{
    "created": "2024-09-25 12:51:10",
    "updated": "2026-08-08 09:15:34",
    "id": "dbf0ea02-ce81-4b17-ad4e-9c9ab829bcd4",
    "version": 8,
    "ds_topic": null,
    "title_cn": "帕米尔-天山地区1979-2013年积雪遥感产品数据集",
    "title_en": "",
    "ds_abstract": "<p>本数据集包含2000年至2011年积雪面积比例产品数据、1979年至2011年微波雪水当量遥感产品数据,1979年至2013年融雪空间分布模拟产品数据和1979年至2013年积雪表面反照率数据，共115份NETCDF格式栅格数据，投影采用WGS84坐标系，空间分辨率0.01°~1°，时间分辨率为逐日和逐月。数据时间序列连续，空间范围完整。</p>",
    "ds_source": "<p>1、2001-2012年Terra卫星的逐日MOD10A1产品。\n2、美国国家雪冰数据中心（NSIDC）处理的SMMR（1978-1987年)，SSM/I（1987-2008年)和AMSR-E(2002-2012)逐日被动微波亮温数据。\n3、GLDAS陆面同化系统CLM、MOSAIC与NOAH陆面过程模型模拟数据。</p>",
    "ds_process_way": "<p>1.在MODIS逐日积雪产品—MOD10A1的基础上，采用基于三次样条函数插值的去云算法进行去云处理以及区域裁剪后得到新的帕米尔-天山地区积雪面积比例产品。\n2.  通过对不同传感器的亮温进行交叉定标提高亮温数据在时间上的一致性。然后利用Chang算法针对中国地区进行修正的算法进行雪深反演。采用亮温梯度法并考虑积雪特性在时间上的变化从SSM/I（S）和AMSR-E亮度温度数据中提取每日雪深，并得到最大雪深分布情况。\n3.  提取研究区GLDAS陆面同化系统中CLM、MOSAIC与NOAH三个陆面过程模型的融雪和反照率模拟结果，采用三次样条函数插值方法进行重采样。</p>",
    "ds_quality": "<ol>\n<li>积雪面积比例产品：时间分辨率，逐日；空间分辨率， 0.01 度。</li>\n<li>微波雪水当遥感量产品：时间分辨率，逐日；空间分辨率，0.25 度。</li>\n<li>陆面反照率产品：时间分辨率，逐月；空间分辨率， 1 度。</li>\n<li>融雪空间分布产品：时间分辨率，逐月；空间分辨率， 1 度。\n以上各项数据时间序列连续，空间范围完整，检查无坏值</li>\n</ol>",
    "ds_acq_start_time": "1979-01-01 00:00:00",
    "ds_acq_end_time": "2013-12-31 00:00:00",
    "ds_acq_place": "帕米尔-天山区",
    "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": 23257275005,
    "ds_files_count": 2,
    "ds_format": "栅格数据",
    "ds_space_res": "0.01°",
    "ds_time_res": "日，月",
    "ds_coordinate": "WGS84",
    "ds_projection": "WGS84",
    "ds_thumbnail": "dbf0ea02-ce81-4b17-ad4e-9c9ab829bcd4.jpg",
    "ds_thumb_from": 2,
    "ds_ref_way": "",
    "paper_ref_way": "",
    "ds_ref_instruction": "方晖. 1979-2010年帕米尔-天山地区500米积雪遥感综合产品数据集. 国家冰川冻土沙漠科学数据中心(http://www.ncdc.ac.cn), 2019. https://cstr.cn/CSTR:11738.11.NCDC.NIEER.2021.1973.\r\n方晖. 1979-2010年帕米尔-天山地区500米积雪遥感综合产品数据集. 国家冰川冻土沙漠科学数据中心(http://www.ncdc.ac.cn), 2019. https://www.doi.org/10.12072/ncdc.nieer.db3091.2023.",
    "ds_from_station": null,
    "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6",
    "doi_value": "",
    "subject_codes": [
        "170.45"
    ],
    "quality_level": 1,
    "publish_time": "2024-09-27 16:45:25",
    "first_publish_time": "2024-09-27 16:45:25",
    "last_updated": "2025-04-09 17:52:51",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.NCDC.NIEER.2021.1973",
    "license": null,
    "extra": null,
    "files_shape": [
        {
            "name": "天山帕米尔微波积雪数据.rar",
            "size": 28401595,
            "is_dir": false
        },
        {
            "name": "天山帕米尔积雪数据.rar",
            "size": 23228873410,
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    ],
    "features": null,
    "data_level": 0,
    "i18n": {
        "en": {
            "title": "Data set of snow remote sensing products in Pamir-Tianshan region from 1979 to 2013",
            "ds_abstract": "<p>This dataset includes snow cover area proportion product data from 2000 to 2011, microwave snow-water equivalent remote sensing product data from 1979 to 2011, snow melt spatial distribution simulation product data from 1979 to 2013, and snow cover surface albedo data from 1979 to 2013. A total of 115 NETCDF format grid data are projected using the WGS84 coordinate system, spatial resolution is 0.01°~1°, and temporal resolution is daily and monthly. The data time series is continuous and the spatial range is complete. </p>",
            "ds_source": "<p>1. Terra satellite's daily MOD10A1 products from 2001 to 2012.\n2. Daily passive microwave brightness temperature data processed by the National Snow and Ice Data Center (NSIDC) from SMMR (1978-1987), SSM/I (1987-2008) and AMSR-E(2002-2012).\n3. Simulated data from GLDAS land surface assimilation systems CLM, MOSAIC and NOAH land surface process models. </p>",
            "ds_process_way": "<p>1. Based on the MODIS daily snow cover product-MOD10A1, a new snow cover area ratio product for Pamir-Tianshan area is obtained by using a cloud removal algorithm based on cubic spline function interpolation to remove clouds and region clipping.\n2.  The temporal consistency of brightness temperature data is improved by cross-calibrating the brightness temperatures of different sensors. Then Chang algorithm is used to retrieve snow depth, which is modified for China. The brightness temperature gradient method was used to extract daily snow depths from SSM/I (S) and AMSR-E brightness temperature data and take into account the temporal changes of snow cover characteristics, and the maximum snow depth distribution was obtained.\n3.  The snowmelt and albedo simulation results of three land surface process models (CLM, MOSAIC and NOAH) in the GLDAS land surface assimilation system in the study area were extracted, and re-sampled using the cubic spline function interpolation method. </p>",
            "ds_quality": "<ol>\n<li>Snow area ratio product: temporal resolution, day-to-day; spatial resolution, 0.01 degrees. </li>\n<li>Microwave snow and water as remote sensing products: temporal resolution, day-to-day; spatial resolution, 0.25 degrees. </li>\n<li>Land surface albedo products: temporal resolution, month by month; spatial resolution, 1 degree. </li>\n<li>Snowmelt spatial distribution products: temporal resolution, month by month; spatial resolution, 1 degree.\nThe time series of the above data is continuous and the spatial range is complete. Check that there are no bad values</li>\n</ol>",
            "ds_acq_place": "Tianshan District",
            "ds_ref_instruction": "Fang Hui. 1979-2010 Comprehensive product data set of remote sensing of the 500-meter snow cover in the Pamir-Tianshan area in 2009. National Glacier, Frozen Soil and Desert Scientific Data Center (www.ncdc.ac.cn), 2019. https://cstr.cn/CSTR:11738.11.NCDC.NIEER.2021.1973.\r\nFang Hui. 1979-2010 Comprehensive product data set of remote sensing of the 500-meter snow cover in the Pamir-Tianshan area in 2009. National Glacier, Frozen Soil and Desert Scientific Data Center (www.ncdc.ac.cn), 2019. https://www.doi.org/10.12072/ncdc.nieer.db3091.2023.",
            "ds_format": "raster data",
            "ds_projection": "WGS84",
            "ds_space_res": "0.01°",
            "ds_time_res": "day, month"
        }
    },
    "license_type": null,
    "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",
        "融雪"
    ],
    "ds_subject_tags": [
        "地理学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "帕米尔",
        "天山"
    ],
    "ds_time_tags": [
        1979,
        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
    ],
    "ds_contributors": [
        "方晖"
    ],
    "ds_meta_authors": [
        "方晖"
    ],
    "ds_managers": [
        "李锦"
    ],
    "category": "遥感再分析数据"
}