通过分析降雨前后ENVISAT ASAR IMM_1P数据,总结出运用ENVISAT ASAR IMM_1P数据反演四湖地区涝渍害的指标,即涝害的反演指标为降水前后向散射系数介于0.12～0.25且降水前后向散射系数大于降水后后向散射系数,渍害的反演指标为降水前后...通过分析降雨前后ENVISAT ASAR IMM_1P数据,总结出运用ENVISAT ASAR IMM_1P数据反演四湖地区涝渍害的指标,即涝害的反演指标为降水前后向散射系数介于0.12～0.25且降水前后向散射系数大于降水后后向散射系数,渍害的反演指标为降水前后向散射系数介于0.12～0.25且降水后的后向散射系数较降水前的后向散射系数高0.30。并运用指标计算出2010年7月8~11日暴雨过后四湖地区涝渍害空间分布,通过利用高分辨率HJ-1-A-CCD1数据对局部地区分析比较表明反演指标能真实反映涝渍害现状。展开更多
Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multitemporal ENVISAT ASAR data with different polarization are used to classify the banana fields in this paper. Principal component ana...Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multitemporal ENVISAT ASAR data with different polarization are used to classify the banana fields in this paper. Principal component analysis (PCA) was applied for six pairs of ASAR dual-polarization data. For its large leaves, banana has high backscatter. So the value of banana fields is high and shows very bright in the 1st component, which makes it much easier for banana fields extraction. Dual-polarization data provide more information, and the VV and VH backscatter of banana show different characters with other land covers. Based on the analysis of the radar signature of banana fields and other land covers and the 1st component, banana fields are classified using object-oriented classifier. Compared to the field survey data and ASTER data, the accuracy of banana fields in the study area is 83.5%. It shows that the principal component analysis provides the useful information in SAR images analysis and makes the extraction of banana fields easier.展开更多
文摘欧洲环境卫星-高级合成孔径雷达(Environmental Satellite-Advanced Synthetic ApertureRadar,Envisat-ASAR)波模式数据提供了全球风、浪要素信息,在海浪模式预报与同化方面有重要作用。该数据合成孔径雷达(Synthetic Aperture Radar,SAR)图像普遍存在海浪条纹清晰度不同的现象,但是否影响数据精度尚无定论。本文通过比较2010年NODC(the National Oceanographic Date Center)浮标观测数据和波模式数据,发现经过官方修正后的海浪参数反而具有更大误差。进而通过对比不同条纹清晰度的SAR图像反演参数误差,揭示了ASAR产品海浪参数与浮标测量值之间的误差与海浪条纹清晰度的关系。结果表明:海浪条纹清晰的SAR图像的主波波长和主波周期的反演误差更小,而条纹不清晰SAR图像的有效波高和风速的反演误差更小。通过分析海浪参数对海浪条纹清晰度的敏感性,证实了有效波高和方位向截断波长对SAR图像条纹清晰度的响应最好,波陡次之,与卫星飞行方位角和入射角无关。因此,在反演和修正SAR波模式数据时,考虑图像的条纹清晰度,将会有效提高反演数据的精度。该研究可为高分三号等卫星的波模式数据波浪要素反演精度的提升提供有价值的参考。
文摘通过分析降雨前后ENVISAT ASAR IMM_1P数据,总结出运用ENVISAT ASAR IMM_1P数据反演四湖地区涝渍害的指标,即涝害的反演指标为降水前后向散射系数介于0.12～0.25且降水前后向散射系数大于降水后后向散射系数,渍害的反演指标为降水前后向散射系数介于0.12～0.25且降水后的后向散射系数较降水前的后向散射系数高0.30。并运用指标计算出2010年7月8~11日暴雨过后四湖地区涝渍害空间分布,通过利用高分辨率HJ-1-A-CCD1数据对局部地区分析比较表明反演指标能真实反映涝渍害现状。
基金Supported by the Program for New Century Excellent Talents in University (NCET-05-0573)Fujian Science and Technology Project (No2006I0018)the Science Project of the Education Department of Fujian Province(No 2006F5022)
文摘Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multitemporal ENVISAT ASAR data with different polarization are used to classify the banana fields in this paper. Principal component analysis (PCA) was applied for six pairs of ASAR dual-polarization data. For its large leaves, banana has high backscatter. So the value of banana fields is high and shows very bright in the 1st component, which makes it much easier for banana fields extraction. Dual-polarization data provide more information, and the VV and VH backscatter of banana show different characters with other land covers. Based on the analysis of the radar signature of banana fields and other land covers and the 1st component, banana fields are classified using object-oriented classifier. Compared to the field survey data and ASTER data, the accuracy of banana fields in the study area is 83.5%. It shows that the principal component analysis provides the useful information in SAR images analysis and makes the extraction of banana fields easier.
