Classification of flying insects in polarimetric weather radar using machine learning and aphid trap data.

Over the past decades, studies have observed strong declines in biomass and the abundance of flying insects. However, there are many locations where no surveys of insect biomass or abundance are available. Weather radars are known to provide quantitative estimates of flying insect biomass and abundance, and can therefore be used to fill knowledge gaps in space and time. In this study, we go beyond previous studies by combining a machine-learning approach with ground-truth observations from an aphid trap network. In this study, radar echoes from Level-II (Base) data of three Next Generation Weather Radar (NEXRAD) stations in the U.S. are classified using machine learning approaches. Weekly aphid counts from suction traps at Manhattan (Kansas), Morris (Illinois), and Rosemount (Minnesota) are used as validation data. Variability and distribution of the radar signals of four scatterer classes (insects, light rain, heavy rain, and plankton) are assessed. Probability density functions (PDF) of reflectivities of insects and plankton were found to be distinct from those of light- and heavy rain. Furthermore, the PDF of radar variables of the insect scatter class was also characterized by a broad distribution of spectrum width, cross-correlation ratio, and a broad range of differential reflectivity values. Decision trees, random forests, and support vector machine models were generated to distinguish three combinations of scatterers. A random forest classifier is found to be the best-performing model. [ABSTRACT FROM AUTHOR]