Radio Frequency Interference 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 ten 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. Sophisticated algorithms are often applied to digital data to detect the presence of an interfering signal within the band. The digitizing hardware is, however, power hungry, consuming about 10 W per 1 GHz of bandwidth. In addition, the amount of generated data can be prohibitive for 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, this 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 (Maddy, Iturbe-Sanchez and Boukabara 2024). 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 resolution.

For example, improving global precipitation mapping requires improved spatial, spectral, and temporal resolution of observations (Kidd, et al. 2021). For precipitation observations, low microwave frequencies (below 60 GHz) are very important. This is also a spectral region where RFI becomes prevalent.

We have been developing an RFI detection algorithm that enables observations in contested RFI environments, in the presence of some interference sources within the observational footprint and within the spectral band. ECO1280 nature run data (Hoffman, Malardel and Peevey 2018) is used for a simulation of the observation of a microwave sensor from orbit. A preliminary RFI detection algorithm has been designed.

We will present the initial results from the evaluation of the RFI detection algorithm.

1. References

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.

Hoffman, R. N., S. Malardel, and T. Peevey. 2018. "ECMWF Nature Run." Cooperative Institute for Research in the Atmosphere. November 3. Accessed June 30, 2023. https://www.cira.colostate.edu/imagery-data/ecmwf-nature-run/.

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.

Maddy, E. S., F. Iturbe-Sanchez, and S. A. Boukabara. 2024. "Toward the Next Generation of Microwave Sounders: Benefits of a Low-Earth Orbit Hyperspectral Microwave Instrument in All-Weather Conditions Using AI." IEEE Journal of selected topics in applied Earth observations and remote sensing 4235-4245. doi:10.1109/JSTARS.2024.3356858.

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.