Geolocation Approach for FY-3A MERSI Remote Sensing Image

Guan Min; Wu Ronghua

Vol.23, NO.5, 2012

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2012 > 23(5): 534-542

Guan Min, Wu Ronghua. Geolocation approach for FY-3A MERSI remote sensing image. J Appl Meteor Sci, 2012, 23(5): 534-542.

Citation:

Guan Min, Wu Ronghua. Geolocation approach for FY-3A MERSI remote sensing image. J Appl Meteor Sci, 2012, 23(5): 534-542.

Guan Min, Wu Ronghua. Geolocation approach for FY-3A MERSI remote sensing image. J Appl Meteor Sci, 2012, 23(5): 534-542.

Citation:

Guan Min, Wu Ronghua. Geolocation approach for FY-3A MERSI remote sensing image. J Appl Meteor Sci, 2012, 23(5): 534-542.

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Geolocation Approach for FY-3A MERSI Remote Sensing Image

Guan Min^,,
Wu Ronghua

National Satellite Meteorological Center, Beijing 100081

Received Date: 2012-02-09
Rev Recd Date: 2012-06-12
Publish Date: 2012-10-31

Abstract

Abstract

Fengyun 3 series are second generation polar-orbiting meteorological satellites in China. The first satellite of Fengyun 3 series, FY-3A, is a research and development satellite and is launched successfully at 1100 BT 27 May 2008. The Medium Resolution Spectral Imager (MERSI) is a main payload of FY-3A spacecraft and since June 2008, it has been acquiring daily global data in 20 spectral bands from the visible to the thermal infrared-15 with 1 km and 5 channels with 250 m spatial resolution at nadir. In order to satisfy the requirements of quantitative application, MERSI data need to be geolocated, which can provide accurate latitude and longitude information for follow-up remote sensing productions. The geolocation algorithm of MERSI data, which is adapted by National Satellite Meteorological Center (NSMC), is introduced in details, as well as the error analysis.MERSI rotates a 45° mirror to get 360° views across the orbit direction. MERSI is a paddle broom electro-optical instrument that uses the forward motion of the satellite to provide the along-track direction of scanning. MERSI simultaneously senses, in each band, 10 rows of 1 km detector pixels and 40 rows of 250 m detector pixels. The MERSI detectors are grouped on four focal planes. Detectors for each band are laid on the focal planes in the along-scan direction. MERSI swath is about 2000 km, and generates about 140 GB data per day.According to the characteristics above, parameter method is used for MERSI remote sensing data geolocation. This approach creates the spatial relationship model between the sensed data and the earth based coordinate system, according to MERSI scanning mode, sensing geometry, satellite/sensor's attitude and position. There are 10 coordinate systems and 9 rotated relationships involved in the model. Since 45° mirror brings image rotation, there is a module removing the rotation in the model.When processing data, the line-of-sight vector from each detector of a band is calculated in the instrument coordinate system first. Then the line-of-sight and satellite position are rotated to the earth centered rotating coordinates. The intersection of the line-of-sight with the WGS-84 ellipsoid is calculated. An iterative search process is used to follow the line-of-sight from the instrument to the intersection of the terrain surface represented by a DEM. This geolocation approach for MERSI has been applied to FY-3A data preprocessing system.Five factors that influence the accuracy of results are analyzed. These factors include satellite position/velocity error, satellite attitude error, satellite-instrument installation error, instrument-inner geometry error and instrument thermal distortion.Compared geolocation results with the true remote sensing image using the land-water mask, it shows that the error alone the orbit direction is about 0.167 km and the error along the scanning direction is about 0.058 km. This geolocation approach for MERSI (250 m) achieves accuracy up to 1 pixel.
- FY-3A;
- MERSI;
- multi-detectors scanning;
- sensing geometry modeling;
- geolocation

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References(21)

Figures and Tables

Fig. 1 Overview of MERSI sensing geometry

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Fig. 2 Transformation of coordinate systems

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Fig. 3 Flow chart of MERSI geolocation

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Fig. 4 MERSI geolocation of Hainan west coastline in China

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Fig. 5 MERSI geolocation of Gulf of Aden in South Yemen

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Fig. 6 MERSI geolocation of Palk Strait in South India

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Fig. 7 Global distribution of 60 ground control points (GCPs)

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Table 1 Definition of coordinate systems

坐标系	原点	参考平面	x轴方向	z轴方向
焦平面坐标系	系统光轴与焦平面的交点	焦平面	沿扫描方向	与焦平面垂直指向焦点方向
扫描镜坐标系	45°扫描镜与系统光轴的交点		沿45°扫描镜的旋转轴方向	45°扫描镜中心点指向星下点的方向
望远镜坐标系	系统光轴与焦平面的交点		x轴垂直于扫描镜的旋转轴，并且沿光线经扫描镜在0°扫描角时反射之后的扫描方向	平行于系统光轴指向卫星飞行方向
K镜坐标系	K镜的几何中心与系统光轴的交点		沿K镜的旋转轴方向	与扫描镜坐标系z轴方向一致
仪器坐标系	仪器质心	仪器安装平面	与卫星滚动轴指向一致	与卫星偏航轴指向一致
卫星本体坐标系	卫星质心	卫星滚动轴与俯仰轴所在平面	与卫星滚动轴指向一致	与卫星偏航轴指向一致
轨道坐标系	卫星质心	卫星轨道平面	与卫星速度方向一致	从卫星质心指向地心
地心惯性坐标系	地心	J2000.0平赤道面	指向J2000.0的平春分点	指向J2000.0的平北天极
地心旋转坐标系	地心	国际时间局 BIH1984.0时元定义的协议地球赤道面	指向国际时间局 BIH1984.0时元定义的零子午面和协议地球赤道的交点	平行于国际时间局 BIH1984.0时元定义的协议地球极轴方向
大地测量坐标系	地心

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Table 2 Ground control point residuals measured before and after updates to the sensor interior orientation parameters

测量	地面控制点个数	扫描方向定位误差/m		跨轨方向定位误差/m
测量	地面控制点个数	平均值	标准偏差	平均值	标准偏差
首次	60	1670	454	304	316
更新后	60	167	343	58	327

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