利用短期静态PPP结果反演海潮负荷位移

采用香港11个GPS测站的观测资料进行1 h、2 h、3 h和4h静态PPP解算,获得4组PPP坐标序列,利用调和分析求取11个测站处8个主要分潮的负荷位移参数（振幅和相位）,将其与海潮模型计算的负荷位移参数进行对比,并比较分析PPP反演值与海潮模型值改正海潮负荷信号的效果。结果表明,垂直和水平方向上,不同PPP结果反演8个分潮的负荷位移分别具有约5 mm和7 mm的差异;PPP反演8个分潮垂向负荷位移优于全球海潮模型,但水平方向上的反演效果稍弱。     

The carotenoid characteristics of the important wild shrimp Trachysalambria curvirostris(Stimpson,1860) in China

Trachysalambria curvirostris is an economically important shrimp in the coastal waters of the Western Indo-Pacific. However, there is no information about its carotenoid composition and distribution.The carotenoid profiles including concentration, composition, molecular forms(free or esterified) and configuration(geometrical and optical isomers) of raw and dried T. curvirostris(Stimpson, 1860) were qualitatively and quantitatively assessed. In the raw shrimp, astaxanthin and β-carotene were the two dominant carotenoids, which contributed 75.18%–88.86% and 2.86%–16.83% of the total carotenoid content. Traces of echinenone, canthaxanthin and astacene were also detected. The carotenoid content in the waste(80.28 mg/kg dry weight) was nearly two-fold higher than that in the meat(49.69 mg/kg dry weight). Of the total astaxanthin in these shrimp, 85.18%–90.59% was esterified. All-trans astaxanthin was the predominant geometrical isomer followed by 13-cis, 9-cis, di-cis and 15-cis isomers. A mix of three stereoisomers was found in all parts and the percentages of meso(3 S, 3' R) and(3 S, 3' S) forms were significantly higher than the(3 R, 3' R) isomer. Compared to the carotenoid content of the raw shrimp,only 26.17 mg/kg remained in the dried shrimp. Nearly half of the carotenoids were lost during the drying process. Substantial hydrolysis and isomerization also occurred.     

Growth inhibition and oxidative stress in two species of marine diatoms exposed to 1-phenylethanol

1-phenylethanol(1-PEA)is a flavor extensively used in the production of co smetics,beverages,and food.The release of 1-PEA into coastal environments has aroused great concern.However,its potential effects on marine organisms are still unknown.In order to provide a better understanding of the ecological risks of 1-PEA in marine environments,this study determined the toxic effects of 1-PEA on two marine diatoms(Phaeodactylum tricornutum and Skeletonema costatum).The diatoms were grown in culture medium containing different concentrations of 1-PEA for 96 h.The contents of chlorophyll a,chlorophyll c,glutathione(GSH),malondialdehyde(MDA),and the activities of superoxide dismutase(SOD),catalase(CAT)and glutathione peroxidase(GPx),were measured at the end of the exposure period.1-PEA was shown to significantly inhibit the growth of diatoms,with 96-h EC_(50) values of 257.14 mg/L and 126.46 mg/L in P.tricornutum and S.costatum,respectively.In P.tricornutum,the levels of SOD,CAT,GPx,GSH,and MDA were stimulated only when 1-PEA concentrations were close to or greater than the 96-h EC_(50)value.However,in S.costatum,the activities of SOD and CAT,and the syntheses of two chlorophylls were inhibited even at an exposure concentration below the 96-h EC_(50) value.Taken together,these findings indicate a potential ecological risk by discharging 1-PEA into coastal areas and its species-specific toxic effects on marine organisms.     

基于EEMD和ARIMA的海温预测模型研究

类型丰富、时空分辨率高的海洋探测数据,为信号分解和机器学习算法的应用提供了可能。本文针对如何建立有效的海温预测模型这一问题,使用高时空分辨率的海表温度(SST)融合产品,引入信号处理领域的集合经验模态分解(EEMD)和机器学习领域的自回归积分滑动平均模型(ARIMA)。首先利用最适于分解自然信号的EEMD方法,将海温数据分解成多个确定频率的序列;再利用ARIMA分别对各个频率的序列进行预测,最后将各个序列的预测结果进行组合。该方法在丰富数据的支撑下,比以往直接使用海温数据所建立的预测模型精度更高,为更好地进行海温预测提供了新方法。     

广东雷州珍稀海洋生物国家级自然保护区 海水环境质量状况分析与评价

2018年9月和12月,两次调查了广东雷州珍稀海洋生物国家级自然保护区海域海水的环境质量。结果表明,该保护区平均综合水质指数为0.38,一类海水平均达标率为93.59%,海水中铅、磷酸盐、锌的含量达标率偏低,分别为53.85%、73.1%、96.15%。12月保护区海水中的无机氮、磷酸盐、溶解氧、叶绿素a含量明显高于9月,保护区实验区和缓冲区站位的水质优于核心区站位的水质。该保护区海域综合水质定性评价结果为优。靠近康港附近站位的营养盐相对偏高,磷酸盐的平均达标率仅为73.1%,因此,需加强对康港附近海域养殖情况的监控,尽量减少养殖废水对保护区海域的排放,保持保护区海水清洁,以利于对保护区内珍稀海洋生物及其栖息地的保护。     

测量系统差对大地水准面精度影响分析

从边值理论出发,推导了顾及Stokes核函数改化的观测值系统误差传播公式,基于超高阶重力场模型设计了仿真试验,以定量分析顾及核函数改化的重力异常系统差对大地水准面计算精度影响。仿真试验结果表明,重力异常系统差对大地水准面的影响表现为系统性特征,顾及核函数改化的Stokes积分公式能显著改进大地水准面计算精度,即使重力异常系统误差达3 mGal,大地水准面精度也优于1 cm,这些成果将为大地水准面精化工程的方案优化和质量评估提供有益参考。     

Semantic segmentation of high spatial resolution images with deep neural networks

Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach.     

遥感数据融合研究进展与文献定量分析(1992—2018)

近年来,遥感应用的快速发展推动了遥感载荷指标性能的不断提升。但由于遥感传感器的硬件技术瓶颈,遥感数据无法同时具有高空间分辨率、高光谱分辨率、高时间分辨率的指标特性。遥感数据融合是解决该问题的有效方法。为了深入了解目前遥感数据融合技术的研究进展情况,本文对国内外1992年—2018年间在该领域有一定影响力的相关成果进行了调研、分析与归纳总结。首先对遥感数据融合相关论文的年发文量、发文国家与机构、发表刊物以及关键词等进行了统计,梳理其发展历史及趋势;系统性的总结了各类数据融合算法,将其分为面向空间维提升的融合算法、面向光谱维提升的融合算法以及面向时间维提升的融合算法3类,并对各类算法的优势与适用性进行了分析;归纳总结了遥感数据融合的质量评价指标,包括有参考影像的融合评价指标以及无参考影像的融合评价指标;最后对遥感数据融合进展进行了总结与展望。     

顾及周期性误差修正的加权平均温度模型构建

针对目前多数大气加权平均温度(Tm)模型没有考虑季节性影响这一问题,该文首先利用IGRA 2005-2010年全球探空数据,分别建立了各探空站点与地表温度有关的线性Tm模型、与地表水汽压有关的指数Tm模型以及与地表温度和水汽压均有关的混合Tm模型。然后以探空站积分Tm值为参考,对上述3类模型的误差时间序列进行了分析,发现这3种模型均存在周期性误差,并在此基础上构建了考虑周期性误差修正的3类Tm新模型。利用2011-2014年全球探空数据对3类新模型进行精度验证,结果表明:3类Tm新模型的精度相比于原模型均有所提升,模型的周期性误差影响基本得以消除,且3类Tm新模型的精度基本一致。     

基于ETOPO1数据的全球及重点海域水深统计与分析

由于不同的水深探测手段及海深反演方法适用于不同深度的海域,且全球海域水深分布不均,因此在探测及反演海深之前,需对目标海域的水深分布有一定认识。本文采用ETOPO1模型对全球及部分海域进行统计分析,结果表明,全球及大西洋、印度洋、太平洋区域的水深大多处于3 000—10 000 m,其中3 000—5 000 m水深海域占比较大,因此,在这些海域主要采用重力数据结合船测数据进行反演,同时应注意避开海岸带、岛屿较多、海脊存在的区域;对于水深小于1 000 m的海域,可以根据外部环境,采用多波束船测、水深遥感、机载激光测深等方式进行探测。通过分析及统计全球和重点海域的水深可以为海深探测及海底地形反演方法的选取提供参考。