  The National Aeronautics and Space Administration's (NASA) Soil Moisture
Active Passive (SMAP) is the latest passive remote sensing satellite operating
in the protected L-band spectrum from 1.400 to 1.427 GHz. SMAP provides
global-scale soil moisture images with point-wise passive scanning of the
earth's thermal radiations. SMAP takes multiple samples in frequency and time
from each antenna footprint to increase the likelihood of capturing RFI-free
samples. SMAP's current RFI detection and mitigation algorithm excludes samples
detected to be RFI-contaminated and averages the remaining samples. But this
approach can be less effective for harsh RFI environments, where RFI
contamination is present in all or a large number of samples. In this paper, we
investigate a bias-free weighted sum of samples estimator, where the weights
can be computed based on the RFI's statistical properties.
