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Effective Compositing Method to Produce Cloud-Free AVHRR Image

机译:生成无云AVHRR图像的有效合成方法

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The Advanced Very High Resolution Radiometer (AVHRR) series of instruments have been frequently used for land cover change and global environment studies. The availability of more than 30 years' records have made important time-series studies possible. Removing cloud effects from AVHRR images is a critical task when using the data to monitor land cover changes. Considering that the maximum normalized difference vegetation index (NDVI) compositing method is not suitable for land cover types with low NDVI (e.g., water, snow, and bare ground), we propose a new compositing method to remove cloud effects while keeping information of all land cover types. The developed method includes three steps: 1) compositing daily AVHRR images using maximum brightness temperature in Channel 4; 2) replacing water pixels with pixels having maximum Channel 1/Channel 2 ratio; and 3) replacing vegetation pixels with pixels having maximum NDVI. The proposed method was tested on the Land Long-Term Data Record-AVHRR dataset in a region of 37$^{circ}{rm E}$ –180$^{circ}{rm E}$, 3$^{circ}{rm S}$–73 $^{circ}{rm N}$ in 1993 to generate monthly minimal cloud-effected images. After compositing, the average ratio of cloud-contaminated and invalid pixels is reduced from 64.8% to 22.0%. Compared with the other two methods (composite using maximum NDVI and composite using cloud masks), our method is shown to be more effective and convenient. Moreover, the compositing process takes consideration of the temporal profile of land surface, which is suited for long time global or continental land cover change studies. Composite images generated in different time periods are also evaluated to identify the proper compositing - eriod.
机译:高级超高分辨率辐射计(AVHRR)系列仪器经常用于土地覆盖变化和全球环境研究。超过30年的记录使重要的时间序列研究成为可能。使用数据监控土地覆盖变化时,从AVHRR图像中消除云影响是一项关键任务。考虑到最大归一化植被指数(NDVI)合成方法不适用于NDVI较低的土地覆盖类型(例如,水,雪和裸露地面),我们提出了一种新的合成方法,在保留所有信息的同时去除云量土地覆盖类型。所开发的方法包括三个步骤:1)使用通道4中的最大亮度温度合成每日AVHRR图像; 2)用具有最大通道1 /通道2比率的像素替换水像素; 3)用具有最大NDVI的像素替换植被像素。在土地长期数据记录-AVHRR数据集上对37 $ ^ {circ} {rm E} $ –180 $ ^ {circ} {rm E} $,3 $ ^ {circ}区域中的方法进行了测试{rm S} $ – 73 $ ^ {circ} {rm N} $,于1993年生成每月最小的云影响图像。合成后,云污染像素和无效像素的平均比例从64.8%降低到22.0%。与其他两种方法(使用最大NDVI的复合方法和使用云遮罩的复合方法)相比,我们的方法显示出更加有效和便捷。此外,合成过程考虑了土地表面的时空分布,这适用于长期的全球或大陆土地覆盖变化研究。还评估了在不同时间段生成的合成图像,以识别适当的合成。

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