本数据集基于MCD19A2、MOD13A3、MCD12Q1、MERRA-2、ERA5和CHAP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、归一化植被指数、土地覆盖类型、气象参数等多种数据提取，将中国环境监测总站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2000年至2022年“一带一路”沿线区域的逐日1公里分辨率沙尘颗粒物产品。本次沙尘颗粒物浓度数据集为“一带一路”沿线区域的沙尘研究提供基础数据。\n
1. 数据集命名：2011001.h22v04.tiff\n
201101表示日期，h22v04对应MCD19A2的瓦片，tiff文件中的波段1即为多对应的浓度值。\n
2.量纲（度量单位）：毫克/立方米\n
3.每张tiff图像大小为1200×1200像素", "ds_source": "
1.MCD19A2、MOD13A3、MCD12Q1数据从MODIS官方网站(https://modis.gsfc.nasa.gov//)下载 。
2.MERRA-2 tavg1_2d_aer_Nx数据从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。\n
3.ERA5-Land 数据从欧洲中期天气预报中心（https://cds.climate.copernicus.eu/） 下载。\n
4.CHAP 数据从青藏高原数据科学中心（https://data.tpdc.ac.cn/home） 下载。", "ds_process_way": "
1. 遥感数据预处理：对MCD19A2不同波段的有效数据取平均，MERRA-2(0.5°×0.625°)和ERA5-Land(0.25°×0.25°)分别通过双线性插值采样至1公里分辨率，用MERRA-2填补MCD19A2的缺失值，然后按照最近点像素的方式时空匹配，提取所需数据。\n
2. 反演方法：采用最终采用LightGBM模型分别训练两个模型：(1)校正模型（主要针对中国区域），即把CHAP的数据也作为预测因子输入到模型中，最后再把反演的结果取平均后作为最终的结果；(2)原始模型（主要针对中国以外的其他区域）。", "ds_quality": "
数据精度（使用2014-2022年的所有样本数据）：\n
校正模型：基于样本的十折交叉验证PM10的R_2、MAE、RMSE分别为0.95、4.83、13.84，PM2.5的R_2、MAE、RMSE分别为0.96、3.27、7.00；\n
原始模型：基于样本的十折交叉验证PM10的R_2、MAE、RMSE分别为0.86、12.05、24.30，PM2.5的R_2、MAE、RMSE分别为0.89、7.23、12.20。", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2022-12-22 00:00:00", "ds_acq_place": "“一带一路”沿线区域", "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": 1903058579162, "ds_files_count": 329925, "ds_format": "*.tiff", "ds_space_res": "1km", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "e4f7ba9a-b93b-4f61-9a79-3c262a6ce767.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": "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": "2025-04-14 18:22:39", "last_updated": "2026-03-31 15:44:51", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6905.2025", "i18n": { "en": { "title": "Daily dust particle data products along the \"the Belt and Road\" (2000-2022)", "ds_format": "*.tiff", "ds_source": "
1. MCD19A2、MOD13A3、MCD12Q1 data: From MODIS official website (https://modis.gsfc.nasa.gov//) to download.\n
2. MERRA-2 tavg1_2d_aer_Nx data: From NASA's Goddard earth science data and information service center (https://disc.gsfc.nasa.gov/) to download. \n
3. ERA5-Land data: From the European centre for medium-range weather forecasts (https://cds.climate.copernicus.eu/) to download. \n
4.CHAP data is downloaded from the Tibetan Plateau Data Science Center (https://data.tpdc.ac.cn/home).", "ds_quality": "
Data accuracy (using all sample data from 2014 - 2022):\n
Calibrated model: Based on ten - fold cross - validation of the samples, for PM10, theR_2 , MAE, and RMSE are 0.95, 4.83, and 13.84 respectively; for PM2.5, the R_2, MAE, and RMSE are 0.96, 3.27, and 7.00 respectively.\n
Original model: Based on ten - fold cross - validation of the samples, for PM10, theR_2 , MAE, and RMSE are 0.86, 12.05, and 24.30 respectively; for PM2.5, theR_2 , MAE, and RMSE are 0.89, 7.23, and 12.20 respectively.", "ds_ref_way": "", "ds_abstract": "
This dataset is based on MCD19A2, MOD13A3, MCD12Q1, MERRA-2, and ERA5 datasets. Through data pre - processing, data up - sampling, and spatio - temporal matching, various data such as aerosol optical thickness, normalized difference vegetation index, land cover type, and meteorological parameters are extracted. The observed values from the ground stations of the China National Environmental Monitoring Centre are used as the target true values. The machine learning algorithm LightGBM is employed to train a model, and then the daily sand - dust particulate matter products with a resolution of 1 km in the regions along the Belt and Road from 2000 to 2022 are retrieved. This sand - dust particulate matter concentration dataset provides basic data for the sand - dust research in the regions along the Belt and Road.\n
1.Dataset Naming: 2011001.h22v04.tiff\n
“201101” represents the date. “h22v04” corresponds to the tile of MCD19A2. Band 1 in the tiff file is the corresponding concentration value.\n
2.Dimension (Measurement Unit):Milligram per cubic meter\n
3.The size of each tiff image is 1200×1200 pixels.
", "ds_time_res": "日", "ds_acq_place": "Regions along the Belt and Road", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "1.Remote Sensing Data Pre - processing: Calculate the average of valid data from different bands of MCD19A2. For MERRA - 2 (0.5°×0.625°) and ERA5 - Land (0.25°×0.25°), they are resampled to a resolution of 1 km through bilinear interpolation respectively. Fill the missing values of MCD19A2 with data from MERRA - 2. Then, perform spatio - temporal matching in the way of the nearest - neighbor pixel method to extract the required data.\n
2.Inversion Method: Finally, two models are trained using the LightGBM model respectively:\n(1) Calibrated model (mainly for the Chinese region). That is, the CHAP data is also input into the model as a predictor. After that, the inversed results are averaged as the final result.\n(2) Original model (mainly for regions other than China).", "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, "ds_topic_tags": [ "一带一路", "沙尘颗粒物" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "一带一路" ], "ds_time_tags": [ 2000, 2022 ], "ds_contributors": [ { "true_name": "姚林军", "email": "yaolj2023@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": "姚林军", "email": "yaolj2023@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": "其他" }