本数据集基于MCD19A2、MERRA-2、ERA5_land数据集，通过数据预处理、数据上采样、时空匹配将气溶胶光学厚度（AOD）、波长指数（AE）、气象参数等多种数据提取，将AERONET地面站点观测值作为目标真值，利用机器学习算法LightGBM训练模型，反演2018年至2022年“一带一路”沙漠地区的逐日1公里分辨率陆地沙尘气溶胶光学厚度。本次沙尘气溶胶光学厚度数据集为“一带一路”地区的沙尘研究提供基础数据。", "ds_source": "
1. MCD19A2数据：从美国MODIS官方网站(https://modis.gsfc.nasa.gov//)下载。\n
2. MERRA-2 tavg1_2d_aer_Nx数据：从美国宇航局戈达德地球科学数据和信息服务中心(https://disc.gsfc.nasa.gov/)下载。 （该数据需要下载全球范围）\n
3. ERA5-Land数据：从欧洲中期天气预报中心(https://cds.climate.copernicus.eu/)下载。 （该数据需要下载东经0°-东经150°，北纬81°-南纬11°的范围）", "ds_process_way": "
1. 遥感数据预处理：MERRA-2(0.5°×0.625°)和ERA5-Land(0.1°×0.1)分别通过双线性插值采样至1公里分辨率，其中ERA5-Land在此之前还需通过最近邻插值填补缺失值，按照最近点像素的方式时空匹配，提取所需数据。将研究范围内的AERONET地面站点观测数据，筛选出波长指数(AE)<1 的气溶胶光学厚度(AOD)作为沙尘AOD的目标真值。
2. 反演方法：采用最终采用LightGBM模型分别训练两个模型：(1)完整模型，即各项预测因子均未缺失作为输入训练的模型（MCD19A2数据存在大量缺失，针对非缺失部分）；(2)非完整模型，即MCD19A2存在数据缺失的部分，仅采用由MERRA-2、ERA5-Land数据提取的预测因子为输入训练的模型。两个模型的输出需要与经降尺度后的MERRA-2的沙尘AOD数据取均值处理，才作为最终反演结果。\n
参考文献：Ke G, Meng Q, Finley T, et al. Lightgbm: A highly efficient gradient boosting decision tree[J]. Advances in neural information processing systems, 2017, 30.", "ds_quality": "
数据精度：取AERONET站点经匹配后未用于训练的数据作验证。平均绝对误差（MAE）为0.0515；均方根误差（RMSE）为0.0805；平均偏差（MBE）为-0.0252；相关系数（R）为0.8559；落在期望误差（EE）为81.74%。", "ds_acq_start_time": "2018-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "亚洲“一带一路”沙漠地区", "ds_acq_lon_east": 117.0, "ds_acq_lat_south": 21.0, "ds_acq_lon_west": 62.0, "ds_acq_lat_north": 49.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 631314883812, "ds_files_count": 36521, "ds_format": "", "ds_space_res": "1km", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "fd89e747-61d3-41fa-98b1-77e6850389a0.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "9de89acc-5714-4927-aba3-ac88067dff8a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "lihongxing@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2024-06-12 11:28:32", "last_updated": "2025-12-08 18:49:11", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6510.2024", "i18n": { "en": { "title": "Dust aerosol optical depth data set over Asian desert", "ds_format": "", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "", "ds_time_res": "日", "ds_acq_place": "", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "", "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_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "亚洲", "陆地沙尘气溶胶" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "亚洲" ], "ds_time_tags": [ 2018, 2022 ], "ds_contributors": [ { "true_name": "吴小林", "email": "220220942371@lzu.edu.cn", "work_for": "兰州大学信息科学与工程学院", "country": "中国" }, { "true_name": "王兆滨", "email": "wangzhb@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "吴小林", "email": "220220942371@lzu.edu.cn", "work_for": "兰州大学信息科学与工程学院", "country": "中国" } ], "ds_managers": [ { "true_name": "王兆滨", "email": "wangzhb@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "沙漠与荒漠化" }