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
介绍一种基于一阶辐射传输的积雪散射模型。该模型考虑了积雪覆盖地表微波散射的3种回波分量:雪层表面散射、下垫面散射以及雪层体散射。对于其中2个面散射分量,文章中应用一种新的面散射模型———AIEM取代原有的IEM模型进行处理。最后...介绍一种基于一阶辐射传输的积雪散射模型。该模型考虑了积雪覆盖地表微波散射的3种回波分量:雪层表面散射、下垫面散射以及雪层体散射。对于其中2个面散射分量,文章中应用一种新的面散射模型———AIEM取代原有的IEM模型进行处理。最后,使用M ich igan大学的实测数据对改进后模型的模拟结果进行验证,并与改进前的模拟结果进行了对比。展开更多
土壤水分是气象预报、农情监测以及水文模型的重要参数之一,利用被动微波遥感技术可以有效获取土壤水分。欧空局(ESA)计划于2009年发射卫星SMOS(Soil Moisture and Ocean Salinity),其主要目的是监测全球范围内的土壤水分和海洋盐度变...土壤水分是气象预报、农情监测以及水文模型的重要参数之一,利用被动微波遥感技术可以有效获取土壤水分。欧空局(ESA)计划于2009年发射卫星SMOS(Soil Moisture and Ocean Salinity),其主要目的是监测全球范围内的土壤水分和海洋盐度变化。根据SMOS的设置情况,寻找精度较高的半经验模型以便为进一步的土壤水分反演提供简化模型。对于裸露地表,地表粗糙度、土壤介电常数等因素影响最终的微波发射率。运用Dobson半经验介电常数模型计算土壤的介电常数,将计算结果输入高级积分方程模型(AIEM)。通过AIEM模拟的数据库,利用回归关系建立了一个精度相对较高的L波段多角度半经验模型。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.
文摘介绍一种基于一阶辐射传输的积雪散射模型。该模型考虑了积雪覆盖地表微波散射的3种回波分量:雪层表面散射、下垫面散射以及雪层体散射。对于其中2个面散射分量,文章中应用一种新的面散射模型———AIEM取代原有的IEM模型进行处理。最后,使用M ich igan大学的实测数据对改进后模型的模拟结果进行验证,并与改进前的模拟结果进行了对比。
文摘土壤水分是气象预报、农情监测以及水文模型的重要参数之一,利用被动微波遥感技术可以有效获取土壤水分。欧空局(ESA)计划于2009年发射卫星SMOS(Soil Moisture and Ocean Salinity),其主要目的是监测全球范围内的土壤水分和海洋盐度变化。根据SMOS的设置情况,寻找精度较高的半经验模型以便为进一步的土壤水分反演提供简化模型。对于裸露地表,地表粗糙度、土壤介电常数等因素影响最终的微波发射率。运用Dobson半经验介电常数模型计算土壤的介电常数,将计算结果输入高级积分方程模型(AIEM)。通过AIEM模拟的数据库,利用回归关系建立了一个精度相对较高的L波段多角度半经验模型。
基金Supported by the National Key Basic Research Program of China(2013CB733400)the National Natural Science Foundation of China(Grant Number:41471299)the Key Projects in the National Science and Technology Pillar Program(2012BAH28B03)
