Radio Frequency Resilient Passive Microwave Sensor

Space based passive microwave sensors offer valuable insights into climate dynamics, the intricate interplay of water and energy cycles, and serve as important data sources for enhancing the accuracy of weather forecasting models, e.g., (English, et al. 2013), (Lupu 2019). These sensors have a lot more to offer if their spatial, temporal, and spectral resolution can be improved. Their observations, however, are threatened by Radio Frequency Interference (RFI). This is a unique problem within the microwave spectrum where RFI contamination reduces the capabilities of active and passive sensors.

There are numerous solutions for detecting the interference within a channel, or a narrow bandwidth (e.g., up to tens of GHz). Such algorithms are currently used for the Soil Moisture Active Passive (SMAP) microwave radiometer, e.g., (Piepmeier, Johnson, et al. 2014), (Piepmeier, Mohammed, et al. 2016), and many other papers describe applications of such algorithms to future sensors, (Kummerow, et al. 2022). These solutions provide detection within a relatively narrow radiometer band by digitizing microwave radiometer channels output. A sophisticated algorithms are applied to these digital data in order to detect a presence of an interfering signal within the band. These solutions are very important, since they can detect a very low level of RFI presence. The digitizing hardware is, however, power hungry. In addition, the amount of generated data can be prohibitive for a download from space. Thus, the data processing must be mostly done onboard the satellite.

BEST’s solution is a hyperspectral radiometer that does not use digitization (yet), is much simpler, doesn’t consume excessive amounts of power, and can be integrated into a compact, economic package. Moreover, the BEST hyperspectral solution has a potential to improve observations with respect to the current state of the art, as documented in literature by many authors, including (Boukabara and Garrett 2011). The proposed class of hyperspectral sensors provides an economical solution, and it could enable the deployment of observational constellations with much improved spatial resolution (by using deployable reflector antennas), temporal resolution (economical sensors), and spectral resolutions.

For example, to improve the global precipitation mapping improved spatial, spectral, a temporal resolution of observations is required, (Kidd, et al. 2021). For precipitation observations, low microwave frequencies (below 50 GHz) are very important. This is also a spectral region where the RFI is most likely. Such future precipitation mapping constellation will have to cope with anthropogenic signals.

Architecture of an RFI resilient passive microwave sensor will be presented.

References:

Boukabara, S. A., and K. Garrett. 2011. "Benefits of a Hyperspectral Microwave Sensor." 2011 IEEE Sensors. Limerick, Ireland: IEEE. 1881-1884.

English, S., T. McNally, N. Bormann, K. Salonen, M. Matricardi, A. Horanyi, M. Rennie, et al. 2013. Impact of satellite data. Technical memorandum, Reading, England: ECMWF.

Kidd, C., G. Huffman, V. Maggioni, P. Chambon, and R. Oki. 2021. "The Global Satellite Precipitation Constellation. Current Status and Future Requirements." Bulletin of the American Meteorological Society. doi:10.1175/BAMS-D-20-0299.1.

Kummerow, C. D., J. C. Poczatek, S. Almond, W. Berg, 0. Jarrett, A. Jones, M. Kantner, and C. P. Kuo. 2022. "Hyperspectral Micowave Sensors - Advantages and Limitations." IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing 764-775. doi:10.1109/JSTARS.2021.3133382.

Lupu, C. 2019. "Data assimilation diagnostics: Assessing the observations impact in the forecast." ECMWF Data assimilation training course 51.

Piepmeier, J. R., P. N. Mohammed, G. DeAmici, E. Kim, J. Peng, and C. Ruf. 2016. Soil Moisture Active Passive (SMAP) Project, Algorithm Theoretical Basis Document, SMAP L1B Radiometer Brightness Temperature, Data Product: L1B_TB (Rev. B). NASA Goddard Space Flight Center.

Piepmeier, J. R., J. T. Johnson, P. N. Mohammed, D. Bradley, C. Ruf, M. Aksoy, R. Garcia, D. Hudson, L. Miles, and M. Wong. 2014. "Radio-frequency Interference Mitigation for the Soil Moisture Active Passive Microwave Radiometer." IEEE Transaction on Geoscience and Remote Sensing 761-775. doi: 10.1109/TGRS.2013.2281266.