基于机器学习的FY-4A气溶胶光学厚度反演
Machine learning-based inversion of aerosol optical depth inversion from FY-4A data
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摘要: 我国新一代静止气象卫星FY-4A搭载了先进静止轨道辐射成像仪(advanced geostationary radiation imager, AGRI), 为了针对AGRI数据的特点开发基于机器学习算法的高时空分辨率AGRI气溶胶数据集, 利用2021年中国及周边地区67个全球地基气溶胶自动观测网络系统(aerosol robotic network, AERONET)站点数据, 选取AGRI数据中表观反射率数据、观测角度数据、高程和MODIS的地表反射率等因子, 采用随机森林(random forest, RF)、梯度增强回归(gradient boosting regression tree, GBRT)、极端梯度增强(extrme gradient boosting, XGBoost)和反向传播神经网络(back propagation neural network, BPNN)4种机器学习方法反演气溶胶光学厚度(aerosol optical depth, AOD)。根据模型评价指标选择最优模型, 实现基于FY-4A的AOD反演, 空间分辨率4 km, 并与时刻相近的MODIS气溶胶产品进行对比分析。结果表明: 4种机器学习建立的AOD反演模型相关系数R均在0.90以上, 平均绝对误差(mean absolute error, MAE)和均方根误差(root mean square error, RMSE)均在0.09和0.14以下, 机器学习模型对AOD反演具有一定的可行性; GBRT模型在4种机器学习中反演精度最优, 验证误差中R为0.82, MAE为0.16, RMSE为0.25, 47%的反演结果落在期望误差内, 表明GBRT反演出的FY-4A AOD与站点观测值基本一致; 将GBRT模型反演的AOD结果与MODIS气溶胶产品进行对比验证, 发现FY-4A AOD反演结果与MODIS AOD在空间分布上具有较好的一致性, 83.57%的网格偏差集中在-1.0, 0)之间, FY-4A AOD反演值相对MODIS AOD略高。Abstract: This study aims to develop a machine learning algorithm based on the characteristics of AGRI data to generate an aerosol dataset with a high spatiotemporal resolution. Using aerosol data from 67 aerosol robotic network (AERONET) sites in China and its surrounding areas in 2021, this study selected data of factors such as apparent reflectance, observation angles, elevation, and MODIS surface reflectance acquired from FY-4A advanced geostationary radiation imager (AGRI)-a new generation geostationary meteorological satellite of China. Then, this study performed the inversion of aerosol optical depth (AOD) using four machine learning methods-random forest (RF), gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN). Using the optimal model determined based on evaluation metrics, this study achieved the AOD inversion with a spatial resolution of 4 km × 4 km based on FY-4A data. Then, this study compared the inversion results with MODIS aerosol products of roughly the same periods. The results indicate that the AOD inversion models based on the four machine learning algorithms yielded correlation coefficients (R) exceeding 0.90, mean absolute errors (MAEs) of less than 0.09, and root mean square errors (RMSE) below 0.14. This indicates that it is feasible to conduct AOD inversion using machine learning-based models. The GBRT-based model exhibited the highest inversion accuracy among the four methods, with a correlation coefficient of 0.82, MAE of 0.16, and RMSE of 0.25, as indicated by the verification results. Additionally, 47% of the inversion results fell within the expected error ranges, indicating that the results of AOD inversion from FY-4A data using the GBRT-based model were generally consistent with observed values. The comparison between the GBRT model-derived AOD inversion results and the results of MODIS aerosol products shows that the former exhibited high consistency with the latter in terms of spatial distribution, with 83.57% of grid deviations falling within the range from -1.0 to 0 and the former slightly higher than the latter.
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