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

Huanyu ZHANG; Bohui TANG

doi:10.11834/jrs.20211217

Remote Sensing Applications | Views : 0 下载量: 177 CSCD: 2

PDF
Export
Share
Collection
Album

Remote sensing retrieval of total precipitable water under clear-sky atmosphere from FY-4A AGRI data by combining physical mechanism and random forest algorithm
Vol. 25, Issue 8, Pages: 1836-1847(2021)
DOI： 10.11834/jrs.20211217

扫描看全文

Quote
张环宇，唐伯惠.2021.融合物理机理与随机森林算法的FY-4A AGRI数据晴空大气可降水量遥感反演.遥感学报，25（8）： 1836-1847

Zhang H Y and Tang B H. 2021. Remote sensing retrieval of total precipitable water under clear-sky atmosphere from FY-4A AGRI data by combining physical mechanism and random forest algorithm. National Remote Sensing Bulletin， 25（8）：1836-1847
张环宇，唐伯惠.2021.融合物理机理与随机森林算法的FY-4A AGRI数据晴空大气可降水量遥感反演.遥感学报，25（8）： 1836-1847 DOI： 10.11834/jrs.20211217.

Zhang H Y and Tang B H. 2021. Remote sensing retrieval of total precipitable water under clear-sky atmosphere from FY-4A AGRI data by combining physical mechanism and random forest algorithm. National Remote Sensing Bulletin， 25（8）：1836-1847 DOI： 10.11834/jrs.20211217.

摘要

作为中国发射的风云四号系列首颗卫星，FY-4A搭载了先进的静止轨道辐射成像仪AGRI。本文利用AGRI传感器的中红外到热红外波段观测数据、ERA5水汽再分析产品以及全球无线电探空数据集IGRA等数据进行晴空大气可降水量反演研究。在海洋表面，分别利用回归分析法与随机森林算法反演大气可降水量，反演结果与ERA5水汽产品相比，均方根误差为0.493 cm和0.247 cm。在陆地表面，利用随机森林模型反演大气可降水量，反演结果与ERA5水汽产品相比，均方根误差为0.155 cm；与IGRA水汽产品相比，均方根误差为0.215 cm。结果表明本文使用的随机森林算法可以有效地提升热红外遥感数据反演大气可降水量的精度。

Abstract

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.

关键词

FY-4A AGRI热红外遥感大气可降水量回归分析随机森林

Keywords

FY-4A AGRIthermal infrared remote sensingTotal Precipitable Water (TPW)regression analysisrandom forest

references

Dalu G. 1986. Satellite remote sensing of atmospheric water vapour. International Journal of Remote Sensing, 7(9): 1089-1097 [DOI: 10.1080/01431168608948911http://dx.doi.org/10.1080/01431168608948911].

Eck T F and Holben B N. 1994. AVHRR split window temperature differences and total precipitable water over land surfaces[J].REMOTE SENSING, 15(3): 567-582.

Hoffmann L, Günther G, Li D, Stein O, Wu X, Griessbach S, Heng Y, Konopka P, Müller R, Vogel B and Wright J S. 2019. From ERA-Interim to ERA5: the considerable impact of ECMWF's next-generation reanalysis on Lagrangian transport simulations[J].Atmospheric Chemistry and Physics, 19(5): 3097-3124

Hu F, Ge J, Lu C F, Li Q Y ,Lv S B, Li Y F, Li Z J, Yuan M, Chen Z, Liu Y M, Liu Y and Lin D D. 2020. Obtaining elevation of Oncomelania Hupensis habitat based on Google Earth and it’s accuracy evaluation: an example from the Poyang lake region, China. Scientific reports, 10(1): 515 [DOI: 10.1038/S41598-020-57458-0http://dx.doi.org/10.1038/S41598-020-57458-0]

Jedlovec G J. 1990. Precipitable water estimation from high-resolution split window radiance measurements. Journal of Applied Meteorology and Climatology, 29(9): 863-877 [DOI: 10.1175/1520-0450(1990)029<0863PWEHHR>2.OCO2http://dx.doi.org/10.1175/1520-0450(1990)029<0863PWEHHR>2.OCO2]

Kleespies T J and McMillin L M. 1990. Retrieval of precipitable water from observations in the split window over varying surface temperatures. Journal of Applied Meteorology and Climatology, 29(9): 851-862 [DOI: 10.1175/1520-0450(1990)029<0851ROPWF0>2.OCOi2http://dx.doi.org/10.1175/1520-0450(1990)029<0851ROPWF0>2.OCOi2]

Lee Y, Han D, Ahn M H, Im J and Lee S J. 2019. Retrieval of total precipitable water from Himawari-8 AHI data: A comparison of random forest, extreme gradient boosting, and deep neural network. Remote Sensing, 11(15): 1741.

Li W B, Liu Y H, Zhu Y J and Zhao B L. 1998. Ret-rieval of Atmospheric Precipitable Water with GMS-5 Infrared Measurements[J].Acta Scientiarum Nat-uralium Universitatis Pekinensis, 34(5): 631-638.

李万彪, 刘盈辉, 朱元竞, 赵柏林. 1998. GMS-5红外资料反演大气可降水量. 北京大学学报(自然科学版), 34(5): 631-638 [DOI: 10.13209/2.0479-8023.1998.050http://dx.doi.org/10.13209/2.0479-8023.1998.050]

Li Z L, Jia L, Su Z B, Wan Z M and Zhang R H. 2003. A new approach for retrieving precipitable water from ATSR2 split-window channel data over land area. International Journal of Remote Sensing, 24(24): 5095-5117 [DOI: 10.1080/0143116031000096014http://dx.doi.org/10.1080/0143116031000096014]

Liaw A and Wiener M. 2002. Classification and regression by randomForest. R news, 23: 18-22.

Lin Y T, Ye J F, Wang Y Q and Zhong S Q. 2018. Water vapor retrieval method based on MODIS thermal infrared band and projection pursuit model[J].Remote Sensing for Land & Resources, 30(03):120-127

林奕桐, 叶骏菲, 王永前, 钟仕全. 2018. 基于MODIS热红外波段与投影寻踪模型的水汽反演方法.国土资源遥感, 30(03):120-127

Ren H Z, Du C, Liu R Y, Qin Q M, Yan G J, Li Z L and Meng J J. 2015. Atmospheric water vapor retrieval from Landsat 8 thermal infrared images. Journal of Geophysical Research: Atmospheres, 120(5): 1723-1738 [DOI: 10.1002/20JDO22619http://dx.doi.org/10.1002/20JDO22619]

Shi J J, Yang S Z, Han L and Lu W Q. 2020. Remote sensing of total precipitable water over the South China Sea from multi-channel infrared data of FY-4A/AGRI.//Proceedings Vdu 11455 Sixth Symposium on Novel Optoelectronic Detection Technology and Applications. Beijing: SPIE 114551W [DOI: 10.11/12.2563291http://dx.doi.org/10.11/12.2563291]

Sobrino J A, Li Z L, Stoll M P and Becker F. 1994. Improvements in the split-window technique for land surface temperature determination IEEE Transactions on Geoscience and Remote Sensing, 32(2): 243-253 [DOI: 10.1109136.295038http://dx.doi.org/10.1109136.295038]

Wu Q, Dou F, Guo Y and Gu S Y. 2020. Validation of FY-3C MWRI Total Precipitable Water Products. Meteorological Monthly, 46(1): 73-79

吴琼, 窦芳丽, 郭杨, 谷松岩. 2020. FY-3C 微波成像仪海上大气可降水产品质量检验. 气象, 46(1): 73-79 [DOI: 10.7519/j.issn.1000-0526.2020.01.007http://dx.doi.org/10.7519/j.issn.1000-0526.2020.01.007]

Yang J, Zhang Z Q, Wei C Y, Lu F and Guo Q. 2017. Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4. Bulletin of the American Meteorological Society, 98(8): 1637-1658 [DOI: 10.1175/BAMS-D-16-0065.1http://dx.doi.org/10.1175/BAMS-D-16-0065.1]

Wu H and Ying W. 2019. Benchmarking machine learning algorithms for instantaneous net surface shortwave radiation retrieval using remote sensing data. Remote Sensing, 11(21): 2520

Zhang S L, Xu L S, Ding J L, Liu H L and Deng X B. 2012. Precipitable Water Vapor Retrieval Using Neural Network from Infrared Hyperspectral Sound-ings//Key Engineering Materials. Trans Tech Pu-blications Ltd, 500: 390-396.

Zhu Y J, Li W B and Chen Y. 1998. Study of Precipitable Water by GMS-5. Quarterly Journal of Applied Meteorology, 1998, 9(1): 8-14

朱元竞, 李万彪, 陈勇. 1998. GMS-5估计可降水量的研究. 应用气象学报, 9(1): 8-14

Alert me when the article has been cited

提交

Refined wetland classification of international wetland cities based on the random forest algorithm and knowledge-driven rules： A case study of Changde city， China

UAV hyperspectral inversion of Suaeda Salsa leaf area index in coastal wetlands combined with multimodal data

Classification of the Yellow River Delta wetland landscape based on ZY-1 02D hyperspectral imagery

A PM_2.5 prediction model based on deep learning and random forest

Integrating high-resolution remote sensing and street view images to identify urban villages： A case study in Yuexiu District， Guangzhou City