One of the limitations in using spaceborne, microwave radiometer data for atmospheric remote sensing is the nonuniform spatial resolution. Remapping algorithms can be applied to the data to ameliorate this limitation....
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One of the limitations in using spaceborne, microwave radiometer data for atmospheric remote sensing is the nonuniform spatial resolution. Remapping algorithms can be applied to the data to ameliorate this limitation. In this paper, two remapping algorithms, the backus-gilbertinversion (BGI) technique and the filter algorithm (AFA), widely used in the operational data preprocessing of the Advanced Technology Microwave Sounder (ATMS), are investigated. The algorithms are compared using simulations and actual ATMS data. Results show that both algorithms can effectively enhance or degrade the resolution of the data. The BGI has a higher remapping accuracy than the AFA. It outperforms the AFA by producing less bias around coastlines and hurricane centers where the signal changes sharply. It shows no obvious bias around the scan ends where the AFA has a noticeable positive bias in the resolution-enhanced image. However, the BGI achieves the resolution enhancement at the expense of increasing the noise by 0.5 K. The use of the antenna pattern instead of the point spread function in the algorithm causes the persistent bias found in the AFA-remapped image, leading not only to an inaccurate antenna temperature expression but also to the neglect of the geometric deformation of the along-scan field-of-views.
One of the limitations in using spaceborne, microwave radiometer data for atmospheric remote sensing is the coarse spatial resolution at low frequencies. backus-gilbert inversion algorithm is widely used for spatial r...
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ISBN:
(纸本)9781665403696
One of the limitations in using spaceborne, microwave radiometer data for atmospheric remote sensing is the coarse spatial resolution at low frequencies. backus-gilbert inversion algorithm is widely used for spatial resolution enhancement. However, the enhancement in resolution is achieved at the expense of the increase in noise. To suppress the noise in the reconstructed data, a new adaptive window method is proposed. Compared to the fixed window method adopted in previous studies, the new method is capable of reducing the noise to 1/10 of the noise induced by the fixed window method while keeping the similar degree of spatial resolution enhancement.
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