积雪作为气候变化的敏感指标，长期积雪数据是开展气候研究不可或缺的基础。目前的积雪范围数据集虽然具有良好的质量和较高的时空分辨率，但是时间跨度较短。本研究使用AVHRR表面反射率数据、Landsat-5TM数据，结合地面雪深观测数据、中国长期日积雪深度数据集、地表温度和DEM等数据源，采用改进的云检测算法、多级积雪判别算法与间隙填补策略，构建了覆盖三江源区域1980–2020年逐日5 km空间分辨率的积雪覆盖范围产品，并通过验证发现，与现有AVHRR积雪范围产品（如JASMES AVHRR产品）相比，本产品的总体精度显著提高约15%，漏分误差从60.8%降至19.7%，错分误差从31.9%降至21.3%，且CK值提升超过114%。该数据可为三江源区域雪盖动态监测及气候变化研究提供数据支持。", "ds_source": "
AVHRR表面反射率第4版（AVHRR SR V4）是由美国国家海洋和大气管理局（NOAA）发布的气候数据记录（CDR）产品。该产品基于 NOAA 极轨气象卫星的高级甚高分辨率辐射计（AVHRR）传感器数据生成，时间覆盖1981-2019年，时间分辨率为逐日，空间分辨率为5 km，经过辐射定标、大气校正和云检测等处理，为生成长期雪盖产品提供基础数据。Landsat-5 TM是由美国地质调查局（USGS）和美国国家航空航天局（NASA）联合运营的陆地卫星（Landsat-5）上搭载的主题制图仪传感器获取的数据，时间分辨率为16 d，空间分辨率为30 m，时间范围为1984-2013年。ERA5-Land地表温度(LST)数据来自欧洲中期天气预报中心(ECMWF)的ERA5-Land再分析数据集(Muñoz Sabater, 2019)。该数据集提供了1981年至今的连续全球覆盖，空间分辨率为0.1°。DEM数据来自NASA航天飞机雷达地形测绘任务（SRTM），空间分辨率为90 m。以上数据通过Google Earth Engine（GEE）云计算平台获取使用。地面雪深观测数据由中国气象局（CMA）全国气象观测站网提供，选取191个气象站点1981-2019年的数据用于验证产品。中国长期日积雪深度数据集由Che et al. (2008)和Dai et al. (2015)基于多颗卫星无源微波传感器的观测数据开发，通过传感器间校准技术处理。该数据集时间跨度为1979年至2020年，空间分辨率为0.25°，每日提供中国区域的积雪深度信息。数据集可通过国家青藏高原科学数据中心获取(https://doi.org/10.11888/Geogra.tpdc.270194)，在本研究中被用作空白填充的补充策略。", "ds_process_way": "
（1）使用AVHRR SR V4自带的质量控制位标志筛选有效观测值，仅保留所有波段均有效的像元用于雪盖提取，无效像元标记为缺失值。\n
（2）基于Hori等人（2017）的方案，利用Landsat-5 TM数据作为真值，重点优化了BT37-BT11的阈值。\n
（3）基于Landsat-5 TM真值数据，提取雪区/非雪区的AVHRR多光谱特征（SR1、BT11、SR3/SR2及NDVI、NDSI），采用三级决策树算法确定最优阈值组合。\n
（4）将中国长期日积雪深度数据集、地表温度 （LST）、数字高程模型 （DEM）重新采样或聚合为5 km，与AVHRR的分辨率相匹配，针对初步记录中由于云或无效观测造成的空缺区域，采用一系列间隙填补技术进行填补，包括基于隐马尔可夫随机场（HMRF）的插值和雪深插值。结合地表温度和数字高程模型（DEM）进行后处理，以剔除错误识别的积雪区域。", "ds_quality": "
（1）使用混淆矩阵以及四种准确率指标包括总体精度（OA）、生产者精度（PA）、用户精度（UA）和Kappa 系数来评估本产品，OA范围在80%–90%之间，PA和UA范围在70%–90%，CK值范围为0.61至0.8。
（2）使用38年的CMA地面积雪深度测量191个站点验证本产品，大多气象站OA较高，普遍在80%–90%之间，但 PA、UA和CK的值较低。\n
（3）使用9张Landsat-5积雪覆盖面积图来进一步评估本产品，OA高达87.3%，UA较高和PA较低表明该产品在一定程度上存在低估积雪覆盖范围的倾向，Kappa值为0.695，这一数值也接近地面观测验证的kappa值（0.717）。\n
因此，无论是从“点”的角度（地面观测）还是“面”的角度（Landsat-5 SCE图）来看，本产品的精度都是可靠的。总体而言，本产品在气候及相关研究中具有良好的应用前景。", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "三江源", "ds_acq_lon_east": 103.0, "ds_acq_lat_south": 31.0, "ds_acq_lon_west": 88.0, "ds_acq_lat_north": 38.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 260391688, "ds_files_count": 8488, "ds_format": "*.tif", "ds_space_res": "5km", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "e27fdedb-c078-413b-ae5e-3c8caf4308be.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "1-01-01 15:15:29", "last_updated": "2025-04-29 14:58:53", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER-SNOW.DB6814.2025", "i18n": { "en": { "title": "Daily 5-km Gap-free AVHRR snow cover extent product over Three-River Source（1980-2020）", "ds_format": "*.tif", "ds_source": "
The AVHRR Surface Reflectance Version 4 (AVHRR SR V4) is a Climate Data Record (CDR) product released by the National Oceanic and Atmospheric Administration (NOAA). This product is generated based on data from the Advanced Very High Resolution Radiometer (AVHRR) sensors onboard NOAA polar-orbiting meteorological satellites. Covering the period from 1981 to 2019, it has a daily temporal resolution and a spatial resolution of 5 km. After undergoing radiometric calibration, atmospheric correction, and cloud detection processes, the dataset serves as a fundamental source for generating long-term snow cover products.Landsat-5 TM data are obtained from the Thematic Mapper sensor onboard the Landsat-5 satellite, which was jointly operated by the United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). The dataset has a 16-day temporal resolution, 30 m spatial resolution, and covers the time period from 1984 to 2013.ERA5-Land land surface temperature (LST) data are sourced from the ERA5-Land reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) (Muñoz Sabater, 2019). This dataset provides continuous global coverage from 1981 to the present, with a spatial resolution of 0.1°.The digital elevation model (DEM) data are derived from the Shuttle Radar Topography Mission (SRTM) led by NASA, with a spatial resolution of 90 m. All the above datasets were accessed and utilized via the Google Earth Engine (GEE) cloud computing platform.Ground-based snow depth observations were provided by the China Meteorological Administration (CMA) through its national meteorological station network. A total of 191 meteorological stations were selected, with data spanning from 1981 to 2019, for validating the snow cover product.The China Long-Term Daily Snow Depth Dataset, developed by Che et al. (2008) and Dai et al. (2015), is based on observations from multiple passive microwave satellite sensors. The dataset has been processed using inter-sensor calibration techniques. Covering the period from 1979 to 2020, it provides daily snow depth information over China at a spatial resolution of 0.25°. This dataset, available from the National Tibetan Plateau Data Center (https://doi.org/10.11888/Geogra.tpdc.270194), was used in this study as a supplementary strategy for gap-filling.", "ds_quality": "
(1) The product was evaluated using a confusion matrix and four accuracy metrics, including Overall Accuracy (OA), Producer’s Accuracy (PA), User’s Accuracy (UA), and the Kappa coefficient. The OA ranged between 80% and 90%, PA and UA ranged from 70% to 90%, and the Kappa coefficient ranged from 0.61 to 0.8.(2) Validation based on 38 years of CMA ground-based snow depth measurements from 191 meteorological stations showed that most stations had high OA values, generally between 80% and 90%. However, the PA, UA, and Kappa values were relatively low.(3) Further validation using nine Landsat-5 snow cover extent maps revealed an OA as high as 87.3%. The higher UA and lower PA indicate a slight tendency of the product to underestimate snow cover extent. The Kappa value was 0.695, which is close to that from ground-based validation (0.717).Therefore, from both the \"point\" perspective (ground observations) and the \"area\" perspective (Landsat-5 SCE maps), the accuracy of this product is reliable. Overall, this product shows great potential for applications in climate and related studies.", "ds_ref_way": "", "ds_abstract": "
As a sensitive indicator of climate change, snow cover plays a vital role in climate research, and long-term snow cover data are an essential foundation for such studies. Although existing snow cover extent datasets offer good quality and relatively high spatial and temporal resolution, their temporal coverage is often limited. In this study, we developed a daily snow cover extent product with a spatial resolution of 5 km covering the Sanjiangyuan region from 1980 to 2020. The product was generated using AVHRR surface reflectance data and Landsat-5 TM imagery, in combination with ground-based snow depth observations, China’s long-term daily snow depth dataset, land surface temperature, and DEM data. By applying an improved cloud detection algorithm, a multi-level snow discrimination algorithm, and a gap-filling strategy, we produced a reliable snow cover product. Validation results show that, compared to existing AVHRR-based snow cover products (e.g., the JASMES AVHRR product), our product demonstrates a significant improvement in overall accuracy by approximately 15%, with the omission error reduced from 60.8% to 19.7%, the commission error reduced from 31.9% to 21.3%, and the Cohen’s kappa coefficient increased by more than 114%. This dataset provides valuable support for monitoring snow cover dynamics and conducting climate change research in the Sanjiangyuan region.
", "ds_time_res": "日", "ds_acq_place": "Three-River Source", "ds_space_res": "5km", "ds_projection": "", "ds_process_way": "(1) Valid observations were selected using the quality control flags provided by AVHRR SR V4. Only pixels with valid values across all bands were retained for snow cover extraction, while invalid pixels were marked as missing values.(2) Based on the scheme proposed by Hori et al. (2017), the brightness temperature difference between BT37 and BT11 was optimized using Landsat-5 TM data as ground truth.(3) Using Landsat-5 TM data as reference, multispectral features of AVHRR over snow-covered and snow-free areas (including SR1, BT11, SR3/SR2, NDVI, and NDSI) were extracted. A three-level decision tree algorithm was then applied to determine the optimal combination of thresholds.(4) The China Long-Term Daily Snow Depth Dataset, land surface temperature (LST), and digital elevation model (DEM) were resampled or aggregated to 5 km resolution to match that of the AVHRR data. For missing areas in the preliminary records caused by clouds or invalid observations, a series of gap-filling techniques were applied, including hidden Markov random field (HMRF)-based interpolation and snow-depth interpolation. Postprocessing was conducted using land surface temperature and DEM data to eliminate falsely identified snow-covered pixels.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "积雪覆盖范围", "长时序", "AVHRR" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国", "三江源" ], "ds_time_tags": [ 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "赵子胜", "email": "zhaozisheng@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "郝晓华", "email": "haoxh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "李杭璇", "email": "2023521174@link.tyut.edu.cn", "work_for": "太原理工大学", "country": "中国" }, { "true_name": "钟歆玥", "email": "xyzhong@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "吴晓东", "email": "wuxd@lzb.ac.cn", "work_for": "中国科学院西北高原生物研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "赵子胜", "email": "zhaozisheng@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "郝晓华", "email": "haoxh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "积雪" }