本数据采用2021年4-10月年哨兵2号光学卫星数据为数据源，采样随机森林回归（RFR）方法构建新疆湖泊叶绿素a浓度遥感估算模型。数据为CGCS2000坐标系阿伯斯投影，精度为10米。
", "ds_source": "采用2021年4-10月年哨兵2号光学卫星数据为数据源
", "ds_process_way": "使用具有较好辐射性能的Sentinle 2 A/B MSI。从欧空局哥白尼数据开放中心获取L1C数据。采用欧空局提供的SEN2COR算法进行大气校正。但是SEN2COR为陆地大气校正算法，需要针对进一步去除天空光、太阳耀斑和残余气溶胶散射的影响。采样随机森林回归（RFR）方法构建水质参数模型。通过调整输入算法的最佳输入变量，各算法最优超参数通过格网化搜索方法获得。
", "ds_quality": "利用大量野外调查和星地同步数据进行模型研究，结果表明RFR叶绿素a浓度算法精度较高，不确定性为（ϵ）为21.92%，偏差（β）为4.29%，斜率为0.64，均方根对数误差为0.235。
", "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": 4154809999, "ds_files_count": 20, "ds_format": "栅格", "ds_space_res": "10米", "ds_time_res": "月", "ds_coordinate": "CGCS2000", "ds_projection": "Albers投影", "ds_thumbnail": "96eeaf90-f24d-4467-9f6a-f0477ab193f7.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "数据来源引用：新疆10m水体叶绿素a数据集（2021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 19:07:35", "first_publish_time": null, "last_updated": "2026-01-14 11:12:05", "protected": false, "protected_to": null, "lang": "zh", "cstr": "33110.11.XIEG.xjsedata.2022.00000112", "license": null, "extra": null, "files_shape": [ { "name": "2021xjkk1400-75-2023031075", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "Chlorophyll a dataset of 10m water body in Xinjiang (April to October 2021)", "ds_abstract": "This data uses Sentinel 2 optical satellite data from April to October 2021 as the data source, and a sampling random forest regression (RFR) method is used to build a remote sensing estimation model for chlorophyll a concentration in Xinjiang lakes. The data is Abers projection in the CGCS2000 coordinate system with an accuracy of 10 meters.
", "ds_source": "Using Sentinel 2 optical satellite data from April to October 2021 as the data source
", "ds_process_way": "Use Sentinel 2 A/B MSI with good radiation performance. Obtained L1C data from ESA's Copernicus Data Open Center. Atmospheric correction is carried out using the SEN2COR algorithm provided by ESA. However, SEN2COR is a terrestrial atmosphere correction algorithm that needs to be targeted to further remove the effects of sky light, solar flares and residual aerosol scattering. A sampling random forest regression (RFR) method was used to build a water quality parameter model. By adjusting the optimal input variables of the input algorithm, the optimal hyperparameters of each algorithm are obtained through the grid search method.
", "ds_quality": "A large number of field surveys and satellite-earth synchronous data were used to conduct model research. The results showed that the RFR chlorophyll a concentration algorithm had high accuracy, with an uncertainty of 21.92%, a deviation of 4.29%, a slope of 0.64, and a root-mean-square logarithmic error of 0.235.
", "ds_ref_instruction": "Data source citation: Xinjiang's 10-meter water body chlorophyll a dataset (April to October 2021) comes from the third Xinjiang comprehensive scientific expedition special project \"Construction of Integrated Comprehensive Scientific Research Monitoring System of Air, Space, Space and Network (2021 xjkk1400)\"", "ds_format": "grid", "ds_projection": "Albers projection", "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": [ "水体叶绿素a" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆" ], "ds_time_tags": [ 2021 ], "ds_contributors": [ "刘铁", "段洪涛" ], "ds_meta_authors": [ "段洪涛" ], "ds_managers": [ "李锦" ], "category": "哨兵" }