An artificial neural network approach for soil moisture retrieval using passive microwave data
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. Pass...
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| Format: | Thesis |
| Language: | English |
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Curtin University
2010
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| Online Access: | http://hdl.handle.net/20.500.11937/1416 |
| _version_ | 1848743661878116352 |
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| author | Chai, Soo See |
| author_facet | Chai, Soo See |
| author_sort | Chai, Soo See |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T05:49:07Z |
| format | Thesis |
| id | curtin-20.500.11937-1416 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T05:49:07Z |
| publishDate | 2010 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-14162021-01-28T05:25:19Z An artificial neural network approach for soil moisture retrieval using passive microwave data Chai, Soo See surface soil moisture Backpropagation Neural Network space-borne L-band mission European Space Agency‟s (ESA) Soil Moisture and Ocean Salinity (SMOS) surface energy soil moisture water balance Artificial Neural Network (ANN) 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. 2010 Thesis http://hdl.handle.net/20.500.11937/1416 en Curtin University fulltext |
| spellingShingle | surface soil moisture Backpropagation Neural Network space-borne L-band mission European Space Agency‟s (ESA) Soil Moisture and Ocean Salinity (SMOS) surface energy soil moisture water balance Artificial Neural Network (ANN) Chai, Soo See An artificial neural network approach for soil moisture retrieval using passive microwave data |
| title | An artificial neural network approach for soil moisture retrieval using passive microwave data |
| title_full | An artificial neural network approach for soil moisture retrieval using passive microwave data |
| title_fullStr | An artificial neural network approach for soil moisture retrieval using passive microwave data |
| title_full_unstemmed | An artificial neural network approach for soil moisture retrieval using passive microwave data |
| title_short | An artificial neural network approach for soil moisture retrieval using passive microwave data |
| title_sort | artificial neural network approach for soil moisture retrieval using passive microwave data |
| topic | surface soil moisture Backpropagation Neural Network space-borne L-band mission European Space Agency‟s (ESA) Soil Moisture and Ocean Salinity (SMOS) surface energy soil moisture water balance Artificial Neural Network (ANN) |
| url | http://hdl.handle.net/20.500.11937/1416 |