本数据为利用sentinel-2号反射率产品数据进行进一步加工，其中原始数据为全疆10米月度哨兵二号反射率镶嵌数据集，陆地生物量通过机器学习进行提取，质量控制主要采用全疆2021-2022年陆地生物量实测数据进行训练样本和验证样本选择，部分利用自然资源部门土壤质量调查数据进行精度检验与评估。
", "ds_source": "哨兵二号反射率数据主要来源为欧空局下载。其余辅助数据来源为实地测量数据，自主产生。
", "ds_process_way": "本数据集是通过对随机森林，梯度回归，支持向量机等多种机器学习进行验证数据对比后，选择随机森林算法进行陆地生物量反演的计算。数据分辨率为10m。其中随机森林算法的训练样本包括由天山西段的实测森林数据进行。
", "ds_quality": "质量控制主要采用全疆2021-2022年陆地生物量实测数据进行训练样本和验证样本选择，结合利用林业自然资源部门调查数据进行精度检验与评估。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "", "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": "login-access", "ds_total_size": 1586494454128, "ds_files_count": 34, "ds_format": "栅格", "ds_space_res": "10米", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "CGCS2000", "ds_thumbnail": "a7e82ebb-501b-4114-b378-1252edee93ab.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "数据来源引用：新疆10m陆地生物量数据集（2024年4-10月）来源于第三次新疆综合科学考察专项 \"空天地网一体化综合科考监测体系建设(2021xjkk1400)\"", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 1, "publish_time": "2025-12-04 19:00:27", "first_publish_time": null, "last_updated": "2026-01-14 11:01:16", "protected": false, "protected_to": null, "lang": "zh", "cstr": "33110.11.ariddc.01333", "license": null, "extra": null, "files_shape": [ { "name": "2021xjkk1400-122-2024120501", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "Xinjiang 10m terrestrial biomass dataset (April to October 2024)", "ds_abstract": "This data is for further processing using sentinel-2 reflectance product data. The original data is the 10-meter monthly Sentinel-2 reflectance mosaic data set in Xinjiang. The land biomass is extracted through machine learning. The quality control mainly uses the 2021-2022 land biomass measurement data for training samples and verification samples selection, and part of it uses soil quality survey data from the natural resources department for accuracy inspection and evaluation.
", "ds_source": "The main source of Sentinel-2 reflectivity data is downloaded from ESA. The rest of the auxiliary data sources are on-site measurement data and are generated independently.
", "ds_process_way": "This dataset is based on comparing verification data from various machine learning such as random forest, gradient regression, and support vector machines, and selecting the random forest algorithm for terrestrial biomass inversion calculation. The data resolution is 10m. Among them, the training samples of the random forest algorithm include measured forest data from the western section of Tianshan Mountains.
", "ds_quality": "Quality control mainly uses the measured land biomass data from 2021 to 2022 in Xinjiang for training samples and verification samples, and combines the use of survey data from the forestry and natural resources department for accuracy inspection and evaluation.
", "ds_ref_instruction": "Data source citation: Xinjiang's 10-meter terrestrial biomass dataset (April to October 2024) comes from the third Xinjiang comprehensive scientific expedition special project \"Construction of an Integrated Comprehensive Scientific Research Monitoring System of Air, Space and Space Network (2021 xjkk1400)\"", "ds_format": "grid", "ds_projection": "CGCS2000", "ds_space_res": "10 meters", "ds_time_res": "months" } }, "license_type": null, "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": [ "生物量" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆" ], "ds_time_tags": [ 2024 ], "ds_contributors": [ "刘铁", "马勇刚" ], "ds_meta_authors": [ "马勇刚" ], "ds_managers": [ "李锦" ], "category": "哨兵" }