Airborne test flight of HY-2A satellite microwave scatterometer and data analysis

This paper introduces the background, aim, experimental design, configuration and data processing for an airborne test flight of the HY-2 Microwave scatterometer (HSCAT). The aim was to evaluate HSCAT performance and a developed data processing algorithm for the HSCAT before launch. There were three test flights of the scatterometer, on January 15, 18 and 22, 2010, over the South China Sea near Lingshui, Hainan. The test flights successfully generated simultaneous airborne scatterometer normalized radar cross section (NRCS), ASCAT wind, and ship-borne-measured wind datasets, which were used to analyze HSCAT performance. Azimuthal dependence of the NRCS relative to the wind direction was nearly cos(2w), with NRCS minima at crosswind directions, and maxima near upwind and downwind. The NRCS also showed a small difference between upwind and downwind directions, with upwind crosssections generally larger than those downwind. The dependence of airborne scatterometer NRCS on wind direction and speed showed favorable consistency with the NASA scatterometer geophysical model function (NSCAT GMF), indicating satisfactory HSCAT performance.     

海洋二号卫星微波散射计面元匹配

面元匹配是散射计海面风场反演的重要预处理步骤。为保障海洋二号卫星散射计(HSCAT)业务化海面风场反演,提出了一种可业务化运行的HSCAT面元匹配算法。HSCAT面元匹配算法包括地面网格划分和后向散射系数观测结果重采样两个关键部分。为简化计算,HSCAT采用以星下点轨迹为中心,以顺轨向及交轨向为坐标轴,分辨率为25 km×25 km的网格划分方式。重采样利用卫星星下点经纬度数据,对每个后向散射系数观测结果,以后向散射系数与各星下点球面距离的最小值为依据进行交轨向重采样,并以取得距离最小值所对应的星下点与该轨数据星下点起始位置的距离为依据进行顺轨向重采样,实现对后向散射系数观测结果的面元匹配。实验和对比结果表明,提出的面元匹配算法可对后向散射系数进行有效的重采样,风矢量面元的位置分布均匀,每个风矢量面元均有足够数量的独立观测后向散射系数,可在全球范围内满足高质量海面风场反演的要求。     

基于HY-1C CZI影像光谱指数重构数据MNF变换的红树林提取

本文基于广西山口国家红树林生态自然保护区的HY-1C卫星的海岸带成像仪(Coastal Zone Imager,CZI)影像,分析了红树林与一般陆地植被的光谱特征及其光谱指数的相关性,采用归一化差值植被指数(Normalized Difference Vegetation Index,NDVI)、归一化差异水分指数(Normalized Difference Water Index,NDWI)、大气阻抗植被指数(Atmospheric Impedance Vegetation Index,ARVI)及利用CZI波段构建的光谱斜率比(CZI Visible Spectrum Slope Ratio,CVSSR)4个指数替代CZI原始波段形成重构数据,基于重构数据的最小噪声分离变换(Minimum Noise Fraction Rotation,MNF)结果分量,建立决策树并实现了红树林信息的自动提取。研究结果表明：结合本文所选光谱指数重构数据及MNF变换方法,能够有效增强CZI影像上红树林与一般陆地植被的光谱差异,基于MNF变换分量建立的决策树可有效提取红树林信息,经与专家解译结果比对,本文方法面积准确率达90%以上;经随机样本点验证,总体检测精度为88%。