|dc.description.abstract||This dissertation is intended to investigate the sensing of surface parameters by microwave radiometry. A backpropagation neural network with error correlation (BNNEC) is developed to manage the nonlinear relationship between surface parameters and radiometric signatures. Its performance of retrieving plant water content (PWC) and soil moisture content (SMC) from brightness temperatures is examined by using both predictions from model simulations and measurements from field experiments.
The backpropagation neural network with error correlation (BNNEC) incorporates a novel rule, error correlation learning, to train a feedforward neural network. The correlated error terms associated with nearby neurons differ from those of the existing neural networks without considering of the correlations. That is, the output layers of its neurons are trained simultaneously and interactively through the error correlation terms. The BNNEC is applied to retrieve soil moisture from simulated brightness temperatures and perform the iris classification. Simulation results demonstrate the superiority of the proposed BNNEC.
In addition, we optimize the observing scheme for sensing surface soil moisture (SM) from simulated brightness temperatures by the BNNEC. The frequencies of interest include 6.9 and 10.7 GHz of the Advanced Microwave Scanning Radiometer (AMSR), and 1.4 GHz (L-band) of the Soil Moisture and Ocean Salinity (SMOS) sensor. The Land Surface Process/Radiobrightness (LSP/R) model is used to provide time series of both SM and brightness temperatures at 6.9 and 10.7 GHz for AMSR’’s viewing angle of 55 degrees, and at L-band for SMOS’’s multiple viewing angles of 0, 10, 20, 30, 40, and 50 degrees. These multiple frequencies and viewing angles allow us to design a variety of observation modes to examine their sensitivity to SM. For example, L-band brightness temperature at any single look angle is regarded as an L-band 1D observation mode. Meanwhile, it can be combined with either the observation at other angles to become an L-band 2D mode or a multiple dimensional observation mode, or with the observation at 6.9 or 10.7 GHz to become a multiple frequency/dimensional observation mode. In this study, it is shown that the L-band 1D radiometric observation is sensitive to SM. The sensitivity can be increased by incorporating radiometric observation either from a second angle, or from multiple look angles, or from any of the two lowest AMSR channels. In addition, the advantage of an L-band 2D mode or a multiple dimensional observation mode over an L-band 1D observation mode is demonstrated.
Moreover, we investigate the best observing configuration for sensing wheat plant water content (PWC) and soil moisture content (SMC) profiles from the measured H- and V-polarized brightness temperatures at 1.4 (L-band), and 10.65 (X-band) GHz by the BNNEC. The brightness temperatures were taken by the PORTOS radiometer over wheat fields through 3 months growth cycles in 1993 (PORTOS-93) and 1996 (PORTOS-96). During both field campaigns, the radiometer was used to measure brightness temperatures at incident angles from 0 to 50 degrees at L-band and at an incident angle of 50 degrees at X-band. The SMC profiles were measured to the depths of 10 cm in 1993 and 5 cm in 1996. The wheat was sampled approximately once a week in 1993 and 1996 to obtain its dry and wet biomass (i.e., PWC). The BNNEC was trained with observations randomly chosen from the PORTOS-93 data, and evaluated by the remaining data from the same set. The trained neural network is further evaluated with data from the PORTOS-96.||en_US|