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Multi‐layer perceptron neural network based algorithm for estimating precipitable water vapour from MODIS NIR data

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This Letter presents a multi-layer perceptron neural network (MLP-NN) based algorithm to quantitatively determine precipitable water vapour (PWV) directly from near infrared (NIR) radiance measured by the Moderate Resolution Imaging Spectroradiometer (MODIS). First, the background of the MLP-NN based algorithm is discussed briefly. Then, the radiance of MODIS NIR channels simulated through a radiative transfer model with a set of input variables covering a broad range of surface reflectance and water vapour content are used to train MLP-NN. Finally, PWV values derived by the MLP-NN based algorithm are compared with radiosonde observations and a root mean squared error of 5.2 kg m -2 is found from this comparison.
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Document Type: Research Article

Affiliations: 1: Department of Civil and Environmental Engineering, Princeton University, USA 2: Institute of Geographical Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS), 100101, Beijing, China

Publication date: 2006-02-10

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