo observation;remote sensing products;high resolution25|16|0更新时间：2025-05-12 - “最新研究进展显示，数据驱动方法在地表动态变化过程建模领域取得突破，为遥感图像时间序列预测提供新思路。”
摘要：Land surface dynamic is one of the key research objects in the field of remote sensing, and the systematic mastery of the law of land surface evolution over time is a long-term and arduous task in the research of earth observation, which has a far-reaching impact in many fields such as natural resources, ecological environment, disaster prevention and mitigation, etc. However, due to the complexity of the driving factors and their effecting mechanisms of land surface changes, most of the modeling of land surface dynamics is simplified or localized, and it is difficult to form a complete physical model with its corresponding mathematical formula expression.From a methodological point of view, along with the rapid development of big data and AI for Science techniques in recent years, a class of data-driven modeling methods for dynamic systems have emerged in addition to physical modeling. Directly using the observed data series as input, data-driven methods can construct models that fit the data well enough to approximate or even replace underlying physical models. In this paper, we introduce three types of data-driven approaches for modeling land surface dynamics, namely, the temporal-spatial mode decomposition, the governing partial differential equation identification, and the state variable discovery network.Temporal-spatial mode decomposition method extracts base modes in the data that represent the basic characteristics of the system state, which helps us to understand the intrinsic properties of the system and further predict the future state of the system in combination with parametric prediction methods. This method is able to separate the temporal and spatial variations of the flow field, decompose the time-varying flow field into the sum of the products of the spatial function that does not vary with time and the temporal function that only relies on the time variations, thus objectively and quantitatively reflecting the flow field's spatial structure and temporal variations of the flow field.Governing partial differential equation identification method assumes that the dynamical system expressed by the differential equations is variably complex and nonlinear and needs to be characterized by nonlinear functions. Thus, it constructs a library of functions covering a wide range of potential nonlinear terms, where the functions and partial derivatives in the library are likely to appear in the unknown governing equation, and the equation is assumed to be a linear regression of these candidate functions. This method does not rely on a priori knowledge but learns the dynamics of the system directly from the observed data.State variable discovery network method discovers the dynamics of physical systems directly from sequential data through a network of self-encoders, which centers on the discovery of hidden state variables from high-dimensional observational data, which can then be used to make predictions about the future state of the system. The state variable of a system refers to the smallest set of variables that are sufficient to completely determine the state of motion of the system.The three types of methods are used to construct dynamic system models from remote sensing time series and the accuracy of modeling is evaluated by image time series prediction. The experimental results preliminarily validate the effectiveness of the data-driven modeling methods and show the characteristics, research value and application prospects of each of the three types of methods.关键词：land surface dynamics;time series;process modeling;mode decomposition;partial differential equation;autoencoder;data driven;sparse regression12|11|0更新时间：2025-05-12 - “在人工智能和大数据时代，时空智能学STI融合时空数据与智能计算，开辟多领域应用，提升决策效率和资源管理水平。”
摘要：The world is material, matter is in motion, and the characteristics of time and space dimensions together reveal the essence of a dynamic material world. With the rapid advancement of artificial intelligence and data acquisition technologies, the intelligent modeling and analysis of large-scale spatiotemporal data have become feasible. This progress drives deeper technological breakthroughs and scientific innovations, giving rise to a new interdisciplinary field: Spatio-Temporal Intelligence (STI). As a multidisciplinary domain, STI integrates spatiotemporal data with intelligent computational methods, opening new avenues for applications across aerospace, terrestrial, maritime, deep space, socio-economic systems, and smart healthcare. It promotes end-to-end intelligence from data collection and analysis to decision-making, thereby enhancing decision-making efficiency and resource management in critical areas. This paper first reviews the historical evolution of geoscience research, tracing its progression from geodesy, which centers on mapping and cartography, to geographic information science, emphasizing spatial information services, and finally to the emerging field of spatio-temporal intelligence. This journey highlights humanity’s continuous advancements in mapping, measuring, and understanding the physical world. With the widespread adoption of artificial intelligence, we are transitioning from static spatial data analysis to intelligent processing and real-time decision-making with dynamic spatiotemporal data. This shift reflects profound societal changes: from the industrial era focused on mechanical and logistical efficiency, to the information age characterized by digitization and connectivity, and now to the intelligence era, defined by data-driven insights and autonomous decision-making. Against this backdrop, this paper systematically explores the core concepts, research objectives, and scope of spatio-temporal intelligence. It analyzes its interdisciplinary integration with related fields, illustrates typical application scenarios, and evaluates its potential value and significance from both scientific and practical perspectives. In today’s complex and dynamic world, STI not only provides new pathways for precise knowledge discovery but also demonstrates immense potential in bridging the gap between scientific understanding and practical implementation.关键词：STI;AI;photogrammetry and remote sensing;interdisciplinary field15|11|0更新时间：2025-05-12 - “中国冰冻圈遥感研究取得新进展，专家提出三项重点行动倡议，为气候应对与适应等提供支持。”
摘要：This paper summarizes the major development trends in cryosphere remote sensing research in China in recent years. With advancements in domestic remote sensing technology and the deepening of international cooperation, the breadth and depth of China’s cryosphere remote sensing research have continuously expanded. In terms of study areas, the focus has gradually extended from China (particularly the Qinghai-Tibet Plateau) to the Arctic, Antarctic regions and the global. Regarding remote sensing data applications, reliance on foreign satellite data has evolved into the joint use of both domestic and foreign remote sensing data (e.g., Gaofen, Fengyun satellites). In algorithm development, traditional approaches that relied on single data sources and had low automation levels have gradually been replaced by new algorithm featuring multi-source data fusion and intelligent processing. Various cryosphere remote sensing products have emerged, making significant contributions to monitoring and understanding global cryosphere changes. This paper also explores frontier issues and potential breakthroughs in cryosphere remote sensing, including remote sensing penetration capabilities, the development of intelligent algorithms, the detection of critical transition, and the advancement of cryosphere data products. To further advance cryosphere remote sensing science, this paper proposes three key action initiatives: (1) conducting a comprehensive tomographic remote sensing experiment for key cryosphere elements, (2) developing internationally influential, China branded data products, and (3) coupling remote sensing, physical model, and artificial intelligence to enhance predictive capabilities. These efforts aim to support national priorities in climate adaptation, disaster prevention, ecological protection, water resource management, and sustainable development.关键词：penetrative remote sensing;snow and permafrost;glacier and ice sheet;river;lake;and sea ice;atmospheric cryosphere;artificial intelligence;data products22|8|0更新时间：2025-05-12 - “全球海洋水色遥感器技术发展50年回顾，分析未来观测需求与发展方向，为中国海洋水色卫星技术发展提供设想。”
摘要：Based on the global demand for ocean color climate change, this paper reviews the development process of ocean color remote sensors in the past 50 years, sorts out the deployment and scientific objectives of ocean color remote sensors in earth observation missions in various countries, expounds the advantages and disadvantages of the current imaging systems of each remote sensor, and analyzes the historical evolution and technical constraints of remote sensor channel selection. According to the spatiotemporal coverage requirements of global observation, the design objectives of the current payload resolution and width are clarified, and the technical means of on-orbit calibration of ocean color remote sensors are listed and the importance of each means is analyzed. According to the list of remote sensors that have been launched and planned to be launched by countries in the past and next decade, the focus of countries’ attention to global changes and coastal disasters is given. Combined with the development history of China’s ocean color observation satellites, this paper clarifies the direction of technological breakthroughs in various countries to cope with ocean color climate change, and gives suggestions on the technical route of China’s ocean color remote sensing and the idea of China’s breakthrough in the field of innovation..关键词：ocean color remote sensing;on-orbit calibration;marine observation satellite;Channel selection;imaging system;load configuration19|7|0更新时间：2025-05-12 - “在林草遥感领域，专家总结了5年技术发展，为国家林草遥感规划提供指导，推动业务化应用。”
摘要：The past five years are the five years when big model and general model of artificial intelligence (AI) are gradually integrated into people’s daily work and life, and the five years when remote sensing + AI technology develops rapidly in the fields of land cover type identification, change detection, etc. It is also the first five-year for the implementation of the national strategy of “ecological civilization” and “beautiful China”. Summarizing the progress made in the research, development and application of forestry and grassland remote sensing technology in these five years is of great significance for the country to formulate the development plan of forestry and grassland remote sensing in the future.The paper summarizes the main progress of the forestry and grassland remote sensing research and development in China in the past five years into four research directions, namely, change detection and classification of forest and grassland cover types, quantitative inversion/estimation of forest parameters by remote sensing, and that of other vegetation parameters such as grasslands, shrubs, and early warning and monitoring of forest and grassland disasters. From a general point of view, the research on forestry and grassland remote sensing technology shows a rapid development trend from traditional shallow machine learning to deep learning, and from “data”-driven to “data + mechanism”-double-driven direction, and the deep learning method develops quickly and deeply in change detection and classification, but not in quantitative parameter inversion/estimation. The production technology of large-scale forest and grassland thematic products, such as global and national products, has also been developed rapidly.An analysis of the integration of remote sensing technology into existing technical standards and technical programs for forestry and grassland resources and ecological monitoring, disaster early warning monitoring and monitoring of nature reserves shows that forest and grassland cover type change detection/monitoring and classification technologies have been widely and deeply applied to various resource supervision and disaster early warning and monitoring operations in the forestry and grassland industry, but the degree of operational application of quantitative inversion/estimation technologies of forest and grassland quality parameter is still very low.In view of the challenges in promoting the comprehensive and in-depth application of forestry and grassland remote sensing technology, it is suggested that the forestry and grassland industry should vigorously integrate the “space-air-ground” multi-source earth observation resources, comprehensively apply remote sensing, artificial intelligence (AI), statistical inference and other cutting-edge technologies to build a “space-air-ground” integrated monitoring technology system, and greatly strengthen the investment in scientific research, technology exchange and talent exchange and cultivation.关键词：forestry and grassland;remote sensing technology;operational application;progress;countermeasures15|11|0更新时间：2025-05-12 - “滨海湿地遥感研究进展：从分布范围到生态系统功能，为湿地保护提供科学依据。”
摘要：Coastal wetlands are transitional zones connecting terrestrial and marine ecosystems, characterized by unique structures and functions, encompassing mangroves, salt marshes, tidal flats, coral reefs, seagrass beds, and so on. These ecosystems collectively represent one of Earth’s most productive biogeochemical systems, where the interplay of tidal dynamics, vegetation zonation, and sediment transport creates distinct ecological niches. They play a crucial role in carbon sequestration and provide critical habitats for various plant and animal species, thereby effectively supporting biodiversity and ecosystem health. The complex feedback mechanisms and dynamic changes in these areas require monitoring approaches capable of capturing multi-scale dynamics. Remote sensing has the capacity to assist in the monitoring, conservation, and sustainable management of these vital ecosystems by providing spatially explicit, cost-effective, and temporally consistent observations. Coastal wetland remote sensing, as an interdisciplinary field, dates back to the 1970s. Over the past 50 years, the focus of coastal wetland remote sensing research has evolved from mapping distribution and retrieving ecological parameters to investigating ecosystem functions and material cycles. This trend indicates that the remote sensing of coastal wetlands has a promising future in supporting ecosystem-based management and global change research. In this study, we retrieved relevant publications on coastal wetland remote sensing from the Web of Science Core Collection and used VOSviewer to identify key research themes, which were then chronologically divided into five historical phases: before 1989, 1990—1999, 2000—2009, 2010—2019 and 2020 to the present. By linking research themes to the emergence of new sensors, we revealed the historical evolution and current status of coastal wetland remote sensing. This sensor-theme coupling analysis revealed pivotal technological transitions that facilitated novel insights into coastal wetland remote sensing processes. For each research theme, we analyzed the first published article and the most cited articles, providing an in-depth examination of the developmental trajectory of each theme. Importantly, our analysis demonstrates that coastal wetland remote sensing has evolved from early extent mapping to structural parameter retrieval, then physiological trait estimation, and ultimately ecosystem function assessment and biogeochemical cycle quantification, reflecting a paradigm shift from pattern observation to in-depth ecological process. Finally, we outlined future research directions from four aspects: large-scale classification and mapping, fine-scale inversion of ecological parameters, remote sensing and climate change model and comprehensive remote sensing of ecosystem services. These priorities emphasize the need for multi-sensor fusion, tighter integration with ecological process models, and transformative applications of artificial intelligence technologies. This study provides valuable insights into the history, key research areas and future directions of coastal wetland remote sensing, and reinforces the significance of coastal wetland remote sensing in ecosystem-based management, particularly in addressing emerging global and climate change challenges. Through a systematic review of multidisciplinary research progress over the past five decades, this study synthesizes the co-evolutionary dynamics between sensor technology advancements and wetland ecological research needs. It also outlines critical future development trajectories, offering guidance for researchers in developing forward-looking and targeted research strategies, and providing policymakers with scientific evidence to promote effective conservation and sustainable management of coastal wetlands under accelerating environmental pressures.关键词：coastal wetlands;remote sensing;review;research hotspots;research progress14|8|0更新时间：2025-05-12 - “最新研究进展显示，专家提出了一种多源卫星数据反演湖泊水色指数FUI的一致性校正方法，显著提升了色度角与FUI反演结果的一致性，为多源卫星数据协同反演湖泊水色参量提供重要方法依据。”
摘要：Objective The color of lake water, as a direct reflection of its optical properties, is an important climate variable of lake ecosystems. In recent years, the Forel-Ule Index (FUI) drived from satellite remote sensing has been widely used to indicate the spatiotemporal variations in lake ecology and water quality over large areas. Multi-source satellite observations can significantly improve observation frequency and spatiotemporal coverage; however, the consistency of FUI retrieval across different satellites remains a challenge.Method This study focuses on six typical lakes on the Tibetan Plateau and develops a consistency correction method for FUI retrieval using multi-source satellite data, including Landsat 5/TM, Landsat 7/ETM+, Landsat 8/OLI, and MODIS surface reflectance products. The method aims to correct the retrieval differences in hue angles and FUI caused by variations in satellite spectral response functions and systematic biases in surface reflectance products. First, a polynomial correction approach based on a water body simulation dataset is applied to perform spectral response correction for the hue angles drived from the visible bands of different satellite sensors. Second, using 112,830 pairs of surface reflectance synchronous observations from the six lakes, a linear regression model is established between MODIS and Landsat TM, ETM+, and OLI retrievals of hue angles for cross-correction. Finally, the consistency of multi-source satellite data is systematically evaluated at both the pixel scale and the time series scale based on the synchronous observations and long-term FUI retrievals.Result Results show that the consistency of the drived hue angles and FUI significantly improves after correction (R² > 0.95, MAPE < 10%), and the annual mean FUI trends derived from the four satellite datasets are consistent.Conclusion This study provides an important methodological reference for the synergistic retrieval of lake water color parameters using Landsat TM, ETM+, OLI, and MODIS multi-source satellite data.关键词：Water color; Multi-source satellite remote sensing;Spectral response function;Consistency correction;Hue angle;Forel-Ule Index (FUI);Water reflectance;Lakes on the Qinghai-Tibet Plateau86|38|0更新时间：2025-04-23 - “非洲森林损毁研究取得新进展，专家构建决策树规则分类框架，揭示人为因素是主要驱动力，为森林保护提供科学依据。”
摘要：Objective Identifying the drivers of forest loss is essential for developing forest management policies, yet accurately and comprehensively classifying these drivers on a large scale remains a significant challenge. In this study, we focused on Africa, a region experiencing severe forest loss due to various human activities and natural disturbances. Our objectives are twofold: (1) to generate a spatially explicit and dynamic dataset for proximate drivers of forest loss, and (2) to quantitatively analyze their spatiotemporal patterns.Method Our approach consists of three main steps. First, we developed a decision tree-based classification framework to attribute annual forest loss in Africa to five human and three natural drivers. This framework integrates multi-source remote sensing data—including a global 30-m land cover dynamic monitoring dataset, human footprint pressure, fire occurrences, forest management practices, and the Standardized Precipitation Evapotranspiration Index (SPEI)—following a hierarchical approach based on identification difficulty. Second, we validated the driver classification results using approximately 15,000 third-party visual interpretation samples at a 1 km × 1 km resolution. Additionally, we compared our results with prior studies and systematically analyzed the sources of observed discrepancies. Finally, we quantified forest loss driven by different drivers across three spatial scales: the entire African continent, distinct geographical zones, and latitudinal gradients. We further examined temporal dynamics and long-term trends at both continental and zonal levels.Result We developed a 30-m annual forest loss driver dataset for Africa (2000–2020) with an overall accuracy of 95.38% in pan-tropical regions. Over this period, Africa experienced an estimated 93.51 Mha of forest loss, with human activities responsible for 86.73% of the loss and natural drivers accounting for 13.27%. Agricultural encroachment was the leading driver, causing 44.02% of the total loss, followed by forestry activity (11.40%). At a finer scale (0.01°×0.01°), agricultural encroachment was the dominant driver in most areas. Its impact increased significantly, from 1.53±0.24 Mha/yr (2001–2011) to 2.71±0.23 Mha/yr (2012–2020), peaking at 3.01 Mha/yr in 2014. Forestry activity declined slightly before 2012 but nearly doubled by 2020 (1.00 Mha/yr vs. 0.52 Mha/yr in 2001). All human drivers, except for impervious surface expansion, displayed significant accelerating trends. Agricultural encroachment showed the most pronounced increase (0.08 Mha/yr², P<0.05), followed by forestry activity (0.03 Mha/yr², P<0.05). In contrast, natural drivers remained stable or declined, with persistent drought showing a decrease of -0.01 Mha/yr² (P<0.05). The dominance of human activities in African forest loss intensified over time, rising from 80.62% in 2001 to 90.38% in 2020. Agricultural encroachment remained the primary driver throughout 2000–2020, peaking at 49.50% of total loss in 2014. The contribution of forestry activity rose sharply from 9.93% in 2001 to 18.61% in 2020, surpassing human-induced fire as the third-largest driver after 2013.Conclusion This study integrates multi-source remote sensing datasets to develop a decision tree-based classification framework for identifying the proximate drivers of forest loss during 2000–2020 in Africa. The driver classification results achieved an overall accuracy of 95.38% in the pan-tropical regions. Our analysis revealed that human activities were responsible for nearly 86.73% of Africa’s forest loss during this period, with their impacts continuing to grow, while natural drivers accounted for only 13.27%. Among the human drivers, agricultural encroachment (44.02%) and forestry activity (11.40%) were the two most significant contributors to forest loss. Notably, the rate of forest loss driven by nearly all human drivers has doubled over time, showing an accelerating trend. These findings highlight the urgent need for stronger forest conservation efforts, as Africa remains far from achieving SDG 15.2 (sustainable forest management), particularly in regions facing rapid agricultural expansion.关键词：forest loss;proximate drivers;remote sensing;decision tree;Africa;time-series;agricultural encroachment;forestry activity75|65|0更新时间：2025-04-23
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