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Atmospheric Correction of Multispectral VNIR Remote Sensing Data: Algorithm and Inter‐sensor Comparison of Aerosol and Surface Reflectance Products
Earth and Space Science ( IF 3.1 ) Pub Date : 2020-09-07 , DOI: 10.1029/2019ea000710
Manoj K. Mishra 1 , Pradyuman S. Rathore 1 , Arundhati Misra 1 , Raj Kumar 1
Affiliation  

Optical imaging satellites, such as SPOT and Cartosat‐2S, provide visible/near infrared (VNIR) multispectral data at very high spatial resolution. The applications of these data sets are associated with precise mapping, monitoring, and change detection of Earth's surface, given that the measurements can be compensated for atmospheric effects. Existing atmospheric correction (AC) algorithms use visible and shortwave infrared channels and therefore cannot be used for AC of data from VNIR sensors. This article describes an algorithm for aerosol optical depth (AOD) retrieval and AC of VNIR imaging data. The AOD algorithm relies on the fact that for vegetated surfaces there exists a visible/NIR surface reflectance relationship due to the absorption of solar radiation by photosynthetic pigments in visible bands, while high reflectance in NIR bands governed by structural discontinuities in the leaves of healthy vegetation. We then describe how retrieved AOD is used to derive surface reflectance. To test the algorithm, the aerosol and surface reflectance products generated from 106 Cartosat‐2S data sets are compared with MODIS‐terra products. The algorithm significantly removes the haze from the images making surface feature visible. The comparison of Cartosat‐2S and MODIS‐terra AOD involving >1,500 data points shows good correlation of 0.95 with a relative difference of ≤25%. Similarly, the comparison of surface reflectance involving >4,500 data points shows good correlation ranging from 0.75 to 0.86 with a relative difference ranging from 24% to 37%. The normalized difference vegetation index shows a correlation of 0.89, with a relative difference of ≤18%. Results show that the given algorithm may be useful for AC of data from VNIR sensors.

中文翻译:

多光谱VNIR遥感数据的大气校正:气溶胶和表面反射产品的算法和传感器间比较

光学成像卫星(例如SPOT和Cartosat-2S)以非常高的空间分辨率提供可见/近红外(VNIR)多光谱数据。这些数据集的应用与地球表面的精确制图,监视和变化检测相关联,前提是可以对大气效应进行补偿。现有的大气校正(AC)算法使用可见光和短波红外通道,因此无法用于VNIR传感器数据的AC。本文介绍了一种用于气溶胶光学深度(AOD)检索和AC的VNIR成像数据的算法。AOD算法基于以下事实:对于植被表面,由于光合色素在可见带中吸收了太阳辐射,因此存在可见/近红外表面反射率关系,而NIR波段的高反射率则取决于健康植被叶片的结构不连续性。然后,我们描述如何将检索到的AOD用于得出表面反射率。为了测试该算法,将106个Cartosat-2S数据集生成的气溶胶和表面反射率产品与MODIS-terra产品进行了比较。该算法从图像中去除了雾度,使表面特征可见。Cartosat-2S和MODIS-terra AOD的比较涉及1,500个以上的数据点,显示出0.95的良好相关性,相对差≤25 将106个Cartosat-2S数据集生成的气溶胶和表面反射率产品与MODIS-terra产品进行了比较。该算法从图像中去除了雾度,使表面特征可见。Cartosat-2S和MODIS-terra AOD的比较涉及1,500个以上的数据点,显示出0.95的良好相关性,相对差≤25 将106个Cartosat-2S数据集生成的气溶胶和表面反射率产品与MODIS-terra产品进行了比较。该算法从图像中去除了雾度,使表面特征可见。Cartosat-2S和MODIS-terra AOD的比较涉及1,500个以上的数据点,显示出0.95的良好相关性,相对差≤25。同样,对涉及> 4,500个数据点的表面反射率的比较显示出良好的相关性,范围在0.75至0.86之间,相对差异在24 至37 %之间。归一化差异植被指数显示出0.89的相关性,相对差异≤18 。结果表明,给定的算法可能对交流来自VNIR传感器的数据有用。
更新日期:2020-09-07
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