A Back Propagation Neural Network-Based Algorithm for Retrieving All-Weather Precipitable Water Vapor From MODIS NIR Measurements

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor can observe precipitable water vapor (PWV) at near-infrared (NIR) bands. In this paper, we proposed a novel Back Propagation Neural Network (BPNN) based water vapor retrieval algorithm to enhance the all-weather retrieval accuracy of the PWV estimation from MODIS NIR observations. The input of the model includes the transmittance, latitude, longitude, elevation, season, cloud, and solar zenith angle information. The water vapor data collected from in-situ 453 Global Positioning System (GPS) sites in Australia in 2017 were employed as the output PWV for the training of the BPNN method. The performance of the retrieval approach was evaluated utilizing reference GPS-measured PWV data during 2018–2019 over Australia, independent of the 2017 training data. The results indicate that the algorithm can notably enhance the accuracy of MODIS NIR PWV retrieval under all weather conditions as well as under each type of weather condition. The BPNN-retrieved weighted mean PWV data calculated with 2-channel ratio approach had the best retrieval accuracy, reducing root-mean-square error (RMSE) by 57.26% from 10.95 mm to 4.68 mm for all-weather conditions and 47.49% from 5.58 mm to 2.93 mm for confident-clear conditions. The new all-weather PWV estimates present a retrieval accuracy superior to official MODIS NIR confident-clear PWV product, illustrating the effectiveness of the retrieval algorithm. This algorithm has been well evaluated in Australia and its performance in other global regions will be investigated in the future, while the MODIS official products are applicable to the global regions.