Detection and Removal of Clouds and Associated Shadows in Satellite Imagery Based on Simulated Radiance Fields. Issue 13 (4th July 2019)

• Record Type: Journal Article
• Authors: Wang, Tianxing; Shi, Jiancheng; Letu, Husi; Ma, Ya; Li, Xingcai; Zheng, Yaomin
• Is Part Of: Journal of geophysical research. Volume 124:Issue 13(2019)
• Page Start: 7207
• Page End: 7225
• Publication Date: 2019-07-04
• Subjects: cloud detection -- shadow detection -- cloud removal -- reconstruction -- MODIS; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2018JD029960
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Clouds and shadows pose a significant barrier for land surface optical and infrared remote sensing image processing and their various applications. The detection and removal of clouds and shadows from satellite images have always been critical preprocessing steps. To date, a variety of methods have been designed to solve this problem. Some require particular channels, while others are heavily dependent on the availability of temporally adjacent images (reference images). Moreover, many methods are too complex to use by common users. For those reasons, in this paper an alternative scheme for detecting clouds and shadows is proposed based on simulated top‐of‐atmosphere radiance fields. At the same time, a simple approach to remove clouds and shadows is also provided. The results indicate that the new method can properly identify both clouds and shadows in satellite images. Especially, it shows obvious advantage over the Moderate Resolution Imaging Spectroradiometer cloud product (MOD35) for shadow detection. Although the proposed cloud removal method is simple, the radiances of a contaminated image can be reasonably reconstructed with root‐mean‐square error < 3.0 W/m 2 ·sr·μm and mean bias < 1.0 W/m 2 ·sr·μm for all seven Moderate Resolution Imaging Spectroradiometer reflective bands for our case studies. These results prove the effectiveness of the proposed scheme in identifying and removing clouds and shadows from remotely sensed images. Meanwhile, these findings provide some new ideas for the remote sensing community, especially in the fields of cloud detection and image processing. Key Points: A new scheme for detecting and removing clouds and shadows is proposed based on simulated TOA radiance fields A simple but effective approach to remove the clouds and shadows in the images is provided based on the simulated image The method works well for both cloud/shadow detection and image reconstruction; it shows obvious advantage over MOD35 in shadow detection