积雪是冰冻圈中分布最广泛、变化最显著的一员。其独特的高反照率、低热导率特性使其在调控地表能量收支、地气相互作用及水文过程方面发挥着关键作用。受限于早期传感器的空间分辨率、时间分辨率与可用时间序列，目前仍缺乏2000年以前、500m空间分辨率的长时间序列积雪范围（Snow Cover Extent, SCE）产品。为填补这一数据空白，研究中基于现有的SCPOT（Similar Conditional Probability & OTSU）算法，将其推广应用至整个中国地区，并针对原算法中的一些不足进行了改进。首先利用覆盖相同时间段的MODIS SCE与AVHRR SCE，计算了不同尺度下影像间的相似条件概率（SCP）。进而以1981—2000年期间内的5km AVHRR SCE为基础，在一系列后处理下生成了该套降尺度产品。产品空间分辨率为500m，时间分辨率为1天，数据命名格式为“YYYYMMDD.tif”，如“19810626.tif”，其中包含六种像元值（0=陆地，1=SCP识别积雪，2=时空立方体（STCPI）插值积雪，3=雪深插值积雪，4=水体，255=填充值）。本产品有效填补了该时段的数据空白，对于长时间序列下的气候变化研究、水资源管理与生态保护具有重要意义。
", "ds_source": "中国科学院西北生态环境资源研究院（NIEER）积雪研究团队基于MODIS地表反射率数据（MOD09GA/MYD09GA）与AVHRR地表反射率数据（AVHRR Surface Reflectance Version 4，AVHRR SR V4），结合多级决策树分类、隐马尔可夫模型及多源数据融合等方法，系统完成了积雪像元识别与云下像元填充工作，分别构建了逐日无云的MODIS SCE和AVHRR SCE数据集，为本研究提供了重要的基础数据支撑。此外，辅助数据包括Che等和Dai等基于多源卫星被动微波观测联合生成的被动微波雪深数据、ERA5陆面再分析地表温度产品和航天飞机雷达地形测量任务（SRTM）提供的90 m分辨率DEM。验证数据包括中国气象局（CMA）提供的积雪深度观测数据和多景Landsat-5影像。
", "ds_process_way": "首先利用覆盖相同时间段的MODIS SCE与AVHRR SCE计算不同尺度下影像间的相似条件概率（SCP），并利用大津法（Otsu’s method）获取到SCP影像的最优分割阈值。基于上述SCP影像与大津阈值，随后对1981-2000年期间内的5km AVHRR SCE进行降尺度处理。对于所得初步的降尺度结果，先后运用STCPI插值、被动微波雪深插值、温度-海拔积雪屏蔽等多种后处理方法，最终得到降尺度产品。
", "ds_quality": "产品基于高精度遥感数据，并在生产过程中对各步骤进行严格的质量把控。基于中国地区地面雪深观测数据的验证结果表明，降尺度产品的总体精度（OA）为0.84，召回率（RC）为0.77，精确率（PC）为0.93，科恩卡帕系数（CK）为0.69。进一步采用同时期Landsat-5 SCE数据进行验证，结果显示OA、RC、PC、CK分别为0.82、0.83、0.81、0.63。此外，精度评估还显示该产品在长时间序列中能够保持较高的年际稳定性，OA持续高于0.8，CK稳定在0.7左右。上述结果均表明降尺度产品具有良好的可靠性与稳定性，能够有效填补当前数据空白。
", "ds_acq_start_time": "1981-06-26 00:00:00", "ds_acq_end_time": "2000-02-27 00:00:00", "ds_acq_place": "中国陆域", "ds_acq_lon_east": 142.0, "ds_acq_lat_south": 16.0, "ds_acq_lon_west": 72.0, "ds_acq_lat_north": 56.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 12509326866, "ds_files_count": 6822, "ds_format": "TIF", "ds_space_res": "500米", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "cdf6d764-1b38-457d-a3f0-aa002a0fce23.jpg", "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": [ "170.45" ], "quality_level": 3, "publish_time": "2025-08-07 16:02:58", "last_updated": "2026-03-10 09:12:30", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.LZU.DB6929.2025", "i18n": { "en": { "title": "A long-term daily 500 m snow cover extent product over China (1981–2000)", "ds_format": "TIF", "ds_source": "Based on MODIS surface reflectance data (MOD09GA/MYD09GA) and AVHRR surface reflectance version 4 (AVHRR SR V4), combined with multi-level decision tree classification, hidden Markov model, multi-source data fusion and other methods, the snow cover research team of the Northwest Institute of Ecological Environment and Resources (NIEER) of the Chinese Academy of Sciences systematically completed snow pixel recognition and cloud pixel filling, respectively constructed the cloud free MODIS SCE and AVHRR SCE datasets day by day, providing important basic data support for this study. In addition, auxiliary data includes passive microwave snow depth data jointly generated by Che et al. and Dai et al. based on multi-source satellite passive microwave observations, ERA5 land surface reanalysis surface temperature products, and a 90 μ m resolution DEM provided by the Space Shuttle Radar Topography Mission (SRTM). The validation data includes snow depth observation data provided by the China Meteorological Administration (CMA) and multiple Landsat-5 images.
", "ds_quality": "The product is based on high-precision remote sensing data and strictly controls the quality of each step in the production process. The validation results based on ground snow depth observation data in China indicate that the overall accuracy (OA) of the downscaled product is 0.84, the recall rate (RC) is 0.77, the precision rate (PC) is 0.93, and the Coampa coefficient (CK) is 0.69. Further validation was conducted using Landsat-5 SCE data from the same period, and the results showed that OA, RC, PC, and CK were 0.82, 0.83, 0.81, and 0.63, respectively. In addition, accuracy evaluation also shows that the product can maintain high interannual stability in a long time series, with OA consistently above 0.8 and CK stable at around 0.7. The above results indicate that the downscaled products have good reliability and stability, and can effectively fill the current data gap.
", "ds_ref_way": "", "ds_abstract": "Snow is the most widely distributed and significantly changing member of the cryosphere. Its unique high albedo and low thermal conductivity characteristics make it play a key role in regulating surface energy balance, ground atmosphere interactions, and hydrological processes. Due to the limited spatial resolution, temporal resolution, and available time series of early sensors, there is still a lack of long-term series Snow Cover Extend (SCE) products with a spatial resolution of 500m before 2000. To fill this data gap, the study extended the existing SCBOT (Similar Conditional Probability&OTSU) algorithm to the entire China region and made improvements to address some of the shortcomings in the original algorithm. Firstly, the similarity conditional probability (SCP) between images at different scales was calculated using MODIS SCE and AVHRR SCE covering the same time period. Furthermore, based on the 5km AVHRR SCE during the period of 1981-2000, this set of downscaled products was generated through a series of post-processing. The spatial resolution of the product is 500m, the temporal resolution is 1 day, and the data naming format is \"YYYYMMDD. tif\", such as \"19810626. tif\", which contains six pixel values (0=land, 1=SCP identified snow, 2=spatiotemporal cube (STCPI) interpolated snow, 3=snow depth interpolated snow, 4=water, 255=fill value). This product effectively fills the data gap during this period and is of great significance for long-term climate change research, water resource management, and ecological protection.
", "ds_time_res": "日", "ds_acq_place": "", "ds_space_res": "500米", "ds_projection": "", "ds_process_way": "Firstly, the similarity conditional probability (SCP) between images at different scales is calculated using MODIS SCE and AVHRR SCE covering the same time period, and the optimal segmentation threshold for SCP images is obtained using Otsu's method. Based on the above SCP images and Otsu threshold, the 5km AVHRR SCE from 1981 to 2000 was subsequently downscaled. For the preliminary downscaling results obtained, various post-processing methods such as STCPI interpolation, passive microwave snow depth interpolation, temperature altitude snow cover shielding, etc. were successively applied to finally obtain the downscaled product.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/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": [ "积雪范围", "降尺度", "500米" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国陆域" ], "ds_time_tags": [ 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000 ], "ds_contributors": [ { "true_name": "沈言龙", "email": "shenyl2023@lzu.edu.cn", "work_for": "兰州大学资源环境学院", "country": "中国" }, { "true_name": "王晓艳", "email": "wangxiaoy@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" }, { "true_name": "祝瑞祥", "email": "zhurx2023@lzu.edu.cn", "work_for": "兰州大学资源环境学院", "country": "中国" }, { "true_name": "梁诗", "email": "liangsh2024@lzu.edu.cn", "work_for": "兰州大学资源环境学院", "country": "中国" }, { "true_name": "车涛", "email": "chetao@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": "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" } ], "ds_meta_authors": [ { "true_name": "沈言龙", "email": "shenyl2023@lzu.edu.cn", "work_for": "兰州大学资源环境学院", "country": "中国" } ], "ds_managers": [ { "true_name": "王晓艳", "email": "wangxiaoy@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "积雪" }