本数据集包含：
\n\n（1）样地1；\n
\n（2）样地2；\n
\n（3）扁都口样方GPS点坐标及样地描述；\n
\n（4）Roughness-预试验扁都口1；\n
\n（5）Roughness-预试验扁都口2；\n
\n本数据可为发展和验证微波遥感算法提供基本数据。数据集主要内容有上游寒区预试验时扁都口样方1和扁都口样方2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。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。
\n", "ds_source": "扁都口各样方均为3Grid×3Grid，每个Grid均为30m×30m。其计算公式见《微波遥感》第二卷234-236页。粗糙度数据文件中，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_process_way": "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\n粗糙度数据文件中，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_quality": "数据质量良好
", "ds_acq_start_time": "2007-10-17 00:00:00", "ds_acq_end_time": "2007-10-18 00:00:00", "ds_acq_place": "黑河流域,上游寒区水文试验区,扁都口加密观测区", "ds_acq_lon_east": 100.44305555555556, "ds_acq_lat_south": 38.04333333333333, "ds_acq_lon_west": 100.58972222222222, "ds_acq_lat_north": 38.973888888888894, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 530887972, "ds_files_count": 2, "ds_format": "jpg", "ds_space_res": null, "ds_time_res": "时", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "6f4da4f4-c996-431d-b897-f032b2ed4c7e.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.db1798.2022", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2022-03-14 16:42:46", "last_updated": "2025-06-30 16:08:37", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.NIEER.2021.1728", "i18n": { "en": { "title": "Heihe integrated remote sensing joint test: surface roughness data set of biandukou intensive observation area during the pre-test period (2007)", "ds_format": "jpg", "ds_source": "Each quadrat of flat mouth is 3grid × 3grid, 30m for each grid × 30m。 The calculation formula is shown in pages 234-236, Volume II, microwave remote sensing. In the roughness data file, 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_quality": "
Good data quality
", "ds_ref_way": "", "ds_abstract": "This dataset contains:
\n(1) Sample plot 1;\n
\n（2) Sample plot 2;\n
\n（3) GPS point coordinates and plot description of biandukou quadrat;\n
\n（4) Rough - pre test flat mouth 1;\n
\n（5) Rough - pre test flat mouth 2;\n
\nThis data can provide basic data for the development and verification of microwave remote sensing algorithms. The data set mainly includes the original photos of roughness measurement at each sampling point of biandukou quadrat 1 and biandukou quadrat 2 during the pre-test in the upstream cold area, and the calculation results of surface height standard deviation (CM) and correlation length (CM). 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.
", "ds_time_res": "时", "ds_acq_place": "Heihe River Basin, upstream cold region, hydrological test area, biandukou intensive observation area", "ds_space_res": "", "ds_projection": "", "ds_process_way": "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
\nIn the roughness data file, 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_ref_instruction": "This data is generated by \"Heihe integrated remote sensing joint test\". When using the data, users should clearly state the source of the data in the text and quote the reference method provided by this metadata in the reference part." } }, "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", "大地测量", "数字化采样" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "上游寒区水文试验区", "黑河流域", "扁都口加密观测区" ], "ds_time_tags": [ 2007 ], "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" } ], "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" } ], "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" }