This paper contrasts predicted X-band sea surface backscattering from slick-free and oil-covered sea surfaces with actual measurements acquired by the X-band satellite TerraSAR-X and COSMO-SkyMed Synthetic Aperture Ra...This paper contrasts predicted X-band sea surface backscattering from slick-free and oil-covered sea surfaces with actual measurements acquired by the X-band satellite TerraSAR-X and COSMO-SkyMed Synthetic Aperture Radar(SAR)missions.Two SAR scenes were acquired with a temporal difference of about 36 minutes,under similar met-ocean conditions,during the North Sea’s Gannet Alpha oil spill accident.The normalized radar cross section of the slick-free sea surface is predicted using the Advanced Integral Equation Model(AIEM)while the backscatter from the oiled sea surface is predicted by the AIEM augmented with the Model of Local Balance(MLB)to include the damping effect of oil slicks.Experimental results show that X-band co-polarized numerical predictions agree reasonably well with both TSX and CSK actual measurements collected over slick-free sea surfaces.When dealing with oil-covered sea surfaces,the predicted backscattering reasonably agrees with TSX measurements,while it overestimates the CSK ones.This is likely due to the different spreading conditions of the oil imaged by the two satellite missions.展开更多
In this paper, an empirical methodology to retrieve bare soil moisture by Synthetic Aperture Radar (SAR) is developed. The model is based on Advanced Integral Equation Model (AIEM). Since AIEM cannot express cross-pol...In this paper, an empirical methodology to retrieve bare soil moisture by Synthetic Aperture Radar (SAR) is developed. The model is based on Advanced Integral Equation Model (AIEM). Since AIEM cannot express cross-polarized backscattering coefficients accurately, we propose an empirical model to retrieve soil moisture for bare farmland only with co-polarized SAR data. The soil moisture can be obtained by solving an equation of HH and VV polarized data without any field measurements. Both simulated and real SAR data are used to validate the accuracy of the model. This method is especially effective in a large area where the surface roughness is difficult to be completely measured.展开更多
The leaf area index(LAI)inversion accuracy of the water cloud model(WCM)based on the SAR images is low.The main reason is that the WCM model assumes two independent contributions from plant and soil surfaces,without t...The leaf area index(LAI)inversion accuracy of the water cloud model(WCM)based on the SAR images is low.The main reason is that the WCM model assumes two independent contributions from plant and soil surfaces,without taking into account their interactions.Based on this,this study proposes an efficient LAI estimation method that combines the advanced integral equation model(AIEM)and modified water cloud model(MWCM).Images collected by GaoFen-3 synthetic aperture radar(GF-3 SAR)in the Xiangfu area in the east of Kaifeng City,Henan Province,are processed with a modified SAR-BM3D method and used as test data of the proposed method.The proposed AIEM-MWCM method that combines the AIEM and MWCM models is employed to perform the remote sensing inversion of winter wheat LAI throughout the growth cycle.The results show that the fitting accuracy of winter wheat LAI in the five growth stages achieved by the proposed AIEMMWCM method inversion is better than that of the Dubois-MWCM model.The R2 value of the proposed method is higher than 0.8,and its root mean squared error(RMSE)is lower than 0.3.展开更多
基金supported by the National Key R&D Program of China[Grant number 2021YFB3901300]the ESA-NRSCC Dragon-5 cooperation project[ID 57979]+1 种基金the Agenzia Spaziale Italiana under the APPLICAVEMARS project[ASI contract n.2021-4-U.0]the China Scholarship Council.
文摘This paper contrasts predicted X-band sea surface backscattering from slick-free and oil-covered sea surfaces with actual measurements acquired by the X-band satellite TerraSAR-X and COSMO-SkyMed Synthetic Aperture Radar(SAR)missions.Two SAR scenes were acquired with a temporal difference of about 36 minutes,under similar met-ocean conditions,during the North Sea’s Gannet Alpha oil spill accident.The normalized radar cross section of the slick-free sea surface is predicted using the Advanced Integral Equation Model(AIEM)while the backscatter from the oiled sea surface is predicted by the AIEM augmented with the Model of Local Balance(MLB)to include the damping effect of oil slicks.Experimental results show that X-band co-polarized numerical predictions agree reasonably well with both TSX and CSK actual measurements collected over slick-free sea surfaces.When dealing with oil-covered sea surfaces,the predicted backscattering reasonably agrees with TSX measurements,while it overestimates the CSK ones.This is likely due to the different spreading conditions of the oil imaged by the two satellite missions.
基金Supported by the Research Program of Science and Technology at Universities of Inner Mongolia Autonomous Region (NJZZ11069)the Natural Science Foundation of Inner Mongolia Autonomous Region (2011BS0904)
文摘In this paper, an empirical methodology to retrieve bare soil moisture by Synthetic Aperture Radar (SAR) is developed. The model is based on Advanced Integral Equation Model (AIEM). Since AIEM cannot express cross-polarized backscattering coefficients accurately, we propose an empirical model to retrieve soil moisture for bare farmland only with co-polarized SAR data. The soil moisture can be obtained by solving an equation of HH and VV polarized data without any field measurements. Both simulated and real SAR data are used to validate the accuracy of the model. This method is especially effective in a large area where the surface roughness is difficult to be completely measured.
基金funded by the 2016 National Key Research and Development Plan(grant number 2016YFC0803103)Research on Key Technology of Agricultural Remote Sensing Monitoring(grant number 12210243)the Henan Provincial University Innovation Team Support Plan(grant number 14IRTSTHN026).
文摘The leaf area index(LAI)inversion accuracy of the water cloud model(WCM)based on the SAR images is low.The main reason is that the WCM model assumes two independent contributions from plant and soil surfaces,without taking into account their interactions.Based on this,this study proposes an efficient LAI estimation method that combines the advanced integral equation model(AIEM)and modified water cloud model(MWCM).Images collected by GaoFen-3 synthetic aperture radar(GF-3 SAR)in the Xiangfu area in the east of Kaifeng City,Henan Province,are processed with a modified SAR-BM3D method and used as test data of the proposed method.The proposed AIEM-MWCM method that combines the AIEM and MWCM models is employed to perform the remote sensing inversion of winter wheat LAI throughout the growth cycle.The results show that the fitting accuracy of winter wheat LAI in the five growth stages achieved by the proposed AIEMMWCM method inversion is better than that of the Dubois-MWCM model.The R2 value of the proposed method is higher than 0.8,and its root mean squared error(RMSE)is lower than 0.3.
