A Back Propagation Neural Network-Based Calibration Approach for Sentinel-3 OLCI Near-Infrared Water Vapor Product

Abstract

A back propagation neural network (BPNN)-based calibration method is developed to enhance the all-weather quality of official integrated water vapor (IWV) products from near-infrared (NIR) observations of the Ocean and Land Color Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B satellites. The model utilizes multiple variables that link with the derivation of satellite NIR IWV products, including official OLCI NIR IWV, latitude, land quality and science flag, month, and solar zenith angle. The in situ IWV observations, collected from 100 Global Positioning System (GPS) stations, are employed as the desired IWV estimates. The model is evaluated using one-year water vapor data at additional 114 GPS stations and 97 radiosonde stations from June 1, 2019 to May 31, 2020 in China and its surrounding regions. The results show that the BPNN-based calibration model reduces the root-mean-square error (RMSE) of official OLCI NIR all-weather IWV products by 24.52% from 3.10 to 2.34 mm for Sentinel-3A and by 25.00% from 3.44 to 2.58 mm for Sentinel-3B, when compared with in situ GPS-observed reference IWV. When compared with in situ radiosonde-observed IWV, the RMSE reduces 21.01% from 4.57 to 3.61 mm and 20.81% from 5.19 to 4.11 mm for Sentinel-3A and Sentinel-3B, respectively.