SMAP Radiometer RFI Prediction with Deep Learning using Antenna Counts

Soil Moisture Active Passive (SMAP) is a NASA's earth observing satellite which is used for global scale soil moisture measurement and differentiating frozen/thawed state. It is employed in 1400–1427 MHz protected band which uses L-Band radiometer for the quantification. But increasing number of wireless equipment such as air surveillance radar signals and 5G communication are making it harder to protect the radiometer microwave sensing in this secured spectrum. These technologies are responsible for the Radio Frequency Interference (RFI) in SMAP's passive observation. In this study, a novel deep learning architecture is developed that uses convolutional neural network (CNN) to predict RFI. Our model uses SMAP's level 1A raw antenna counts as well as level 1B quality flags to dynamically label these antenna raw measurements as RFI contaminated and RFI free footprints. This example study shows around 94% accuracy in detecting RFI and such result may recommend a lucrative technique in detecting RFI.