本数据集针对吉林省典型农田区，主要包括吉林省长春市、松原市、白城市、四平市，基于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(包括浅层土壤温度数据、雪深数据)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年东北典型农田区春季逐日土壤温度数据集。首先利用MATLAB软件编程把一小时步长的浅层土壤温度数据、雪深数据处理成日平均数据集，然后利用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温度数据与产品的浅层土壤温度数据、雪深数据、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浅层(0-10cm)土壤温度拟合模型。利用2019年站点实测土壤温度数据对浅层土壤温度拟合模型进行验证，表明结果具有较高的模拟精度（RMSE=1.6℃）。最后根据构建的浅层土壤温度拟合模型得到2017年–2020年10km×10km分辨率的东北典型农田区春季（2月1日–3月20日）逐日0–10cm的土壤温度数据集。
", "ds_source": "原始数据的主要来源于ERA5产品数据
", "ds_process_way": "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温度数据与浅层土壤温度数据、雪深数据、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浅层土壤温度拟合模型，模型如下：\nY = (–23.1034 + 0.8101 * (ERA5_Temp–273.15) + 2.5341 * ERA5_SD + 0.2465 * Lon–0.2077 * Lat) + 273.15；\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，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土壤温度。\n最后根据构建的东北典型农田区春季逐日0–10cm土壤温度拟合模型得到2017年–2020年10km*10km分辨率的东北典型农田区春季（2月1日–3月20日）逐日0–10cm的土壤温度数据集。
", "ds_quality": "\"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），通过日平均获得东北典型农田区春季逐日的10km10km分辨率的浅层土壤温度数据和雪深数据。对2017–2018年的气象站点实测土壤温度数据与浅层土壤温度数据、雪深数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温度数据与浅层土壤温度数据、雪深数据、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浅层土壤温度拟合模型。最后根据构建的东北典型农田区春季逐日0–10cm土壤温度拟合模型得到2017年–2020年10km10km分辨率的东北典型农田区春季（2月1日–3月20日）逐日0–10cm的土壤温度数据集。\n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，并利用实测土壤温度值对浅层土壤温度拟合模型进行验证，表明结果具有较高的模拟精度（RMSE=1.6℃）。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。\n\"
", "ds_acq_start_time": "2017-02-01 00:00:00", "ds_acq_end_time": "2020-03-20 00:00:00", "ds_acq_place": "本图集的研究区域主要为吉林省典型农田地区（包括吉林省长春市、松原市、白城市、四平市）", "ds_acq_lon_east": 131.71666666666667, "ds_acq_lat_south": 41.596944444444446, "ds_acq_lon_west": 122.66666666666667, "ds_acq_lat_north": 45.47222222222222, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 1802526, "ds_files_count": 5, "ds_format": "tif", "ds_space_res": "10000", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "cbd65373-f70f-471c-b5e0-2c5696b4a2cc.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "本数据集可使用地理信息系统软件进行读取,也可使用MATLAB等软件读取。", "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.I-SNOW.db1662.2022", "subject_codes": [], "quality_level": 3, "publish_time": "2022-01-06 12:11:15", "last_updated": "2023-05-12 18:37:07", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2512.2022", "i18n": { "en": { "title": "Daily soil temperature data set of typical farmland areas in Northeast China in spring (2017-2020)", "ds_format": "", "ds_source": " <pre><code> <pre><code> The original data mainly comes from era5 product data\n <pre><code> <pre><code> The data quality control stage is divided into two steps: firstly, the data provider checks the data accuracy of the validation data set, removes the outliers of the measured soil temperature with a residual error greater than 95%, and verifies the shallow soil temperature fitting model with the measured soil temperature value, which shows that the result has high simulation accuracy (RMSE = 1.6 ℃). 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 review the integrity and accuracy of the data.\n <pre><code> <pre><code> This data set is aimed at typical farmland areas in Jilin Province, mainly including Changchun City, Songyuan City, Baicheng city and Siping City in Jilin Province, based on the 1-hour step length and 10km from February 1 to March 20, 2017-2020 × Based on the era5 data set with a resolution of 10km (including shallow soil temperature data and snow depth data) and the longitude and latitude information corresponding to each grid in the data set, the daily soil temperature data set in spring in typical farmland areas in Northeast China from 2017 to 2020 was prepared by using multiple linear regression analysis. Firstly, the shallow soil temperature data and snow depth data in one hour steps are processed into daily average data sets by MATLAB software programming, and then the relationship between the measured soil temperature data of meteorological stations in 2017-2018 and the shallow soil temperature data, snow depth data, longitude information and latitude information of products is determined by multiple linear regression analysis in mathematical statistics, The fitting model of daily shallow soil temperature (0-10cm) in spring in typical farmland area of Northeast China is obtained. The shallow soil temperature fitting model is verified by using the measured soil temperature data of the station in 2019, which shows that the result has high simulation accuracy (RMSE = 1.6 ℃). Finally, 10km from 2017 to 2020 is obtained according to the constructed shallow soil temperature fitting model × Soil temperature data set of 0 – 10cm day by day in spring (February 1 – March 20) in typical farmland area of Northeast China with a resolution of 10km.\n <pre><code> <pre><code> The original data mainly comes from era5 product data, and era5 products are processed. Firstly, read the era5 data set (shallow soil temperature data and snow depth data) in 1-hour steps, and obtain the shallow soil temperature data and snow depth data with daily resolution of 10km * 10km in the typical farmland area of Northeast China in spring through daily average. Test the outliers of the measured soil temperature data, shallow soil temperature data and snow depth data of meteorological stations from 2017 to 2018, and eliminate the outliers. Then, according to the multiple linear regression analysis in mathematical statistics, the relationship between the measured soil temperature data of meteorological stations from 2017 to 2018 and the shallow soil temperature data, snow depth data, longitude information and latitude information is determined, so as to obtain the daily shallow soil temperature fitting model of typical farmland areas in Northeast China in spring. The model is as follows:\nY = (–23.1034 + 0.8101 * (ERA5_Temp–273.15) + 2.5341 * ERA5_ SD + 0.2465 * Lon–0.2077 * Lat) + 273.15；\nIncluding era5_ Temp represents the 0 – 7cm soil temperature value of era5 product at this pixel point, era5_ SD represents the snow depth value of era5 product of the pixel point, lon represents the longitude of the pixel point, lat represents the latitude of the pixel point, and Y is the fitted 0 – 10cm soil temperature.\nFinally, according to the constructed daily 0-10cm soil temperature fitting model of northeast typical farmland area in spring, the daily 0-10cm soil temperature data set of northeast typical farmland area in spring (February 1-March 20) with a resolution of 10km * 10km from 2017 to 2020 is obtained.
", "ds_ref_instruction": " This data set can be read by GIS software or MATLAB and other software." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "东北典型农田区", "积雪影响", "春季土壤温度" ], "ds_subject_tags": [], "ds_class_tags": [], "ds_locus_tags": [ "吉林省长春市、松原市、白城市、四平市的典型农田区" ], "ds_time_tags": [ 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "陈秀雪", "email": "", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" }, { "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" }, { "true_name": "姜波", "email": "", "work_for": "吉林省墒情监测中心", "country": "中国" }, { "true_name": "卫颜霖", "email": "", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" }, { "true_name": "王广蕊", "email": "", "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" }