Correction of atmospheric effect on ADEOS/OCTS ocean color data: Algorithm description and evaluation of its performance

This paper first describes the atmospheric correction algorithm for OCTS visible band data used at NASDA/EOC. Sharing a basic structure with Gordon and Wang’s Sea WiFS algorithm, it uses 10 candidate aerosol models including the “Asian dust model” introduced in consideration of the unique feature of aerosols over the east Asian waters. Based on the observations at 670 and 865 nm bands, the algorithm selects a pair of aerosol models that account best for the observed spectral reflectances, and synthesizes the aerosol reflectance used for the atmospheric correction. Two different schemes for determining the value of the parameter for the aerosol model selection are presented and their anticipated estimation error is analyzed in terms of retrieved water reflectance at 443 nm. The results of our numerical simulation show that the standard deviation of the estimation error of the “weighted average” scheme is mostly within the permissible level of ±0.002, reducing the error by 18% on average compared to the “simple average” scheme. The paper further discusses the expected error under the old CZCS-type atmospheric correction, which assumes constant aerosol optical properties throughout the given image. Although our algorithm has a better performance than the CZCS algorithm, further analysis shows that the error induced by the assumption taken in the algorithm that the water-leaving radiance at 670 nm band is negligibly small may be large in high pigment concentration waters, indicating the necessity for future improvements.     

卫星遥感信息处理与多元统计分析

本文针对遥感信息的数据特征探讨了卫星遥感信息处理与多元统计算法之间的关系,并从实际出发对最小二乘残差技术,目标模式识别,增强主成份分析等方法的应用作了介绍。     

卫星遥感结合地面观测估算非均匀地表区域能量通量

卫星遥感在研究非均匀陆面上地－气间能量和水循环过程时有其独到的作用。文中介绍了一种利用ＬＡＮＤＳＡＴＴＭ资料进行非均匀陆面上区域能量平衡研究的参数化方案。并以夏季和深秋两个景的ＴＭ资料为个例,结合“黑河实验”（ＨＥＩＦＥ）期间的地面观测资料分析了实验区非均匀陆面上地表特征参数及能量平衡各分量的区域分布及季节差异,同时将所得的结果与地面观测的“真值”作了比较,所得的这些结果是基本可信的。这种方法仍然处于发展的初始阶段,对此也进行了讨论。     

福建省近海区域海面表层温度的卫星遥感应用研究

分析了海洋热红外辐射的传输特性,建立了海温定量反演计算的统计回归模型,介绍了福建省近海区域海温产品生成业务化软件系统及其应用情况。     

利用卫星遥感资料估算海面太阳辐照度的初步结果

本文在分析了若干由卫星信息提取地表的太阳辐照度物理模式的基础上,将１９９５年５月２６日的ＮＯＡＡ卫星ＡＶＨＲＲ资料经适当变换后,选用Ｈａｙ和Ｈａｎｓｏｎ模式估算并分析了黄渤海３５°～４０°Ｎ、１２０°～１２５°Ｅ海区的海面太阳辐照度值。文中指出,卫星测量可成为海面太阳辐照度观测的有效手段。建议：１）尽快在国内开展这方面的研究;２）应进行海洋实况测量（最好是卫星过境时同步进行）;３）建立从卫星资料提取我国海区海面辐照度的适用算法。     

卫星高度计反演海面风速——模式函数与应用实例

对迄今为止国际上公开发表的１４ 种卫星高度计反演海面风速的模式函数进行了系统的分析和比较。在此基础上提出了一种“折中”模式函数,用于ＴＯＰＥＸ 资料海面风速反演效果良好。文中对高度计获取的全球海面风速分布特征进行了分析,最后探讨了改善海面风速模式函数反演精度的途径。     

利用GPS三频数据计算电离层垂直总电子含量变化率

研究了通过采样间隔为1 s的GPS三频载波相位观测数据计算1 Hz电离层垂直总电子含量变化率(RVTEC)的方法,推导了直接采用双频载波相位观测量计算RVTEC的公式,对一般空间环境与电离层活动剧烈两种条件下L1L2、L1L5、L2L5三种载波相位组合计算的RVTEC进行了一致性分析,给出了通过三频载波相位观测数据计算电离层RVTEC的修正方法,比较了X射线太阳耀斑期间RVTEC与由传统方法计算的总电子含量变化率(ROT)响应,在双星串飞编队测高模式下对计算方法进行了应用.结果表明,L1L2、L1L5两种组合之间的一致性较好,由L1L2、L1L5组合计算的RVTEC的中误差约为0.004 TECu/s,RVTEC较ROT更能体现TEC变化的细节,在双星串飞编队测高模式下海面高梯度的计算中,电离层延迟之差可以忽略.     

基于GRACE的空间约束方法监测华北平原地下水储量变化

华北平原作为我国重要的工农业基地和政治经济中心,面临着严重的水资源危机.因此,开展对华北平原地下水储量变化的监测工作具有重要现实意义与科学价值.本文基于GRACE重力卫星的空间约束方法,研究了华北平原地下水储量变化的时空分布规律,并与地面水井实测与地下水模型结果进行了综合比较和分析.结果表明:2002-2014年,华北平原地下水存在明显的长期亏损,GRACE估计的亏损速率为-7.4±0.9 km~3·a~(-1),而地面水井资料估计的浅层地下水亏损速率为-1.2 km~3·a~1,对比两者之间的差异可以发现,华北平原的地下水亏损以深层地下水为主.2002-2008年,GRACE估计的华北平原地下水亏损速率为-5.3±2.2 km~3·a~(-1),这与华北平原两个地下水模型得到的平均亏损速率-5.4 km~3·a~(-1)十分吻合.通过华北平原区域地下水模型的独立验证,说明GRACE可以有效评估华北平原的地下水储量变化趋势.除了长期亏损的趋势项之外,华北平原地下水还存在明显的年际变化特征,并与该地区年降雨量变化特征一致.在降雨偏少的2002年、2005-2009年和2014年,华北平原地下水储量显著减少.在空间分布上,GRACE结果表明,华北平原的地下水储量减少主要发生在山前平原和中部平原区,这也与水井实测资料和区域地下水模型结果较为吻合.与GRACE和区域地下水模型相比,目前的全球水文模型仍无法准确估计华北平原地下水变化的空间分布和亏损速率.上述研究表明,GRACE提供了评估华北平原地下水储量变化的重要监测手段.     

基于贝叶斯方法的深层充电效应风险模型评估

高能电子穿透航天器并在其内部沉积电荷从而引发深层充电效应,是导致卫星故障的重要因素之一.为了评估深层充电效应诱发卫星异常的风险,本文基于贝叶斯方法,使用一颗地球同步轨道卫星的异常数据和GOES-8卫星的电子通量探测数据,计算了不同能量阈值及累积时间的电子注量、不同卫星配置下模拟仿真的沉积电荷,并分别与卫星异常建立一系列概率风险模型.本文从模型中随机抽样得到模拟异常,并与实测异常构造混淆矩阵以评估模型拟合优度,结果表明1.0MeV电子3日累积注量-卫星异常概率风险模型为该卫星最优模型.本文利用最优模型对该卫星深层充电效应风险进行了计算,在1.0MeV电子3日累积注量达到2.0×10~(10)cm~(-2)·sr~(-1)时,该卫星发生深层充电异常的平均后验概率为27%,且95%最小可信值为22%.根据最优模型,我们对该卫星最可能导致异常的部件的材料和结构等特征做出了推断.     

Predicting plant water content in Eucalyptus grandis forest stands in KwaZulu-Natal,South Africa using field spectra resampled to the Sumbandila Satellite Sensor

The measurement of plant water content is essential to assess stress and disturbance in forest plantations. Traditional techniques to assess plant water content are costly, time consuming and spatially restrictive. Remote sensing techniques offer the alternative of a non-destructive and instantaneous method of assessing plant water content over large spatial scales where ground measurements would be impossible on a regular basis. In the context of South Africa, due to the cost and availability of imagery, studies focusing on the estimation of plant water content using remote sensing data have been limited. With the scheduled launch of the South African satellite SumbandilaSat evident in 2009, it is imperative to test the utility of this satellite in estimating plant water content. This study resamples field spectral data measured from a field spectrometer to the band settings of the SumbandilaSat in order to test its potential in estimating plant water content in a Eucalyptus plantation. The resampled SumbandilaSat wavebands were input into a neural network due to its ability to model non-linearity in a dataset and its inherent ability to perform better than conventional linear models. The integrated approach involving neural networks and the resampled field spectral data successfully predicted plant water content with a correlation coefficient of 0.74 and a root mean square error (RMSE) of 1.41% on an independent test dataset outperforming the traditional multiple regression method of estimation. The best-trained neural network algorithm that was chosen for assessing the relationship between plant water content and the SumbandilaSat bands was based on a few points only and more research is required to test the robustness and effectiveness of this sensor in estimating plant water content across different species and seasons. This is critical for monitoring plantation health in South Africa using a cheaply available local sensor containing key vegetation wavelengths.