尽管中国是世界上最大的大豆消费国和第四大豆生产国，但缺乏描绘中国大豆种植面积的高分辨率年度地图。为了弥补这一差距，我们开发了一种称为基于物候和像素的大豆面积制图（PPS）的新方法，该方法基于来自Google Earth Engine（GEE）平台的Sentinel-2遥感图像。我们利用各种辅助数据（例如，耕地层、详细的物候观测）来选择最有效地将大豆与不同地区其他作物区分开来的独特特征。然后将这些特征输入到无监督分类器（K-means）中，并通过基于动态时间扭曲（DTW）的后分类方法确定最可能的类型。我们首次生成了2017-2021年中国大豆种植面积数据集，空间分辨率高达10米。2017-2020年，县级和地级的制图结果与人口普查数据之间的值始终在0.85左右。此外，2017年、2018年和2019年现场制图结果的总体准确率分别为77%、84%和88%。与现有基于田间样本和监督分类方法的东北地区10m作物类型地图相比，制图精度显著提高31%，根据其与人口普查数据的一致性，特别是在县一级。ChinaSoyArea10m在空间上与现有的两个数据集吻合较好。ChinaSoyArea10m为可持续大豆生产和管理以及农业系统建模和优化提供了重要信息。", "ds_source": "
Google Earth Engine（GEE）平台的Sentinel-2遥感图像。", "ds_process_way": "
无监督分类器（K-means），并通过基于动态时间扭曲（DTW）的后分类方法确定最可能的类型。", "ds_quality": "
为了弥补我国长期大豆种植面积图的不足，我们首先生成了2017—2021年中国主要产区大豆种植面积数据集，空间分辨率为10 m（ChinaSoyArea10m）。与现有数据集相比，ChinaSoyArea10m与人口普查数据具有更高的一致性，空间细节进一步完善。该数据集可为后续产量监测和粮食安全研究提供可靠支持。", "ds_acq_start_time": "2017-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 140.0, "ds_acq_lat_south": 20.0, "ds_acq_lon_west": 70.0, "ds_acq_lat_north": 40.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 8579915077, "ds_files_count": 6, "ds_format": "tif", "ds_space_res": "10 m", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "0a5a8216-6eb6-44be-b283-ca611e6d8360.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" ], "quality_level": 3, "publish_time": "2024-04-25 15:54:04", "last_updated": "2026-01-14 10:37:48", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6433.2024", "i18n": { "en": { "title": "ChinaSoyArea10m: a dataset of soybean planting areas with a spatial resolution of 10 m across China from 2017 to 2021", "ds_format": "tif", "ds_source": "
Sentinel-2 remote sensing images on the Google Earth Engine (GEE) platform.", "ds_quality": "
In order to compensate for the lack of long-term soybean planting area maps in China, we first generated a dataset of soybean planting areas in major production areas from 2017 to 2021, with a spatial resolution of 10 meters (ChinaSoyArea10m). Compared with existing datasets, ChinaSoyArea10m has higher consistency with census data and further improves spatial details. This dataset can provide reliable support for subsequent yield monitoring and food security research.", "ds_ref_way": "", "ds_abstract": "
Although China is the world's largest soybean consumer and fourth largest soybean producer, there is a lack of high-resolution annual maps depicting the area of soybean cultivation in China. To address this gap, we have developed a new method called Phenology and Pixel Based Soybean Area Mapping (PPS), which is based on Sentinel-2 remote sensing images from the Google Earth Engine (GEE) platform. We use various auxiliary data, such as cultivated land layers and detailed phenological observations, to select the unique features that most effectively distinguish soybeans from other crops in different regions. Then these features are input into an unsupervised classifier (K-means) and the most likely type is determined through a post classification method based on dynamic time warping (DTW). We have generated the first dataset of soybean planting area in China from 2017 to 2021, with a spatial resolution of up to 10 meters. From 2017 to 2020, the value between the mapping results at the county and prefecture levels and the census data remained around 0.85. In addition, the overall accuracy of on-site mapping results in 2017, 2018, and 2019 was 77%, 84%, and 88%, respectively. Compared with the existing 10m crop type map in Northeast China based on field samples and supervised classification methods, the mapping accuracy has significantly improved by 31%, especially at the county level, based on its consistency with census data. ChinaSoyArea10m spatially matches well with the existing two datasets. ChinaSoyArea10m provides important information for sustainable soybean production and management, as well as agricultural system modeling and optimization.
", "ds_time_res": "年", "ds_acq_place": "China", "ds_space_res": "10 m", "ds_projection": "", "ds_process_way": "Unsupervised classifier (K-means) and determines the most likely type through a post classification method based on dynamic time warping (DTW).", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/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, "belong_to_nieer": false, "ds_topic_tags": [ "大豆", "中国", "10m" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "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": "张朝", "email": "zhangzhao@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "张朝", "email": "zhangzhao@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "category": "生态" }