art aot_论文的背后：能见度得出的AOT估计是否适合参数化卫星数据大气校正算法？

art aot

This has been a bit slow coming, but I am now sticking to my promise to write a Behind the paper post for each of my published academic papers. This is about:

来的有点慢，但是我现在仍然信守诺言，为我发表的每篇学术论文写一篇“幕后花”。 这是关于：

Wilson, R. T., E. J. Milton, and J. M. Nield (2015). Are visibility-derived AOT estimates suitable for parameterising satellite data atmospheric correction algorithms? International Journal of Remote Sensing 36 (6) 1675-1688

威尔逊，RT，EJ米尔顿和JM Nield（2015）。 可见性得出的AOT估计是否适合参数化卫星数据大气校正算法？ 国际遥感杂志 36（6）1675-1688

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出版商链接 印后PDF

莱曼的摘要 (Layman’s summary)

Aerosol Optical Thickness (AOT) is a measure of how hazy the atmosphere is – that is, how easy it is for light to pass through it. It’s important to measure this accurately, as it has a range of applications. In this paper we focus on satellite image atmospheric correction. When satellites collect images of the Earth, the light has to pass through the atmosphere twice (once on the way from the sun to the Earth, once again on the way back from the Earth to the satellite) and this affects the light significantly. For example, it can be scattered or absorbed by various atmospheric constituents. We have to correct for this before we can use satellite images – and one of the ways to do that is to simulate what happens to light under the atmospheric conditions at the time, and then use this simulated information to remove the effects from the image.

气溶胶光学厚度（AOT）可以衡量大气的朦胧度，也就是说，光线通过大气的难易程度。 准确地进行测量很重要，因为它具有广泛的应用范围。 在本文中，我们着重于卫星图像大气校正。 当卫星收集地球图像时，光必须两次穿过大气层（一次从太阳到地球的途中，一次又一次从地球到人造卫星的途中），这会严重影响光。 例如，它可能被各种大气成分散射或吸收。 在使用卫星图像之前，我们必须对此进行校正-实现此目的的方法之一是模拟当时在大气条件下光照会发生什么，然后使用此模拟信息从图像中消除影响。

To do this simulation we need various bits of information on what the atmosphere was like when the image was acquired – and one of these is the AOT value. The problem is that it’s quite difficult to get hold of AOT values. There are some ground measurements sites – but only about 300 of them across the whole world. Therefore, quite a lot of people use measurements of atmospheric visibility as a proxy for AOT. This has many benefits, as loads of these measurements are taken (by airports, and local meteorological organisations), but is a bit scientifically questionable, because atmospheric visibility is measured horizontally (as in “I can see for miles!”) and AOT is measured vertically. There are various ‘standard’ ways of estimating AOT from visibility – and some of these are built in to tools that do atmospheric correction of images – and I wanted to investigate how well these worked.

要进行此模拟，我们需要有关获取图像时的大气状况的各种信息，其中之一就是AOT值。 问题在于很难掌握AOT值。 有一些地面测量站点，但全世界只有大约300个。 因此，很多人将大气能见度的测量值用作AOT的替代指标。 这是有很多好处的，因为这些测量是由机场（和当地气象组织）进行的，但是在科学上有点可疑，因为大气能见度是水平测量的（如“我能看到英里！”），AOT是垂直测量。 有多种“标准”方式可以根据可见度估算AOT，其中一些内置于对图像进行大气校正的工具中，我想研究一下这些方式的效果。

I used a few different datasets which had both visibility and AOT measurements, collected at the same time and place, and investigated the relationship. I found that the relationship was often very poor – and the error in the estimated AOT was never less than half of the mean AOT value (that is, if the mean AOT was 0.2, then the error would be 0.1 – not great!), and sometimes more than double the mean value! Simulating the effect on atmospheric correction showed that significant errors could result – and I recommended that visibility-derived AOT data should only be used for atmospheric correction as a last resort.

我使用了几个具有可见性和AOT测量值的不同数据集，这些数据集是在同一时间和地点收集的，并研究了这种关系。 我发现这种关系通常很差-估计的AOT的误差永远不会小于平均AOT值的一半（也就是说，如果平均AOT为0.2，则误差将是0.1 –不大！），有时甚至超过平均值的两倍！ 模拟对大气校正的影响表明可能会导致重大误差，因此，我建议只能将能见度得出的AOT数据仅用于大气校正。

重要结论 (Key conclusions)

• Estimation of AOT from horizontal visibility measurements can produce significant errors.
• Radiative transfer simulations using different models (eg. MODTRAN and 6S) with the same visibility may produce significantly different results due to the differing methods used for estimating AOT from visibility
• Errors can be significant for both radiance values (significantly larger than the noise level of the sensor) and vegetation indices such as NDVI and ARVI.
• Overall: other methods for estimating AOT should be used wherever possible – as they nearly all have smaller errors than visibility-based estimates – and great care should be taken at low visibilities, when the error is even higher.

• 根据水平能见度测量估算AOT会产生重大误差。
• 使用不同模型（例如MODTRAN和6S）的具有相同可见性的辐射传输模拟可能会产生明显不同的结果，这是因为根据可见性估算AOT的方法不同
• 对于辐射值（显着大于传感器的噪声水平）和植被指数（例如NDVI和ARVI），误差都可能很大。
• 总体而言：应尽可能使用其他估算AOT的方法-因为它们几乎都比基于可见性的估算值具有较小的误差-当误差更高时，应在低可见性时格外小心。

关键结果 (Key results)

• Error in visibility-derived AOT is highest at low visibilities
• Root Mean Square Error ranges from 1.05 for visibilities < 10km to 0.05 for visibilities > 40km
• The error for low visibilities is many times the mean AOT at those visibilities (for example, an average error of 0.76 for visibilities < 10km, when the average AOT is only 0.38!)
• Overall, MODTRAN appears to perform poorly compared to the other methods (6S and the Koschmieder formula) – and this is particularly pronounced at low visibilities
• Atmospheric correction with these erroneously-estimate AOT values can produce significant errors in radiance, which range from three times the Noise Equivalent Delta Radiance to over thirty times the NEDR!
• There can still be significant errors in vegetation indices (NDVI/ARVI), of up to 0.12 and 0.09 respectively.

• 低能见度时，能见度衍生的AOT的误差最高
• 均方根均方根误差范围从对于<10km的可见性的1.05到对于> 40km的可见性的0.05
• 低能见度的误差是这些能见度的平均AOT的许多倍（例如，对于小于10 km的能见度，平均误差为0.76，而平均AOT仅为0.38！）。
• 总体而言，与其他方法（6S和Koschmieder公式）相比，MODTRAN的性能似乎较差-这在可见度较低的情况下尤其明显
• 使用这些错误估计的AOT值进行大气校正会产生显着的辐射误差，范围从噪声等效Delta辐射强度的三倍到NEDR的三十倍以上！
• 植被指数（NDVI / ARVI）仍然可能存在明显误差，分别高达0.12和0.09。

历史和评论 (History and Comments)

This work developed from one of my previous papers (Spatial variability of the atmosphere over southern England, and its effects on scene-based atmospheric corrections). In that paper I investigated a range of methods for estimating AOT – and one of these was by estimating it from from visibility.

这项工作是根据我以前的一篇论文（ 英格兰南部大气的空间变化及其对基于场景的大气校正的影响 ）发展而来的。 在那篇论文中，我研究了多种估计AOT的方法-其中之一是从可见性中进行估计。

I had always been a bit frustrated that lots of atmospheric correction tools required visibility as an input parameter – and wouldn’t allow you enter AOT even if you had an actual AOT measurement (eg. from an AERONET site or a Microtops measurement). I started to wonder about the error involved in the use of visibility rather than AOT – and did a very brief assessment of the accuracy as part of my investigation for the spatial variability paper. An extension of that accuracy assessment turned into this paper.

我一直感到有些沮丧，因为许多大气校正工具都需要可见性作为输入参数，即使您进行了实际的AOT测量（例如，从AERONET站点或Microtops测量），也不允许您输入AOT。 我开始怀疑使用能见度而不是AOT所涉及的错误，并在对空间变异性论文进行调查的过程中对准确性进行了非常简短的评估。 该准确性评估的扩展变成了本文。

数据，代码与方法 (Data, Code & Methods)

The good news is that the analysis performed for this paper was designed from the beginning to be reproducible. I used the R programming language, and the ProjectTemplate package to make this really nice and easy. All of the code is available on Github, and the README file there explains that all you need to do to reproduce the analysis is run:

好消息是，对本文进行的分析从一开始就被设计为可重复的。 我使用R编程语言和ProjectTemplate软件包使它变得非常好用和容易。 Github上提供了所有代码，并且其中的README文件说明已运行了所有用于重现分析的操作：

source('go.r')

to initialise everything, install all of the packages etc. You can then run any of the files in the src directory to reproduce a specific analysis.

要初始化所有组件，请安装所有软件包等。然后，您可以运行src目录中的任何文件以重现特定的分析结果。

That’s all good news, so what is the bad news? Well, the problem with reproducing this work is that you need access to the data – and most of the data I used is only available to academics, or cannot be ‘rehosted’ by me. There are instructions in the repository showing how to get hold of the data, but it’s quite a complex process which requires registering with the British Atmospheric Data Centre, requesting access to datasets, and then downloading the various pieces of data.

那都是好消息，那么坏消息是什么？ 好吧，复制此作品的问题是您需要访问数据，而我使用的大多数数据仅对学者可用，或者不能被我“托管”。 存储库中有说明如何获取数据的说明，但这是一个非常复杂的过程，需要在英国大气数据中心进行注册，请求访问数据集，然后下载各种数据。

翻译自: https://www.pybloggers.com/2016/01/behind-the-paper-are-visibility-derived-aot-estimates-suitable-for-parameterising-satellite-data-atmospheric-correction-algorithms/

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