最新刊期
- “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 Plateau4|0|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 activity4|0|0更新时间：2025-04-23 - “在海洋遥感领域，人工智能技术取得重要进展，推动了人工智能海洋学体系的形成，为海洋大数据的处理和分析提供了高效解决方案。”
摘要：Over the past 40 years, remote sensing technology has advanced significantly, facilitating ocean observation and propelling the field into the era of big data. However, the enormous amount of data generated by ocean remote sensing, coupled with the complexity of these datasets, presents a major challenge for traditional data processing methods. As Artificial Intelligence (AI), particularly Deep Learning (DL), has developed rapidly in recent years, it has emerged as a powerful tool to address these challenges.This study highlights key areas where AI has been applied to ocean remote sensing, notably in ocean parameter retrieval, subsurface data reconstruction, super-resolution techniques, and information extraction from ocean remote sensing images. For instance, AI-driven DL models have been developed to improve the accuracy of sea surface wind field retrieval using Synthetic Aperture Radar (SAR) data, especially under extreme weather conditions such as typhoons or hurricanes. Additionally, AI methods have enhanced the retrieval of chlorophyll-a concentrations in the ocean, with AI models outperforming traditional algorithms in precision and adaptability.Another significant application of AI in ocean remote sensing discussed in the article is the reconstruction of three-dimensional ocean fields. Traditional observation methods, both in situ and satellite-based, face limitations in spatial and temporal resolution, particularly in subsurface ocean observations. AI-based models, such as Convolutional Neural Networks (CNN), have been employed to reconstruct subsurface ocean data, filling gaps in observational datasets and providing a more comprehensive understanding of ocean dynamics.We also explore the use of AI for super-resolution tasks in Sea Surface Temperature (SST) estimation. Through advanced deep learning models, low-resolution SST data can be enhanced to meet the increasing demand for high-resolution data in coastal and regional studies. The integration of AI has not only improved the spatial resolution of SST data but has also enabled better predictions and monitoring of ocean ecosystems, including coral bleaching events and fishery management.Furthermore, AI plays a crucial role in extracting information from ocean remote sensing images, such as identifying mesoscale eddies and internal waves. These oceanographic features have important implications for ocean circulation, climate variability, and ocean biogeochemical processes. AI-based models, with their superior feature extraction capabilities, can identify and classify these features with high precision, surpassing traditional image processing methods.Finally, we concludes by discussing future trends and challenges in the application of AI to ocean remote sensing. It emphasizes the need for further research to integrate physical oceanographic knowledge with data-driven AI models. The combination of AI technology and ocean science theories will enhance the accuracy and reliability of future oceanographic models and contribute to a deeper understanding of ocean processes.关键词：ocean remote sensing;artificial intelligence;deep learning;ocean parameter inversion;super resolution techniques;3D ocean field reconstruction;ocean information extraction22|14|0更新时间：2025-04-21 - “本报道聚焦农作物早期分类研究，梳理了2014年以来的相关研究进展，为理解农作物早期分类的方法与策略、把握早期分类的难点与发展方向提供依据。”
摘要：Timely and accurate acquisition of spatial distribution information on crop planting is critically important for crop growth monitoring, yield forecasting, agricultural planning, and management, among other applications. Such information serves as a critical data foundation for ensuring global food security. Thus far, the limitations of postseason crop classification in meeting the demand for timeliness have prompted a gradual shift in research focus toward early-season crop classification. This study systematically reviews relevant studies published since 2014 to clarify the developmental directions of early-season crop classification research. These studies are retrieved from the Web of Science and CNKI databases. The review categorizes and summarizes the recent research progress in terms of data and preprocessing methods, sample selection, feature selection, classification strategies, and accuracy evaluation metrics. The analysis indicates that several unresolved challenges persist in current studies, including the enhancement of early-season features, the optimization of classification strategies, and the selection of reliable samples. These areas represent key focal points and challenges for future research, thereby meriting further in-depth exploration. This review aims to provide valuable insights into the methods and strategies of early-season crop classification, understand its challenges and developmental directions deeply, and contribute to the advancement of theories and technologies in early-season crop classification.关键词：remote sensing;crop classification;early-season;time-series analysis;key phenological period;machine learning;deep learning14|14|0更新时间：2025-04-21 - “资源一号02E卫星热红外载荷采用双向加密采样技术，实现影像超分辨率重建，有效提高分辨率，为遥感领域提供新解决方案。”
摘要：The advancement of Super-Resolution (SR) imaging in remote sensing holds significant potential for enhancing spatial resolution in applications. Since SPOT-5, researchers have been investigating a new super-resolution imaging mode based on “sub-pixel shifted sampling”. On this basis, researchers have further proposed the concept of “bidirectional overlapping sub-pixel shifted sampling”. Similar to the SPOT-5 mode, it acquires 2 frames shifted by only 0.5 pixels in the along-track direction and then 0.5 pixels in the vertical direction additionally, i.e., it can acquire 4 frames with half-pixel relative shifts. The thermal infrared camera of ZY-1 02E satellite is the first remote sensor in China that supports image super-resolution by means of “bidirectional overlapping sub-pixel shifted sampling”. This paper describes in detail the principle of bidirectional overlapping sub-pixel shifted sampling of the thermal infrared camera of ZY-1 02E. However, since common super-resolution algorithms are not compatible with the special imaging mode, it is necessary to put forward a super-resolution method that is specifically suitable for the “sub-pixel shifted sampling” mode. Based on the hardware features of bidirectional overlapping sub-pixel shifted sampling, an observation equation model and maximum a posteriori probability (MAP) method are introduced to realize image super-resolution in this paper. In order to obtain a higher resolution image from 4-frame “bidirectional overlapping sub-pixel shifted sampled” original images, it is necessary to go through two steps: multi-frame overlapping up-sampling and multi-frame sequential super-resolution reconstruction. For the multi-frame overlapping up-sampling step, this paper proposes an observation equation model to describe the fixed half-pixel shifted relationship between frames, which can effectively utilize the complementary information between frames and significantly suppress the noise. Then the result of the first step is used as the estimated initial value, and a improved MAP method is proposed for sequence super-resolution reconstruction. In this paper, we use the real thermal infrared overlapping sampling mode data of ZY-1 02E to validate and analyze super-resolution, and the experiments show that the hardware mode of bidirectional encrypted sampling can effectively improve the resolution compared with the conventional imaging mode. Judging from the visual effect, the result images of this paper’s algorithm are clear in detail and the noise is not significantly enlarged. In order to quantitatively evaluate the super-resolution results, three unsupervised metrics, average gradient, entropy, and signal-to-noise ratio, are used. By visually and quantitatively comparison, this paper’s algorithm outperforms the method considering only hardware characteristics and the conventional MAP method considering only software algorithms, respectively. The method proposed in this paper achieves the maximum average gradient and entropy due to the integration of the observation equation and the principle of maximum a posteriori probability, while the signal-to-noise ratio remains high, indicating that this method effectively enhances the high-frequency detail information of the image while the noise is not significantly increased. The method in this paper effectively solves the super-resolution problem of ZY-1 02E thermal infrared bidirectional sub-pixel shifted sampling mode.关键词：thermal infrared remote sensing;ZY-1 02E satellite;super resolution;bidirectional overlapping sampling;sub-pixel shifted5|6|0更新时间：2025-04-21 - “中国城市遥感技术在城市规划、环境保护等领域取得显著成果，为城市高质量发展提供重要支撑。”
摘要：The rapid urbanization in China has imposed higher demands on urban management and planning. Urban remote sensing technology, with its unique advantages, has emerged as a critical tool for addressing these challenges and promoting high-quality urban development. This review aims to systematically summarize the research progress in China’s urban remote sensing field over the past 40 years, providing a comprehensive understanding of its definitions, characteristics, and key applications.This paper conducts a thorough review of the literature on urban remote sensing in China. It outlines the basic definitions and core features of urban remote sensing technology, categorizes its developmental stages, and organizes typical application areas. The key areas analyzed include land use and urban spatial structure detection, urban environmental monitoring and management, disaster monitoring and emergency response, as well as socioeconomic development analysis. Additionally, future research directions and challenges in urban remote sensing are discussed from the perspectives of data, models, and methodologies.The review reveals that urban remote sensing in China has developed significantly, becoming an essential tool for various urban applications due to its high spatiotemporal resolution, extensive coverage, and multi-dimensional data fusion. It highlights key achievements in dynamic urban monitoring, environmental protection, and emergency disaster response. The categorization of application areas provides a clear understanding of the roles urban remote sensing plays in addressing critical urban issues.Urban remote sensing technology is expected to continue playing a crucial role in urban management as China’s urbanization process accelerates. Future research will need to address challenges related to data management, processing accuracy, and multi-scale coordination. The findings of this review offer a comprehensive overview of China’s urban remote sensing research, providing valuable insights for both domestic and international scholars, and serve as a useful reference for future studies and applications in the field.关键词：urban remote sensing;remote sensing technology;sensing platforms;urban applications;future development;challenges23|12|0更新时间：2025-04-21 - “遥感技术发展迅速，高光谱图像和深度学习模式识别算法不断突破，图神经网络在高光谱遥感图像解译中发挥重要作用，为遥感领域研究提供新方向。”
摘要：The rapid development of remote sensing technology has brought a variety of remote sensing data. Hyperspectral images, with the highest spectral resolution among these data, are always a crucial source for various Earth observation applications. In the field of computer vision, pattern recognition algorithms represented by deep learning also constantly developing and breaking through the limitations, providing more effective technologies for hyperspectral remote sensing applications. In recent years, Graph Neural Networks (GNNs) have been widely utilized in hyperspectral remote sensing image interpretation tasks, which can leverage the underlying relationships between samples to extract both local and global contextual information, producing high-precision classification results even with a limited number of labeled samples. This paper summarizes the most commonly used GNN frameworks from existing studies, analyzing the characteristics of these methods by decomposing their structures and categorizing them. We first extract the commonalities of existing GNN architectures and propose a basic module of GNN, which consists of an information aggregation function and a feature updating function. Building upon this module, we reinterpret various popular GNN architectures, including spectral-based Graph Convolutional Networks (GCNs), spatial-based GCNs, and Graph Autoencoders (GAEs). In the context of GAEs, current approaches are analyzed from three perspectives: loss functions, decoders, and graph reconstruction methods. These methods formulate loss functions to incorporate various graph-based constraints, thereby embodying the implicit assumptions and specific characteristics inherent to each method. Then, the analyses of GNN methods in the remote sensing field are conducted from three perspectives: graph connections, graph nodes, and network models. The existing research outcomes are classified based on the spatial range of connections, the information hierarchy of nodes, and the uncertainty of models. These GNN-based algorithms extract either local information or non-local information (e.g., global or local-global interactions) by using graph connections across different spatial ranges. The concept of non-local modeling has been extensively explored in GNNs over the past four years. Among GNNs with different node information hierarchies, approaches of using superpixels as graph node representations are the most prevalent. This is because superpixels can serve as a generalized form of node representation for other hierarchies, and their construction is relatively straightforward. Additionally, this paper introduces the application of GNNs in hyperspectral remote sensing image classification under varying modal and label quantities. For single-modal applications, we summarize the characteristics of several representative algorithms and provide their corresponding code implementations. For a limited number of multi-modal applications, we categorize and introduce methods based on the role of GNNs in multi-modal feature fusion. We conduct detailed analyses of the performance of GNN-based classification models in relevant literature, evaluating the applicability of these methods by considering the number of labeled samples and their corresponding classification accuracies. Furthermore, we elaborate on the theoretical foundations and integrated techniques of these models in the fields of supervised, semi-supervised, and unsupervised classification. Finally, the paper summarizes and looks forward to the frontier technologies of GNNs from three aspects: deep graph networks, GNN integrated with other deep learning techniques, and GNN-based foundation models, providing directions and insights for future research in the remote sensing field.关键词：hyperspectral remote sensing;classification;graph neural network;graph convolutional networks;deep learning11|4|0更新时间：2025-04-21 - “最新研究揭示红树林遥感技术在碳储存和气候变化领域的重大进展，为全球红树林保护提供科学依据。”
摘要：Mangroves are one of the most productive ecosystems globally, playing a crucial role in carbon sequestration and climate change mitigation. With the increasing awareness of mangrove conservation both domestically and internationally, as well as the rapid development of remote sensing sensors and artificial intelligence technologies, significant progress has been made in the field of mangrove remote sensing research. Literature retrieval shows that only 18.7% of SCI papers focus on the quantitative remote sensing of mangroves, and there is a lack of systematic reviews in this field. Quantitative remote sensing of mangroves refers to the use of electromagnetic wave information obtained by remote sensing sensors to quantitatively retrieve or estimate parameters related to mangrove biophysics (such as biomass, canopy height, Leaf Area Index (LAI), etc.), biochemistry (such as chlorophyll, nitrogen content, etc.), and ecological functions (such as gross/primary productivity, biodiversity, etc.), with the support of prior knowledge and computer systems, through mathematical or physical models. This study systematically reviews the research progress and future prospects of mangrove quantitative remote sensing through bibliometric analysis and literature review, providing insights for related fields of research. The results show that:(1) Compared to the period 1996—2012, the period from 2013—2024 has witnessed an explosive increase in research on various parameters, with the types of parameters gradually diversifying. Research has expanded from traditional biophysical parameters to include biochemical parameters such as nutrient elements and equivalent water thickness, as well as ecological function parameters like productivity, phenology, photosynthesis, and biodiversity. Among these studies, above-ground biomass (or carbon storage) is the most studied parameter, followed by tree height, LAI, chlorophyll content, and nutrient element content.(2) Remote sensing data sources have diversified, and researchers are increasingly focusing on the comparative analysis of the performance of different remote sensing sensors and the coupled application of multi-source remote sensing data, which has improved the inversion accuracy of various parameters.(3) Inversion models are primarily based on machine learning models, with random forest (RF) and XGBoost being the mainstream methods in current mangrove quantitative remote sensing inversion studies. However, there is insufficient research on physical models.(4) Quantitative remote sensing of mangroves is mainly focused on the landscape scale. In recent years, the spatial scope of research has gradually expanded to a global scale and a single tree scale, providing important data at different levels for mangrove health assessment and carbon cycle research.(5) The application of mangrove quantitative remote sensing in health status, disaster assessment, and ecological restoration management is receiving increasing attention.Future research should focus on strengthening the development of quantitative remote sensing products for mangrove biochemical and ecological function parameters at the national/global scale and constructing a cross-scale (canopy layer, understory vegetation, roots, and soil) 3D remote sensing monitoring system for mangroves. Specifically, this can be achieved by integrating hyperspectral satellite data with Sentinel series satellite imagery, combined with deep learning (DL) models and physical models, to further enhance the quantitative retrieval capabilities for mangrove ecosystems. Promoting the integration of various remote sensing technologies, including unmanned aerial vehicle (UAV) remote sensing (such as chlorophyll fluorescence, thermal infrared, LiDAR, hyperspectral, etc.), high-resolution satellite imagery, and ground-based LiDAR data, will facilitate the construction of a multi-scale, multi-dimensional 3D remote sensing monitoring system, spanning from single-tree scale to species and landscape scales through data fusion. In addition, future research should focus on the deep integration of mangrove ecosystem process models and artificial intelligence models, aiming to reveal the underlying mechanisms and dynamic patterns of key ecological processes such as mangrove growth, carbon sequestration, and nutrient cycling. This will provide a more scientific and reliable theoretical and methodological foundation for the protection, restoration, and sustainable management of mangroves, thereby contributing to addressing urgent environmental challenges such as global climate change.关键词：quantitative remote sensing;mangrove;UAV;Satellite image;biophysical parameter;biochemical parameter;bibliometric8|3|0更新时间：2025-04-21 - “在遥感领域，专家基于2023年综述论文，进一步阐述了绿潮和金潮遥感研究的技术路线，为避免常见错误提供解决方案。”
摘要：Since the first report of a massive Ulva prolifera bloom (also called green tide) in 2008 in the Yellow Sea and the first report of a massive Sargassum horeri bloom (also called golden tide) in 2017 in the East China Sea, remote sensing studies have increased exponentially, with at least 350 papers published on either remote sensing technique, methodology, or on interpretation of remote sensing results. However, because of the lack of rigor, many of these studies are problematic, thus often leading to contradicting results that create obstacles for follow-on studies. Here, I continue a group effort in 2023 to further elaborate on the requirements of remote sensing data products for different purposes. These data products are categorized into four levels: Level-1 data products are quick look images for visual inspection of presence/absence of macroalgae; Level-2 data products are interpreted Level-1 images through “automatic” model extractions; Level-3 data products are the same as Level-2 but after pixel unmixing so the spatial coverage of the macroalgae can be estimated correctly; and Level-4 data products are image composites to remove data gaps. Each level of data products has specific requirements to meet various research and management needs. Unfortunately, many common mistakes in the remote sensing literature of green tides and golden tides are found, which often led to contradicting results. For example, many papers reported 2009 as the maximum year for green tides, while others showed 2021 or 2019 as the maximum year. Obviously, they cannot be all correct, but many of them are due to common mistakes that include incomplete proof, lack of pixel unmixing, incorrect use of threshold, and lack of statistics. These problems have significant impacts on both remote sensing specialists and non-specialists: the former would find it difficult to find a baseline reference to improve technology/methodology, and the latter would find it nearly impossible to select the correct remote sensing results to interpret driving factors and/or environmental impacts. Indeed, attempts to use environmental variables such as water temperature, wind, nutrient levels, etc. would be meaningless if the long-term time-series of macroalgae coverage data are incorrect to begin with. Therefore, with both green tides and golden tides being projected to increase in future years and more and more satellites being launched to observe the Earth, I suggest that the concepts presented here may be used as a reference, and the guidelines presented in the 2023 study may be followed to avoid similar mistakes in future remote sensing efforts. Otherwise, adding more diversified results with increased number of publications will only add more chaos to the current situation.关键词：Ulva prolifera;Sargassum horneri;green tides;golden tides;coverage;biomass;remote sensing;MODIS;VIIRS;OLCI;OCI;GOCI;MSI;OLI8|4|0更新时间：2025-04-21 - “在遥感影像智能解译领域，专家分析了双时相高分辨率遥感影像变化检测的典型算法和最新进展，为相关研究提供参考。”
摘要：Bi-temporal remote sensing image change detection stands as a prevalent direction in the realm of intellectual interpretation and applied research of remote sensing imagery. It aims to acquire information regarding changes in land cover types or geophysical attributes within a monitored area over a specified time span, according to practical application requirements. Over the past few years, remote sensing image change detection techniques have undergone a rapid evolution and upgrading, fueled by the synergetic forces of the ever-growing remote sensing big data (especially the proliferation of very-high-resolution remote sensing images) and the revolutionary advancements in deep learning. In this paper, we delve into and analyze the existing popular algorithms for common change detection tasks using bi-temporal very-high-resolution remote sensing images, encompassing binary change detection that aims to identify the presence or absence of changes, semantic change detection that delves deeper into the semantic categories of the changed areas, building damage assessment that applies to natural disasters, and change captioning that focuses on generating meaningful descriptions of the detected changes using natural language. Finally, we present an outlook on the pivotal research trends in remote sensing image change detection and highlight lingering open issues under the current developmental trajectory, with the intention of offering some valuable insights and perspectives for future research endeavors in this field.Through this review, we raise two observations as follows. 1) Deep learning solutions for the common change detection tasks in remote sensing have been continuously evolving, with many innovative algorithms proposed; 2) The advanced capabilities of current vision and language foundation models have subtly permeated into this field. In fact, the remote sensing domain itself has witnessed a highly encouraging development trend of foundation models. On this basis, remote sensing change detection tends to embark on new development opportunities, and would face a reshaping of its technological landscape in the future.Though, researchers have to spend efforts in developing specialized solutions to (1) enhance the reliability of change detection in complex scenes. At present, even for the relatively mature fully supervised binary change detection, the detection results often show a complete loss of changed entities when faced with some complex scenarios. In semantic change detection, there may even be a phenomenon where the segmented changed semantics are not consistent with the binary change detection results. (2) reduce the dependence on bi-temporal image registration. This requires new algorithms to be developed to adaptively handle small misalignments and deformations in the spatial positions of corresponding objects in bi-temporal images and complete change detection in this case. (3) improve the practicality of multi-modal change detection. This may ask for a comprehensive framework that integrates multi-modal information extraction, feature registration and fusion, and change detection. Meanwhile, semi-supervised or weakly supervised learning methods are expected to become a research focus due to the annotation difficulty of heterogeneous remote sensing data. Besides, it is also believed that the synergy between image modality and language modality has broad prospects for future research in remote sensing change detection, owing to the increasing advancement of large language models as well as vision and remote sensing foundation models.关键词：very-high-resolution (VHR) remote sensing images;bi-temporal images;deep learning;change detection;literature review26|13|0更新时间：2025-04-21 - “在航空航天领域，多模态对齐技术为遥感数据融合提供新途径，专家分析了其挑战、现状与机遇，为对地观测任务提供解决方案。”
摘要：In recent years, the advancement of sensor technology has significantly expanded the application of data from visible light, multispectral, infrared, and synthetic aperture radar sensors across various fields such as precision agriculture, military reconnaissance, and hydrological detection. Multimodal alignment in remote sensing has gained increasing attention, aiming to establish spatiotemporal or semantic relationships between different modal data, thereby achieving efficient data fusion and interoperability. Multimodal alignment is categorized into two main types: semantic and spatiotemporal alignments. Spatiotemporal alignment seeks to synchronize or associate data from different modalities in both time and space, while semantic alignment involves mapping different modal data into a shared high-level semantic space to ensure consistency at the semantic level.Firstly, this study explores the primary application needs of multimodal alignment technology in remote sensing. Key applications include multimodal image registration and fusion, vision-based UAV positioning, and target matching for spatiotemporal alignment, as well as multimodal fusion recognition, image retrieval, and remote sensing change detection for semantic alignment. These applications focus on the spatiotemporal alignment of multimodal remote sensing images to support subsequent image fusion and recognition, and on the semantic alignment of images and text to optimize multimodal information fusion in multi-sensor interactions. Additionally, the study compiles and organizes typical multimodal alignment datasets to support various applications, providing a solid foundation for further research and development in this area.Then,the study reviews the research status of multimodal alignment technology in remote sensing, noting a significant increase in attention over the past decade, particularly in semantic alignment research. It discusses multimodal spatiotemporal alignment from region-based, feature-based, and deep learning perspectives, highlighting the advantages and disadvantages of each method. It then examines semantic alignment through contrastive learning and generative models, including extensions to image-text-geographic coordinate alignment and addressing high-level issues like logical reasoning, hallucination, and the integration of external structured knowledge in generative models. The study summarizes typical multimodal alignment methods and their successful applications, demonstrating the progress and potential of these technologies.Furthermore, the study details the challenges encountered in real-world remote sensing applications of different multimodal alignment technologies. In spatiotemporal alignment, challenges include large image scale differences, modality specificity, temporal discrepancies, and high computational costs. In semantic alignment, challenges arise from semantic gaps between modalities, poor generality of foundational models, and data scarcity. These challenges underscore the need for continued innovation and refinement in alignment techniques to enhance their applicability and effectiveness in diverse scenarios.Finally, the study forecasts future development directions for multimodal alignment technology in remote sensing. These include researching resource-constrained multi-platform alignment mechanisms, developing foundational models specifically for the remote sensing domain, enriching multimodal alignment datasets, and integrating spatiotemporal with semantic alignment. The study predicts that advancements in these areas will drive innovative applications in environmental monitoring, resource investigation, and national defense security. By addressing current challenges and exploring new frontiers, multimodal alignment technology is poised to significantly impact the field of remote sensing, enabling more accurate, efficient, and comprehensive data analysis and interpretation. This will ultimately lead to enhanced decision-making capabilities and more effective solutions to complex real-world problems.关键词：multimodal alignment;spatiotemporal alignment;semantic alignment;remote sensing scenario5|6|0更新时间：2025-04-21 - “遥感机理建模是认知自然界规律的基础，也是定量遥感反演的前提。中国已经构建了较为完善的对地观测体系，发射了系列国产遥感卫星。如何充分发挥国产卫星的应用效能并服务于国民经济建设，已对高精度遥感机理模型发展提出了迫切需求。二向反射和热红外辐射方向性模型是光学遥感两类重要的机理模型，模型场景从均质地表发展到复杂地表，涵盖了叶片、土壤、冠层、以及由破碎地类和复杂地形构成的复杂地表。论文从组分尺度、冠层尺度和复杂地表尺度综述了当前光学遥感机理模型的主要进展，组分尺度模型包括叶片和土壤模型，冠层尺度模型包括连续植被、离散植被和行结构植被模型，复杂地表尺度模型包括不同地类混合和山地模型。形成了叶片—土壤—冠层—复杂地表的多尺度光学遥感机理模型体系，可解释多尺度场景的二向反射和热红外辐射方向性的基本规律。光学遥感机理模型是支撑定量遥感应用的关键，在光学载荷有效评估、观测技术改进、地表参数反演和陆面模式模拟中起着重要作用。文章最后对光学遥感机理模型的未来发展进行了展望。”
摘要：Remote sensing mechanism modeling stands as a cornerstone for deciphering radiative transfer dynamics and enabling quantitative inversion of surface parameters, where bidirectional reflectance distribution function (BRDF) and thermal infrared directional temperature/emissivity models play pivotal roles in optical remote sensing applications. China has established a robust Earth observation infrastructure through indigenously developed satellite constellations such as Gaofen, Fengyun, and Resources, yet unlocking their full potential to address region-specific challenges—including fragmented agricultural landscapes, complex terrains, and densely vegetated mountainous regions—necessitates the development of high-fidelity, localized models tailored to the nation’s heterogeneous geospatial conditions.This paper comprehensively reviews advancements in multi-scale optical remote sensing modeling frameworks, systematically spanning component-scale models (e.g., leaf optical properties, soil spectral anisotropy), canopy-scale models (encompassing continuous vegetation canopies, discrete tree stands, and row-structured crops), and complex surface-scale models (integrating mixed land cover types, urban-rural interfaces, and mountainous topography), collectively forming a hierarchical system to unravel bidirectional reflectance and thermal radiation patterns across spatial and temporal scales. Methodologically, transformative progress includes the paradigm shift from homogeneous to heterogeneous scene modeling to encapsulate China’s geospatial diversity, emphasizing intricate terrain-textured surfaces and dynamic land cover interactions; the transition from 2D empirical approximations to 3D vertically stratified representations empowered by high-resolution satellite imagery, LiDAR-derived canopy profiles, and hyperspectral datasets to resolve vertical vegetation structures and urban morphology; the integration of coupled surface-atmosphere radiative transfer models to disentangle intertwined atmospheric scattering effects (e.g., aerosols, water vapor) and surface anisotropy, thereby enhancing the accuracy of satellite signal interpretation and enabling synergistic retrievals of atmospheric and terrestrial parameters; the unification of multi-band radiative transfer mechanisms (optical, thermal, microwave) within cohesive frameworks to exploit spectral complementarity for holistic Earth system monitoring; and the fusion of physics-based models with machine learning architectures to synergize mechanistic interpretability with data-driven adaptability, facilitating rapid parameter inversion, uncertainty quantification, and cross-scenario generalization.Notably, the localization of models using domestic satellite data has catalyzed applications such as high-precision 3D terrain reconstruction for karst rocky desertification monitoring using Gaofen stereo imagery and thermal anomaly detection in agro-ecosystems via Fengyun geostationary sensors. Despite these strides, persistent challenges include the parameterization of multi-scale spatial heterogeneity, computational bottlenecks in 3D-coupled model simulations, and gaps in closing the data-model validation loop through long-term, multi-source observational networks. Future trajectories emphasize intelligent modeling platforms for automated multi-scale fusion, scalable AI-physics hybrid tools to balance accuracy and efficiency, and enhanced interoperability with global Earth observation initiatives such as GEOSS and Copernicus to foster cross-regional knowledge transfer.By advancing optical remote sensing models toward ultra-fine spatial resolution, synergistic multi-band assimilation, and seamless AI-physics integration, China can harness its sovereign satellite capabilities to address pressing strategic priorities—including precision agriculture, ecological degradation mitigation, disaster resilience, and carbon neutrality monitoring—while positioning itself as a global leader in next-generation Earth observation innovation, thereby bridging the gap between cutting-edge remote sensing science and transformative societal applications.关键词：quantitative remote sensing;optical remote sensing;mechanism model;bidirectional reflectance model10|6|0更新时间：2025-04-21 - “光伏潜力评估研究取得新进展，专家构建了评估框架，为光伏资源开发提供方法参考。”
摘要：Photovoltaic (PV) potential assessment is a vital method for evaluating the developable solar energy resources and PV power generation potential in specific areas. It serves as the foundation for scientific regional energy planning and rational utilization. Advances in remote sensing and GIS technologies have significantly enriched multi-scale, long time-series, high spatial and temporal resolution solar radiation data products. These advancements have also propelled the multidimensional potential assessment of both centralized and distributed PV systems. However, there is currently a lack of systematic review and summary of the application of remote sensing and GIS technologies in solar resource assessment, PV suitability area evaluation, and PV potential estimation. Although some studies have reviewed the application of GIS technology in acquiring solar radiation data, evaluating PV suitability areas, and assessing urban rooftop PV potential, these studies either focus solely on building rooftop PV systems or fail to comprehensively cover all steps of PV potential assessment. Consequently, they do not fully and clearly reveal the technical framework and research pathways for PV potential assessment. Furthermore, a comprehensive framework for PV potential assessment, addressing both centralized and distributed PV systems, remains to be explored. This paper systematically analyzes the current applications of remote sensing and GIS technologies in PV potential assessment, covering key steps from radiation data acquisition, PV suitability area evaluation/usable area determination (for centralized and distributed PV systems), slope and aspect analysis, shadow simulation, to PV potential estimation. It delves into how different methodologies and tools are integrated into each of these steps, providing a holistic view of the process. By summarizing and organizing the assessment processes for both centralized and distributed PV systems, this paper aims to provide a more complete technical framework for related research. This framework is intended to foster a comprehensive understanding and further development of the PV potential assessment field, helping to standardize methods and improve accuracy across different studies. Additionally, considering the current trends in PV applications, this paper explores the potential role of remote sensing and GIS technologies in the future development of the PV industry. It highlights how these technologies can support advanced applications such as integrating PV systems with other renewable energy sources, optimizing energy storage solutions, and improving grid management. By addressing these emerging areas, the paper seeks to underscore the ongoing and future importance of remote sensing and GIS in maximizing the efficiency and effectiveness of solar energy utilization, thus contributing to the broader goals of energy sustainability and carbon neutrality.关键词：solar energy;irradiation data;concentrated/distributed photovoltaic (PV) systems;PV site selection;energy planning8|5|0更新时间：2025-04-21 - “热红外遥感技术在自然资源调查、生态环境监测等领域取得显著进展，为国民经济和社会服务做出重要贡献。”
摘要：Thermal infrared (TIR) remote sensing is a branch of remote sensing that focuses on the acquisition, processing, and interpretation of data in the TIR region of the electromagnetic spectrum, ranging from 3 μm to 15 μm. This technology is critical for studying the thermal radiation characteristics of the Earth’s surface and its changes. Over the last 6 decades, remarkable achievements have been achieved in sensor development, theoretical understanding, and methodological approaches within TIR remote sensing. At present, more than 60 operational satellites are equipped with TIR sensors, thereby offering substantial improvements in spatial resolution, detection channels, and capabilities. For example, the thermal bands of the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) provide a spatial resolution of 1 km; however, the recent HotSat-1, launched in June 2023, offers the highest-resolution commercial TIR sensor in orbit, capable of identifying features as small as 3.5 m. This progress marks a new era in Earth observation and climate monitoring. Moreover, hyperspectral TIR data offers unique insights into the Earth’s surface and atmosphere.Alongside sensor advancements, notable progress has been made in the retrieval of atmospheric and surface parameters. Various methods, such as the single-channel, split-window, temperature, and emissivity separation, as well as the day–night methods, have been developed to derive Land Surface Temperature (LST) accurately. LST is a critical parameter in the land–atmosphere energy balance. Advancements in TIR observation retrieval methods have driven the development of various LST products. At present, more than 30 types of LST products are publicly available, including those from Landsat, the Advanced Spaceborne Thermal Emission and Reflection Radiometer, MODIS, the Advanced Baseline Imager, and the Satellite Application Facility on Land Surface Analysis. The integration of physics-based models with data-driven Artificial Intelligence (AI), the “TIR + AI” paradigm, can considerably enhance the interpretation of TIR images and information extraction capabilities.These innovations have expanded TIR’s applications across diverse fields, including natural resource monitoring, ecological assessment, disaster response, planetary exploration, human health evaluation, and public safety. For example, security agencies typically rely on fingerprints and documentation to verify individuals’ identities. Moreover, current research indicates that analyzing skin temperatures with infrared cameras makes detecting fraudulent identities possible with an acceptable margin of error. Furthermore, TIR remote sensing is expected to play increasingly important roles in the exploration of deep space, thereby providing insights into planetary components, mineral exploration, space weather, and human habitation. Therefore, the benefits of TIR are significant and have contributed significantly to national economies and the societal well-being of individuals and populations.This study presents a comprehensive review of the state of the art in the field of TIR remote sensing by discussing the historical development of TIR sensors, the evolution of retrieval methods, and various applications. It also explores future trends and challenges, thereby highlighting opportunities for further innovation. In the coming years, several key technologies, namely, advanced sensor systems, AI integration with physical models, real-time remote sensing, and data fusion with optical and microwave sources, are expected to enhance the understanding and management of environmental, geological, anthropogenic, and disaster phenomena, thereby positioning TIR remote sensing as an indispensable tool for researchers, policymakers, and emergency responders.关键词：thermal infrared remote sensing;sensor;parameter retrieval;remote sensing application;development and challenges6|8|0更新时间：2025-04-21 -
Airborne remote sensing system： The innovative verification platform of Earth observation technology AI导读
“航空遥感技术发展迅速，已成为地球系统科学研究和行业应用的重要手段，为解决重大科学问题和国家需求提供解决方案。”
摘要：Remote sensing is a technology that involves the use of the electromagnetic spectrum to obtain and process noncontact, multidimensional information of land-ocean-atmospheric elements. The development of the global economy and society has made remote sensing a vital tool for Earth system science research and information acquisition across various industries. Moreover, remote sensing has become a key area of competition among the world’s scientific and technological powers.As two main pillars of Earth observation technology, spaceborne and airborne remote sensing have their unique advantages and are indispensable. Airborne remote sensing is highly conducive to achieving high-resolution, fine, and flexible observations. Airborne remote sensing systems have highly advanced and diverse Earth observation payloads. Thus, they serve as in-flight laboratories for remote sensing science and technology and provide the most important technical verification platform for the innovative development of Earth observation technologies.This study systematically summarizes the development history of airborne remote sensing technology, introduces the major innovations of China’s new-generation airborne remote sensing systems as a major national science and technology infrastructure, and briefly outlines their applications in agriculture, rural areas, emergency disaster reduction, forestry, land and natural resources, and international cooperation and exchange. In particular, it looks ahead to major scientific issues and national needs, such as new-generation Earth observation technologies for “Transparent Earth-Observing,” the authenticity verification of remote sensing satellite products, and the development trends of airborne remote sensing technologies and systems, to promote deep scientific and industrial applications of airborne remote sensing systems.In the future, airborne collaborative and transparent observation technologies will be further developed based on airborne remote sensing systems. This future development direction will involve many open public service flights and cross-industry remote sensing research, thereby creating an open remote sensing research ecosystem. The open sharing of resources will be further expanded with active engagement in science popularization to increase the public’s understanding of Earth system science research, thereby inspiring and cultivating the next generation’s interest in Earth sciences.关键词：airborne remote sensing system;satellite-aircraft-ground collaborative remote sensing;transparent earth-observing;validation;earth observation technology;major national science and technology infrastructure;multimodal sensors;remote sensing aircraft5|5|0更新时间：2025-04-21 - “在冻土参量遥感反演领域，东北典型冻土区黑土的L波段微波辐射响应特性研究取得新进展，为冻土参数反演提供重要参考。”
摘要：In the detection of frozen soil parameters, the L-band offers stronger penetration capability compared to other microwave bands, making it more advantageous for studying soil properties in frozen conditions. However, there has been limited research on the microwave radiation response depth of the L-band in such environments. Additionally, the microwave radiation response characteristics of soil under freezing conditions remain not fully understood. This study aims to investigate the microwave radiation response depth of black soil in typical permafrost regions of Northeast China, focusing on its behavior under natural freezing conditions during winter.To address the research gap, this study employed a dual-polarized L-band microwave radiometer with a frequency of 1.414 GHz to perform near-field experiments on black soil samples with different initial moisture contents. The experiments were conducted in winter under natural freezing conditions. The study examined the changes in microwave radiation response depth during and after the freezing process, considering four initial moisture contents: 0%, 10%, 20%, and 30%. By analyzing the experimental results, the study aimed to explore how the initial moisture content affects the microwave radiation response depth and to determine the dominant factors influencing brightness temperature in frozen soil. This approach allowed for a comprehensive understanding of the interactions between soil moisture, freezing processes, and microwave radiation.The results of the study revealed several key findings. During the freezing process, the L-band microwave radiation response depth for soils with 10% and 30% initial moisture content was found to exceed 5 cm. This suggests that the L-band is capable of detecting soil characteristics at relatively greater depths during freezing. Notably, the soil moisture content remained the dominant factor influencing brightness temperature during this process. After freezing, the initial moisture content continued to impact the microwave radiation response depth by affecting the amount of unfrozen water present in the soil. The measured response depths for black soil with an initial moisture content of 0% (frozen soil) and 10% were found to range from 100 cm to 105 cm and 50 cm to 60 cm, respectively, following freezing. For soils with higher initial moisture contents of 20% and 30%, the post-freezing microwave radiation response depths were recorded as between 35 cm to 50 cm and 25 cm to 35 cm, respectively. These results highlight the significant influence of soil moisture content on the penetration depth of the L-band signal. Furthermore, the study confirmed that under certain conditions, the microwave radiation response depth of frozen soil exceeded the penetration depth calculated using the Ulaby (1981) model, indicating that the actual response depth could be greater than previously estimated for frozen environments.This study provides new insights into the L-band microwave radiation response depth of frozen soil with varying initial moisture contents. The findings demonstrate that initial moisture content plays a significant role in determining the microwave radiation response depth by influencing the unfrozen water content in the soil. Additionally, the observed response depth surpassing the Ulaby model’s predicted penetration depth emphasizes the complexity of microwave interactions with frozen soil. These results have important implications for the remote sensing inversion of frozen soil parameters and the use of L-band microwave radiometry in monitoring permafrost and frozen ground. The ability to accurately measure the microwave radiation response depth in these environments can improve our understanding of the physical properties of frozen soils and enhance the accuracy of remote sensing systems designed for frozen soil monitoring and analysis.关键词：Microwave radiometer;Response Depth;L-band;Frozen soil;Black Soil38|23|0更新时间：2025-04-17 - “在空间信息度量领域，专家提出了基于像元邻域边界方形度的分类栅格数据空间信息度量方法，有效度量分类栅格数据的空间信息。”
摘要：The category encoding values of classified raster data only represent semantic types without numerical significance. This characteristic poses challenges in directly applying current spatial information measurement algorithms to classified raster data, and leads to higher costs in applications such as cognition, analysis, comparison, and screening of classified raster data. To address this issue, this paper proposes a spatial information measurement method for classified raster data based on the squareness of pixel neighborhood boundaries, from the perspectives of feature similarity and diversity.In the method proposed in this article, the degree of similarity between pixel neighborhood boundaries and a reference square is utilized to quantitatively measure the spatial morphological information expressed by homogeneous pixels. The diversity probability entropy of heterogeneous pixels within the neighborhood is employed to quantitatively measure the spatial structural information conveyed by these pixels. This method designs an adaptive weight parameter w based on the maximum diversity entropy associated with the number of heterogeneous pixels within the neighborhood space, coupling spatial morphological information with spatial structural information. Combining two types of information components to comprehensively express the spatial information content of classified raster data.Three sets of experiments were conducted to validate the effectiveness of the proposed method. The experimental results indicate that the measurement results of the proposed method are unaffected by changes in category encoding values and exhibit strong correlations with thermodynamic consistency and the fragmentation degree of raster data, with correlation coefficients all exceeding 0.98. Compared with existing representative methods, the trend of information change in empirical cognition is more consistent. In the application example in Xi'an, China, this method can quickly and intuitively compare the classification raster data of different resolutions and data sources, and provide objective measurement data. The application results indicate that there is strong heterogeneity in the information content of different data sources between urban and rural areas, which provides a theoretical basis for the comprehensive utilization of categorical raster data.In conclusion, the experimental results support the effectiveness of the proposed method in measuring the spatial information of classified raster data. It will play an important role in reducing costs and improving efficiency in applications such as cognitive, comparative, and filtering of classified raster data. However, current information content is limited to the spatial information dimension and fail to fully express the rich connotations of data. In future research, spatial information will be used as the basis to expand the coupling measurement of thematic information and spatial information in classification raster data, and further explore the key role of information indicators in quantitative cognition and evaluation of classification grid data.关键词：Spatial Information Measurement;Classified Raster Data;information entropy;Morphological Information;Spatial Structural Information;Thermodynamic Consistency;Shape Similarity;data filtering29|32|0更新时间：2025-04-17 - “遥感时谱理论揭示地表物体变化特征，应用于多领域，专家展望未来研究方向。”
摘要：Long term remote sensing data contains information from four dimensions: time, space, and spectrum. Currently, the description is only based on spatial and spectral dimensions, and there is no unified concept to describe long-term remote sensing data. In traditional research, using a three-dimensional cube model to store long-term remote sensing data would separately store data from different time periods, which cannot meet the efficient storage, fast retrieval, and deep analysis of its time dimension, and thus cannot fully explore the characteristics of different land features changing over time. Essentially, it is still discrete three-dimensional data, and the four-dimensional information of long-term data has not been effectively managed and analyzed uniformly. Therefore, based on the research foundation of predecessors, the concept of time spectrum has emerged, bringing new breakthroughs to the organization and storage of remote sensing data, and improving the utilization value of long-term remote sensing data. Similar to the concept of spectra, remote sensing feature sequences at different times constitute spectrograms.Remote sensing spectrogram theory mainly describes the information of remote sensing data in spectral, temporal, and spatial dimensions. By analyzing the temporal spectrum of remote sensing images, the changing characteristics of surface objects at different time scales and spectral ranges can be revealed, which can be used in various application fields. Experts and scholars in the remote sensing field have fully recognized the scientific value and broad application prospects of MDD.Land change detection and fine classification of crops require the use of multiple spectral information from different time periods. Unlike traditional 3D datasets, the study of remote sensing data has been elevated from 3D to 4D, providing a different way for land features to be represented. This helps to solve the problem of “same spectrum foreign objects, same object different spectrum”. The analysis of cultivated land types based on spatiotemporal spectra also has the characteristics of complete phenology. It can effectively eliminate false changes caused by seasonal factors. The MDD format has significantly improved the efficiency and accuracy of remote sensing analysis, and has been widely promoted and applied in multiple industry user units, generating good social and economic benefits.This article summarizes and generalizes the basic concepts, main methods, and techniques of remote sensing spectrogram theory, introduces the application fields and latest research progress of remote sensing spectrogram theory, and finally looks forward to the future research directions of spectrogram theory. The future research directions in this field include the study of spatiotemporal consistency and data fusion of multi-source data, the research of deep learning time-frequency analysis methods, and the research of real-time and high-resolution analysis.In the field of next-generation remote sensing data storage, the multidimensional data format developed by Chinese scientists is expected to meet the needs of real-time online data processing, be used to develop a new spatiotemporal spectral integrated data organization and storage mode, and serve as one of the technical foundations of remote sensing cloud platforms. This innovation can improve the storage, retrieval, and sharing efficiency of remote sensing big data, providing key support for China to build an independent and controllable remote sensing cloud platform.关键词：remote sensing;multi-dimensional dataset;spectrotemporal dataset;spectrotemporal characteristics;spectrotemporal applications26|21|0更新时间：2025-04-17 - “国内商业航天企业航天宏图成功发射航天宏图一号卫星，验证了其在地形测绘领域的高精度能力，为“女娲星座”业务化应用提供重要参考。”
摘要：On March 30, 2023, the PIESAT-1 satellite, operated by the domestic commercial space enterprise Piesat Information Technology Co., Ltd. (hereinafter referred to as Piesat), was successfully launched, injecting new impetus into the development of domestic SAR satellites. As the first star of “Nüwa Constellation”, it has the ability of high-precision topographic mapping, high-resolution wide-range imaging and high-precision deformation monitoring. However, there are fewer studies on this satellite, and it is urgent to explore its real mapping capability. In this paper, we utilize the on-board InSAR to produce DEMs technology process, and make corresponding technical adjustments for the unique “1 main + 3 auxiliary” wheel formation configuration of PIESAT-1.Objective To explore the PIESAT-1 interferometric terrain mapping capability and the advantages of the unique formation format.Method Firstly, three special terrains, namely, mountain, hill and plain, are selected as the research objects, combined with the L1B data acquired during the in-orbit test of PIESAT-1 satellite; Then the DEM is extracted using InSAR technology. In the extraction step, the multi-baseline phase unwrapping brought by the four-star constellation of PIESAT-1 is fully utilized, and the high-precision DEM is extracted by combining with the TSPA multi-baseline phase unwrapping method; Finally, the ICESat-2 control points and the Copernicus DEM are used as the references to validate the accuracy of the terrain mapping of PIESAT-1.Results (1) The vertical accuracy of PIESAT-1 DEM product is higher than that of Copernicus DEM under the three terrain conditions of mountains, hills and plains, with the RMSE values of 4.8727m, 7.8329m and 0.9857m, respectively.(2) The unique formation configuration of PIESAT-1 can effectively reduce the elevation error caused by vegetation, which also makes the spatial resolution of PIESAT-1 DEM products better than that of Copernicus DEM products, and is able to depict the detailed features of the terrain more clearly.Conclusion The experiments in this paper show that when PIESAT-1 is used in the global topographic mapping mission, the unique formation method gives it a great advantage in spatial resolution, mapping efficiency, and measurement accuracy, which provides an important reference for the operational application of “Nuwa Constellation”.关键词：PIESAT-1;Satellite-based InSAR terrain mapping;ICESat-2 control points;Multi-baseline phase unwrapping;DEM accuracy validation;Vegetation impacts22|23|0更新时间：2025-04-17 - “风云四号B星晴空图像合成算法，为生态遥感应用提供高频次单日晴空图像。”
摘要：Objective The clear-sky image synthesis in a single day is of great significance for daily water body recognition and other business applications. This paper proposes a clear sky image synthesis algorithm based on a binary Gaussian mixture model for the 1-minute continuous imaging sequence data of Geostationary High-speed Imager (GHI) of Fengyun-4B satellite.关键词：Clear Sky Synthesis Image;FY-4B;GHI;Gaussian model;Water Body Identification;Multi- temporal Remote Sensing Data21|22|0更新时间：2025-04-16
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