土壤湿度是区域水循环的关键变量，在水资源和农业干旱管理中有着重要的应用。各种全球土壤湿度产品大多是从微波遥感数据中检索到的。然而，目前在国家尺度上，空间明确、时间连续、高分辨率的土壤湿度信息很少。本数据集通过随机森林(RF)算法，基于大量土壤湿度的日常原位观测，生成了1993-2018年中国旱地小麦和玉米的1公里土壤湿度数据集(ChinaCropSM1 km)。
\n本数据独立使用全国农业气象站(AMSs)的现场观测数据(181 327个样本)进行训练(164 202个样本)，使用其他数据(17 125个样本)进行测试。首先根据作物类型(即小麦、玉米)、土壤深度(0-10厘米、10-20厘米)和物候特征开发灌溉模块。根据作物类型和土壤深度分别制作了4个每日数据集，它们的精度都令人满意(小麦r 0.93, ubRMSE 0.033 m3 m−3;玉米r 0.93, ubRMSE 0.035 m3 m−3)。包括基于全球遥感的地表土壤湿度数据集(RSSSM)和欧洲空间局(ESA)气候变化倡议(CCI) SM在内的全球土壤湿度产品，ChinaCropSM1 km的时空分辨率和精度均显著优于前者(r提高116%，ubRMSE降低64%)。该方法可应用于全球其他地区和作物，改进后的数据集对农业干旱监测和作物产量预测等研究和田间管理具有重要价值。", "ds_source": "
全国农业气象站(AMSs)的现场观测数据。", "ds_process_way": "
通过随机森林(RF)算法，基于大量土壤湿度的日常原位观测，生成了1993-2018年中国旱地小麦和玉米的1公里土壤湿度数据集(ChinaCropSM1 km)。", "ds_quality": "
RF模型预测的ChinaCropSM1 km与原位SM观测结果吻合良好（ubRMSE范围为0.028-0.037，偏差范围为 -0.0011-0.0009，r范围为0.925-0.944，R 2范围为0.860-0.895）。", "ds_acq_start_time": "1993-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "北方干旱半干旱地区,黄土高原,黄淮海平原,四川盆地,长江中下游平原,云贵高原及华南地区,青藏地区", "ds_acq_lon_east": 135.0, "ds_acq_lat_south": 4.25, "ds_acq_lon_west": 73.0, "ds_acq_lat_north": 53.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 35038691339, "ds_files_count": 2, "ds_format": "TIFF", "ds_space_res": "1000m", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "2aa82bcd-5cb9-4809-9bcb-6d087d8b4eb2.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45", "170.55" ], "quality_level": 3, "publish_time": "2024-03-26 14:01:28", "last_updated": "2026-01-14 11:08:34", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6453.2024", "i18n": { "en": { "title": "Fine dataset of daily soil moisture for 1km of wheat and corn in arid regions of China (1993-2018)", "ds_format": "TIFF", "ds_source": "
On site observation data from National Agricultural Meteorological Stations (AMSs).", "ds_quality": "
The ChinaCropSM1 km predicted by the RF model is in good agreement with the in-situ SM observation results (ubRMSE range is 0.028-0.037, deviation range is -0.0011-0.0009, r range is 0.925-0.944, R2 range is 0.860-0.895).", "ds_ref_way": "", "ds_abstract": "
Soil moisture is a key variable in regional water cycle and has important applications in water resources and agricultural drought management. Most global soil moisture products are retrieved from microwave remote sensing data. However, currently at the national scale, there is a lack of spatially clear, temporally continuous, and high-resolution soil moisture information. This dataset uses the Random Forest (RF) algorithm to generate a 1-kilometer soil moisture dataset (ChinaCropSM1 km) for dryland wheat and corn in China from 1993 to 2018 based on a large number of daily in-situ observations of soil moisture.
\nThis data was independently trained using on-site observation data from the National Agricultural Meteorological Stations (AMSs) (181327 samples) (164202 samples), and tested using other data (17125 samples). Firstly, develop irrigation modules based on crop types (i.e. wheat, corn), soil depth (0-10 cm, 10-20 cm), and phenological characteristics. Four daily datasets were created based on crop type and soil depth, and their accuracy was satisfactory (wheat r 0.93, ubRMSE 0.033 m3 m − 3; Corn r 0.93, ubRMSE 0.035 m3 m − 3). The global soil moisture products, including the Surface Soil Moisture Dataset based on Global Remote Sensing (RSSSM) and the European Space Agency (ESA) Climate Change Initiative (CCI) SM, show significantly better spatiotemporal resolution and accuracy of ChinaCropSM1 km compared to the former (with a 116% increase in r and a 64% decrease in ubRMSE). This method can be applied to other regions and crops around the world, and the improved dataset has important value for research and field management such as agricultural drought monitoring and crop yield prediction.
", "ds_time_res": "日", "ds_acq_place": "Arid and semi-arid areas in the north, Loess Plateau, Huang Huai Hai Plain, Sichuan Basin, the Middle and Lower Yangtze Valley Plain, the Yunnan-Guizhou Plateau and South China, Qinghai Tibet region", "ds_space_res": "1000m", "ds_projection": "", "ds_process_way": "A 1-kilometer soil moisture dataset (ChinaCrop SM1 km) for dryland wheat and corn in China from 1993 to 2018 was generated based on daily in-situ observations of a large amount of soil moisture using the Random Forest (RF) algorithm.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "旱地小麦", "旱地玉米", "土壤湿度" ], "ds_subject_tags": [ "地理学", "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "北方干旱半干旱地区", "黄土高原", "黄淮海平原", "四川盆地", "长江中下游平原", "云贵高原及华南地区", "青藏地区" ], "ds_time_tags": [ 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 ], "ds_contributors": [ { "true_name": "庄慧敏", "email": "zhuanghuimin@mail.bnu.edu.cn", "work_for": "北京师范大学减灾与应急管理研究院", "country": "中国" }, { "true_name": "张朝", "email": "zhangzhao@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "庄慧敏", "email": "zhuanghuimin@mail.bnu.edu.cn", "work_for": "北京师范大学减灾与应急管理研究院", "country": "中国" }, { "true_name": "张朝", "email": "zhangzhao@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "庄慧敏", "email": "zhuanghuimin@mail.bnu.edu.cn", "work_for": "北京师范大学减灾与应急管理研究院", "country": "中国" }, { "true_name": "张朝", "email": "zhangzhao@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "category": "生态" }