本数据集针对吉林典型农田区，主要包括吉林省长春市、松原市、白城市、四平市，利用2017–2020年2月1日至3月20日的1小时步长、10km×10km分辨率的ERA5数据集(主要数据要素包括浅层土壤温度、雪深、浅层土壤湿度)以及数据集中每个格网对应的经度和纬度信息，利用多元线性回归分析制作了2017–2020年东北典型农田区积雪影响下的春季土壤墒情专题图。\n
首先利用MATLAB软件编程把一小时步长的浅层土壤温度数据、雪深数据、浅层土壤湿度数据处理成日平均数据集。接着根据数理统计中多元线性回归分析确定2017–2018年的气象站点实测土壤湿度数据与浅层土壤温度数据、雪深数据、浅层土壤湿度数据、经度信息、纬度信息的关系，从而得到东北典型农田区积雪影响下的春季逐日0-10cm土壤墒情拟合模型。\n
利用2019年站点实测土壤湿度数据对土壤墒情拟合模型进行验证，结果表明模型具有较高的模拟精度（RMSE=0.04 m3/m3）。最后根据构建土壤墒情拟合模型得到2017年–2020年10km×10km分辨率的东北典型农田区积雪影响下春季（2月1日–3月20日）土壤墒情专题图数据。数据采用完全开放共享。", "ds_source": "
原始数据的主要来源于ERA5产品数据。", "ds_process_way": "
原始数据的主要来源于ERA5产品数据，对ERA5产品进行加工处理。首先读取1小时步长的ERA5数据集（浅层土壤温度数据、雪深数据、浅层土壤湿度数据），通过日平均获得东北典型农田区春季逐日的10km×10km分辨率的浅层土壤温度数据、雪深数据、浅层土壤湿度数据；\n
对2017–2018年的气象站点实测土壤湿度数据与浅层土壤温度数据、雪深数据、浅层土壤湿度数据进行数据离群值的检验，剔除离群值。接着根据数理统计中多元线性回归分析确定2017–2018年的气象站点实测土壤湿度数据与浅层土壤温度数据、雪深数据、浅层土壤湿度数据、经度信息、纬度信息的关系，从而得到东北典型农田区春季逐日0–10cm土壤墒情拟合模型，模型如下：Y = –266.2921 + 0.2732 * (ERA5_Temp – 273.15) + 9.2996 * ERA5_SD + 0.0540 * ERA5_Mois * 100 + 2.0190 * Lon+0.5245 * Lat;\n
其中ERA5_Temp代表该像元点ERA5产品的0–7cm土壤温度值，ERA5_SD代表该像元点ERA5产品的雪深值，ERA5_Mois代表该像元点ERA5产品的0–7cm土壤湿度值，Lon代表该像元点经度，Lat代表该像元点纬度，Y为拟合的0–10cm土壤湿度；\n
最后根据构建的土壤墒情拟合模型得到2017年–2020年10km×10km分辨率的东北典型农田区积雪影响下春季（2月1日–3月20日）土壤墒情专题图数据。", "ds_quality": "
数据质量控制阶段分为两步：首先数据提供者对验证数据集进行了数据准确性检查，对残差大于95% 的实测土壤温度的离群点进行了剔除，并利用实测土壤湿度值对土壤墒情拟合模型进行验证，表明结果具有较高的模拟精度（RMSE=0.04 m3/m3）；\n
另一方面对数据的存储方式、数据格式进行统一与规范。第二阶段邀请专家对数据进行审核，审核数据的完整性和准确性。", "ds_acq_start_time": "2017-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "吉林省中部农田区", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 3707925, "ds_files_count": 2, "ds_format": "tif.geotif.jpg", "ds_space_res": "10km*10km", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "35681e7a-2d35-4021-98fa-941a3edea3e8.png", "ds_thumb_from": 0, "ds_ref_way": "李晓峰，2017–2020年东北典型农田区积雪影响下春季土壤墒情数据，国家冰川冻土沙漠科学数据中心(www.ncdc.ac.cn)，2023，doi：10.12072/ncdc.isnow.db2718.2023", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "aba68fe5-65d3-41b1-b036-bc274a834b5e", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.isnow.db2718.2023", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2023-01-25 16:49:39", "last_updated": "2023-02-16 08:51:04", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2718.2023", "i18n": { "en": { "title": "Spring soil moisture data under the influence of snow cover in typical agricultural areas in Northeast China from 2017 to 2020", "ds_format": "", "ds_source": "
\nThe raw data mainly comes from ERA5 product data.", "ds_quality": "
\nThe data quality control stage is divided into two steps: first, the data provider checked the data accuracy of the validation data set, removed the outliers of the measured soil temperature with the residual error greater than 95%, and verified the soil moisture fitting model with the measured soil moisture value, indicating that the results have high simulation accuracy (RMSE=0.04 m3/m3);\n
On the other hand, unify and standardize the data storage mode and data format. In the second stage, experts are invited to review the data to verify the integrity and accuracy of the data.", "ds_ref_way": "", "ds_abstract": "
\nThis data set is aimed at typical agricultural areas in Jilin Province, mainly including Changchun City, Songyuan City, Baicheng City, Siping City, Jilin Province. It uses the 1-hour step length and 10km from February 1, 2017 to March 20, 2020 × The ERA5 data set with 10km resolution (the main data elements include shallow soil temperature, snow depth, and shallow soil moisture) and the longitude and latitude information corresponding to each grid in the data set have been used to produce a thematic map of spring soil moisture under the influence of snow cover in typical agricultural areas in Northeast China from 2017 to 2020 using multiple linear regression analysis.\n
Firstly, the one-hour step shallow soil temperature data, snow depth data and shallow soil moisture data are processed into daily average data sets by using MATLAB software. Then, according to the multiple linear regression analysis in mathematical statistics, determine the relationship between the measured soil moisture data of meteorological stations in 2017 – 2018 and the shallow soil temperature data, snow depth data, shallow soil moisture data, longitude information, latitude information, so as to obtain the daily 0-10cm soil moisture fitting model under the influence of snow cover in typical agricultural areas in the northeast.\n
The soil moisture fitting model was verified by using the measured soil moisture data at the station in 2019, and the results showed that the model had high simulation accuracy (RMSE=0.04 m3/m3). Finally, 10km from 2017 to 2020 is obtained according to the construction of soil moisture fitting model × The thematic map data of soil moisture in spring (from February 1 to March 20) under the influence of snow cover in the typical farmland area in northeast China with a resolution of 10km. Data is fully open and shared.
", "ds_time_res": "日", "ds_acq_place": "Farmland area in central Jilin Province", "ds_space_res": "10km*10km", "ds_projection": "", "ds_process_way": "\nThe original data mainly comes from ERA5 product data, and ERA5 products are processed. First, read the ERA5 data set (shallow soil temperature data, snow depth data, and shallow soil moisture data) in 1-hour steps, and obtain the daily average of 10km in spring in typical agricultural areas in Northeast China × Shallow soil temperature data, snow depth data and shallow soil moisture data with 10km resolution;\n
Test the outliers of the measured soil moisture data, shallow soil temperature data, snow depth data and shallow soil moisture data at meteorological stations in 2017 – 2018, and eliminate the outliers. Then, according to the multiple linear regression analysis in mathematical statistics, determine the relationship between the measured soil moisture data at meteorological stations in 2017 – 2018 and the shallow soil temperature data, snow depth data, shallow soil moisture data, longitude information, and latitude information, so as to obtain the daily 0 – 10cm soil moisture fitting model of typical agricultural areas in the northeast of China in spring. The model is as follows: Y=– 266.2921+0.2732 * (ERA5_Temp – 273.15)+9.2996 * ERA5_ SD + 0.0540 * ERA5_ Mois * 100 + 2.0190 * Lon+0.5245 * Lat;\n
Where ERA5_ Temp represents the 0 – 7cm soil temperature value of the pixel ERA5 product, ERA5_ SD represents the snow depth value of ERA5 product at the pixel point, ERA5_ Mois represents the 0 – 7cm soil humidity value of the ERA5 product of the pixel point, Lon represents the longitude of the pixel point, Lat represents the latitude of the pixel point, and Y represents the fitted 0 – 10cm soil humidity;\n
Finally, 10km from 2017 to 2020 is obtained according to the constructed soil moisture fitting model × The thematic map data of soil moisture in spring (from February 1 to March 20) under the influence of snow cover in the typical farmland area in northeast China with a resolution of 10km.", "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": [ "土壤温度", "雪深", "浅层土壤湿度" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "吉林省中部农田区" ], "ds_time_tags": [ 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "李晓峰", "email": "lixiaofeng@iga.ac.cn", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李晓峰", "email": "lixiaofeng@iga.ac.cn", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "李晓峰", "email": "lixiaofeng@iga.ac.cn", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" } ], "category": "积雪" }