地表水精细制图对水资源管理、生态环境保护以及洪涝灾害的防治具有重要作用。然而，松辽流域尚缺乏高精度、长时间序列的地表水分布数据集。为弥补这一空白，本文基于多源卫星遥感数据（Landsat 5/7/8/9），依托Google Earth Engine（GEE）平台，构建了一套适用于松辽流域的地表水提取算法，生成了1984至2023年的年度地表水分布数据集。该数据集包括年度水体频率数据（WBF）和水体分类数据（SWC），覆盖季节性水体、永久性水体以及最大水体分布。通过精度评估，水体提取方法的总体精度达到了98%，Kappa系数为0.95。为了验证数据的可靠性，将SWC数据与全球水体分类历史数据集（GWS）进行了对比分析，结果表明，松辽流域SWC数据集的分类精度在多个区域优于GWS数据集。本研究提供的高精度水体数据集将为松辽流域的水资源监测和管理提供重要的支持，也将为相关领域的科学研究奠定坚实的数据基础。
", "ds_source": "本数据集涵盖了1984至2023年间中国东北松辽流域的年度地表水频率数据（Water Body Frequency, WBF）和地表水分类数据（Surface Water Classification, SWC）。SWC数据包含永久性水体、季节性水体和最大水体的类别信息。数据以栅格格式呈现，采用WGS-84地理坐标系，空间分辨率为30 m，文件格式为*.tif，命名规则为“YYYY.tif”（其中YYYY表示对应年份）。地表水频率数据（WBF）的波段值范围为0至1，表示水体观测频率的浮点数值，用于反映不同年份水体出现的频率。地表水分类数据（SWC）的波段值为0和1，其中0表示非水体，1表示水体，用于区分水体与非水体的空间分布。该数据集为松辽流域的地表水动态监测和水资源管理提供了精确的时空数据支持，可在水文、生态及环境保护等领域发挥重要作用。
", "ds_process_way": "（1）利用GEE工具，对Landsat-5/7/8/9进行数据去云、预处理，投影坐标系为WGS84；
（2）对Landsat-5/7/8/9影像进行水体提取；
（3）使用HAND地形指数对水体数据进行矫正并对水体提取方法的精度进行评估；
（4）计算水体频率数据集和水体类别数据集。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "1984-01-01 00:00:00", "ds_acq_end_time": "2023-12-31 00:00:00", "ds_acq_place": "松辽流域", "ds_acq_lon_east": 135.14249999999998, "ds_acq_lat_south": 38.25, "ds_acq_lon_west": 115.75, "ds_acq_lat_north": 53.25, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 45989811335, "ds_files_count": 176, "ds_format": "*.tif", "ds_space_res": "30米", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "Albers Equal Area Conic Projection System", "ds_thumbnail": "361a036a-299e-4c06-a025-376101d9ee8c.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "lihongxing@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170" ], "quality_level": 3, "publish_time": "2024-12-19 13:28:32", "last_updated": "2026-01-13 08:49:29", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.CSD.DB6687.2024", "i18n": { "en": { "title": "Annual Surface Water Distribution Dataset of the Songliao Basin, 1984-2023", "ds_format": "*.tif", "ds_source": "This dataset covers annual surface water frequency (WBF) and surface water classification (SWC) data for the Songliao River Basin in Northeast China from 1984 to 2023. SWC data contains category information for permanent water bodies, seasonal water bodies, and maximum water bodies. The data is presented in raster format using the WGS-84 geographic coordinate system, with a spatial resolution of 30 meters and a file format of *. tif. The naming convention is \"YYYY. tif\" (where YYYY represents the corresponding year). The band value range of surface water frequency data (WBF) is 0 to 1, which represents the floating point value of water observation frequency and is used to reflect the frequency of water occurrence in different years. The band values of surface water classification data (SWC) are 0 and 1, where 0 represents non water bodies and 1 represents water bodies, used to distinguish the spatial distribution of water bodies and non water bodies. This dataset provides accurate spatiotemporal data support for surface water dynamic monitoring and water resource management in the Songliao River Basin, and can play an important role in hydrology, ecology, and environmental protection", "ds_quality": "
The data quality is good", "ds_ref_way": "", "ds_abstract": "
Fine mapping of surface water plays an important role in water resource management, ecological environment protection, and flood prevention and control. However, the Songliao River Basin still lacks high-precision, long-term surface water distribution datasets. To fill this gap, this article constructs a surface water extraction algorithm suitable for the Songliao River Basin based on multi-source satellite remote sensing data (Landsat 5/7/8/9) and the Google Earth Engine (GEE) platform, generating an annual surface water distribution dataset from 1984 to 2023. This dataset includes annual water body frequency data (WBF) and water body classification data (SWC), covering seasonal water bodies, permanent water bodies, and maximum water body distribution. Through precision evaluation, the overall accuracy of the water extraction method reached 98%, with a Kappa coefficient of 0.95. In order to verify the reliability of the data, a comparative analysis was conducted between the SWC data and the Global Water Classification History (GWS) dataset. The results showed that the classification accuracy of the SWC dataset in the Songliao Basin was better than that of the GWS dataset in multiple regions. The high-precision water body dataset provided in this study will provide important support for water resource monitoring and management in the Songliao River Basin, and will also lay a solid data foundation for scientific research in related fields
", "ds_time_res": "年", "ds_acq_place": "Songliao River Basin", "ds_space_res": "30米", "ds_projection": "Albers Equal Area Conic Projection System", "ds_process_way": "(1) Using the GEE tool, cloud removal and preprocessing were performed on Landsat-5/7/8/9 data, with a projection coordinate system of WGS84
(2) Extract water bodies from Landsat-5/7/8/9 images
(3) Use HAND terrain index to correct water data and evaluate the accuracy of water extraction methods
(4) Calculate the dataset of water frequency and water category", "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": [ "松辽流域", "地表水分布", "水体分类", "水体频率" ], "ds_subject_tags": [ "地球科学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国，松辽流域" ], "ds_time_tags": [ 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, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "张兴旺", "email": "zhangxw@henu.edu.cn", "work_for": "河南大学地理与环境学院", "country": "中国" }, { "true_name": "张延玲", "email": "157866918@qq.com", "work_for": "松辽水利委员会松辽流域水土保持监测中心站", "country": "中国" }, { "true_name": "夏浩铭", "email": "xiahm@vip.henu.edu.cn", "work_for": "河南大学地理与环境学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张兴旺", "email": "zhangxw@henu.edu.cn", "work_for": "河南大学地理与环境学院", "country": "中国" }, { "true_name": "张延玲", "email": "157866918@qq.com", "work_for": "松辽水利委员会松辽流域水土保持监测中心站", "country": "中国" }, { "true_name": "夏浩铭", "email": "xiahm@vip.henu.edu.cn", "work_for": "河南大学地理与环境学院", "country": "中国" } ], "ds_managers": [ { "true_name": "夏浩铭", "email": "xiahm@vip.henu.edu.cn", "work_for": "河南大学地理与环境学院", "country": "中国" } ], "category": "水文" }