Articles | Volume 13, issue 10
https://doi.org/10.5194/amt-13-5459-2020
https://doi.org/10.5194/amt-13-5459-2020
Research article
 | 
14 Oct 2020
Research article |  | 14 Oct 2020

Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations

Willem J. Marais, Robert E. Holz, Jeffrey S. Reid, and Rebecca M. Willett

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Cited articles

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I. J., Harp, A., Irving, G., Isard, M., Jia, Y., Józefow-icz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D. G., Olah, C., Schuster, M., Shlens, J.,Steiner, B., Sutskever, I., Talwar, K., Tucker, P. A., Vanhoucke,V., Vasudevan, V., Viégas, F. B., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: : Tensorflow: Large-scale machine learning on heterogeneous distributed systems, arXiv [preprint], arXiv:1603.04467, 14 March 2016. a, b
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Blackwell, W. J.: A neural-network technique for the retrieval of atmospheric temperature and moisture profiles from high spectral resolution sounding data, IEEE T. Geosci. Remote, 43, 2535–2546, 2005. a
Boukabara, S.-A., Krasnopolsky, V., Stewart, J. Q., Maddy, E. S., Shahroudi, N., and Hoffman, R. N.: Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges, B. Am. Meteorol. Soc., 100, ES473–ES491, 2019. a
Chilson, C., Avery, K., McGovern, A., Bridge, E., Sheldon, D., and Kelly, J.: Automated detection of bird roosts using NEXRAD radar data and Convolutional neural networks, Remote Sensing in Ecology and Conservation, 5, 20–32, 2019. a, b
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Space agencies use moderate-resolution satellite imagery to study how smoke, dust, pollution (aerosols) and cloud types impact the Earth's climate; these space agencies include NASA, ESA and the China Meteorological Administration. We demonstrate in this paper that an algorithm with convolutional neural networks can greatly enhance the automated detection of aerosols and cloud types from satellite imagery. Our algorithm is an improvement on current aerosol and cloud detection algorithms.