Atmospheric correction for HY-1C CZI images using neural network in western Pacific region

Abstract

ABSTRACT With a spatial resolution of 50 m, a revisit time of three days, and a swath of 950 km, the coastal zone imager (CZI) offers great potential in monitoring coastal zone dynamics. Accurate atmospheric correction (AC) is needed to exploit the potential of quantitative ocean color inversion. However, due to the band setting of CZI, the AC over coastal waters in the western Pacific region with complex optical properties cannot be realized easily. This research introduces a novel neural network (NN) AC algorithm for CZI data over coastal waters. Total 100,000 match-ups of HY-1 C CZI-observed reflectance at the top-of-atmosphere and Operational Land Imager (OLI)-retrieved high-quality remote sensing reflectance (R rs) at the CZI bands are built to train the NN model. These reflectance data are obtained from the standard AC algorithm in the SeaDAS. Results indicate that the distributions of the CZI retrieved R rs were consistent with the quasi-synchronous OLI data, but the spatial information from the CZI is more detailed. Then, the accuracy of the CZI data for AC is evaluated using the multi-source in-situ data. Results further show that the NN-AC can successfully retrieve R rs for CZI and the coefficients of determination in the blue, green, red, and near-infrared bands were 0.70, 0.77, 0.76, and 0.67, respectively. The NN algorithm does not depend on shortwave-infrared bands and runs very fast once properly trained.