For many years, the status of surface vegetation has been monitored by using polar-orbiting satellite imagers such as Moderate Resolution Imaging Spectroradiometer(MODIS). However, limited availability of clear-sky sa...For many years, the status of surface vegetation has been monitored by using polar-orbiting satellite imagers such as Moderate Resolution Imaging Spectroradiometer(MODIS). However, limited availability of clear-sky samples makes the derived vegetation index dependent on multiple days of observations. High-frequency observations from the geostationary Fengyun(FY) satellites can significantly reduce the influence of clouds on the synthesis of terrestrial normalized difference vegetation index(NDVI). In this study, we derived the land surface vegetation index based on observational data from the Advanced Geostationary Radiation Imager(AGRI) onboard the FY-4B geostationary satellite. First, the AGRI reflectance of visible band and near-infrared band is corrected to the land surface reflectance by the 6S radiative transfer model. The bidirectional reflectance distribution function(BRDF) model is then used to normalize the AGRI surface reflectance at different observation angles and solar geometries, and an angle-independent reflectance is derived. The AGRI surface reflectance is further corrected to the MODIS levels according to the AGRI spectral response function(SRF). Finally, the daily AGRI data are used to synthesize the surface vegetation index. It is shown that the spatial distribution of NDVI images retrieved by single-day AGRI is consistent with that of 16-day MODIS data. At the same time, the dynamic range of the revised NDVI is closer to that of MODIS.展开更多
为了在天基远距离条件下估计空间目标的姿态参数,提出了基于时序光谱信号分级求解目标表面参数及姿态参数的方法.第一级,在三轴稳定状态下将空间目标等效为“双面模型”,引入双向反射分布函数(bidirectional reflectance distribution f...为了在天基远距离条件下估计空间目标的姿态参数,提出了基于时序光谱信号分级求解目标表面参数及姿态参数的方法.第一级,在三轴稳定状态下将空间目标等效为“双面模型”,引入双向反射分布函数(bidirectional reflectance distribution function,BRDF)的多级融合模型表征复杂材料的光谱反射特性,基于时谱信号与时谱信号模型对双面光谱BRDF与面积乘积进行重构.第二级,为了抑制双面耦合特性对姿态估计的影响,构建双面特性分离度,并基于该度量最大化实现光谱波段优选.第三级,构建目标姿态运动状态下的时序光谱信号模型,以模型值与观测值之间的误差为目标函数,利用Levenberg-Marquardt算法对姿态参数进行估计.仿真表明,该方法更适用于方形本体的目标,且反演误差会随相位角和探测器噪声的增大而增大,在信噪比SNR≥10条件下反演误差在2%以内.展开更多
基金Supported by the National Key Research and Development Program of China (2021YFB3900400)National Natural Science Foundation of China (U2142212 and U2242211)。
文摘For many years, the status of surface vegetation has been monitored by using polar-orbiting satellite imagers such as Moderate Resolution Imaging Spectroradiometer(MODIS). However, limited availability of clear-sky samples makes the derived vegetation index dependent on multiple days of observations. High-frequency observations from the geostationary Fengyun(FY) satellites can significantly reduce the influence of clouds on the synthesis of terrestrial normalized difference vegetation index(NDVI). In this study, we derived the land surface vegetation index based on observational data from the Advanced Geostationary Radiation Imager(AGRI) onboard the FY-4B geostationary satellite. First, the AGRI reflectance of visible band and near-infrared band is corrected to the land surface reflectance by the 6S radiative transfer model. The bidirectional reflectance distribution function(BRDF) model is then used to normalize the AGRI surface reflectance at different observation angles and solar geometries, and an angle-independent reflectance is derived. The AGRI surface reflectance is further corrected to the MODIS levels according to the AGRI spectral response function(SRF). Finally, the daily AGRI data are used to synthesize the surface vegetation index. It is shown that the spatial distribution of NDVI images retrieved by single-day AGRI is consistent with that of 16-day MODIS data. At the same time, the dynamic range of the revised NDVI is closer to that of MODIS.
文摘为了在天基远距离条件下估计空间目标的姿态参数,提出了基于时序光谱信号分级求解目标表面参数及姿态参数的方法.第一级,在三轴稳定状态下将空间目标等效为“双面模型”,引入双向反射分布函数(bidirectional reflectance distribution function,BRDF)的多级融合模型表征复杂材料的光谱反射特性,基于时谱信号与时谱信号模型对双面光谱BRDF与面积乘积进行重构.第二级,为了抑制双面耦合特性对姿态估计的影响,构建双面特性分离度,并基于该度量最大化实现光谱波段优选.第三级,构建目标姿态运动状态下的时序光谱信号模型,以模型值与观测值之间的误差为目标函数,利用Levenberg-Marquardt算法对姿态参数进行估计.仿真表明,该方法更适用于方形本体的目标,且反演误差会随相位角和探测器噪声的增大而增大,在信噪比SNR≥10条件下反演误差在2%以内.
