{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:19:51Z","timestamp":1774120791258,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"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 describes a high-speed correction method for geolocation information of geostationary satellite data for accurate physical analysis. Geostationary satellite observations with high temporal resolution provide instantaneous analysis and prompt reports. We have previously reported the quasi real-time analysis of solar radiation at the surface and top of the atmosphere using geostationary satellite data. Estimating atmospheric parameters and surface albedo requires accurate geolocation information to estimate the solar radiation accurately. The physical analysis algorithm for Earth observations is verified by the ground truth. In particular, downward solar radiation at the surface is validated by pyranometers installed at ground observation sites. The ground truth requires that the satellite observation data pixels be accurately linked to the location of the observation equipment on the ground. Thus, inaccurate geolocation information disrupts verification and causes complex problems. It is difficult to determine whether error in the validation of physical quantities arises from the estimation algorithm, satellite sensor calibration, or a geolocation problem. Geolocation error hinders the development of accurate analysis algorithms; therefore, accurate observational information with geolocation information based on latitude and longitude is crucial in atmosphere and land target analysis. This method provides the basic data underlying physical analysis, parallax correction, etc. Because the processing speed is important in geolocation correction, we used the phase-only correlation (POC) method, which is fast and maintains the accuracy of geolocation information in geostationary satellite observation data. Furthermore, two-dimensional fast Fourier transform allowed the accurate correction of multiple target points, which improved the overall accuracy. The reference dataset was created using NASA\u2019s Shuttle Radar Topography Mission 1-s mesh data. We used HIMAWARI-8\/Advanced HIMAWARI Imager data to demonstrate our method, with 22,709 target points for every 10-min observation and 5826 points for every 2.5 min observation. Despite the presence of disturbances, the POC method maintained its accuracy. Column offset and line offset statistics showed stability and characteristic error trends in the raw HIMAWARI standard data. Our method was sufficiently fast to apply to quasi real-time analysis of solar radiation every 10 and 2.5 min. Although HIMAWARI-8 is used as an example here, our method is applicable to all geostationary satellites. The corrected HIMAWARI 16 channel gridded dataset is available from the open database of the Center for Environmental Remote Sensing (CEReS), Chiba University, Japan. The total download count was 50,352,443 on 8 July 2020. Our method has already been applied to NASA GeoNEX geostationary satellite products.<\/jats:p>","DOI":"10.3390\/rs12152472","type":"journal-article","created":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T06:16:47Z","timestamp":1596435407000},"page":"2472","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Geolocation Correction for Geostationary Satellite Observations by a Phase-Only Correlation Method Using a Visible Channel"],"prefix":"10.3390","volume":"12","author":[{"given":"Hideaki","family":"Takenaka","sequence":"first","affiliation":[{"name":"Center for Environmental Remote Sensing (CEReS), Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan"}]},{"given":"Taiyou","family":"Sakashita","sequence":"additional","affiliation":[{"name":"Weathernews Inc. (WNI), Makuhari Techno Garden, Nakase 1-3 Mihama-ku, Chiba-shi, Chiba 261-0023, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7067-5164","authenticated-orcid":false,"given":"Atsushi","family":"Higuchi","sequence":"additional","affiliation":[{"name":"Center for Environmental Remote Sensing (CEReS), Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan"}]},{"given":"Teruyuki","family":"Nakajima","sequence":"additional","affiliation":[{"name":"National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba City, Ibaraki 305-8506, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1175\/1520-0469(1967)024<0241:TEOTAW>2.0.CO;2","article-title":"Thermal equilibrium of the atmosphere with a given distribution of relative humidity","volume":"24","author":"Manabe","year":"1967","journal-title":"J. Atmos. 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