本数据来自大野口流域关滩森林站超级样地，该超级样地乔木植被为青海云杉纯林，样地大小为100m×100m。 该数据应用中科院寒旱所自制的粗糙度测量板和数码相机，在整个超级样地内选取了41个格网点，其中有25个角点和16个中心点（代表16个超级样地的子样方），每个位置观测2次，并拍照。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。\n
\n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。b包括原始照片，及表面高度标准离差（cm）和表面相关长度（cm）的计算结果。相片处理是在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
\n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。
", "ds_source": "该数据应用中科院寒旱所自制的粗糙度测量板和数码相机，在整个超级样地内选取了41个格网点，其中有25个角点和16个中心点（代表16个超级样地的子样方），每个位置观测2次，并拍照。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。
\n", "ds_process_way": "相片处理是在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", "ds_quality": "数据质量良好
\n", "ds_acq_start_time": "2008-06-21 00:00:00", "ds_acq_end_time": "2008-06-21 00:00:00", "ds_acq_place": "黑河流域,大野口流域加密观测区,森林水文试验区,大野口关滩森林站超级样地", "ds_acq_lon_east": 100.2875, "ds_acq_lat_south": 38.46666666666667, "ds_acq_lon_west": 100.30333333333333, "ds_acq_lat_north": 38.666666666666664, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 118442205, "ds_files_count": 2, "ds_format": "word,txt, jpg", "ds_space_res": null, "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "5deb0203-a663-4807-93b8-b4247cacc99c.png", "ds_thumb_from": 0, "ds_ref_way": "谭俊磊, 白云洁, 车涛, 屈永华, 周红敏，黑河综合遥感联合试验：大野口关滩森林站超级样地地表粗糙度观测数据集（2008年），国家冰川冻土沙漠科学数据中心(www.ncdc.ac.cn)，2019，doi：10.12072/ncdc.NIEER.db1901.2022", "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.db1901.2022", "subject_codes": null, "quality_level": 3, "publish_time": "2022-03-15 18:15:11", "last_updated": "2022-03-15 18:15:11", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.NIEER.2021.1908", "i18n": { "en": { "title": "Heihe River Integrated Remote Sensing joint experiment: surface roughness observation data set of super sample plot of Dayekou guantan forest station (2008)", "ds_format": "", "ds_source": "Using the roughness measurement board and digital camera made by the Institute of cold and drought of Chinese Academy of Sciences, 41 grid points are selected in the whole super sample plot, including 25 corner points and 16 center points (representing the sub sample square of 16 super samples). Each position is observed twice and photographed. 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\n
", "ds_quality": "Good data quality
\n", "ds_ref_way": "", "ds_abstract": "This data comes from the super sample plot of guantan forest station in Dayekou basin. The tree vegetation of the super sample plot is pure Qinghai Spruce Forest, and the sample plot size is 100m × 100m。 Using the roughness measurement board and digital camera made by the Institute of cold and drought of Chinese Academy of Sciences, 41 grid points are selected in the whole super sample plot, including 25 corner points and 16 center points (representing the sub sample square of 16 super samples). Each position is observed twice and photographed. 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.\n
\nThis data can provide a basic ground data set for the development and verification of microwave remote sensing algorithms. B include the original photos and the calculation results of the standard deviation of surface height (CM) and surface correlation length (CM). Photo processing is to manually digitize the top of each spoke and the four corners of the board in the photo under ArcView software to obtain its image coordinate value. After geometric correction, calculate the height of each spoke, and then calculate the surface height standard deviation and surface correlation length according to the formula. The calculation formula is shown in pages 234-236, Volume II, microwave remote sensing.\n
\nIn 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
", "ds_time_res": "日", "ds_acq_place": "Heihe River Basin, Dayekou basin intensive observation area, forest hydrological test area, Dayekou guantan Forest Station Super sample plot", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Photo processing is to manually digitize the top of each spoke and the four corners of the board in the photo under ArcView software to obtain its image coordinate value. After geometric correction, calculate the height of each spoke, and then calculate the surface height standard deviation and surface correlation length according to the formula. The calculation formula is shown in pages 234-236, Volume II, microwave remote sensing\n
", "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": [ "地表粗糙度", "表面高度标准离差", "大地测量/重力" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黑河流域", "大野口流域加密观测区", "森林水文试验区", "大野口关滩森林站超级样地" ], "ds_time_tags": [ 2008 ], "ds_contributors": [ { "true_name": "谭俊磊", "email": "tanjunlei@163.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "白云洁", "email": "baiyj27@163.com", "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": "屈永华", "email": "qyh@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" }, { "true_name": "周红敏", "email": "zhouhm@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "谭俊磊", "email": "tanjunlei@163.com", "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": "谭俊磊", "email": "tanjunlei@163.com", "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" }