An artificial neural network approach for soil moisture retrieval using passive microwave data
MetadataShow full item record
Soil moisture is a key variable that defines land surface-atmosphere (boundary layer) interactions, by contributing directly to the surface energy and water balance. Soil moisture values derived from remote sensing platforms only accounts for the near surface soil layers, generally the top 5cm. Passive microwave data at L-band (1.4 GHz, 21cm wavelength) measurements are shown to be a very effective observation for surface soil moisture retrieval. The first space-borne L-band mission dedicated to observing soil moisture, the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, was launched on 2nd November 2009.Artificial Neural Network (ANN) methods have been used to empirically ascertain the complex statistical relationship between soil moisture and brightness temperature in the presence of vegetation cover. The current problem faced by this method is its inability to predict soil moisture values that are 'out-of-range' of the training data.In this research, an optimization model is developed for the Backpropagation Neural Network model. This optimization model utilizes the combination of the mean and standard deviation of the soil moisture values, together with the prediction process at different pre-determined, equal size regions to cope with the spatial and temporal variation of soil moisture values. This optimized model coupled with an ANN of optimum architecture, in terms of inputs and the number of neurons in the hidden layers, is developed to predict scale-to-scale and downscaling of soil moisture values. The dependency on the accuracy of the mean and standard deviation values of soil moisture data is also studied in this research by simulating the soil moisture values using a multiple regression model. This model obtains very encouraging results for these research problems.The data used to develop and evaluate the model in this research has been obtained from the National Airborne Field Experiments in 2005.
Showing items related by title, author, creator and subject.
Chai, Soo See; Walker, J.; Makarynskyy, Oleg; Kuhn, Michael; Veenendaal, Bert; West, Geoffrey (2010)Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is ...
Chai, Soo See; Veenendaal, Bert; West, Geoffrey; Walker, J. (2009)Factors other than soil moisture which influence the intensity of microwave emission from the soil include surface temperature, surface roughness, vegetation cover and soil texture which make this a non-linear and ill-posed ...
Explicit inverse of soil moisture retrieval with an artificial neural network using passive microwave remote sensing dataChai, S.; Veenendaal, Bert; West, Geoff; Walker, J. (2008)Soil moisture is an important variable that controls the partition of rainfall into infiltration and run-off. This plays an important role in the prediction of erosion, flood or drought. Passive microwave remote sensing ...