Estimation of water vapor content in near-infrared bands around 1 μm from MODIS data by using RM–NN

K B Mao,J X Huang,Q B Zhou,H T Li,Z L Li,J Wang,H J Tang,D Y Hu

doi:10.1364/oe.18.009542

Abstract

An algorithm based on the radiance transfer model (RM) and a dynamic learning neural network (NN) for estimating water vapor content from moderate resolution imaging spectrometer (MODIS) 1B data is developed in this paper. The MODTRAN4 is used to simulate the sun-surface-sensor process with different conditions. The dynamic learning neural network is used to estimate water vapor content. Analysis of the simulation data indicates that the mean and standard deviation of estimation error are under 0.06 gcm(-2 )and 0.08 gcm(-2). The comparison analysis indicates that the estimation result by RM-NN is comparable to that of a MODIS water vapor content product (MYD05_L2). Finally, validation with ground measurement data shows that RM-NN can be used to accurately estimate the water vapor content from MODIS 1B data, and the mean and standard deviation of the estimation error are about 0.12 gcm(-2 )and 0.18 gcm(-2).

Highlights

Water vapor content is an important tropospheric greenhouse gas, which is very important in the study of energy balance and global climate change [1,2]
The analysis indicates that the radiance transfer model (RM) neural network (NN) can be competent for accurately estimating water vapor content, because some potential information between geophysical parameters were fully used in previous algorithms
The comparison analyses between estimation results by RM–NN and the moderate resolution imaging spectrometer (MODIS) product provided by NASA indicate that the MODIS product underestimates the water vapor content when the values of water vapor content are over 3.5 gcm 2 and below 0.7 gcm 2

Summary

Introduction

Water vapor content is an important tropospheric greenhouse gas, which is very important in the study of energy balance and global climate change [1,2]. The ratios partially eliminate the effects of the variations of surface reflectance with wavelengths and give approximate atmospheric water vapor transmittances. This method is influenced by the spectral reflectance of the ground surface and mixed pixels. The overall water vapor error estimated by using the ratio method is about ± 13% [2,7], which demonstrates the need for further improvement for estimation accuracy of water vapor content in many applications such as atmospheric correction in visible spectral remote sensing and land surface temperature retrieval in thermal remote sensing [8,9].

Utilizing RM–NN to estimate water vapor content from MODIS data

Why Use RM–NN

Comparison with MODIS water vapor content product and validation

Conclusion