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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Dust source identification using MODIS: A comparison of techniques applied to the Lake Eyre Basin, Australia
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Dust source identification using MODIS: A comparison of techniques applied to the Lake Eyre Basin, Australia

机译：使用MODIS识别尘源：澳大利亚艾尔湖盆地应用技术的比较

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The impact of mineral aerosol (dust) in the Earth's system depends on particle characteristics which are initially determined by the terrestrial sources from which the sediments are entrained. Remote sensing is an established method for the detection and mapping of dust events, and has recently been used to identify dust source locations with varying degrees of success. This paper compares and evaluates five principal methods, using MODIS Level 1B and MODIS Level 2 aerosol data, to: (a) differentiate dust (mineral aerosol) from nondust, and (2) determine the extent to which they enable the source of the dust to be discerned. The five MODIS LIB methods used here are: (1) un-processed false colour composite (FCC), (2) brightness temperature difference, (3) Ackerman's (1997: J.Geophys. Res., 102, 17069-17080) procedure, (4) Miller's (2003:Geophys. Res. Lett. 30, 20, art.no.2071) dust enhancement algorithm and (5) Roskovensky and Liou's (2005: Geophys. Res. Lett. 32, L12809) dust differentiation algorithm; the aerosol product is MODIS Deep Blue (Hsu et al., 2004: IEEE Trans. Geosci. Rem. Sensing, 42, 557-569), which is optimised for use over bright surfaces (i.e. deserts). These are applied to four significant dust events from the Lake Eyre Basin, Australia. OMl Al was also examined for each event to provide an independent assessment of dust presence and plume location. All of the techniques were successful in detecting dust when compared to FCCs, but the most effective technique for source determination varied from event to event depending on factors such as cloud cover, dust plume mineralogy and surface reflectance. Significantly, to optimise dust detection using the MODIS L1B approaches, the recommended duston-dust thresholds had to be considerably adjusted on an event by event basis. MODIS L2 aerosol data retrievals were also found to vary in quality significantly between events; being affected in particular by cloud masking difficulties. In general, we find that OMI AI and MODIS AQUA L7 B and L2 data are complementary; the former are ideal for initial dust detection, the latter can be used to both identify plumes and sources at high spatial resolution. Overall, approaches using brightness temperature difference (BT10-11) are the most consistently reliable technique for dust source identification in the Lake Eyre Basin. One reason for this is that this enclosed basin contains multiple dust sources with contrasting geochemical signatures. In this instance, BTD data are not affected significantly by perturbations in dust mineralogy. However, the other algorithms tested (including MODIS Deep Blue) were all influenced by ground surface reflectance or dust mineralogy; making it impossible to use one single MODIS LIB or L2 data type for all events (or even for a single multiple-plume event). There is, however, considerable potential to exploit this anomaly, and to use dust detection algorithms to obtain information about dust mineralogy.
机译：矿物气溶胶（粉尘）对地球系统的影响取决于颗粒特征，这些颗粒特征最初是由夹带沉积物的陆地来源决定的。遥感是一种用于检测和绘制粉尘事件的既定方法，最近已被用来识别粉尘源位置，并取得了不同程度的成功。本文使用MODIS 1B级和MODIS 2级气溶胶数据对五种主要方法进行了比较和评估，以：（a）区分粉尘（矿物气溶胶）与非粉尘，以及（2）确定它们使粉尘来源的程度有待观察。这里使用的五种MODIS LIB方法是：（1）未处理的假彩色复合（FCC），（2）亮度温差，（3）Ackerman's（1997：J.Geophys.Res。，102，17069-17080）过程，（4）Miller（2003：Geophys.Res.Lett.30，20，art.no.2071）除尘算法和（5）Roskovensky and Liou's（2005：Geophys.Res.Lett.32，L12809）除尘算法;气溶胶产品是MODIS深蓝色（Hsu等人，2004：IEEE Trans。Geosci。Rem。Sensing，42，557-569），该产品经过优化可在明亮的表面（例如沙漠）上使用。这些被应用于来自澳大利亚艾尔湖盆地的四个重大沙尘事件。还针对每个事件检查了OM1A1，以独立评估灰尘的存在和羽流的位置。与FCC相比，所有技术均能成功检测灰尘，但最有效的源确定技术因事件而异，具体取决于云量，尘埃羽状矿物学和表面反射率等因素。重要的是，要使用MODIS L1B方法优化灰尘检测，必须逐个事件对推荐的灰尘/非灰尘阈值进行大量调整。还发现MODIS L2气溶胶数据检索在两次事件之间的质量差异很大。尤其受到云掩蔽困难的影响。通常，我们发现OMI AI和MODIS AQUA L7 B和L2数据是互补的;前者是初始灰尘检测的理想选择，后者可用于以高空间分辨率识别羽流和源。总体而言，使用亮度温差（BT10-11）的方法是艾尔湖流域中最一致可靠的灰尘源识别技术。其原因之一是该封闭式盆地中包含多种具有不同地球化学特征的粉尘源。在这种情况下，粉尘矿物学的扰动不会显着影响BTD数据。但是，测试的其他算法（包括MODIS深蓝色）都受到地面反射率或粉尘矿物学的影响。因此，不可能对所有事件（甚至对于单个多线程事件）都使用单个MODIS LIB或L2数据类型。但是，有很大的潜力可以利用这种异常，并使用灰尘检测算法来获取有关灰尘矿物学的信息。

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