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Effects of forward models on the semi-analytical retrieval of inherent optical properties from remote sensing reflectance

机译:远程模型对遥感反射近近光学性能半分析检索的影响

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摘要

Inherent optical properties (IOPs) play a key role in modulating an aquatic light field; they are the core link for remotely sensing water constituents based on ocean color remote sensing. Many semi-analytical algorithms (SAAs) have been developed to obtain IOPs from remote sensing reflectance (R-rs) data; these algorithms require a forward model (FM) to link the IOPs to R-rs. Most currently available SAAs use the FM presented by Gordon et al. [J. Geophys. Res. 93, 10909 (1988)] (G88 hereafter) without knowledge of how other models would impact the retrieval of IOPs from R-rs. This study evaluates the effects of two popular SAAs, namely, the quasi-analytical algorithm (QAA) and the generalized IOP algorithm (GIOP), combined with six different FMs on the retrieval of IOPs from a synthetic data set generated with Hydrolight software. The results indicated that different FMs can have quite different effects on the computed R-rs(lambda), and the effects were not uniform across the R-rs spectrum. Of the six FMs tested, G88 and P05 [Appl. Opt. 44, 1236 (2005)] produced the best estimates of R-rs(lambda) at 350, 440, and 550 nm in both oceanic and coastal sub-datasets; they also were less impacted by changes in the particle phase function. M02 also produced a good estimation of R-rs but only at 440 nm, and L04 performed well only in the oceanic condition. When the two SAAs were combined with the six FMs, in the oceanic condition, QAA and GIOP combined with M02 (QM02 and GM02) provided better quality for the absorption coefficient [a(lambda)] at 350, 440, and 550 nm when compared with the SAAs combined with the other models. However, for the retrieval of the particle backscattering coefficient [b(bp)(lambda)] in the oceanic condition, QAA and GIOP combined with L04 (QL04 and GL04) performed better than the others, and GL04 always provided a better estimation of b(bp)(lambda) than QL04. In the coastal condition, QAA and GIOP combined with G88 or P05 produced slightly better quality of IOPs compared with the other four FMs. Compared with GIOP in the coastal condition, QAA combined with G88 or P05 always showed better quality of retrieval of a(lambda) but weaker quality of retrieval of b(bp)(lambda). (C) 2019 Optical Society of America
机译:固有的光学属性(IOPs)在调制水生光场中起关键作用;它们是基于海洋颜色遥感的远程传感水成分的核心环节。已经开发出许多半分析算法(SaaS)以获得来自遥感反射率(R-RS)数据的IOPS;这些算法需要前向模型(FM)来将IOPS链接到R-RS。最目前的SaaS使用Gordon等人提供的FM。 [J.地球症。 res。 93,10909(1988)](以下G88以下)没有了解其他模型如何影响来自R-R级的IOPS。本研究评估了两个流行的SaaS的影响,即准分析算法(QAA)和广义IOP算法(GIOP),与六种不同的FMS从用水强软件生成的合成数据集中的IOP中检索。结果表明,不同的FMS对计算的R-RS(Lambda)具有相当不同的影响,并且在R-RS光谱上的效果并不均匀。六个FMS测试,G88和P05 [Appl。选择。 44,1236(2005)]在海洋和沿海子数据集中产生350,440和550nm的R-Rs(Lambda)的最佳估计值;它们的影响较小地受到粒子相位函数的变化。 M02还良好地估计了R-R级,但仅在440nm处,L04仅在海洋状况下进行。当两种SAAs与六个FMS结合时,在海洋病症中,QAA和GIOP与M02(QM02和GM02)结合使用,为350,440和550nm的吸收系数[A(Lambda)]提供更好的质量萨斯结合其他型号。然而,对于在海洋病症中的粒子反向散射系数[B(BP)(Lambda)]中的检索,C104(QL04和GL04)结合的QAA和GIOP比其他方式更好地进行,GL04总是提供更好地估计B. (BP)(Lambda)比QL04。在沿海情况下,与其他四个FMS相比,QAA和GIOP与G88或P05相结合产生稍微更好的IOP质量。与沿海情况下的GIOP相比,QAA与G88或P05相结合总是显示出更好的A(Lambda)检索质量,而是较弱的B(BP)(Lambda)的质量。 (c)2019年光学学会

著录项

  • 来源
    《Applied optics》 |2019年第13期|共19页
  • 作者单位

    Chinese Acad Sci South China Sea Inst Oceanol State Key Lab Trop Oceanog Guangzhou 510301 Guangdong Peoples R China;

    Univ Massachusetts Sch Environm Boston MA 02125 USA;

    Chinese Acad Sci Nanjing Inst Geog &

    Limnol Key Lab Watershed Geog Sci Nanjing 210008 Jiangsu Peoples R China;

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  • 正文语种 eng
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