Improving soil moisture data retrieval from airborne L-band radiometer data by considering spatially varying roughness

Marion Pause, Angela Lausch, Matthias Bernhardt, Jorg Hacker, Karsten Schulz

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)


    This study presents the retrieval of near-surface soil moisture data below crop canopies (winter rye and winter barley) from airborne L-band radiometer observations using a radiative transfer model at very dry soil moisture conditions (<15 Vol.%). Using physically based models, the roughness parameterization plays a crucial role for the description of the surface emissivity. A two-step optimization procedure was performed for choosing an optimal roughness value to minimize the uncertainty of soil moisture estimates. A crop-type specific roughness parameterization within the model did not show satisfactory soil moisture results. Instead, a “pixel”-based (spatially varying) roughness parameter optimization provided significantly improved results, also indicating a strong relationship between the optimal roughness parameter value and the Normalized Difference Vegetation Index (NDVI) derived from imaging spectrometer data. Our results demonstrate the importance of treating surface roughness as spatially variable when retrieving soil moisture information from high spatial resolution L-band brightness temperature data. Furthermore, the results strongly indicate that a combination of passive microwave observations and optical remote sensing data of the vegetation improve the mapping and monitoring of surface soil moisture.

    Original languageEnglish
    Pages (from-to)15-25
    Number of pages11
    JournalCanadian Journal of Remote Sensing
    Issue number1
    Publication statusPublished - Feb 2014


    • Soil Moisture
    • L-Band
    • Radiometer
    • pixel based
    • spatially varying
    • Airborne


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