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Li J.,Beijing Normal University | Li J.,Demonstration Center for Spaceborne Remote Sensing National Space Administration | Gu X.-F.,Beijing Normal University | Gu X.-F.,Demonstration Center for Spaceborne Remote Sensing National Space Administration | And 7 more authors.
Nonlinear Dynamics | Year: 2016

A variable-coefficient nonisospectral modified Kadomtsev–Petviashvili equation with two Painlevé branches is investigated in this paper. Through the generalized singular manifold method, a couple of Lax pairs for such an equation are constructed on account of the relationship between manifolds and eigenfunctions. Meanwhile, utilizing the aforementioned Lax pairs, a binary Darboux transformation of this equation has been presented. © 2015, Springer Science+Business Media Dordrecht.


Li J.,Beijing Normal University | Li J.,Demonstration Center for Spaceborne Remote Sensing National Space Administration | Gu X.-F.,Beijing Normal University | Gu X.-F.,Demonstration Center for Spaceborne Remote Sensing National Space Administration | And 8 more authors.
Shuidonglixue Yanjiu yu Jinzhan/Chinese Journal of Hydrodynamics Ser. A | Year: 2011

Utilizing the analytical two-soliton solutions of the variable-coefficient Gardner equation including the quadratic and cubic terms, the nonlinear interaction of ocean internal waves is investigated in this paper. In the Luzon Strait, the variation of signature characteristic for the elastic interaction on SAR image is discussed. Furthermore, the effects of dissipative, perturbed and dispersive terms on the nonlinear interaction of internal waves and horizontal velocity of the ocean surface current are also presented, which are consistent with the characteristic on the SAR images.


Li X.,Beijing Normal University | Li X.,Demonstration Center for Spaceborne Remote Sensing National Space Administration | Gu X.,Beijing Normal University | Gu X.,Demonstration Center for Spaceborne Remote Sensing National Space Administration | And 9 more authors.
International Journal of Remote Sensing | Year: 2012

Atmospheric turbulence and aerosol scattering can produce the blurring of the remotely sensed image. The degrading effect is usually quantified by atmospheric modulation transfer function (MTF). However, this effect behaves differently with different remote sensors. The effort of this article is to study the different degrading effects of aerosol MTF and turbulence MTF between charge-coupled device (CCD) camera on China-Brazil Earth Resources Satellite-02b (CBERS-02b) and on Huan Jing-1A/1B satellite (HJ-1A/1B). Specifically, a corrected aerosol MTF model is established based on classical solution of small-angle approximation (SAA) model by considering the MTF and the effective instantaneous field of view (EIFOV) of CCD cameras. By assuming many different atmospheric conditions, the aerosol MTF and turbulence MTF for two CCD cameras are evaluated. It is found that the output aerosol MTF of CCD camera on HJ-1A/1B causes more degrading effect than that of CBERS-02b under the same atmospheric condition. However, the situation reverses for the turbulence MTF. Furthermore, CCD images acquired over Beijing, China, by CBERS-02b and HJ-1A/1B on four different dates are selected. The overall atmospheric MTF for these images are determined based on the aerosol products from Aerosol Robotic Network (Aeronet) and radiosounding data. Results indicate that the overall atmospheric MTF of CBERS-02b CCD camera reduces image quality more seriously than that of HJ-1A/1B CCD camera. Additionally, the atmospheric MTF compensation is performed and evaluated for these CCD images based on the overall atmospheric MTF. © 2012 Taylor and Francis Group, LLC.


Liu P.,CAS Institute of Remote Sensing | Liu P.,Demonstration Center for Spaceborne Remote Sensing National Space Administration | Liu P.,University of Chinese Academy of Sciences | Gu X.F.,CAS Institute of Remote Sensing | And 7 more authors.
Science China Earth Sciences | Year: 2014

Rain can significantly degrade the wind vector retrieval from Precipitation Radar (PR) by three mechanisms, namely, two-way rain attenuation, rain volume-backscattering, and ocean surface roughening from the rain splash effect. Here we first derive the radar equation for PR in rainy conditions. Then we use the rain attenuation model for Ku band, volume backscatter model for spherical raindrops and PR-TMI (TRMM Microwave Imager, TMI) matchup datasets from June to August in 2010 to solve the radar equation, and quantitatively analyze the influence of rainfall on PR radar measurement of ocean surface wind speed. Our results show that the significant effect of rain on radar signal is dominated by two-way rain attenuation and rain splash effect, and the effect of rain volume-backscattering is relatively the weakest, which can even be neglected in rain-weak conditions. Moreover, both the two-way rain attenuation and rain splash effect increase with the increasing of integration rain rate and incident angle. Last, we combine volume-backscattering effect and splash effect into a simple phenomenological model for rain calibration and select three typhoon cases from June to August in 2012 to verify the accuracy of this model. Before calibration, the mean difference and mean square error (MSE) between PR-observed σ0 and wind-induced σ0 are about 2.95 dB and 3.10 dB respectively. However, after calibration, the mean difference and MSE are reduced to 0.64 dB and 1.61 dB respectively. The model yields an accurate calibration for PR near-nadir normalized radar cross section (NRCS) in rainy conditions. © 2014, Science China Press and Springer-Verlag Berlin Heidelberg.

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