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中国科学院空天信息创新研究院
企业中国科学院空天信息创新研究院成立于2020年，主营开展空天信息科学与技术研究，促进科技发展、空间地球系统科学、遥感与数字地球、空天观测等。面向遥感技术、温度监测、无人机等领域提供数据服务，开放中国及周边地区长时序Landsat系列卫星地表温度（遥感技术、温度监测）、AerialAgent（无人机、智能代理）、中国及周边地区长时序Landsat系列卫星增强植被指数（遥感监测、植被指数），支撑遥感监测与空间分析。
遥感技术温度监测无人机应用科研院所
数据集列表
Airport Damage Dataset
We have constructed the first open-source airport damage dataset, covering diverse disaster scenarios including natural calamities and armed conflicts. This dataset encompasses damage sustained by 102 representative airports across 27 countries worldwide over the past two decades due to warfare and natural disasters. Utilizing high-resolution wide-swath satellite images from Google Earth Pro, we performed pixel-level annotation for ten core facility categories, including aircraft, aprons, runways, terminals, and more.This dataset is used to reproduce the conclusions presented in the paper. In the initial release, we published sample data from the large-scale airport images used for training and testing in the paper. In subsequent releases, we will continuously update the dataset to enhance the contributions of this research. All samples are strictly limited to academic research purposes and are prohibited from commercial use.
科学数据银行2025-12-26 更新40
MuSyQ SHG-LCC: A high-resolution global leaf chlorophyll content dataset derived from Sentinel-2 imagery(2022)
Leaf chlorophyll content (LCC) is a critical indicator of plant photosynthetic capacity and plays a key role in understanding the terrestrial carbon cycle. However, existing global LCC products, primarily derived from coarse-resolution remote sensing data, are unable to accurately capture the spatiotemporal variations of LCC in heterogeneous regions. Currently, high-resolution global LCC products remain limited, significantly constraining their application at fine scales. To address this gap, this dataset developed a high-resolution global LCC product (MuSyQ SHG-LCC) with 100-meter/10-day resolution from Sentinel-2 MSI data for the 2019-2023. The dataset was generated using the leaf-scale MERIS Terrestrial Chlorophyll Index (MTCIleaf), which effectively corrects for canopy structural effects on LCC retrieval. Validation results demonstrated that the dataset achieved high retrieval accuracy across various vegetation types, providing important data support for global carbon cycle and ecological research.
科学数据银行2025-10-21 更新60
MuSyQ SHG-LCC: A high-resolution global leaf chlorophyll content dataset derived from Sentinel-2 imagery(2023)
Leaf chlorophyll content (LCC) is a critical indicator of plant photosynthetic capacity and plays a key role in understanding the terrestrial carbon cycle. However, existing global LCC products, primarily derived from coarse-resolution remote sensing data, are unable to accurately capture the spatiotemporal variations of LCC in heterogeneous regions. Currently, high-resolution global LCC products remain limited, significantly constraining their application at fine scales. To address this gap, this dataset developed a high-resolution global LCC product (MuSyQ SHG-LCC) with 100-meter/10-day resolution from Sentinel-2 MSI data for the 2019-2023. The dataset was generated using the leaf-scale MERIS Terrestrial Chlorophyll Index (MTCIleaf), which effectively corrects for canopy structural effects on LCC retrieval. Validation results demonstrated that the dataset achieved high retrieval accuracy across various vegetation types, providing important data support for global carbon cycle and ecological research.
科学数据银行2025-10-21 更新80
Industrial heat sources in China during 2012 and 2023
Comprehensively grasping the high spatiotemporal dynamic information of industrial heat sources (IHS) in China is of great significance for the green, high-quality and sustainable development of industry under the background of "dual carbon". At present, there is still a lack of dynamic data on industrial heat sources for large regions, long time series, and high spatiotemporal, and measures such as structural adjustment and capacity reduction in China have not been effectively tracked and monitored in space. This article utilizes a long time sequence of 375m NPP VIIRS（United States Suomi National Polar-orbiting Partnership, Visible Infrared Imaging Radiometer Suite） Active fire/hotspot data (ACF), based on an improved Kmeans industrial heat source identification method, combined with POI topology analysis and high-resolution remote sensing image features of different types of factories and mines, is used to identify and classify industrial heat sources in China from 2021 to 2023. A dataset of industrial heat sources in China from 2021 to 2023 including type information in vector format is first constructed and public free. The dynamic remote sensing monitoring results of industrial heat sources in China from 2021 to 2023 provide independent scientific basis for China to actively respond to the upgrading of the "structural adjustment and capacity reduction" industrial model, domestic and international carbon tax trading, and improvement of atmospheric environment and other sustainable development processes. The results show that extending the time span to 2012-2023, combined with POI topology analysis based industrial heat source category recognition, can effectively reveal the spatiotemporal evolution laws of different types of industrial heat sources during the critical period of industrial transformation and upgrading; Based on the improved Kmeans industrial heat source recognition method, while ensuring recognition accuracy (98.14%), the number, accuracy, average particle size, and spatial coverage of industrial heat source recognition have been effectively improved; The dataset includes 20 characteristic parameters such as factory and mine locations, annual operating conditions, and categories, which fully record the radiation flux characteristics and production activity intensity of different types of industrial heat sources, providing richer data support for industrial carbon emission estimation and regional economic development assessment.
科学数据银行2025-07-25 更新30
长江经济带11省市（2米以内）尾矿库、露天开采、化工厂等工矿企业位置分布图集（2015-2019年）
通过获取长江经济带11省市的高分1号（2米）等高空间分辨率遥感影像，对其进行融合、几何、大气校正等预处理；采用深度学习目标检测方法解译提取工矿企业目标；在此基础上，结合目视检验，引入地理位置信息得到尾矿库、露天开采和化工厂等工矿企业位置分布遥感提取产品，并绘制专题图形成的图集。
国家地球系统科学数据中心2024-10-21 更新230
MuSyQ SHG-LCC: A high-resolution global leaf chlorophyll content dataset derived from Sentinel-2 imagery
Leaf chlorophyll content (LCC) is a critical indicator of plant photosynthetic capacity and plays a key role in understanding the terrestrial carbon cycle. However, existing global LCC products, primarily derived from coarse-resolution remote sensing data, are unable to accurately capture the spatiotemporal variations of LCC in heterogeneous regions. Currently, high-resolution global LCC products remain limited, significantly constraining their application at fine scales. To address this gap, this dataset developed a high-resolution global LCC product (MuSyQ SHG-LCC) with 100-meter/10-day resolution from Sentinel-2 MSI data for the 2019-2023. The dataset was generated using the leaf-scale MERIS Terrestrial Chlorophyll Index (MTCIleaf), which effectively corrects for canopy structural effects on LCC retrieval. Validation results demonstrated that the dataset achieved high retrieval accuracy across various vegetation types, providing important data support for global carbon cycle and ecological research.
科学数据银行2025-12-11 更新40
MuSyQ SHG-LCC: A high-resolution global leaf chlorophyll content dataset derived from Sentinel-2 imagery(2021)
Leaf chlorophyll content (LCC) is a critical indicator of plant photosynthetic capacity and plays a key role in understanding the terrestrial carbon cycle. However, existing global LCC products, primarily derived from coarse-resolution remote sensing data, are unable to accurately capture the spatiotemporal variations of LCC in heterogeneous regions. Currently, high-resolution global LCC products remain limited, significantly constraining their application at fine scales. To address this gap, this dataset developed a high-resolution global LCC product (MuSyQ SHG-LCC) with 100-meter/10-day resolution from Sentinel-2 MSI data for the 2019-2023. The dataset was generated using the leaf-scale MERIS Terrestrial Chlorophyll Index (MTCIleaf), which effectively corrects for canopy structural effects on LCC retrieval. Validation results demonstrated that the dataset achieved high retrieval accuracy across various vegetation types, providing important data support for global carbon cycle and ecological research.
科学数据银行2025-10-21 更新60
IRSAMap
IRSAMap是一个全球性的遥感数据集，专为大规模、高分辨率、多特征的陆地覆盖矢量映射设计。该数据集提供了四个关键优势：首先，一个全面的元素矢量标注系统，包括超过180万个实例的10个典型自然和人造对象；其次，一个智能标注工作流程，提高了标注效率；第三，全球覆盖范围，涵盖六大洲79个区域，总面积超过1000平方公里；最后，多任务适应性，支持像素级土地覆盖分类、建筑物轮廓规整提取、道路中心线提取和全景分割等任务。该数据集对于全球地理信息更新和数字孪生构建等应用具有重大价值。
arXiv2025-08-22 更新1490
Landsat surface reflectance products based on BRDF correction over Qinghai-Tibetan Plateau
The surface reflectance products based on BRDF correction can decrease the variability of surface reflectance caused by changes in solar incidence angles and satellite viewing angles, so these products perform higher accuracy and radiative consistency by comparing with surface reflectance products that only undergo atmospheric correction. By using 6S-based atmospheric correction method and the C-factor BRDF correction method, the high-quality surface reflectance products over the Qinghai-Tibetan Plateau in 1990, 2000, 2010, 2015, 2020, and 2022 are generated. The processing steps to these products include radiometric calibration, atmospheric correction, and BRDF correction. The results are finally saved in GeoTIFF format, accompanied by corresponding quality and metadata files. The product can be applied in fields such as quantitative retrieval of remote sensing surface parameters, agriculture and forestry, and climate change research in Qinghai-Tibetan Plateau. The dataset consists of 6 subsets with folders in total. Folder XXX means the surface reflectance product in year XXX, e.g. Folder 1990 is the surface reflectance product in 1990. Each folder comprises about 170 folders, and each folder includes surface reflectance results of each band, QA file, thumbnail and metadata file.
科学数据银行2024-10-22 更新30
长江经济带11省市（2米以内）尾矿库、露天开采、化工厂等工矿企业位置分布遥感提取产品（2015-2019年）
通过获取长江经济带11省市的高分1号（2米）等高空间分辨率遥感影像，对其进行融合、几何、大气校正等预处理；采用深度学习目标检测方法解译提取工矿企业目标；在此基础上，结合目视检验，引入地理位置信息得到尾矿库、露天开采和化工厂等工矿企业位置分布遥感提取产品。
国家地球系统科学数据中心2024-10-21 更新120