Backscattering Statistics of Labeled Sentinel-1 Wave Mode Imagettes for Ten Geophysical Phenomena

Abstract

Synthetic aperture radar (SAR) is a sensor that is proven to have great potential in observing atmospheric and oceanic phenomena at high-spatial resolutions (∼10 m). The statistics of SAR backscattering that describe the image characteristics are essential to help interpret the properties of the geophysical processes. In this study, we took advantage of a hand-labeled database of ten commonly observed geophysical processes created based on the Sentinel-1 wave mode vignettes to document the SAR backscattering statistics. The probability density function (PDF), normalized variance, skewness, and kurtosis were investigated among the ten labeled categories. We found that the NRCS PDFs differ between types, implying the influences of these large-scale features on the radar backscattering distribution. The statistical parameters exhibited distinct variations among classes at the two incidence angles of 23.5∘ and 36.5∘. In particular, the normalized variance of low wind area at 23.5∘ exceeded other phenomena by an order of magnitude. This lays the basis for directly identifying the SAR images of low wind areas in terms of this parameter. Sea ice and rain cells at 36.5∘ span within a similar range of kurtosis values, much higher than the other groups. While sea ice could be excluded using a latitude threshold, the rain cells are readily detected. The global percentage map of directly identified rain cells is consistent with the deep-learning results but with higher efficiency. The influence of these large-scale atmospheric and oceanic features on radar backscattering statistics must be considered in the future wave retrieval algorithm for improved accuracy.