A scheme that utilizes satellite radiobrightness to infer surface parameters without losing the nonlinearity between the measured quantities and desired variables is examined. The approach is to incorporate products from the 1-dimensional hydrology/radiobrightness (1dH/R) model into the dynamic learning neural network (DLNN) manages the nonlinear mappings.
|Number of pages||3|
|State||Published - 1997|
|Event||Proceedings of the 1997 IEEE International Geoscience and Remote Sensing Symposium, IGARSS'97. Part 3 (of 4) - Singapore, Singapore|
Duration: 3 Aug 1997 → 8 Aug 1997
|Conference||Proceedings of the 1997 IEEE International Geoscience and Remote Sensing Symposium, IGARSS'97. Part 3 (of 4)|
|Period||3/08/97 → 8/08/97|