Fair and efficient management of the radio-frequency (RF) spectrum as used by different scientific and commercial services is becoming more and more challenging, and this can have a considerable impact on microwave remote sensing. Here, we provide an overview of those spectrum management aspects relevant to the remote-sensing community, along with an introduction to the actors and processes involved in spectrum regulation, including the World Radiocommunication Conference (WRC).

The Soil Moisture Active/Passive (SMAP) satellite mission measures Earth’s radiation in the protected portion of the spectrum at 1.413 GHz (L-band) to retrieve geophysical quantities of the surface, such as soil moisture and the frozen/thawed state of the soil. The presence of radio-frequency interference (RFI) in this band is significant and impacts the quality of SMAP measurements. Knowing the location of the sources of RFI is important, because it can help to identify the source itself and also be used to develop strategies to mitigate its impact of the RFI on the data. This paper presents an algorithm that takes advantage of the viewing geometry of SMAP to locate sources of RFI. The results are validated using known locations of RFI sources and by comparison with the measurements of Soil Moisture and Ocean Salinity (SMOS) and Aquarius, two other satellite missions with L-band microwave radiometers operating in the protected band. Comparison with RFI of known location suggests that the algorithm is accurate to 1–2 km. The median distance between the locations reported by SMOS and this algorithm is 2.27 km. A study of the relationship between the localization error and the number of observations of RFI sources shows that the median localization error is about 2 km with 12 observations and about 1 km with 30 observations.

The Soil Moisture Active/Passive (SMAP) satellite microwave radiometer has been providing global measurements of L-band thermal emission from Earth since April 2015. Although the radiometer operates in the protected 1400-1427 MHz portion of the radio spectrum, its measurements are still corrupted by either Radio Frequency Interference (RFI) from out-of-band emissions via legal sources or by sources operating in-band illegally. The SMAP radiometer includes a digital backend that enables implementation of multiple ground-based RFI detection and filtering algorithms. This data is used to collect statistics and trends of Earths RFI environment. This paper examines properties of the global RFI environment as observed by SMAP, including information on RFI source properties (obtained from analysis of SMAPs multiple detector outputs) and the evolution of the RFI environment in time. Residual RFI contributions after the application of SMAP RFI processing are also examined as preliminary information for the development of future methods to address their effect.

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Radio frequency interference (RFI) is a well-documented problem for passive remote sensing of the Earth at L-band even though the measurements are made in the protected band at 1.413 GHz. Consequently, filtering for RFI is an important early step in the processing of measurements made by the SMAP (Soil Moisture Active/Passive) radiometer. However, the filtered data still include regions with suspiciously high antenna temperatures. One possible cause of these “hot spots” is interference not fully detected during RFI filtering. This paper presents evidence supporting this hypothesis and describes an algorithm to identify these “hot spots” so that they can be removed from the measurements. The impact of removing these “hot spots” is generally small, but evidence is presented that the brightness temperature and soil moisture improve when the hot spots are removed.

The range of salinity and temperature measurements used to develop models for the dielectric constant of sea water have in the past been limited to values appropriate for the open ocean. But there are important water bodies such as the Great Salt Lake in Utah with salinities much larger than those encountered in the open ocean. Extrapolating existing models to values of salinity beyond the limits of the data used to create the model can result in unrealistic predictions in remote sensing applications. This can be prevented by using a model for conductivity based on the definition of salinity and ensuring that polynomials used to model the other unknown parameters result in bounded behavior at high salinity. New laboratory measurements with high salinity (up to 150 pss) are reported and used to test a model with these adjustments.