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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (1) : 213-220     DOI: 10.6046/zrzyyg.2023220
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Machine learning-based inversion of aerosol optical depth inversion from FY-4A data
CHEN Xin(), SHI Guoping()
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
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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.
Keywords machine learning      AOD      FY-4A/AGRI      AERONET      MODIS AOD     
ZTFLH:  TP79  
  X513  
Issue Date: 17 February 2025
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Xin CHEN
Guoping SHI
Cite this article:   
Xin CHEN,Guoping SHI. Machine learning-based inversion of aerosol optical depth inversion from FY-4A data[J]. Remote Sensing for Natural Resources, 2025, 37(1): 213-220.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023220     OR     https://www.gtzyyg.com/EN/Y2025/V37/I1/213
Fig.1  Study area and site distribution
通道序号 通道类型 中心波长/μm 光谱带宽/μm 主要用途
B1 可见光与近红外 0.47 0.45~0.49 气溶胶
B2 0.65 0.55~0.75 植被
B3 0.825 0.75~0.90 植被
B4 短波红外 1.375 1.36~1.39 卷云
B5 1.61 1.58~1.64 云、雪
B6 2.25 2.1~2.35 卷云、气溶胶
B7 中波红外 3.75 3.5~4.0(高) 火点
B8 3.75 3.5~4.0(低) 地表
B9 水汽 6.25 5.8~6.7 云导风
B10 7.1 6.9~7.3 云导风
B11 长波红外 8.5 8.0~9.0 云导风、云
B12 13.5 13.2~13.8 云顶高度
Tab.1  AGRI channels
Fig.2  Importance of features
Fig.3  Scatterplot of modeling results for AOD and ground-based data for BPNN, RF, XGBoost and GBRT model
Fig.4  Scatterplot of validation results for AOD and ground-based data for BPNN, RF, XGBoost and GBRT model
时间 R MAE RMSE =EE/% >EE/% <EE/%
2022年7月 0.748 8 0.120 6 0.188 6 52 20 28
2022年10月 0.826 9 0.117 8 0.182 8 56 12 32
2023年1月 0.903 7 0.125 3 0.197 8 51 6 43
2023年4月 0.802 2 0.251 3 0.364 7 32 2 66
Tab.2  Monthly validation table
Fig.5  Comparison of the regional distribution of MODIS/Terra, Aqua/MODIS and FY-4A/AGRI products
Fig.6  Histogram of frequency differences between MODIS AOD and FY-4A AOD
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