Abstract
This work made an improvement upon and a further evaluation of previous work for estimating water vapor content from near-infrared around 1 μm from MODIS data. The accuracy of RM-NN is determined by the complicated relationship of the geophysical parameters. An advanced scheme is proposed for building different training databases for different seasons in different regions to reduce the complexity. The training database includes three parts. The first part is a simulation database by MODTRAN for different weather conditions, which is made as a basic database; the second part is reliable field measurement data in observation stations; and the third part is the MYD05_L2 product on clear days, which is produced by the standard product algorithm for water vapor content. The comparative analyses based on simulation data indicate that maximum accuracy of single condition could be improved by about 34% relative to the “all conditions” results. Two study regions in China and America are selected as test areas, and the evaluation shows that the mean and the standard deviation of estimation error are about 0.08 g cm−2 and 0.09 g cm−2, respectively. All the analysis indicates that the advanced scheme can improve the retrieval accuracy of water vapor content, which can make full use of the advantages of previous methods.
Highlights
The Moderate Resolution Imaging Spectrometer (MODIS) on the Earth Observing System (EOS)is an instrument that has near-IR channels within and around the 0.94 μm water vapor band for remote sensing of water vapor content over the globe from a satellite platform [1]
Mao et al proposed to use the combination of radiative transfer model MODTRAN4 (RM) [7] and neural network (NN) to estimate water vapor content from MODIS data, and the initial analysis indicates that the accuracy has been improved well [1]
The MODTRAN4 is used to simulate the radiative transfer of MODIS bands 2, 5, 17, 18 and 19
Summary
The Moderate Resolution Imaging Spectrometer (MODIS) on the Earth Observing System (EOS). Many algorithms have been proposed to estimate water vapor content from near-infrared at around 1 μm from MODIS data [2,3,4,5,6]. The general method uses ratios of water vapor absorbing channels at 0.905, 0.936 and 0.94 μm, with atmospheric window channels at 0.865 and 1.24 μm, to estimate water vapor content [2,3,4,5,6]. (RM) [7] and neural network (NN) to estimate water vapor content from MODIS data, and the initial analysis indicates that the accuracy has been improved well [1].
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