{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T02:20:31Z","timestamp":1780366831993,"version":"3.54.1"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T00:00:00Z","timestamp":1591660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study proposes a new technique for retrieving temperature and humidity profiles based on Artificial Neural Networks (ANNs) using data acquired from the GIIRS (Geosynchronous Interferometric Infrared Sounder) L1 and ERA-Interim (European Centre for Medium-Range Weather Forecasts Reanalysis). The approach is also compared against another method that uses simulated data from the radiative transfer model to construct the retrieval network. Furthermore, the two methods of network construction are evaluated in the North China Plain for July and August 2018, for which ground validated observations concurrent to the satellite data were available. In summary, the results showed that: (1) the ANN built with the GIIRS L1 and the EC data is superior to that built with the forward simulation and EC data in retrieval accuracy; (2) the retrieval accuracy for the troposphere exceeds that for the stratosphere; (3) the root mean square errors (RMSEs) of the relative humidity in the troposphere as retrieved by the two ANNs are 6.003% and 10.608%, respectively; (4) a relatively low correlation (R) between the simulated and observed radiance of the GIIRS is found, ranging between 720 and 736.875 cm\u22121, and the correlation between the simulated and observed radiance of the water vapor channels exceeds that between the temperature channels; (5) compared with Atmospheric Infrared Sounder\u2019s (AIRS\u2019) products, our retrieved temperature profiles exhibit preferable consistency and the humidity retrievals also show an acceptable accuracy. Our study offers important insights towards improving our ability to retrieve atmospheric temperature and humidity profiles from the most sophisticated Earth Observation instruments such as the GIIRS of the FY-4 satellite, which could assist in expanding the use of those products globally.<\/jats:p>","DOI":"10.3390\/rs12111872","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T08:49:11Z","timestamp":1591692551000},"page":"1872","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Temperature and Humidity Profile Retrieval from FY4-GIIRS Hyperspectral Data Using Artificial Neural Networks"],"prefix":"10.3390","volume":"12","author":[{"given":"Xi","family":"Cai","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol-Cloud-Precipitation, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"},{"name":"School of Atmospheric Physics, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yansong","family":"Bao","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol-Cloud-Precipitation, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"},{"name":"School of Atmospheric Physics, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1442-1423","authenticated-orcid":false,"given":"George P.","family":"Petropoulos","sequence":"additional","affiliation":[{"name":"Department of Geography, Harokopio University of Athens, El. Venizelou 70, Kallithea, 17671 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites \/National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifeng","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites \/National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liuhua","family":"Zhu","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol-Cloud-Precipitation, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"},{"name":"School of Atmospheric Physics, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Wu","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol-Cloud-Precipitation, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"},{"name":"School of Atmospheric Physics, Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,9]]},"reference":[{"key":"ref_1","first-page":"243","article-title":"0\u201310 km temperature and humidity profiles retrieval from ground-based microwave radiometer","volume":"24","author":"Bao","year":"2018","journal-title":"J. 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