An introduction to factor analysis for radio frequency interference detection on satellite observations

Abstract

A novel radio frequency interference (RFI) detection method is introduced for satellite-borne passive microwave radiometer observations. This method is based on factor analysis, in which variability among observed and correlated variables is described in terms of factors. In the present study, this method is applied to the Tropical Rainfall Measuring Mission (TRMM)/TRMM Microwave Imager (TMI) and Aqua/Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) satellite measurements over the land surface to detect the RFI signals, respectively, in 10 and 6 GHz channels. The RFI detection results are compared with other traditional methods, such as spectral difference method and principal component analysis (PCA) method. It has been found that the newly proposed method is able to detect RFI signals in the C-and X-band radiometer channels as effectively as the conventional PCA method.

Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

“…During cloudy conditions soil moisture retrieval from visible/IR satellite produce is not possible. Microwave can provide retrievals under all weather conditions however, they are strongly affected by Radio Frequency Interference (RFI) [92]. In addition, SSM retrieval in areas with high pixel heterogeneity, specially in agro-ecosystems with varying crop type, can cause very significant differences in the behaviour of retrieved SSM [93,94].…”

Experimental data on radio frequency interference in microwave links using frequency scan measurements at 6 GHz, 7 GHz, and 8 GHz

“…RFI triggers churn of such mobile services by customers for reliable and efficient network solutions. Potential RFI sources include but are not limited to spurious signals from lower frequency bands and noise from different coherent or incoherent interference waves [3 , 4] .…”

Forecasting Arabian Sea level rise using exponential smoothing state space models and ARIMA from TOPEX and Jason satellite radar altimeter data

“…Sea level rise forecasting is important for making strategies for future development and planning, and also for mitigating its serious consequences (Ali Ghorbani et al , ). Forecasting sea level at different geographical locations is an important topic of research (Islam et al , ). On the one hand, forecasting methods can be classified as qualitative when historical data on the variable being forecast are either not applicable or unavailable and forecasts are developed based purely on the use of expert judgment (Makridakis et al , ).…”