remote sensing Article Retrieval of Cloud Optical Thickness from Sky-View Camera Images using a Deep Convolutional Neural Network based on Three-Dimensional Radiative Transfer Ryosuke Masuda 1, Hironobu Iwabuchi 1,* , Konrad Sebastian Schmidt 2,3 , Alessandro Damiani 4 and Rei Kudo 5 1 Graduate School of Science, Tohoku University, Sendai, Miyagi 980-8578, Japan 2 Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO 80309-0311, USA 3 Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, CO 80303-7814, USA 4 Center for Environmental Remote Sensing, Chiba University, Chiba 263-8522, Japan 5 Meteorological Research Institute, Japan Meteorological Agency, Tsukuba 305-0052, Japan * Correspondence:
[email protected]; Tel.: +81-22-795-6742 Received: 2 July 2019; Accepted: 17 August 2019; Published: 21 August 2019 Abstract: Observation of the spatial distribution of cloud optical thickness (COT) is useful for the prediction and diagnosis of photovoltaic power generation. However, there is not a one-to-one relationship between transmitted radiance and COT (so-called COT ambiguity), and it is difficult to estimate COT because of three-dimensional (3D) radiative transfer effects. We propose a method to train a convolutional neural network (CNN) based on a 3D radiative transfer model, which enables the quick estimation of the slant-column COT (SCOT) distribution from the image of a ground-mounted radiometrically calibrated digital camera. The CNN retrieves the SCOT spatial distribution using spectral features and spatial contexts. An evaluation of the method using synthetic data shows a high accuracy with a mean absolute percentage error of 18% in the SCOT range of 1–100, greatly reducing the influence of the 3D radiative effect.