气溶胶细模分数（FMF）是区分人为气溶胶和天然气溶胶的一个关键参数，但它在卫星检索中具有很大的不确定性，尤其是在陆地上。 本数据集整合了物理和深度学习（Phy-DL）方法，以MODIS数据为基础，在全球陆地尺度上检索气溶胶微模分数，并以日时间分辨率和 1° 空间分辨率生成了20年（2001-2020 年）Phy-DL气溶胶精细模式分数（500 nm）。", "ds_source": "
（1）在本研究中，获得了 2001 至 2020 年 MODIS C6.1 L1B MOD02SSH 数据（即波段 1 至波段 7 的大气顶部（TOA）反射率）、MODIS C6.1 L3 MOD09CMG 数据（波段 1 至波段 7 的地表反射率）和 MODIS C6.1 L3 MOD08 每日数据，以检索 FMF。\n
（2）由于没有足够的 2.0 级数据作为建模的训练数据，因此我们使用了从全球 1170 个 AERONET 站点的数据中生成的 1.5 级 SDA FMF 数据集，作为进一步建模和验证的基础数据。\n
（3）由于气象因素对 FMF 的影响，五个气象变量（即 2 米气温、PBLH、表面气压、10 米风分量和 2 米露点温度）均来自欧洲中程天气预报中心（ERA5）的第五代产品，该产品提供 1950 年以来的每小时数据，空间分辨率为 0.25°。然后根据 2 米露点温度和空气温度计算相对湿度（Tetens，1930 年）。由于 MODIS 数据的时间和空间分辨率较高，因此只使用了当地时间 10:00 至 11:00 收集的监测时间气象数据，并将其重新采样为 1° × 1°，以获得日平均值。", "ds_process_way": "
在本研究中，我们采用串联模式将物理模型和深度学习模型结合起来，即把物理模型的输出作为深度学习模型的输入。使用的物理模型是 LUT-SDA（Yan 等人，2017 年）。LUT-SDA 专为仅有两个波长的 AOD（如 DT AOD 产品）时的卫星 FMF 检索而设计。首先需要三个波长的最少 AOD 才能获得 AE 导数（α′）。然后就可以计算出细模式 AOD 的 AE（αf）和 FMF。", "ds_quality": "
基于对来自全球 1170 个 AERONET 站点的 361089 个数据样本的分析，Phy-DL FMF 数据集与气溶胶机器人网络（AERONET）的测量结果具有可比性。总体而言，Phy-DL FMF 的均方根误差 (RMSE) 为 0.136，相关系数为 0.68，在 ±20 % 预期误差 (EE) 范围内的结果比例为 79.15 %。此外，地表辐射预算（SURFRAD）观测结果的场外验证显示，Phy-DL FMF 的均方根误差为 0.144（72.50% 的结果在 ±20 % 的预期误差（EE）范围内）。Phy-DL FMF 的性能优于其他深度学习或物理方法（如我们之前研究中提出的光谱解卷积算法），尤其是在森林、草地、耕地以及城市和贫瘠土地类型方面。作为一个长期数据集，Phy-DL FMF 能够显示出全球陆地区域总体上显著下降的趋势（显著性水平为 95%）", "ds_acq_start_time": "2001-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 179.05, "ds_acq_lat_south": -81.05, "ds_acq_lon_west": -171.06666666666666, "ds_acq_lat_north": 89.01666666666667, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 153038533, "ds_files_count": 2, "ds_format": " Geotiff ", "ds_space_res": "", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "8cc0406e-571e-4c5f-b542-fe5b566abdde.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "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": "2024-06-21 10:05:03", "last_updated": "2026-01-14 10:51:16", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6527.2024", "i18n": { "en": { "title": "Physics and Deep Learning Retrieval Fine Mode Fraction (Phy DL FMF)", "ds_format": " Geotiff ", "ds_source": "
(1) In this study, MODIS C6.1 L1B MOD02SSH data (i.e. top of atmosphere (TOA) reflectance of bands 1 to 7), MODIS C6.1 L3 MOD09CMG data (surface reflectance of bands 1 to 7), and MODIS C6.1 L3 MOD08 daily data were obtained from 2001 to 2020 to retrieve FMF.\n
(2) Due to insufficient level 2.0 data as training data for modeling, we used the 1.5-level SDA FMF dataset generated from data from 1170 AERONET sites worldwide as the foundational data for further modeling and validation.\n
(3) Due to the influence of meteorological factors on FMF, the five meteorological variables (i.e. 2-meter temperature, PBLH, surface pressure, 10 meter wind component, and 2-meter dew point temperature) all come from the fifth generation product of the European Centre for Medium Range Weather Forecasts (ERA5), which provides hourly data since 1950 with a spatial resolution of 0.25 °. Then calculate the relative humidity based on the 2-meter dew point temperature and air temperature (Tetens, 1930). Due to the high temporal and spatial resolution of MODIS data, only meteorological data collected between 10:00 and 11:00 local time was used and resampled to 1 °× 1 ° to obtain daily averages.", "ds_quality": "
Based on the analysis of 361089 data samples from 1170 AERONET sites worldwide, the Phy DL FMF dataset is comparable to the measurement results of the Aerosol Robot Network (AERONET). Overall, the root mean square error (RMSE) of Phy DL FMF is 0.136, the correlation coefficient is 0.68, and the proportion of results within the expected error (EE) range of ± 20% is 79.15%. In addition, off-site validation of the Surface Radiation Budget (SURFRED) observation results showed that the root mean square error of Phy DL FMF was 0.144 (72.50% of the results were within ± 20% of the expected error (EE) range). The performance of Phy DL FMF is superior to other deep learning or physical methods (such as the spectral deconvolution algorithm proposed in our previous research), especially in forest, grassland, cultivated land, as well as urban and barren land types. As a long-term dataset, Phy DL FMF is able to show a significant downward trend in the overall terrestrial regions worldwide (with a significance level of 95%)", "ds_ref_way": "", "ds_abstract": "
Aerosol fine fraction (FMF) is a key parameter for distinguishing between anthropogenic and natural aerosols, but it has significant uncertainty in satellite retrieval, especially on land. This dataset integrates physics and deep learning (Phy DL) methods, based on MODIS data, to retrieve aerosol micromodule fractions at the global land scale, and generates 20-year (2001-2020) Phy DL aerosol micromodule fractions (500 nm) at daily time resolution and 1 ° spatial resolution
", "ds_time_res": "日", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "In this study, we combined a physical model and a deep learning model using a cascade mode, where the output of the physical model is used as the input of the deep learning model. The physical model used is LUT-SDA (Yan et al., 2017). LUT-SDA is designed for satellite FMF retrieval when there are only two wavelengths of AOD (such as DT AOD products). Firstly, a minimum AOD of three wavelengths is required to obtain the AE derivative (α '). Then, the AE (αf) and FMF of the fine mode AOD can be calculated.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC 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": [ "Phy-DL FMF", "气溶胶", "FMF" ], "ds_subject_tags": [ "大气科学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "晏星", "email": "yanxing@bnu.edu.cn", "work_for": " 北京师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "晏星", "email": "yanxing@bnu.edu.cn", "work_for": " 北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "晏星", "email": "yanxing@bnu.edu.cn", "work_for": " 北京师范大学", "country": "中国" } ], "category": "大气本底" }