{
    "created": "2019-12-23 09:42:32",
    "updated": "2026-05-02 01:33:14",
    "id": "40a110eb-dd3c-463b-8c48-b79e65e504ef",
    "version": 3,
    "ds_topic": null,
    "title_cn": "黑河综合遥感联合试验：阿柔加密观测区地表粗糙度数据集（2008年）",
    "title_en": "Joint experiment of Heihe comprehensive remote sensing: surface roughness data set of ARW intensive observation area",
    "ds_abstract": "<p>&emsp;&emsp;本数据包括上游寒区水文试验中阿柔样方1（A1），阿柔样方2（A2），阿柔样方3（A3），阿柔样带1（L1），阿柔样带2（L2），阿柔样带3（L3），阿柔样带4（L4），阿柔样带5（L5）和阿柔样带6（L6）中各采样点的粗糙度测量原始照片，及表面高度标准离差（cm）和相关长度（cm）的计算结果。\n</p>\n<p>&emsp;&emsp;每个采样点均按照南北向和东西向分别测量1次，粗糙度板长110cm，测量点间距1cm。阿柔各样方在预试验期时为3Grid×3Grid，加强试验期时扩展为4Grid×4Grid，每个Grid均为30m×30m；阿柔各样带为南北向朝向，样带上各采样点间距为100m。相片命名规则如下，以A3-1EW为例，表示阿柔样方3（A3）中的1号采样点东西向的粗糙度板测量照片。本数据可为发展和验证微波遥感算法提供基本的地面数据集。\n</p>\n<p>&emsp;&emsp;粗糙度数据通过数码照相采集，然后在ArcView软件下，对照片中每根辐条的顶端以及板子的四角做手工数字化采样，获得其图像坐标值，经过几何校正后，计算得到每根辐条的高度，然后按公式计算表面高度标准离差和表面相关长度。其计算公式见《微波遥感》第二卷234-236页。粗糙度数据中首先是样点名称，之后数据正文包括4列（编号、文件名、标准离差、相关长度）。每一个文件名，即txt文件对应一张采样照片，标准离差（cm）与相关长度（cm）即代表了粗糙度。之后是每张照片中101根辐条的长度，属于中间结果，用以检查校正。</p>",
    "ds_source": "<p>&emsp;&emsp;每个采样点均按照南北向和东西向分别测量1次，粗糙度板长110cm，测量点间距1cm。阿柔各样方在预试验期时为3Grid×3Grid，加强试验期时扩展为4Grid×4Grid，每个Grid均为30m×30m；阿柔各样带为南北向朝向，样带上各采样点间距为100m。相片命名规则如下，以A3-1EW为例，表示阿柔样方3（A3）中的1号采样点东西向的粗糙度板测量照片。本数据可为发展和验证微波遥感算法提供基本的地面数据集。</p>",
    "ds_process_way": "<p>&emsp;&emsp;粗糙度数据通过数码照相采集，然后在ArcView软件下，对照片中每根辐条的顶端以及板子的四角做手工数字化采样，获得其图像坐标值，经过几何校正后，计算得到每根辐条的高度，然后按公式计算表面高度标准离差和表面相关长度。其计算公式见《微波遥感》第二卷234-236页。\n<p>&emsp;&emsp;粗糙度数据中首先是样点名称，之后数据正文包括4列（编号、文件名、标准离差、相关长度）。每一个文件名，即txt文件对应一张采样照片，标准离差（cm）与相关长度（cm）即代表了粗糙度。之后是每张照片中101根辐条的长度，属于中间结果，用以检查校正。</p>",
    "ds_quality": "<p>&emsp;&emsp;数据质量良好",
    "ds_acq_start_time": "2008-04-06 00:00:00",
    "ds_acq_end_time": "2008-04-08 00:00:00",
    "ds_acq_place": "黑河流域,上游寒区水文试验区,阿柔加密观测区",
    "ds_acq_lon_east": 100.44305555555556,
    "ds_acq_lat_south": 38.04333333333333,
    "ds_acq_lon_west": 100.58972222222222,
    "ds_acq_lat_north": 38.97666666666667,
    "ds_acq_alt_low": null,
    "ds_acq_alt_high": null,
    "ds_share_type": "login-access",
    "ds_total_size": 718353361,
    "ds_files_count": 2,
    "ds_format": "JPG",
    "ds_space_res": null,
    "ds_time_res": "时",
    "ds_coordinate": "WGS84",
    "ds_projection": "",
    "ds_thumbnail": "40a110eb-dd3c-463b-8c48-b79e65e504ef.png",
    "ds_thumb_from": 0,
    "ds_ref_way": "",
    "paper_ref_way": "",
    "ds_ref_instruction": "本数据由“黑河综合遥感联合试验”产生，用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。",
    "ds_from_station": null,
    "organization_id": "14df1d8b-6362-4c0f-b88e-b46d4abe5db9",
    "ds_serv_man": "敏玉芳",
    "ds_serv_phone": "0931-4967596 ",
    "ds_serv_mail": "ncdc@lzb.ac.cn",
    "doi_value": "10.12072/ncdc.NIEER.db1908.2022",
    "subject_codes": [
        "170.4510"
    ],
    "quality_level": 3,
    "publish_time": "2022-03-15 18:12:49",
    "last_updated": "2025-06-30 16:05:45",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.ncdc.NIEER.2021.1768",
    "i18n": {
        "en": {
            "title": "Joint experiment of Heihe comprehensive remote sensing: surface roughness data set of ARW intensive observation area",
            "ds_format": "JPG",
            "ds_source": "<p>&emsp; Each sampling point is measured once in north-south direction and east-west direction respectively. The roughness plate is 110cm long and the distance between measurement points is 1cm. Argyle quadrat is 3grid in the pre-test period × 3grid, extended to 4grid during the enhanced test period × 4grid, 30m for each grid × 30m； The A'rou belt is north-south, and the spacing of sampling points on the sample belt is 100m. The photo naming rules are as follows. Taking a3-1ew as an example, it represents the East-West roughness plate measurement photo of No. 1 sampling point in flexible quadrat 3 (A3). This data can provide a basic ground data set for the development and verification of microwave remote sensing algorithms</p>",
            "ds_quality": "<p>&emsp; Good data quality",
            "ds_ref_way": "",
            "ds_abstract": "<p>  This data includes the original photos of roughness measurement at each sampling point in agile sample 1 (A1), agile sample 2 (A2), agile sample 3 (A3), agile sample 1 (L1), agile sample 2 (L2), agile sample 3 (L3), agile sample 4 (L4), agile sample 5 (L5) and agile sample 6 (L6) in the hydrological test in the upstream cold region, And the calculation results of surface height standard deviation (CM) and correlation length (CM).\n</p>\n<p>  Each sampling point is measured once in north-south direction and east-west direction respectively. The roughness plate is 110cm long and the distance between measurement points is 1cm. Argyle quadrat is 3grid in the pre-test period × 3grid, extended to 4grid during the enhanced test period × 4grid, 30m for each grid × 30m； The A'rou belt is north-south, and the spacing of sampling points on the sample belt is 100m. The photo naming rules are as follows. Taking a3-1ew as an example, it represents the East-West roughness plate measurement photo of No. 1 sampling point in flexible quadrat 3 (A3). This data can provide a basic ground data set for the development and verification of microwave remote sensing algorithms.\n</p>\n<p>  The roughness data is collected by digital photography, and then the top of each spoke in the photo and the four corners of the board are manually digitally sampled under ArcView software to obtain the image coordinate value. After geometric correction, the height of each spoke is calculated, and then the surface height standard deviation and surface correlation length are calculated according to the formula. The calculation formula is shown in pages 234-236, Volume II, microwave remote sensing. In the roughness data, the first is the sample name, and then the data body includes 4 columns (number, file name, standard deviation and correlation length). Each file name, i.e. TXT file, corresponds to a sampling photo, and the standard deviation (CM) and correlation length (CM) represent the roughness. This is followed by the length of 101 spokes in each photo, which belongs to the intermediate result to check and correct</p>",
            "ds_time_res": "时",
            "ds_acq_place": "Heihe River Basin, upstream cold region, hydrological test area",
            "ds_space_res": "",
            "ds_projection": "",
            "ds_process_way": "<p>&emsp; The roughness data is collected by digital photography, and then the top of each spoke in the photo and the four corners of the board are manually digitally sampled under ArcView software to obtain the image coordinate value. After geometric correction, the height of each spoke is calculated, and then the surface height standard deviation and surface correlation length are calculated according to the formula. The calculation formula is shown in pages 234-236, Volume II, microwave remote sensing.\n<p>&emsp; In the roughness data, the first is the sample name, and then the data body includes 4 columns (number, file name, standard deviation and correlation length). Each file name, i.e. TXT file, corresponds to a sampling photo, and the standard deviation (CM) and correlation length (CM) represent the roughness. This is followed by the length of 101 spokes in each photo, which belongs to the intermediate result to check and correct</p>",
            "ds_ref_instruction": "This data was generated by the \"Heihe Comprehensive Remote Sensing Joint Experiment\". When using the data, please clearly state the source of the data in the main text and cite the citation provided by this metadata in the reference section."
        }
    },
    "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": [
        "黑河流域",
        "地表高度标准差",
        "地表粗糙度数据",
        "大地测量"
    ],
    "ds_subject_tags": [
        "自然地理学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "黑河联合观测",
        "阿柔加密观测区",
        "黑河流域"
    ],
    "ds_time_tags": [
        2008
    ],
    "ds_contributors": [
        {
            "true_name": "晋锐",
            "email": "jinrui@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": "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": "韩旭军",
            "email": "hanxj@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": "李新",
            "email": "lixin@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": "王树果",
            "email": "sgwang@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": "晋锐",
            "email": "jinrui@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": "王树果",
            "email": "sgwang@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_managers": [
        {
            "true_name": "晋锐",
            "email": "jinrui@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": "遥感及产品"
}