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Cloud screening in IRS-P4 OCM satellite data: potential of spatial coherence method in the absence of thermal channel information

机译:IRS-P4 OCM卫星数据中的云筛查:在缺乏热通道信息的情况下空间相干方法的潜力

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摘要

Cloud screening of satellite data for the remote sensing of atmospheric aerosols, ocean sediments, chlorophyll, and phytoplankton in the marine environment is a major problem in the absence of information from thermal channel. This is particularly the case with the data from some of the highly potential satellite sensors such as the Ocean Colour Monitor (OCM-on board the Indian Remote Sensing Satellite, IRS-P4) and the SeaWiFS. Two main tests conventionally used for cloud screening of data from such satellite sensors are the threshold method applied to visible and near-IR bands and the visible to near-IR channel ratio method. These methods do not have the potential to eliminate the pixels with small cloud fractions, leading to overestimation of the aerosol optical depth (AOD) derived from satellite data, and might also identify the pixels with high values of AOD as cloudy. The puipose of this paper is to study the potential of Spatial Coherence Test (SCT) applied to the data from the near-IR bands for cloud screening of satellite data over the oceanic environment. We use here the data from IRS-P4 OCM. Though more computationally intensive, the SCT does not suffer from the serious limitations of the threshold and channel ratio methods and is found to be superior in identifying the clear sky pixels that are not affected by clouds. Although the SCT applied to near-IR channel data may be overestimating the number of cloud affected pixels, it neither leads to overestimation of AOD nor identifies the pixels with high AOD values as cloudy.
机译:在缺少热通道信息的情况下,对卫星数据进行云筛查以遥感海洋环境中的大气气溶胶,海洋沉积物,叶绿素和浮游植物是一个主要问题。来自某些高潜力卫星传感器的数据尤其如此,例如海洋颜色监视器(OCM,位于印度遥感卫星上,IRS-P4)和SeaWiFS。常规用于云筛选来自此类卫星传感器的数据的两个主要测试是应用于可见光和近红外波段的阈值方法和可见光与近红外通道比方法。这些方法无法消除云量较小的像素,从而导致对卫星数据得出的气溶胶光学深度(AOD)的估计过高,并且可能会将AOD值较高的像素识别为浑浊。本文的目的是研究将空间相干性测试(SCT)应用于近红外波段数据进行海洋环境中卫星数据云筛选的潜力。我们在这里使用来自IRS-P4 OCM的数据。尽管计算量更大,但SCT并未受到阈值和通道比率方法的严重限制,并且在识别不受云影响的晴朗天空像素方面具有优势。尽管应用于近红外通道数据的SCT可能高估了受云影响的像素的数量,但它既不会导致AOD的高估,也不会将具有高AOD值的像素识别为多云。

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