{
    "created": "2026-09-29 11:23:54",
    "updated": "2026-10-08 08:55:54",
    "id": "d9141f2b-16dc-48d4-b054-23b0572b3909",
    "version": 10,
    "ds_topic": null,
    "title_cn": "高亚洲MODIS逐日无云500m积雪面积比例产品（2000-至今）",
    "title_en": "Daily Cloud-Free 500 m Snow Cover Fraction Product over High Mountain Asia from MODIS (2000–present)",
    "ds_abstract": "<p>亚洲高山区是北半球积雪分布最广、变化最剧烈的区域之一，积雪面积比例能在亚像元尺度上定量描述积雪的覆盖程度，相比二值积雪更适合反映复杂山区积雪的分布情况，是山区融雪径流模拟、气候变化预测的重要输入参数。本数据集针对高亚洲积雪特性，以MODIS反射率产品MOD09GA为数据源，采用分地类特征选择的多元自适应回归样条模型 LC-MARS 发展 MODIS FSC 反演算法，进而引入一套结合时空滤波、时空反距离权重插值及多源数据融合方法的时空多步融合算法STMF实现产品的完全去云，得到高亚洲MODIS逐日无云500m积雪面积比例数据集。该数据集以TIF文件格式存储，每个TIF文件为单波段栅格数据，像元值域为0–100，表示积雪面积比例（0=无雪，100=完全积雪），230=水体。以Landsat-8提取的FSC为参考真值验证模型的反演精度，总体精度、召回率分别为93.4%、97.1%，RMSE为0.148，MAE为0.093。本数据集将根据卫星遥感数据和算法的更新情况持续更新，并采用完全开放共享。</p>",
    "ds_source": "",
    "ds_process_way": "",
    "ds_quality": "",
    "ds_acq_start_time": null,
    "ds_acq_end_time": null,
    "ds_acq_place": "",
    "ds_acq_lon_east": 61.0,
    "ds_acq_lat_south": 23.0,
    "ds_acq_lon_west": 106.0,
    "ds_acq_lat_north": 46.0,
    "ds_acq_alt_low": null,
    "ds_acq_alt_high": null,
    "ds_share_type": "apply-access",
    "ds_total_size": 1307778679200,
    "ds_files_count": 0,
    "ds_format": "",
    "ds_space_res": "500米",
    "ds_time_res": "日",
    "ds_coordinate": "无",
    "ds_projection": "",
    "ds_thumbnail": "136c83f9-0f5e-412d-a813-c9976f4b5774.png",
    "ds_thumb_from": 0,
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    "paper_ref_way": "",
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    "ds_serv_phone": null,
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    "doi_value": "",
    "subject_codes": [
        "170"
    ],
    "quality_level": 0,
    "publish_time": "2026-10-08 15:15:31",
    "last_updated": "2026-10-08 16:44:54",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.ncdc.db7815.2026",
    "i18n": {
        "en": {
            "title": "Daily Cloud-Free 500 m Snow Cover Fraction Product over High Mountain Asia from MODIS (2000–present)",
            "ds_format": "",
            "ds_source": "",
            "ds_quality": "",
            "ds_ref_way": "",
            "ds_abstract": "High Mountain Asia (HMA) is one of the areas with the most widespread distribution and most severe changes in snow cover in the Northern Hemisphere. Fractional Snow Cover (FSC) can quantitatively describe the snow cover on a sub-pixel scale. The degree is more suitable to reflect the distribution of snow cover in complex mountainous areas than binary snow cover, and is an important input parameter for snowmelt runoff simulation and climate change prediction in mountainous areas. Aiming at the characteristics of high Asian snow cover, this dataset uses the MODIS reflectance product MOD09GA as the data source, and uses multiple adaptive regression splines selected by classification characteristics.(Multivariate Adaptive Regression Splines (MARS) model LC-MARS develops the MODIS FSC inversion algorithm and prepares FSC products for Asian alpine regions. It then introduces a spatio-temporal multi-step fusion algorithm that combines spatio-temporal filtering, spatio-temporal inverse distance weight interpolation and multi-source data fusion methods.(spatiotemporal multi-step fusion (STMF) achieves complete cloud removal of the product and obtains a high Asian MODIS daily cloud-free 500-meter snow cover area ratio data set. The dataset is stored in the TIF file format. Each TIF file is single-band raster data with a cell range of 0 - 100, which represents the proportion of the snow area (0= no snow, 100= complete snow), and 230= water body. The total Accuracy and Recall of FSC retrieved by the LC-MARS model are 93.4% and 97.1% respectively, the overall RMSE is 0.148, and the MAE is 0.093. The overall accuracy is high. This dataset will be updated in real time based on satellite remote sensing data and algorithm updates, and will be fully open and shared.",
            "ds_time_res": "on",
            "ds_acq_place": "",
            "ds_space_res": "500 meters",
            "ds_projection": "",
            "ds_process_way": "",
            "ds_ref_instruction": ""
        }
    },
    "submit_center_id": "ncdc",
    "data_level": 0,
    "recommendation_value": 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,
    "belong_to_nieer": true,
    "allow_update_data": true,
    "created_from": "uc",
    "ds_topic_tags": [
        "积雪",
        "积雪面积比例"
    ],
    "ds_subject_tags": [
        "地球科学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "亚洲高山区"
    ],
    "ds_time_tags": [
        2000,
        2001,
        2002,
        2003,
        2004,
        2005,
        2006,
        2007,
        2008,
        2009,
        2010,
        2011,
        2012,
        2013,
        2014,
        2015,
        2016,
        2017,
        2018,
        2019,
        2020,
        2021,
        2022
    ],
    "ds_contributors": [
        {
            "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": "赵琴",
            "email": "zhaoqin@tyust.edu.cn",
            "work_for": "太原科技大学车辆与交通工程学院",
            "country": "中国"
        },
        {
            "true_name": "高伟强",
            "email": "weiqiang_97@163.com",
            "work_for": "太钢集团岚县矿业有限公司",
            "country": "中国"
        },
        {
            "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_meta_authors": [
        {
            "true_name": "赵琴",
            "email": "zhaoqin@tyust.edu.cn",
            "work_for": "太原科技大学车辆与交通工程学院",
            "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"
        },
        {
            "true_name": "赵琴",
            "email": "zhaoqin@tyust.edu.cn",
            "work_for": "太原科技大学车辆与交通工程学院",
            "country": "中国"
        }
    ],
    "category": "积雪"
}