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    Input parameters selection for soil moisture retrieval using an artificial neural network

    134042_16047_chai_veenendaal_west_walker.pdf (241.6Kb)
    Access Status
    Open access
    Authors
    Chai, Soo See
    Veenendaal, Bert
    West, Geoffrey
    Walker, J.
    Date
    2009
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Chai, Soo-See and Veenendaal, Bert and West, Geoff and Walker, Jeffrey P. 2009. Input parameters selection for soil moisture retrieval using an artificial neural network, in Ostendorf B., Baldock, P., Bruce, D., Burdett, M. and Corcoran, P. (ed), Surveying & Spatial Sciences Institute Biennial International Conference 2009, Sep 28 2009, pp. 1181-1193. Adelaide, Australia.
    Source Title
    Surveying & Spatial Sciences Institute Biennial International Conference
    Source Conference
    Surveying & Spatial Sciences Institute Biennial International Conference 2009
    ISBN
    9780958136686
    Faculty
    Department of Spatial Sciences
    Faculty of Science and Engineering
    WA School of Mines
    Remarks

    Published by Surveying & Spatial Sciences Institute (SSSI)

    URI
    http://hdl.handle.net/20.500.11937/38643
    Collection
    • Curtin Research Publications
    Abstract

    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 problem. Artificial Neural Networks (ANNs) have been demonstrated to be good solutions to this type of problem. Since an ANN is a data driven model, proper input selection is a crucial step in its implementation as the presence of redundant or unnecessary inputs can severely impair the ability of the network to learn the target patterns. In this paper, the input parameters are chosen in combination with the brightness temperatures and are based on the use of incremental contributions of the variables towards soil moisture retrieval. Field experiment data obtained during the National Airborne Field Experiment 2005 (NAFE'05) are used. The retrieval accuracy with the input parameters selected is compared with the use of only brightness temperature as input and the use of brightness temperature in conjunction with a range of available parameters. Note that this research does not aim at selecting the best features for all ANN soil moisture retrieval problems using passive microwave. The paper shows that, depending on the problem and the nature of the data, some of the data available are redundant as the input of ANN for soil moisture retrieval. Importantly the results show that with the appropriate choice of inputs, the soil moisture retrieval accuracy of ANN can be significantly improved.

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    • Explicit inverse of soil moisture retrieval with an artificial neural network using passive microwave remote sensing data
      Chai, 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 ...
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