{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T16:24:03Z","timestamp":1783787043526,"version":"3.55.0"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T00:00:00Z","timestamp":1611878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41875037"],"award-info":[{"award-number":["41875037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In our study, a retrieval method of temperature profiles is proposed which combines an improved one-dimensional variational algorithm (1D-Var) and artificial neural network algorithm (ANN), using FY-4A\/GIIRS (Geosynchronous Interferometric Infrared Sounder) infrared hyperspectral data. First, according to the characteristics of the FY-4A\/GIIRS observation data using the conventional 1D-Var, we introduced channel blacklists and discarded the channels that have a large negative impact on retrieval, then used the information capacity method for channel selection and introduced a neural network to correct the satellite observation data. The improved 1D-Var effectively used the observation information of 1415 channels, reducing the impact of the error of the satellite observation and radiative transfer model, and realizing the improvement of retrieval accuracy. We subsequently used the improved 1D-Var and ANN algorithms to retrieve the temperature profiles, respectively, from the GIIRS data. The results showed that the accuracy when using ANN is better than using improved 1D-Var in situations where the pressure ranges from 800 hPa to 1000 hPa. Therefore, we combined the improved 1D-Var and ANN method to retrieve temperature profiles for different pressure levels, calculating the error by taking sounding data published by the University of Wyoming as the true values. The results show that the average error of the retrieved temperature profiles is smaller than 2 K when using our method, this method makes the accuracy of the retrieved temperature profiles superior to the accuracy of the GIIRS products from 10 hPa to 575 hPa. All in all, through the combination of the physical retrieval method and the machine learning retrieval method, this paper can certainly provide a reference for improving the accuracy of products.<\/jats:p>","DOI":"10.3390\/rs13030481","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T11:34:33Z","timestamp":1611920073000},"page":"481","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["An Improved Method Combining ANN and 1D-Var for the Retrieval of Atmospheric Temperature Profiles from FY-4A\/GIIRS Hyperspectral Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Pengyu","family":"Huang","sequence":"first","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2266-1163","authenticated-orcid":false,"given":"Qiang","family":"Guo","sequence":"additional","affiliation":[{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"National Satellite Meteorological Center, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9162-7367","authenticated-orcid":false,"given":"Changpei","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7931-9759","authenticated-orcid":false,"given":"Chunming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianhang","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,29]]},"reference":[{"key":"ref_1","first-page":"602","article-title":"Research of the infrared high spectral (IASI) satellite remote sensing atmospheric temperature and humidity profiles based on the one-dimensional variational algorithm","volume":"42","author":"Guan","year":"2019","journal-title":"Trans. Atmos. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4393","DOI":"10.5194\/amt-13-4393-2020","article-title":"Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature","volume":"13","author":"Degenstein","year":"2020","journal-title":"Atmos. Meas. Tech."},{"key":"ref_3","unstructured":"Dong, C.H., Li, J., and Zhang, P. (2013). The Principle and Application of Satellite Hyperspectral Infrared Atmospheric Remote Sensing, Science Press."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s00703-018-0588-3","article-title":"An improvement of the retrieval of temperature and relative humidity profiles from a combination of active and passive remote sensing","volume":"131","author":"Che","year":"2019","journal-title":"Meteorol. Atmos. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1029\/97RS03656","article-title":"Radiometric profiling of temperature, water vapor and cloud liquid water using various inversion methods","volume":"33","author":"Solheim","year":"1998","journal-title":"Radio Sci."},{"key":"ref_6","first-page":"72","article-title":"The Chinese next-generation geostationary meteorological satellite FY-4 compared with the Japanese Himawari-8\/9 satellite","volume":"6","author":"Zhang","year":"2016","journal-title":"J. Adv. Met. S T"},{"key":"ref_7","first-page":"285","article-title":"Application of FY-4 atmospheric verticals sounder in weather forecast","volume":"38","author":"Chen","year":"2019","journal-title":"J. Infrared Millim. Waves"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Guo, Q., Yang, J., Wei, C.Y., Chen, B.Y., Wang, X., Han, C.P., Hui, W., Xu, W.W., Wen, R., and Liu, Y.N. (2021). Spectrum Calibration of the First Hyperspectral Infrared Measurements from a Geostationary Platform: Method and Preliminary Assessment. Q. J. R. Meteorol. Soc.","DOI":"10.1002\/qj.3981"},{"key":"ref_9","first-page":"411","article-title":"On-orbit test to FY-4A AGRI and generating RBG image","volume":"37","author":"Chen","year":"2018","journal-title":"J. Infrared Millim. Waves"},{"key":"ref_10","first-page":"765","article-title":"Study on FY-4A\/GIIRS infrared spectrum detection capability based on information content","volume":"38","author":"Luo","year":"2019","journal-title":"J. Infrared Millim. Waves"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1175\/BAMS-D-16-0293.1","article-title":"Satellite-Based Atmospheric Infrared Sounder Development and Applications","volume":"99","author":"Menzel","year":"2018","journal-title":"B Am. Meteorol. Soc."},{"key":"ref_12","first-page":"756","article-title":"Retrieving Atmospheric Profiles from MODIS\/AIRS Observations. I. Eigenvector Regression Algorithms","volume":"6","author":"Guan","year":"2006","journal-title":"J. Nanjing Inst. Meteorol."},{"key":"ref_13","first-page":"586","article-title":"Preliminary Study on the Capacity of High Spectral Resolution Infrared Atmospheric Sounding Instrument Using AIRS Measurements","volume":"10","author":"Jiang","year":"2006","journal-title":"J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1007\/s00376-013-3094-z","article-title":"Ensemble Retrieval of Atmospheric Temperature Profiles from AIRS","volume":"31","author":"Zhang","year":"2014","journal-title":"Adv. Atmos. Sci."},{"key":"ref_15","first-page":"1894","article-title":"Study on Simulation of infrared hyperspectral CrIS data retrieval of atmospheric temperature and humidity profiles","volume":"07","author":"Ma","year":"2014","journal-title":"J. Spectrosc. Spectr. Anal."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shi, L., Matthews, J.L., Ho, S.-P., Yang, Q., and Bates, J.J. (2016). Algorithm development of temperature and humidity profile retrievals for long-term HIRS observations. Remote Sens., 8.","DOI":"10.3390\/rs8040280"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1109\/TGRS.2005.855071","article-title":"A neural-network technique for the retrieval of atmospheric temperature and moisture profiles from high spectral resolution sounding data","volume":"43","author":"Blackwell","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1109\/TGRS.2018.2863948","article-title":"Using artificial neural network ensembles with crogging resampling technique to retrieve sea surface temperature from hy-2a scanning microwave radiometer data","volume":"57","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1414","DOI":"10.1002\/2015JD024008","article-title":"Neural network temperature and moisture retrieval algorithm validation for AIRS\/AMSU and CrIS\/ATMS","volume":"121","author":"Milstein","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1029\/2018SW001955","article-title":"Prediction of Solar Wind Speed at 1 AU Using an Artificial Neural Network","volume":"16","author":"Yang","year":"2018","journal-title":"Space Weather"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1109\/36.477189","article-title":"Passive microwave relative humidity retrievals using feedforward neural networks","volume":"33","author":"Cabreramercader","year":"2002","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.3390\/rs12111872","article-title":"Temperature and Humidity Profile Retrieval from FY4-GIIRS Hyperspectral Data Using Artificial Neural Networks","volume":"12","author":"Cai","year":"2020","journal-title":"J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1127\/0941-2948\/2006\/0099","article-title":"Temp. and humidity profile retrievals from ground-based microwave radiometers during TUC","volume":"15","author":"Cimini","year":"2006","journal-title":"J. Meteorologische Z."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1175\/1520-0426(1994)011<0105:TPWNNI>2.0.CO;2","article-title":"Temperature Profiling with Neural Network Inversion of Microwave Radiometer Data","volume":"11","author":"Churnside","year":"1994","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7415","DOI":"10.1002\/2016JD024808","article-title":"A 1DVAR retrieval applied to GMI: Algorithm description, validation, and sensitivities","volume":"121","author":"Duncan","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6687","DOI":"10.5194\/acp-13-6687-2013","article-title":"Performance of the Line-By-Line Radiative Transfer Model (LBLRTM) for temperature, water vapor, and trace gas retrievals: Recent updates evaluated with IASI case studies","volume":"13","author":"Alvarado","year":"2013","journal-title":"Atmos. Chem. Phys."},{"key":"ref_27","first-page":"199","article-title":"A study on the inversion of atmospheric temperature and humidity profiles by using CrIS infrared hyperspectral satellite data","volume":"3","author":"Shen","year":"2019","journal-title":"J. East China Norm. Univ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhu, L.H., Bao, Y.S., Petropoulos, G.P., Zhang, P., Lu, F., Lu, Q.F., Wu, Y., and Xu, D. (2020). Temperature and Humidity Profiles Retrieval in a Plain Area from Fengyun-3D\/HIRAS Sensor Using a 1D-VAR Assimilation Scheme. J. Remote Sens., 12.","DOI":"10.3390\/rs12030435"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111203","DOI":"10.3788\/COL201816.111203","article-title":"Review of Geostationary Interferometric Infrared Sounder","volume":"16","author":"Hua","year":"2018","journal-title":"J. Chin. Opt. Lett."},{"key":"ref_30","first-page":"648","article-title":"Post-launch calibration and validation of the Geostationary Interferometric Infrared Sounder(GIIRS) on FY-4A","volume":"38","author":"Feng","year":"2019","journal-title":"J. Infrared Millim. Waves."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1175\/BAMS-D-16-0065.1","article-title":"Introducing the new generation of Chinese geostationary weather satellites FENGYUN-4. Bull","volume":"98","author":"Yang","year":"2018","journal-title":"Amer. Meteor. Soc."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Di, D., Li, J., Han, W., Bai, W., Wu, C., and Menzel, W.P. (2018). Enhancing the Fast Radiative Transfer Model for FengYun-4 GIIRS by Using Local Training Profiles. J. Geophys. Res. Atmos., 123.","DOI":"10.1029\/2018JD029089"},{"key":"ref_33","first-page":"377","article-title":"Radiation Calibration Accuracy Assessment of FY-3D Hyperspectral Infrared Atmospheric Sounder Based on Inter-Comparison","volume":"39","author":"Yang","year":"2019","journal-title":"Acta Optica Sin."},{"key":"ref_34","first-page":"28","article-title":"Preliminary Study on Atmospheric Temperature Profiles Retrieval from GIIRS Based on FY-4A Satellite","volume":"34","author":"Bao","year":"2017","journal-title":"Aerosp. Shanghai"},{"key":"ref_35","first-page":"80","article-title":"On structures of supervised linear basis function feedforward three-layered neural networks","volume":"21","author":"Gao","year":"1998","journal-title":"Chin. J. Comput."},{"key":"ref_36","first-page":"136","article-title":"Information content and optimization of high spectral resolution remote measurements","volume":"21","author":"Rodgers","year":"1996","journal-title":"Adv. Space Res."},{"key":"ref_37","first-page":"545","article-title":"Channel selection of atmosphere vertical sounder (GIIRS) on board the FY-4A geostationary satellite","volume":"37","author":"Yang","year":"2018","journal-title":"J. Infrared Millim. Waves"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7563","DOI":"10.1029\/2018JD028272","article-title":"Retrieval of Atmospheric Profiles in the New York State Mesonet Using One-Dimensional Variational Algorithm","volume":"123","author":"Yang","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1175\/JTECH-D-16-0186.1","article-title":"Improved AIRS temperature and moisture soundings with local a priori information for the 1DVAR method","volume":"34","author":"Jang","year":"2017","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7633","DOI":"10.1002\/2014JD021706","article-title":"One-dimensional variational (1D-Var) retrieval of middle to upper tropospheric humidity using AIRS radiance data","volume":"119","author":"Ishimoto","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"27925","DOI":"10.3402\/tellusa.v67.27925","article-title":"1D-Var temperature retrievals from microwave radiometer and convective scale model","volume":"67","author":"Martinet","year":"2015","journal-title":"Tellus A"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/481\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:17:23Z","timestamp":1760159843000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/481"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,29]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13030481"],"URL":"https:\/\/doi.org\/10.3390\/rs13030481","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,29]]}}}