本数据使用自动机器学习算法，融合多源数据，基于国际土壤湿度网络和中国气象局土壤湿度观测数据，得到青藏高原2000-2021年5层0.1度日土壤湿度数据集。使用不同算法得到了三个版本的数据，分别为AMSMQTP_base、AMSMQTP_best和AMSMQTP_ensemble存放在三个文件夹内。每个文件夹共五层从layer1-layer5，分别代表0-10cm、10-30cm、30-50cm、50-80cm和80-110cm。每个文件的命名方式为\"%Y_%m.nc\"，\"%Y\"代表年（2000-2021），\"%m\"代表月（01-12）。
", "ds_source": "土壤湿度观测数据来自国际土壤湿度网络和中国气象局。用于模型输入的气象数据来自ERA5-Land，地表数据来自ERA5-Land、GLDAS-2.1和reprocessed MODIS V6.1，土壤质地数据来自GSDE，地形数据来自GTOPO30。
", "ds_process_way": "本数据基于FLAML自动机器学习框架，利用多源数据作为模型输入，根据站点观测土壤湿度进行训练，使用模型推理得到土壤湿度数据。
", "ds_quality": "AMSMQTP_ensemble在三个版本中最优，在青藏高原五层土壤湿度评价指标如下：(R=0.73, RMSE=0.074, ubRMSE=0.047); (R=0.665, RMSE=0.069, ubRMSE=0.042); (R=0.433, RMSE=0.08, ubRMSE=0.045); (R=0.503, RMSE=0.077, ubRMSE=0.035); (R=0.351, RMSE=0.084, ubRMSE=0.038)。AMSMQTP_base, AMSMQTP_best和AMSMQTP_ensemble土壤湿度误差总体上低于GLDAS-2.1, ERA5-Land, SMCI1.0_9km和GSSM1km。
", "ds_acq_start_time": "2000-01-27 00:00:00", "ds_acq_end_time": "2021-12-27 00:00:00", "ds_acq_place": "青藏高原", "ds_acq_lon_east": 105.0, "ds_acq_lat_south": 20.0, "ds_acq_lon_west": 65.0, "ds_acq_lat_north": 45.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 82510848965, "ds_files_count": 4, "ds_format": "NetCDF", "ds_space_res": "0.1度", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "b8fd29de-75ff-42f3-9691-a0ec46f2d02a.png", "ds_thumb_from": 0, "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": "09314967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15", "170.55" ], "quality_level": 3, "publish_time": "2024-05-15 11:10:05", "last_updated": "2024-09-27 08:57:24", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6463.2024", "i18n": { "en": { "title": "The dataset of daily soil moisture at 0.1-degree resolution over five layers on the Qinghai-Tibet Plateau from 2000 to 2021.", "ds_format": "", "ds_source": "The soil moisture observation data is sourced from the International Soil Moisture Network and the China Meteorological Administration. Meteorological data used for model inputs are obtained from ERA5-Land, surface data from ERA5-Land, GLDAS-2.1, and reprocessed MODIS V6.1, soil texture data from GSDE, and terrain data from GTOPO30.
", "ds_quality": "AMSMQTP_ensemble performs the best among the three versions, with the following evaluation metrics for soil moisture across five layers on the Qinghai-Tibet Plateau: (R=0.73, RMSE=0.074, ubRMSE=0.047); (R=0.665, RMSE=0.069, ubRMSE=0.042); (R=0.433, RMSE=0.08, ubRMSE=0.045); (R=0.503, RMSE=0.077, ubRMSE=0.035); (R=0.351, RMSE=0.084, ubRMSE=0.038). AMSMQTP_base, AMSMQTP_best, and AMSMQTP_ensemble overall exhibit lower soil moisture errors compared to GLDAS-2.1, ERA5-Land, SMCI1.0_9km, and GSSM1km.
", "ds_ref_way": "", "ds_abstract": "This dataset was generated using automatic machine learning algorithms, integrating multiple data sources including the International Soil Moisture Network and soil moisture observation data from the China Meteorological Administration. It provides daily soil moisture data at a resolution of 0.1 degrees over five layers on the Qinghai-Tibet Plateau from 2000 to 2021. Three versions of the data were produced using different algorithms: AMSMQTP_base, AMSMQTP_best, and AMSMQTP_ensemble, stored in three separate folders. Each folder contains data for five layers, representing depths from layer 1 to layer 5, corresponding to 0-10cm, 10-30cm, 30-50cm, 50-80cm, and 80-110cm, respectively. The files are named following the format \"%Y_%m.nc\", where \"%Y\" represents the year (2000-2021), and \"%m\" represents the month (01-12).
", "ds_time_res": "日", "ds_acq_place": "The Qinghai Tibet Plateau", "ds_space_res": "0.1度", "ds_projection": "", "ds_process_way": "This data is based on the FLAML automatic machine learning framework, utilizing multiple data sources as model inputs. It is trained based on soil moisture observations from monitoring stations, and soil moisture data is obtained through model inference.
", "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": [ "土壤湿度" ], "ds_subject_tags": [ "大气科学", "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "罗斯琼", "email": "lsq@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": "李卓群", "email": "m15004059308@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": "谭晓晴", "email": "t892398232@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": "郝晓华", "email": "haoxh@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": "罗斯琼", "email": "lsq@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": "李卓群", "email": "m15004059308@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": "罗斯琼", "email": "lsq@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": "李卓群", "email": "m15004059308@163.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "水文" }