A neural network based method for land surface temperature retrieval from AMSR-E passive microwave data

Abstract

In this paper, a generalized regression neural network (GRNN) is used for land surface temperature (LST) retrieval from advanced microwave scanning radiometer-earth (AMSR-E) passive microwave data. To make neural network method more representative of the real situations, the simulated data under various atmospheric and surface conditions is generated with the aid of monochromatic radiative transfer model and the advances integral equation model, and is used to train GRNN, combined with AMSR-E measurements and MODIS LST product on the same platform (Aqua satellite). Because of the lack of simultaneous ground LST measurements in large scale, MODIS LSTs are taken as actual ground LST measurements. Through detailed analysis, the datasets in AMSR-E channels 23.8 V, 36.5 V, 89.0 V and 89.0 H GHz with the smallest root mean square error (RMSE) are used for LST retrieval, and the results show that more than 70% of errors are within 3 K, and the RMSE is 4.66 K.