Neural Network Model Tree Prediction for Passive Microwave Sensor Quality Control

Earth remote sensing algorithms for spaceborne passive microwave sensors depend on high quality observations of top-of-atmosphere radiances. When these observations are adversely affected by non-geophysical processes, such as radio frequency interference (RFI), calibration drifts, or sensor anomalies, they can have detrimental impacts on retrievals or their assimilation into models. The rise of machine learning applications has exacerbated this problem because non-linear schemes can reveal even small issues with the data. A simple and computationally inexpensive method is presented for assessing the quality of observations both instantaneously and over time using a fully connected neural network model tree. The basic premise of this scheme involves using n-1 channels to predict the nth channel, and then identifying when the prediction error is larger than normal in order to identify potentially anomalous observations. Prediction error thresholds are chosen based on the application, and well-trained models with high correlations between channels can have prediction errors on the order of the noise-equivalent differential temperature (NEDT) of the sensor (less than 1 K in most cases). We find that this method is effective for identifying various types of observational problems and present case studies to this effect that demonstrate radio frequency interference, sensor electronic and physical anomalies, sun glint, and channel performance degradation. We also find that its sensitivity is ideal for quality control, even when the observational data has previously passed operational quality checks, and that this tool can be used to effectively prescreen problematic observations that may not be initially identifiable in the data application. This tool is readily available to the remote sensing community through freely distributed code with examples on training and application via a link provided in the Data Availability statement of this article.