此数据集分享了中国大陆高空间（1x1km2）和时间（日）分辨率，2015-2018年的完整气溶胶光学深度数据集，以及北京1954投影（https://epsg.io/2412）。该数据库包含四个数据集：2015年1月1日至2018年12月31日中国大陆每日完整高分辨率AOD影像数据集。存档资源包含存储1461个文件中的1461幅图像，以及3个 Excel摘要文件：表 \"CHN_AOD_INFO.xlsx \"描述了1461幅图像的属性，包括投影、训练R2和RMSE、测试R2和RMSE、最小值、平均值、中位数和我们预测的最大AOD； 表“Model_and_Accuracy_of_Meteorological_Elements.xlsx” 描述了高分辨率气象数据集插值的性能指标统计；表 \"Evaluation_Using_AERONET_AOD.xlsx \"显示AERONET的评估结果，包括R2、RMSE和本研究使用的监测信息。
", "ds_source": "原始气溶胶光学深度图像来自多角度大气校正气溶胶光学深度（MAIAC AOD）（https://lpdaac.usgs.gov/products/mcd19a2v006/），具有相似的时空分辨率和投影（https://en.wikipedia.org/wiki/Sinusoidal_projection）。
", "ds_process_way": "经过投影转换，18 幅MAIAC AOD 被合并在一起，得到了覆盖整个中国大陆地区的 AOD 大图像。由于云和高地表反射率的条件，每幅原始 MAIAC AOD 图像通常都有许多缺失值，每幅 AOD 图像的平均缺失率可能超过 60%。如此高的缺失率严重限制了原始 MAIAC AOD 数据集产品的适用性。我们采用复杂的全残差深度网络方法对 MAIAC 每日缺失的 AOD 进行了估算，从而获得了覆盖中国大陆的完整（无缺失值）的高分辨率 AOD 数据产品。估算中使用的协变量包括坐标、海拔、MERRA2 粗分辨率 PBLH 和 AOD 变量、云分量、高分辨率气象变量（气压、气温、相对湿度和风速）和/或时间指数等。地面监测数据用于生成高分辨率气象变量，以确保插值的可靠性。", "ds_quality": "
总体而言，我们的每日估算模型的平均训练 R^2 为 0.90，范围在 0.75 至 0.97 之间（平均 RMSE：0.075，范围在 0.026 至 0.32 之间），平均测试 R^2 为 0.90，范围在 0.75 至 0.97 之间（平均 RMSE：0.075，范围在 0.026 至 0.32 之间）。训练指标和测试指标之间几乎没有差异，高测试 R^2 和低测试 RMSE 显示了 AOD 估算的可靠性。在利用中国大陆气溶胶机器人网络（AERONET）监测站的地面AOD数据进行的评估中，我们的方法获得了0.78的R^2和0.27的RMSE，进一步说明了该方法的可靠性。", "ds_acq_start_time": "2015-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 135.86083333333332, "ds_acq_lat_south": 6.323333333333333, "ds_acq_lon_west": 73.5111111111111, "ds_acq_lat_north": 53.56083333333333, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 118494272989, "ds_files_count": 1465, "ds_format": "tif", "ds_space_res": "1000", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "bad455dc-95d6-4529-a7a0-8a335bb1a7a9.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "Lianfa, Li; Jiajie, Wu, 2020, \"A Complete Aerosol Optical Depth Dataset with High Spatiotemporal Resolution for Mainland China\", https://doi.org/10.7910/DVN/RNSWRH, Harvard Dataverse, V4, UNF:6:1THSdIiPb8sMAoqm62PDUg== [fileUNF]", "ds_from_station": null, "organization_id": "a3ce23a2-c353-4383-a544-65c8f218579f", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15" ], "quality_level": 3, "publish_time": "2024-06-18 10:57:28", "last_updated": "2025-05-29 11:31:39", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.DVN.DB6522.2024", "i18n": { "en": { "title": "A Complete Aerosol Optical Depth Dataset with High Spatiotemporal Resolution for Mainland China（2015-2018）", "ds_format": "tif", "ds_source": "
The original aerosol optical depth images are from Multi-Angle Implementation of Atmospheric Correction Aerosol Optical Depth (MAIAC AOD) (https://lpdaac.usgs.gov/products/mcd19a2v006/) with the similar spatiotemporal resolution and the sinusoidal projection (https://en.wikipedia.org/wiki/Sinusoidal_projection).
", "ds_quality": "Overall, our daily estimation model has an average training R ^ 2 of 0.90, ranging from 0.75 to 0.97 (average RMSE: 0.075, ranging from 0.026 to 0.32), and an average testing R ^ 2 of 0.90, ranging from 0.75 to 0.97 (average RMSE: 0.075, ranging from 0.026 to 0.32). There is almost no difference between the training and testing metrics, and the high test R ^ 2 and low test RMSE demonstrate the reliability of AOD estimation. In the evaluation using the ground AOD data from AERONET monitoring stations in Chinese Mainland, our method obtained 0.78 R ^ 2 and 0.27 RMSE, which further demonstrated the reliability of the method.", "ds_ref_way": "", "ds_abstract": "
We share the complete aerosol optical depth dataset with high spatial (1x1km2) and temporal (daily) resolution and the Beijing 1954 projection (https://epsg.io/2412) for mainland China (2015-2018).This database contains four datasets: - Daily complete high-resolution AOD image dataset for mainland China from January 1, 2015 to December 31, 2018. The archived resources contain 1461 images stored in 1461 files, and 3 summary Excel files. The table “CHN_AOD_INFO.xlsx” describing the properties of the 1461 images, including projection, training R2 and RMSE, testing R2 and RMSE, minmum, mean, median and maximum AOD that we predicted. - The table “Model_and_Accuracy_of_Meteorological_Elements.xlsx” describing the statistics of performance metrics in interpolation of high-resolution meteorological dataset. - The table “Evaluation_Using_AERONET_AOD.xlsx” showing the evaluation result of AERONET, including R2, RMSE, and monitoring information used in this study.
", "ds_time_res": "日", "ds_acq_place": "China", "ds_space_res": "1000", "ds_projection": "", "ds_process_way": "After projection conversion, eighteen tiles of MAIAC AOD were merged to obtain a large image of AOD covering the entire area of mainland China. Due to the conditions of clouds and high surface reflectance, each original MAIAC AOD image usually has many missing values, and the average missing percentage of each AOD image may exceed 60%. Such a high percentage of missing values severely limits applicability of the original MAIAC AOD dataset product. We used the sophisticated method of full residual deep networks to impute the daily missing MAIAC AOD, thus obtaining the complete (no missing values) high-resolution AOD data product covering mainland China. The covariates used in imputation included coordinates, elevation, MERRA2 coarse-resolution PBLH and AOD variables, cloud fraction, high-resolution meteorological variables (air pressure, air temperature, relative humidity and wind speed) and/or time index etc. Ground monitoring data were used to generate high-resolution meteorological variables to ensure the reliability of interpolation. Overall, our daily imputation models achieved an average training R2 of 0.90 with a range of 0.75 to 0.97 (average RMSE: 0.075, with a range of 0.026 to 0.32) and an average test R2 of 0.90 with a range of 0.75 to 0.97 (average RMSE: 0.075 with a range of 0.026 to 0.32). With almost no difference between training metrics and test metrics, the high test R2 and low test RMSE show the reliability of AOD imputation. In the evaluation using the ground AOD data from the monitoring stations of the Aerosol Robot Network (AERONET) in mainland China, our method obtained a R2 of 0.78 and RMSE of 0.27, which further illustrated the reliability of the method.
", "ds_ref_instruction": "When using data, please clearly state the source of the data in the main text and cite the citation method provided in this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "气溶胶光学深度", "高分辨率", "完全残差深度网络" ], "ds_subject_tags": [ "大气科学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2015, 2016, 2017, 2018 ], "ds_contributors": [ { "true_name": "李连发", "email": "lilf@lreis.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李连发", "email": "lilf@lreis.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "李连发", "email": "lilf@lreis.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "category": "大气本底" }