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    Estimating soil water content in tallgrass prairie using remote sensing

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    Author
    Starks, P. J.
    Jackson, T. J.
    Issue Date
    2002-09-01
    Keywords
    algorithms
    microwave radiation
    data assimilation
    particle size distribution
    soil profiles
    data analysis
    simulation models
    water balance
    remote sensing
    leaf area index
    sampling
    Oklahoma
    soil water content
    prairies
    biomass
    prairie soils
    hydrology
    microwave
    water budget
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    Metadata
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    Citation
    Starks, P. J., & Jackson, T. J. (2002). Estimating soil water content in tallgrass prairie using remote sensing. Journal of Range Management, 55(5), 474-481.
    Publisher
    Society for Range Management
    Journal
    Journal of Range Management
    URI
    http://hdl.handle.net/10150/643686
    DOI
    10.2307/4003225
    10.2458/azu_jrm_v55i5_starks
    Additional Links
    https://rangelands.org/
    Abstract
    Increased demand for available water supplies necessitates that tools and techniques be developed to quantify soil water reserves over large land areas as an aid in management of water resources and watersheds. Microwave remote sensing can provide measurements of volumetric water content of the soil surface (theta(vSL)) up to about 10 cm deep. The objective of this study was to examine the feasibility of inferring the volumetric water content of the soil profile (theta(vBL)) by combining remotely sensed estimates of theta(vSL), in situ measurements, and modeling techniques. A simple soil water budget model was modified to estimate theta(vBL) from assimilated values of theta(vSL). Four modeling scenarios were evaluated at 4 tallgrass prairie sites located in central and south central Oklahoma: 1) unmodified model, 2) assimilation of field-measured theta(vSL) at 2-day intervals, 3) assimilation of field-measured theta(vSL) matching dates of remote sensing data acquisitions during the study period, and 4) assimilation of remotely sensed theta(vSL). The unmodified model (scenario 1) underestimated measurements with root mean square errors (RMSE) between 0.03 and 0.06 m3m(-3) and mean errors (ME) between 0.02 and 0.04 m3m(-3). Model output from scenario 2 agreed well with measurements at all study sites (ME less than or equal to 0.01 m3m(-3), RMSE less than or equal to 0.03 m3m(-3)). The RMSE and ME values from scenario 3 were comparable to those of scenario 2. Simulations from scenario 4 agreed well with measured data at 2 study sites (0.00 m3m(-3) greater than or equal to ME less than or equal to 0.02 m3m(-3), RMSE less than or equal to 0.03 m3m(-3)) but underestimated measurements at the remaining sites, in one case by as much as 0.15 m3m(-3). The underestimation was due largely to inaccurate remotely sensed theta(vSL) values. These preliminary results suggest that it is feasible to infer theta(vSL) in tallgrass prairies by combining remotely sensed estimates of theta(vSL), in situ field measurements, and modeling, provided that the remotely sensed data correctly estimates surface conditions.
    Type
    text
    Article
    Language
    en
    ISSN
    0022-409X
    ae974a485f413a2113503eed53cd6c53
    10.2307/4003225
    Scopus Count
    Collections
    Journal of Range Management, Volume 55, Number 5 (September 2002)

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