Publication Abstracts

Nied et al. 2023

Nied, J., M. Jones, S. Seaman, T. Shingler, J. Hair, B. Cairns, D. Van Gilst, A. Bucholtz, S. Schmidt, S. Chellappan, P. Zuidema, B. van Diedenhoven, A. Sorooshian, and S. Stamnes, 2023: A cloud detection neural network for above-aircraft clouds using airborne cameras. Front. Remote Sens., 4, 1118745, doi:10.3389/frsen.2023.1118745.

For aerosol, cloud, land, and ocean remote sensing, the development of accurate cloud detection methods, or cloud masks, is extremely important. For airborne passive remotesensing, it is also important to identify when clouds are above the aircraft since their presence contaminates the measurements of nadir-viewing passive sensors. We describe the development of a camera-based approach to detecting clouds above the aircraft via a convolutional neural network called the cloud detection neural network (CDNN). We quantify the performance of this CDNN using human-labeled validation data where we report 96% accuracy in detecting clouds in testing datasets for both zenith viewing and forward-viewing models. We present results from the CDNN basedon airborne imagery from the NASA Aerosol Cloud meteorology Interactions oVer the western Atlantic Experiment (ACTIVATE) and the Clouds, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex). We quantify the ability of the CDNN to identify the presence of clouds above the aircraft using a forward-looking camera mounted inside the aircraft cockpit compared to the use of an all-sky upward-looking camera that is mounted outside the fuselage on top of the aircraft. We assess our performance by comparing the flight-averaged cloud fraction of zenith and forward CDNN retrievals with that of the prototype hyperspectral total-diffuse Sunshine Pyranometer (SPN-S) instrument's cloud optical depth data. A comparison of the CDNN with the SPN-S on time-specific intervals resulted in 93% accuracy for the zenith viewing CDNN and 84% for the forward-viewing CDNN. The comparison of the CDNNs with the SPN-S on flight-averaged cloud fraction resulted in an agreement of .15 for the forward CDNN and .07 for the zenith CDNN. For CAMP2Ex, 53% of flight dates had above-aircraft cloud fraction above 50%, while for ACTIVATE, 52% and 54% of flight dates observed above-aircraft cloud fraction above 50% for 2020 and 2021, respectively. The CDNN enables cost-effective detection of clouds above the aircraft using an inexpensive camera installed in the cockpit for airborne science research flights where there are no dedicated upward-looking instruments for cloud detection, the installation of which requires time-consuming and expensive aircraft modifications, in addition to added mission cost and complexity of operating additional instruments.

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BibTeX Citation

  author={Nied, J. and Jones, M. and Seaman, S. and Shingler, T. and Hair, J. and Cairns, B. and Van Gilst, D. and Bucholtz, A. and Schmidt, S. and Chellappan, S. and Zuidema, P. and van Diedenhoven, B. and Sorooshian, A. and Stamnes, S.},
  title={A cloud detection neural network for above-aircraft clouds using airborne cameras},
  journal={Front. Remote Sens.},

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RIS Citation

ID  - ni08100s
AU  - Nied, J.
AU  - Jones, M.
AU  - Seaman, S.
AU  - Shingler, T.
AU  - Hair, J.
AU  - Cairns, B.
AU  - Van Gilst, D.
AU  - Bucholtz, A.
AU  - Schmidt, S.
AU  - Chellappan, S.
AU  - Zuidema, P.
AU  - van Diedenhoven, B.
AU  - Sorooshian, A.
AU  - Stamnes, S.
PY  - 2023
TI  - A cloud detection neural network for above-aircraft clouds using airborne cameras
JA  - Front. Remote Sens.
VL  - 4
SP  - 1118745
DO  - 10.3389/frsen.2023.1118745
ER  -

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