Improving Soil Moisture Data Retrieval From AirborneL-Band Radiometer Data by Considering SpatiallyVarying Roughness

摘要

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 soilmoisture estimates. A crop-type specific roughness parameterization within the model did not show satisfactory soil moistureresults. 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 VegetationIndex (NDVI) derived from imaging spectrometer data. Our results demonstrate the importance of treating surface roughnessas 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 dataof the vegetation improve the mapping and monitoring of surface soil moisture.