Using statistical inversion for the retrieval of the geophysical parameters from remote sensing data

Juha-Petri Kärnä

    Research output: ThesisLicenciate's thesis

    Abstract

    In this thesis the use of statistical inversion method for retrieving geophysical parameters from different remote sensing data was studied. The statistical inversion method is rather universal. In this work it was demonstrated by retrieving the following snow and forest parameters: snow depth, snow water equivalent, snow-covered area and forest stem volume. One of the benefits of the statistical inversion method is that it can combine data from different sources based on their statistical accuracy. The method can also estimate the accuracy of the estimation result based on the accuracy of the input data and the models used. In this work the statistical inversion method was demonstrated by retrieving snow depth of Eurasia from microwave radiometer data, snow covered area from microwave and optical data, forest stem volume from ERS INSAR data, and enhancing the accuracy of the discharge forecasts of the operational watershed simulation and forecasting system (WSFS) using SAR data. The statistical inversion method utilises remote sensing models. The Helsinki University of Technology (HUT) microwave snow emission model, the HUT forest backscattering model, and the optical reflectance model developed at the Finnish Environment Institute were used as such. In addition to these remote sensing models, a dynamic environmental model (WSFS) was used to assimilate SAR measurements to it. In addition to the studies mentioned above, two software applications were developed. The first one was developed to simulate brightness temperatures observed by a multichannel microwave radiometer and to test the performance of the available inversion algorithms and the statistical inversion method. The second software application developed is a general purpose statistical inversion tool that can be used either independently or as a part of an image processing system.
    Original languageEnglish
    QualificationLicentiate's degree
    Awarding Institution
    • Aalto University
    Supervisors/Advisors
    • Hallikainen, Martti, Supervising Professor
    • Pulliainen, Jouni, Thesis Advisor, External person
    Publication statusPublished - 2010
    MoE publication typeG3 Licentiate thesis

    Keywords

    • Remote sensing
    • Statistical inversion

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