本数据集为基于 Sentinel-3 OLCI 传感器的时间序列积雪参数栅格产品（.img 格式）。支持 ENVI、ArcGIS、QGIS 等主流遥感与地理信息软件直接读取。原始处理生成积雪像元点云表格后，通过空间插值将离散点数据转换为连续的面状栅格影像，使数据能够反映积雪参数的空间连续分布特征，满足区域积雪时空分析的需求。按年度-月份-日期对每日栅格文件命名，月度合成 NDSI 文件按年度-月份-合成序号命名。包含以下数据：\n1）NDSI（归一化积雪指数）：无单位（范围 -1~1，是积雪覆盖监测的经典指标，可有效区分积雪与非积雪地表，反映区域积雪覆盖范围与覆盖度）\n2）雪粒径有效半径（Snow Grain Size / Snowgrain）：μm（微米）反映积雪物理结构的关键参数，其大小与积雪的融化、反射特性密切相关，可支撑积雪物理过程研究；\n3）污染物相对质量浓度（Pollution Amount）：ppm（百万分之一）表征积雪中粉尘、气溶胶等污染物的相对含量，可反映积雪的污染程度，为积雪生态环境评估提供数据支撑。\n其中雪粒径有效半径，污染物相对质量浓度参数生成每日栅格影像，NDSI参数进一步进行月度最大值无云合成（每月约 4 景）。栅格采用等经纬度投影，重采样分辨率 200 米，支持积雪动态监测、粒径与污染量时空分析等应用。
", "ds_source": "原始数据选取欧洲空间局（ESA）/ EUMETSAT Sentinel-3A/B 卫星搭载的 Ocean and Land Colour Instrument（OLCI）传感器 L1B 级 Earth Observation Full Resolution (EFR) 产品，原因在于 Sentinel-3 系列卫星是欧洲哥白尼计划的核心卫星，其搭载的 OLCI 传感器专为海陆观测设计，具备高光谱、高空间分辨率、高覆盖频率的优势，适合开展大尺度积雪长期动态监测。OLCI 传感器拥有 21 个光谱波段，覆盖 400-1020nm 光谱范围，其中第 21 波段的中心波长为 1.02 微米，该波段对积雪粒径的散射特性变化高度敏感，是反演雪粒径的核心波段，为积雪物理参数的定量反演提供了关键光谱基础。该传感器原始空间分辨率约 300 米，幅宽达 1270km，且 Sentinel-3A、3B 两颗卫星协同工作，可实现全球范围内 1 天一次的全覆盖观测，能够保障数据的空间覆盖范围与时间观测密度，满足 2016-2025 年长时间序列、中亚与中国北方大区域的积雪参数监测需求。
", "ds_process_way": "使用 iCOR 插件（支持 SNAP 模块可视化操作或批量调用 icor.bat 脚本）对 L1B 级数据进行大气校正处理。原因在于 L1B 级数据为传感器原始辐射定标数据，包含大气散射、吸收、反射等多种干扰，无法直接用于地表参数反演，大气校正是定量遥感的基础前提，可将传感器接收到的辐射值转换为地表真实反射率，为后续积雪参数反演提供准确的光谱数据。选择 iCOR 插件而非传统大气校正模型，是因为 iCOR 插件专为 Sentinel-3 OLCI 等中等空间分辨率、大覆盖面积的遥感数据设计，能够有效处理异质性较高的大气模式，校正精度更高、适配性更强。处理过程中去除受大气强吸收影响的 Oa13、Oa14、Oa15、Oa19、Oa20 五个波段，仅输出 16 个可靠的地表反射率波段（Oa01–Oa12、Oa16–Oa18、Oa21）及经纬度信息，原因在于上述五个波段处于大气水汽、二氧化碳的强吸收波段，受大气干扰严重，光谱数据可靠性低，去除后可提升后续数据处理的准确性，同时保留的 16 个波段覆盖了积雪监测的核心光谱范围，能够满足积雪分类与参数反演的需求。\n采用 SNAP IdePix OLCI 处理器识别影像中的云、云影及积雪像元，并仅保留 1216、1232、1248、1264、3264 五类积雪相关编码像元。遥感影像中云、云影会与积雪产生光谱混淆，成为积雪识别与参数反演的主要干扰源，必须通过专业分类算法剔除云、云影及其他非积雪像元，才能精准提取积雪像元。SNAP IdePix OLCI 处理器是针对 OLCI 传感器开发的专用像素分类算法，能够基于光谱特征与纹理特征实现云、云影、积雪、陆地等下垫面类型的精准区分，分类精度高于通用分类算法；而 1216、1232 等五类编码为该算法中积雪像元的专属编码，仅保留此类像元可有效剔除非积雪干扰，确保后续处理仅针对积雪像元，提升积雪参数反演的针对性与准确性。\n参数反演：基于 Kokhanovsky 等积雪反射率物理模型（球形反照率、几何光学模型、外部混合模式），使用 400 nm、560 nm、865 nm、1020 nm 等关键波段，反演雪粒径（考虑形状参数 3.62、4.53、5.8）和污染物量。该模型是针对积雪反射率特性开发的经典定量模型，能够综合考虑积雪的散射、吸收特性及污染物的混合效应，相比经验模型，物理模型的机理性更强、反演精度更高，适用于不同区域、不同积雪状况的参数反演；融合球形反照率、几何光学模型、外部混合模式，可分别模拟积雪的球形散射特征、几何光学散射特性及冰与污染物的外部混合状态，更贴合实际的积雪物理与光学特征。在进行粒径的算法过程中，还需要计算冰的负折射指数的虚部，这里用Spline插值即可。同时需要计算相当方位角，直接采用使用传感器的观测方位角与太阳的方位角进行计算，如果它们的绝对值大于180°时，用360°减去其绝对值。\n栅格化与插值：将离散的积雪像元点云数据进行克里金空间插值，生成 200 米分辨率的等经纬度投影栅格影像。点云数据为离散的点特征，无法直观反映积雪参数的空间分布特征，也难以进行区域尺度的空间分析与可视化表达，通过栅格化与插值将离散点数据转换为连续的面状栅格数据，能够实现积雪参数的空间连续表达，满足区域积雪时空分布分析的需求。将栅格分辨率设置为 200 米提升数据空间精细度；采用等经纬度投影，该投影方式可保持地理坐标的唯一性与一致性，便于数据的空间拼接、叠加分析及与其他地理数据的融合使用。\n月度合成：仅对 NDSI 参数采用最大值合成方法，生成每月约 4 景无云栅格产品，雪粒径和污染物参数则保留每日单景产品，不进行合成，最终所有数据均输出为 ENVI .img 栅格文件。对 NDSI 进行月度最大值合成的原因在于，NDSI 主要用于积雪覆盖范围监测，受云、云影干扰较大，每日影像中存在大量云覆盖缺失区域，而最大值合成法可选取月度内同一像元的最大 NDSI 值，有效剔除云、云影干扰，提升数据的无云覆盖率与空间连续性，满足积雪覆盖宏观监测的需求；雪粒径与污染物参数对积雪短期变化响应敏感，且云覆盖对其反演结果的影响可通过前期积雪分类有效剔除，因此保留每日单景产品，保障数据的时间精细度，满足积雪短期动态变化研究的需求。将数据输出为 ENVI .img 格式，该格式是遥感领域的标准栅格数据格式，支持 ENVI、ArcGIS、QGIS 等主流遥感与地理信息软件直接读取与处理，无需额外的格式转换，降低数据使用门槛，同时该格式可完整保留栅格数据的空间参考、波段信息等元数据，保障数据的完整性。
", "ds_quality": "大气校正质量：iCOR 在 Sentinel-3 OLCI 陆地应用中表现良好，校正后的地表反射率数据能够真实反映地表的实际光谱特征，为后续积雪参数反演提供了准确的基础数据；蓝光和近红外个别波段差异略高，主要与 OLCI 传感器的 “excess of brightness” 现象相关，该现象为传感器自身系统特性导致，属于可控误差范围，对整体积雪参数反演的影响较小。\n反演不确定性：受观测几何角度、大气残留误差、云污染及地形影响。月度 NDSI 最大值合成可显著提高数据连续性和无云覆盖率。 \n整体适用性：数据质量适合中高纬度积雪区的定量遥感分析，但建议在使用时结合具体应用场景进行进一步验证。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 1418620240255, "ds_files_count": 3900, "ds_format": "", "ds_space_res": "200m", "ds_time_res": "雪粒径、污染量：每日； NDSI：月度最大值合成（每月约 4 景无云影像）", "ds_coordinate": "WGS84", "ds_projection": "WGS84", "ds_thumbnail": "5917f373-60fa-41c5-9ced-6002b6bce9d3.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 1, "publish_time": "2026-03-26 13:05:47", "first_publish_time": null, "last_updated": "2026-04-22 16:40:19", "protected": false, "protected_to": null, "lang": "zh", "cstr": "33110.11.ariddc.01421", "license": null, "extra": null, "files_shape": [ { "name": "数据加工方法.docx", "size": 171931, "is_dir": false }, { "name": "DATA-FOR-UPLOAD", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "Time-series snow cover parameter grid dataset based on Sentinel-3 OLCI (snow particle size, pollution amount and NDSI)", "ds_abstract": "This dataset is a time-series snow parameter grid product (in.img format) based on the Sentinel-3 OLCI sensor. Support direct reading by mainstream remote sensing and geographical information software such as ENVI, ArcGIS, and QGIS. After the original processing generates a snow pixel point cloud table, the discrete point data is converted into a continuous area grid image through spatial interpolation, so that the data can reflect the spatial continuous distribution characteristics of snow cover parameters and meet the needs of regional snow cover spatio-temporal analysis. The daily grid files are named year-month-date, and the monthly composite NDSI files are named year-month-composite serial numbers. Contains the following data:\n1) NDSI (normalized snow cover index): unit-free (range-1 to 1, is a classic indicator for snow cover monitoring. It can effectively distinguish snow cover and non-snow cover surfaces and reflect regional snow cover and coverage)\n2) Snow Grain Size / Snowgrain: μm (microns) reflects a key parameter of the physical structure of snow cover. Its size is closely related to the melting and reflection characteristics of snow cover, and can support research on snow cover physical processes;\n3) Pollution Amount: ppm (one part per million) characterizes the relative content of dust, aerosols and other pollutants in snow cover, can reflect the pollution level of snow cover, and provide data for snow cover ecological environment assessment. support.\nAmong them, the effective radius of snow particle size and the relative mass concentration parameters of pollutants generate daily grid images, and the NDSI parameters are further synthesized with cloud-free monthly maximum values (about 4 scenes per month). The grid adopts constant latitude and longitude projection with a resampling resolution of 200 meters, supporting applications such as dynamic monitoring of snow cover, spatio-temporal analysis of particle size and pollution amount.
", "ds_source": "The raw data selects the L1B-class Earth Observation Full Resolution (EFR) product of the Ocean and Land Colour Instrument (OLCI) sensor carried by the European Space Agency (ESA)/ EUMETSAT Sentinel-3A/B satellite. The reason is that the Sentinel-3 series satellites are the core satellites of the European Copernicus project. The OLCI sensors they carry are specially designed for land and sea observations and have the advantages of high spectrum, high spatial resolution, and high coverage frequency. Suitable for long-term dynamic monitoring of large-scale snow cover. The OLCI sensor has 21 spectral bands covering the spectral range of 400-1020nm, of which the center wavelength of the 21st band is 1.02 microns. This band is highly sensitive to changes in the scattering characteristics of snow particle size and is the core band for retrieving snow particle size., provides a key spectral basis for quantitative inversion of snow physical parameters. The original spatial resolution of the sensor is about 300 meters and the width is 1270 kilometers. The two satellites, Sentinel-3A and 3B, work together to achieve full coverage observation once a day on a global scale, which can ensure the spatial coverage of the data. and time observation density to meet the monitoring needs of snow parameters in the long-term series from 2016 to 2025, Central Asia and northern China.
", "ds_process_way": "Use the iCOR plug-in (which supports visual operations of the SNAP module or batch calls of the icor.bat script) to perform atmospheric correction processing on L1B level data. The reason is that the L1B-level data is the original radiation calibration data of the sensor, which includes various disturbances such as atmospheric scattering, absorption, and reflection, and cannot be directly used for surface parameter inversion. Atmospheric correction is the basic prerequisite for quantitative remote sensing, and the radiation received by the sensor can be converted. The value is converted into the true reflectivity of the surface, providing accurate spectral data for subsequent inversion of snow cover parameters. The iCOR plug-in was chosen instead of the traditional atmospheric correction model because the iCOR plug-in is specially designed for remote sensing data with medium spatial resolution and large coverage such as Sentinel-3 OLCI. It can effectively process atmospheric models with high heterogeneity and have higher correction accuracy., more adaptable. During the processing process, the five bands Oa13, Oa14, Oa15, Oa19, and Oa20 affected by strong atmospheric absorption are removed, and only 16 reliable surface reflectance bands are output.(Oa01-Oa12, Oa16-Oa18, Oa21) and latitude and longitude information, the reason is that the above five bands are in the strong absorption band of atmospheric water vapor and carbon dioxide, are seriously interfered by the atmosphere, and the reliability of spectral data is low. After removal, it can improve the accuracy of subsequent data processing. At the same time, the 16 bands retained cover the core spectral range of snow monitoring, which can meet the needs of snow classification and parameter inversion.\nThe SNAP IdePix OLCI processor is used to identify clouds, cloud shadows and snow elements in the image, and only five types of snow-related coded pixels of 1216, 1232, 1248, 1264, and 3264 are retained. Clouds and cloud shadows in remote sensing images will be confused with snow cover and become the main source of interference in snow cover identification and parameter inversion. Clouds, cloud shadows and other non-snow cover pixels must be eliminated through professional classification algorithms to accurately extract snow cover pixels. SNAP IdePix OLCI processor is a dedicated pixel classification algorithm developed for OLCI sensors. It can accurately distinguish the types of underlying surfaces such as clouds, cloud shadows, snow, and land based on spectral features and texture features, and the classification accuracy is higher than that of the general classification algorithm; The five types of codes such as 1216 and 1232 are exclusive codes for snow elements in this algorithm. Only retaining such pixels can effectively eliminate non-snow interference, ensure that subsequent processing is only targeted at snow elements, and improve the inversion of snow parameters. Targeted and accurate.\nParameter inversion: Based on Kokhanovsky's physical model of snow reflectance (spherical albedo, geometric optical model, external mixed model), using key bands such as 400 nm, 560 nm, 865 nm, and 1020 nm to retrieve snow particle sizes (considering shape parameters 3.62, 4.53, and 5.8) and pollutant amounts. This model is a classic quantitative model developed for the reflectivity characteristics of snow cover. It can comprehensively consider the scattering and absorption characteristics of snow cover and the mixing effect of pollutants. Compared with the empirical model, the physical model has stronger mechanism and higher inversion accuracy. It is suitable for parameter inversion in different regions and different snow cover conditions; Integrating the spherical albedo, geometric optical model, and external mixing model can respectively simulate the spherical scattering characteristics, geometric optical scattering characteristics, and external mixing state of ice and pollutants, which is more consistent with the actual physical and optical characteristics of snow cover. In the process of calculating the particle size, it is also necessary to calculate the imaginary part of the negative refractive index of ice, which can be interpolated here. At the same time, it is necessary to calculate the equivalent azimuth angle, which is directly calculated by using the observation azimuth angle using the sensor and the azimuth angle of the sun. If their absolute values are greater than 180°, subtract their absolute values by 360°.\nRasterization and interpolation: Kriging space interpolation is performed on discrete snow pixel point cloud data to generate iso-latitude and longitude projection raster images with a resolution of 200 meters. Point cloud data is discrete point features, which cannot intuitively reflect the spatial distribution characteristics of snow cover parameters, and it is difficult to carry out spatial analysis and visual expression on a regional scale. The discrete point data is converted into continuous area grid data through rasterization and interpolation, which can realize spatial continuous expression of snow cover parameters and meet the needs of regional snow cover spatio-temporal distribution analysis. Set the grid resolution to 200 meters to improve the spatial fineness of the data; use equal latitude and longitude projection, which can maintain the uniqueness and consistency of geographical coordinates, facilitate spatial splicing, overlay analysis and integration with other geographical data.\nMonthly synthesis: Only the maximum synthesis method is used for NDSI parameters to generate cloud-free grid products for about 4 scenes per month. The snow particle size and pollutant parameters are retained for daily single-scene products and are not synthesized. Finally, all data is output as ENVI .img grid file. The reason for synthesizing the monthly maximum value of NDSI is that NDSI is mainly used for snow cover monitoring and is greatly interfered by clouds and cloud shadows. There are a large number of missing areas of cloud coverage in daily images. The maximum value synthesis method can select the maximum NDSI value of the same pixel in the month, effectively eliminates the interference of clouds and cloud shadows, improves the cloud-free coverage and spatial continuity of data, and meets the needs of macro monitoring of snow cover; Snow particle size and pollutant parameters are sensitive to short-term changes in snow cover, and the impact of cloud cover on the retrieval results can be effectively eliminated through previous snow cover classification. Therefore, daily single-scene products are retained to ensure the temporal precision of the data and meet the needs of short-term dynamic changes in snow cover. Output the data into ENVI .img format, which is a standard raster data format in the field of remote sensing. It supports direct reading and processing by mainstream remote sensing and geographical information software such as ENVI, ArcGIS, and QGIS. No additional format conversion is required and data usage is reduced. Threshold, at the same time, this format can completely retain metadata such as spatial reference and band information of raster data to ensure data integrity.
", "ds_quality": "Atmospheric correction quality: iCOR has performed well in Sentinel-3 OLCI land applications. The corrected surface reflectance data can truly reflect the actual spectral characteristics of the surface, providing accurate basic data for subsequent snow cover parameter inversion; the difference between blue light and near-infrared individual bands is slightly high, mainly related to the \"excess of brightness\" phenomenon of OLCI sensor. This phenomenon is caused by the sensor's own system characteristics and belongs to the controllable error range and has little impact on the overall snow cover parameter inversion.\nInversion uncertainty: Affected by observation geometric angle, atmospheric residual error, cloud pollution and terrain. Composite monthly NDSI maximum values can significantly improve data continuity and cloud-free coverage. \nOverall applicability: The data quality is suitable for quantitative remote sensing analysis of snow cover areas in mid-to-high latitudes, but it is recommended to conduct further verification in conjunction with specific application scenarios when using it.
", "ds_projection": "WGS84", "ds_space_res": "200m", "ds_time_res": "Snow particle size and pollution amount: daily; NDSI: monthly maximum value composite (about 4 cloud-free images per month)" } }, "ds_topic_tags": [ "污染量", "Sentinel-3", "雪粒径" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [], "ds_contributors": [ "陆科儒", "杨辽", "杨海平", "王杰" ], "ds_meta_authors": [ "王杰" ], "ds_managers": [ "杨辽" ], "category": "哨兵" }