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Wang, W, Sun, X, Zhang, R, Li, Z, Zhu, Z, Su, H (2006). Multi-layer perceptron neural network based algorithm for estimating precipitable water vapour from MODIS NIR data. INTERNATIONAL JOURNAL OF REMOTE SENSING, 27(3), 617-621.

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 kgm 22 is found from this comparison.



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