A Simplified Coastline Inflection Method for Correcting Geolocation Errors in FengYun-3D Microwave Radiation Imager Images

Author:

Chen Zhuoqi123ORCID,Xie Jin4,Heygster Georg5,Chi Zhaohui6ORCID,Yang Lei7,Wu Shengli7ORCID,Hui Fengming123,Cheng Xiao123ORCID

Affiliation:

1. School of Geospatial Engineering and Science, Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

2. Key Laboratory of Natural Resources Monitoring in Tropical and Subtropical Area of South China, Ministry of Natural Resources, Zhuhai 519082, China

3. University Corporation for Polar Research, Zhuhai 519082, China

4. College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

5. GEORG-Lab (Geophysical Remote Sensing Lab), 28209 Bremen, Germany

6. Department of Geography, Texas A&M University, College Station, TX 77843, USA

7. National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China

Abstract

Passive microwave (PMW) sensors are popularly applied to Earth observations. However, the satellite PMW radiometer data sometimes have non-negligible errors in geolocation. Coastline inflection methods (CIMs) are widely used to improve geolocation errors of PMW images. However, they commonly require accuracy satellite flight parameters, which are difficult to obtain by users. In this study, a simplified coastline inflection method (SCIM) is proposed to correct the geolocation errors without demanding for the satellite flight parameters. SCIM is applied to improve geolocation errors of FengYun-3D (FY-3D) Microwave Radiation Imager (MWRI) brightness temperature images from 2018 and 2019. It reduces the geolocation errors of MWRI images to 0.15 pixels in the along-track and cross-track direction. This means reductions of 75% and 86% of the geolocation errors, respectively. The mean brightness temperature differences between the ascending and descending MWRI images are reduced by 34%, demonstrating the improved geolocation accuracy of SCIM. The corrected images are also used to estimate Arctic sea ice concentration (SIC). By comparing with SICs retrieved from the un-corrected images, the root mean square error (RMSE) and mean absolute error (MAE) of the SICs from the corrected images are reduced from 13.7% to 10.2% and 8.9% to 6.9%, respectively. The mean correlation coefficient (R) increases from 0.91 to 0.95. All these results indicate that SCIM can reduce geolocation errors of satellite-based PMW images significantly. As SCIM is very simple and easy to be applied, it could be a useful method for users of PMW images.

Funder

Guangdong Basic and Applied Basic Research Foundation

National Key Research and Development Program of China

National Science Fund for Distinguished Young Scholars

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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