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Remote sensing retrieval of total precipitable water under clear-sky atmosphere from FY-4A AGRI data by combining physical mechanism and random forest algorithm

  • role： First author第一作者
  • Affiliation：

    School of Geodesy and Geomatics, Wuhan University, Wuhan 430072, China

    State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

  • Email：2017301610128@whu.edu.cn
  • Introduction：张环宇，1999年生，男，博士研究生，研究方向为热红外遥感定量反演。E-mail：2017301610128@whu.edu.cn
ZHANG Huanyu13,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China

    State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

  • Email：tangbh@kust.edu.cn
  • Introduction：唐伯惠，1973年生，男，教授，研究方向为热红外遥感定量反演。E-mail：tangbh@kust.edu.cn
TANG Bohui23*

реферат

Atmospheric water vapour, an important component of global greenhouse gases, is one of the key factors influencing global climate change, hydrologic cycle, matter-energy exchange and so on. The total water vapour content in the atmosphere is usually quantified with the parameter of Total Precipitable Water (TPW). As the first launched satellite of Chinese Fengyun-4 series, FY-4A satellite has been equipped with the Advanced Geosynchronous Radiation Imager (AGRI). This paper aims to retrieve the TPW under the clear-sky atmosphere with the combination of the middle- and thermal-infrared remote sensing observations of AGRI, ERA5 total column water vapour reanalysis product and Integrated Global Radiosonde Archive (IGRA).On the ocean surface, the regression analysis method and random forest algorithm have been introduced to retrieve the TPW successively. In random forest modeling, the standard deviation of every regression tree’s output value is used as quality control criterion, and the retrieval results are considered as reliable only when the standard deviation is less than 0.4 cm.On the land surface, the Split-Window Covariance-Variance Ratio (SWCVR) method and random forest algorithm have been developed to retrieve the TPW, and the elevation and Normalized Difference Vegetation Index (NDVI) estimated at the top of atmosphere have been added to the random forest model in order to reflect the information of land surface.In terms of TPW retrieval over ocean, the determination coefficient is 0.817, the Root Mean Square Error (RMSE) is 0.493 cm, and the Mean Absolute Error (MAE) of the retrieval results is 0.393 cm compared with the ERA5 total column water vapour product when using the regression analysis method. For the random forest model, the quality control passing rate of retrieval results accounts for 84.0%, the determination coefficient is 0.956, RMSE is 0.247 cm, and MAE is 0.183 cm when compared with the ERA5 total column water vapour product.In terms of TPW retrieval over land, for the reason that the spatial resolution of AGRI is only 4 km, the SWCVR method is unable to retrieve the TPW accurately using the observations of AGRI. For the random forest model, the passing rate of retrieval results accounts for 93.2%, the determination coefficient is 0.972, RMSE is 0.155 cm, and MAE is 0.106 cm when compared with the ERA5 total column water vapour product. In the end, the radio-sounding water vapour is introduced to evaluate the accuracy of the random forest model, and the passing rate of retrieval results accounts for 75.6%, the determination coefficient is 0.946, RMSE is 0.215 cm, and MAE is 0.163 cm.First of all, TPW over ocean can be retrieved with the middle- and thermal-infrared remote sensing observations of AGRI by using the regression analysis method and random forest algorithm, and the retrieval accuracy of random forest model is obviously higher. Besides, the SWCVR method is unable to retrieve the TPW over land accurately due to the low spatial resolution of AGRI, meanwhile the random forest algorithm performs well. The results indicate that the random forest algorithm can effectively improve the TPW retrieval accuracy with thermal infrared remote sensing observations.

ключеви́че слова́

FY-4A AGRI;thermal infrared remote sensing;Total Precipitable Water (TPW);Regression analysis;Random Forest

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