Use of multi-spectral visible and near-infrared satellite data for timely estimates of the Earth's surface reflectance in cloudy conditions: Part 2 - image restoration with HICO satellite data in overcast conditions

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Authors

Joanna Joiner , Zachary Fasnacht , Bo-Cai Gao, Wenhan Qin 

Abstract

Satellite-based visible and near-infrared imaging of the Earth's surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using multi-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion of
the panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image.
This case study demonstrates that our approach may be useful for many science and applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment as well as disturbance, change, hazard, and disaster detection.

DOI

https://doi.org/10.31223/X5WW4Z

Subjects

Physical Sciences and Mathematics

Keywords

cloud contamination, cloud-clearing, image reconstruction, image restoration, cloud-removal, de-hazing

Dates

Published: 2021-06-08 20:26

Last Updated: 2021-06-09 03:26

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
Authors ZF and WQ are employed by Science Systems and Applications, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data Availability (Reason not available):
All data used in this study are publicly available. We used the HICO data provided by NASA at https://oceancolor.gsfc.nasa.gov/data/hico/. The IDL AUTO\_ALIGN\_IMAGES code was obtained from https://hesperia.gsfc.nasa.gov/ssw/gen/idl/image/auto\_align\_images.pro.

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