Microwave brightness temperature prediction of plane targets by a neural network

Studies on microwave radiation of many targets, such as air, ocean, ice, snow, vegetation, rock, sand, and so on, lead to the radiometric models of the targets. The model uses one or more formulas to represent the radiation of one target. A neural network (NN) is introduced to represent the antenna temperature (AT) or brightness temperature (BT) of the seven types of plane targets: water, concrete road, asphalt road, loess, grassland, crushed stone, and vegetation. The same NN can simulate the relationship of AT (or BT) to observation angle, surface temperature, and polarization of the seven types of plane targets. The agreement between the prediction of NN and the measured AT (or inverted BT) shows that the same NN can give good prediction of the AT (or BT) of the seven types of plane targets.