Retrieval of cloud optical thickness from sky-view camera images using a deep convolutional neural network based on three-dimensional radiative transfer

Ryosuke Masuda, Hironobu Iwabuchi, Konrad Sebastian Schmidt, Alessandro Damiani, Rei Kudo

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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. As an initial analysis result, COT is estimated from a sky image taken by a digital camera, and a high correlation is shown with the effective COT estimated using a pyranometer. The discrepancy between the two is reasonable, considering the difference in the size of the field of view, the space-time averaging method, and the 3D radiative effect.

Original languageEnglish
Article number1962
JournalRemote Sensing
Volume11
Issue number17
DOIs
Publication statusPublished - 2019 Jan 1

Keywords

  • 3D radiative transfer
  • Cloud
  • Convolutional neural network
  • Deep learning
  • Sky-view camera

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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