A Remote Sensing-based Biophysical Modelling Approach for Estimation of Crop Yield in Irrigated Agriculture for Australian Conditions

Indira Wittamperuma Arachchige

    Research output: ThesisDoctoral Thesis

    309 Downloads (Pure)

    Abstract

    With rising world population, irrigated agriculture faces the problem of
    accommodating increases in demand for food while maintaining the
    sustainable use of limited water resources. By 2030, demand for cereal for
    human and animal consumption is expected to increase by 50% from its
    level in 2000. Such high demand must be met despite less water being
    available for irrigated agriculture. Irrigated agriculture accounts for 40% of the world's food production and uses over 70% of consumptive water.
    However with climate change and the pressure of population, there is no
    possibility that this level of water usage can be sustained into the future and
    thus there is an urgent need to improve water use efficiency and water
    productivity in the irrigated agriculture sector. In order to achieve improved
    efficiency and productivity, accurate crop yield estimation is crucial. This
    will also contribute to improved water resource management, food security
    at national and international levels, and food trade planning.

    Many biophysical models have been developed for the estimation of crop
    yields in Australia and globally. For example, the Maizeman model was
    developed in Australia for estimating the yield of maize crops at farm scale
    while the ORYZA2000 model has been widely used for the estimation of
    rice crop yields. However, these models are only able to provide crop yield
    information at the farm scale and are not able to provide total crop yield
    production data at the irrigation system or catchment/basin scale. A number
    of research projects have been undertaken to develop catchment-scale yield
    models for forecasting the crop yields of different crop types using state-ofthe-art remote sensing techniques in many countries. However, there have
    been very limited applications of remote sensing of broad acre irrigated
    crops grown in Australian conditions. This study has tried to estimate the
    spatial variability of crop yield using a remote sensing modelling technique
    in order to fill this gap in the research and to provide a practical and accurate model over the Coleambally Irrigation Area located in the Riverina
    region of NSW.

    This study focused on yield estimation for maize/corn, rice and wheat, three
    crops commonly grown in the Coleambally Irrigation Area (CIA) which is
    located in the Murray Darling Basin. This study developed CIA-specific
    relationships between the Leaf Area Index (LAI) and the Normalised
    Difference Vegetation Index (NDVI) using satellite and on-ground
    measurements for the three crops grown in various soil types. The novel
    biophysical models developed are able to use the estimation of groundbased
    LAI in the CIA. In addition the biophysical models, incorporated
    with the object-oriented modelling technique, have been used for classifying
    the three irrigated crops in the area. In the classification phase, high and
    medium level spatial resolution optical satellite imagery was used in order
    to obtain highly accurate classification maps. The overall accuracy of the
    classification map obtained in this research was 78% for winter 2010 and
    85% for summer 2010/11. The average producer and user accuracies for
    winter were 79% and 78% respectively and for summer 85% and 86%
    respectively.

    The biophysical models and classification maps developed were integrated
    to extract biomass growth over the cropping season in the CIA. By
    integrating biomass production with the crop specific harvest index, the crop
    yield for each crop for the two seasons, winter and summer, was estimated a
    few weeks before the harvest. The estimation of crop yield for corn, rice
    and wheat were in 93, 91% and 86% in agreement with the published data.
    In conclusion it can be stated that the general reliability and accuracy of this
    robust method is promising and the method has proved to be an effective
    tool in regional yield estimation in Australian conditions. The method
    proved to be stable and accurate for operational use for crop yield
    estimation at the irrigation system level across Australia.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Awarding Institution
    • Charles Sturt University
    Supervisors/Advisors
    • Hafeez, Muhammad, Principal Supervisor
    • Louis, John, Co-Supervisor
    Award date01 Dec 2014
    Place of PublicationAustralia
    Publisher
    Publication statusPublished - 2014

    Fingerprint

    Dive into the research topics of 'A Remote Sensing-based Biophysical Modelling Approach for Estimation of Crop Yield in Irrigated Agriculture for Australian Conditions'. Together they form a unique fingerprint.

    Cite this