Abstract
Known as input in the Numerical Weather Prediction (NWP) models, Microwave Radiation Imager (MWRI) data have been widely distributed to the user community. With the development of remote sensing technology, improving the geolocation accuracy of MWRI data are required and the first step is to estimate the geolocation error accurately. However, the traditional method, such as the coastline inflection method (CIM), usually has the disadvantages of low accuracy and poor anti-noise ability. To overcome these limitations, this paper proposes a novel ℓ p iterative closest point coastline inflection method ( ℓ p -ICP CIM). It assumes that the field of views (FOVs) across the coastline can degenerate into a step function and employs an ℓ p ( 0 ≤ p < 1 ) sparse regularization optimization model to solve the coastline point. After estimating the coastline points, the ICP algorithm is employed to estimate the corresponding relationship between the estimated coastline points and the real coastline. Finally, the geolocation error can be defined as the distance between the estimated coastline point and the corresponding point on the true coastline. Experimental results on simulated and real data sets show the effectiveness of our method over CIM. The accuracy of the geolocation error estimated by ℓ p -ICP CIM is up to 0.1 pixel, in more than 90 % of cases. We also show that the distribution of brightness temperature near the coastline is more consistent with the real coastline and the average geolocation error is reduced by 63 % after geolocation error correction.
Highlights
FY-3 satellites are the second generation of Chinese polar orbital series meteorological satellites
The cloud-free field of views (FOVs) at Microwave Radiation Imager (MWRI) 89 GHz data was defined by FY-3C Medium Resolution Spectrum Imager (MERSI) data [11]
We greatly improve the accuracy of geolocation error estimation through the following two ways: (1) Increasing the number of FOVs per group from 4 to 12 and using an p(0 ≤ p < 1) sparse regularization optimization model to improve the stability of an estimated geolocation error; (2) Using an ICP algorithm to determine the ‘corresponding point’ instead of the ‘nearest point’ of the estimated coastline point on the real coastline
Summary
FY-3 satellites are the second generation of Chinese polar orbital series meteorological satellites. FY-3 series satellites are equipped with various instruments in the visible, infrared and microwave bands, which provide abundant information for global climate prediction and weather prediction. The Microwave Radiation Imager (MWRI) is an important remote sensor onboard the FY-3 meteorological satellite, including 10 channels in five frequency bands: 10.65, 18.7, 23.8, 36.5 and 89.0 GHz V/H. The MWRI radiometer weighs 175 kg and consumes 125 W of power. It consists of an offset parabolic main reflector of size 977.4 mm × 897.0 mm and four independent feed horns. According to the characteristics of MWRI, a variety of engineering functions have been developed, including precipitation intensity at surface (liquid or solid), sea-ice cover, wind speed over the surface (horizontal), sea surface temperature, cloud liquid water (CLW) total column, snow cover and so on [2,3]
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