本数据为利用sentinel2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，地表生物量数据通过机器学习进行提取，质量控制主要采用全疆2021-2023年地表生物量实测数据进行训练样本和验证样本选择，同时结合林业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": "哨兵二号反射率数据由于已经经过了欧空局处理，故未作进一步处理。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对比后，选择随机森林算法进行生物量反演的反演的计算。数据分辨率为10m。其中随进森林算法的训练样本包括由吐鲁番哈密地区，艾比湖流域，南疆塔里木河流域北缘的实测数据集，地表生物量的收集采用标准枝或全收获方式进行，森林采用森林资源调查规范进行。用于本模型计算的植被生物量数据采用10m×10m标准样方，采集方法为标准枝采集法和全采集法。
", "ds_quality": "质量控制主要采用全疆2021-2023年地表生物量实测数据进行训练样本和验证样本选择，同时结合林业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": 1517021639482, "ds_files_count": 44, "ds_format": "栅格", "ds_space_res": "10米", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "CGCS2000", "ds_thumbnail": "36bb9f57-87db-430f-b6be-47b82ba37426.jpg", "ds_thumb_from": 2, "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（2023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 18:08:01", "first_publish_time": null, "last_updated": "2026-01-21 10:25:31", "protected": false, "protected_to": null, "lang": "zh", "cstr": "33110.11.XIEG.XJSEDATA.2024.00100179", "license": null, "extra": null, "files_shape": [ { "name": "2021xjkk1400-21-2023121821", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "Xinjiang 10m terrestrial biomass dataset (April to October 2023)", "ds_abstract": "This data is for further processing using the sentinel2 reflectance product data. The original data is the 10-meter monthly Sentinel 2 reflectance mosaic data set in Xinjiang. The surface biomass data is extracted through machine learning. The quality control mainly uses the 2021-2023 surface biomass measurement data in Xinjiang for training samples and verification samples, and at the same time, precision inspection and evaluation are carried out based on forestry survey data.
", "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": "The Sentinel-2 reflectivity data were not further processed because they had already been processed by ESA. This dataset is based on comparing various machine learning methods such as random forest, gradient regression, and support vector machines, and selecting random forest algorithm for biomass inversion calculation. The data resolution is 10m. The training samples of the Follow-up Forest Algorithm include measured data sets from Hami City in Turpan, Aibi Lake Basin, and the northern edge of the Tarim River Basin in southern Xinjiang. The collection of surface biomass is carried out using standard branches or full harvest methods, and forests are carried out using forest resource survey specifications. The vegetation biomass data used for calculation of this model uses a 10m × 10m standard sample square, and the collection methods are standard branch collection method and full collection method.
", "ds_quality": "Quality control mainly uses the measured surface biomass data from 2021 to 2023 in Xinjiang to select training samples and verification samples, and at the same time combines forestry survey data for accuracy inspection and evaluation.
", "ds_ref_instruction": "Data source citation: Xinjiang's 10-meter terrestrial biomass dataset (April to October 2023) 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": [ 2023 ], "ds_contributors": [ "刘铁", "马勇刚" ], "ds_meta_authors": [ "马勇刚" ], "ds_managers": [ "李锦" ], "category": "哨兵" }