Accurately detecting nocturnal cloud over land using next-generation geostationary satellite imagery: A case study using advanced Himawari imager data for Australia

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment Pub Date : 2025-06-19 DOI:10.1016/j.rse.2025.114861

Yi Qin, Tim R. McVicar, Randall J. Donohue

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引用次数: 0

Abstract

Cloud detection is a requisite step of almost all terrestrial applications using optical remote sensing imagery, as many applications are sensitive to cloud contamination. In this research, a new algorithm has been developed to detect nocturnal cloud (from sunset to sunrise) in Himawari-8/9 AHI (Advanced Himawari Imager) imagery over land, with simultaneous aims of maximising accuracy, simplicity and efficiency. The algorithm consists of two cloud detection methods: (i) proxy emissivity temporal variation, measured by pixel wise standard deviation within an hour; and (ii) monthly clear surface proxy emissivity database which is updated daily. Results from the two components are combined based on their respective confidence. A validation was conducted against 6 years of CALIPSO LiDAR data over the Australian continent, showing an overall accuracy of 96 %. The algorithm requires no ancillary data. It is also computationally efficient and so is suitable for near real-time (i.e., within 2 h) operation and can be readily adopted to similar operational geostationary sensors.

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使用下一代地球同步卫星图像精确探测陆地上的夜间云：使用澳大利亚先进Himawari成像仪数据的案例研究

云检测是使用光学遥感图像的几乎所有地面应用的必要步骤，因为许多应用对云污染很敏感。在这项研究中，开发了一种新的算法，用于在Himawari-8/9高级Himawari成像仪（Advanced Himawari Imager）的陆地图像中检测夜间云（从日落到日出），同时以最大限度地提高精度、简单性和效率为目标。该算法包括两种云检测方法：(i)代理发射率时间变化，以一小时内逐像元标准偏差测量；（ii）逐月更新的晴空地表代理辐射率数据库。两个分量的结果根据各自的置信度组合在一起。对CALIPSO在澳大利亚大陆6年的激光雷达数据进行了验证，显示出96%的总体精度。该算法不需要辅助数据。它的计算效率也很高，因此适合于近实时（即在2小时内）的操作，并且可以很容易地采用类似的操作地球静止传感器。

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来源期刊

Remote Sensing of Environment 环境科学-成像科学与照相技术

CiteScore

25.10

自引率

8.90%

发文量

455

审稿时长

53 days

期刊介绍： Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.