本数据集反映了我国松嫩平原土壤粉砂含量的分布情况，可为土壤物理性状和各种农业研究提供数据支持。数据集基于2020年的实地采样和实验室分析，覆盖了松嫩平原的主要农业区域，空间分辨率为10米，能够提供高精度的土壤粉砂含量信息。数据集的建立采用了先进的遥感技术和地理信息系统（GIS）方法，结合地面采样数据，确保了数据的准确性和可靠性。", "ds_source": "
本数据集来源于Science Data Bank网站（https://www.scidb.cn/en/detail?dataSetId=DS_8c64142471e54b5a95b410976976793b&version=V1）。", "ds_process_way": "
本数据集通过分析松嫩平原Sentinel-2的波段和光谱指数与土壤粉粒含量的关系，结合随机森林算法 (Random Forest, RF)，生产了松嫩平原10m分辨率的土壤粉粒含量。", "ds_quality": "
原始资料为Sentinel-2产品，使用广泛，精度可靠。对于粉粒，Sentinel-2影像和RF算法的决定系数（R2）和均方根误差（RMSE）分别为0.75和9.38%。", "ds_acq_start_time": "2020-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "松嫩平原", "ds_acq_lon_east": 127.0, "ds_acq_lat_south": 43.0, "ds_acq_lon_west": 122.0, "ds_acq_lat_north": 49.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 4741718806, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "10米", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "9db28847-c08d-4cef-bd13-322fcd3fca7c.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2025-02-27 15:43:54", "last_updated": "2026-01-14 10:54:05", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.SCIDB.DB6761.2025", "i18n": { "en": { "title": "10 meter soil particle content dataset in Songnen Plain (2020)", "ds_format": "TIFF", "ds_source": "
This dataset is sourced from the Science Data Bank website（ https://www.scidb.cn/en/detail?dataSetId=DS_8c64142471e54b5a95b410976976793b&version=V1 ）.", "ds_quality": "
The original data is Sentinel-2 product, which is widely used and has reliable accuracy. For powder particles, the coefficient of determination (R2) and root mean square error (RMSE) of Sentinel-2 imaging and RF algorithm are 0.75 and 9.38%, respectively.", "ds_ref_way": "", "ds_abstract": "
This dataset reflects the distribution of soil silt content in the Songnen Plain of China, and can provide data support for soil physical properties and various agricultural research. The dataset is based on field sampling and laboratory analysis in 2020, covering the main agricultural areas of the Songnen Plain with a spatial resolution of 10 meters, providing high-precision information on soil silt content. The establishment of the dataset adopts advanced remote sensing technology and geographic information system (GIS) methods, combined with ground sampling data, to ensure the accuracy and reliability of the data.
", "ds_time_res": "年", "ds_acq_place": "Songnen Plain", "ds_space_res": "10米", "ds_projection": "", "ds_process_way": "This dataset analyzed the relationship between the band and spectral index of Sentinel-2 and soil particle content in the Songnen Plain, and combined it with the Random Forest algorithm (RF) to produce soil particle content at a resolution of 10m in the Songnen Plain.", "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": [ "松嫩平原", "10米分辨率", "2020年", "土壤粉砂含量" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "松嫩平原" ], "ds_time_tags": [ 2020 ], "ds_contributors": [ { "true_name": "宋开山", "email": "songks@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": "宋开山", "email": "songks@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": "宋开山", "email": "songks@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": "生态" }