Each year, the United States Department of Agriculture (USDA) releases a Cropland Data Layer (CDL) that serves as a nationwide classification system and statistical service for the United States agriculture industry. The CDL is compiled using Landsat 8 Optical Land Imager (OLI) and Sentinel-2 Multispectral Imager (MSI) data, among other sources. This optical satellite imagery is susceptible to temporal restrictions and inclement weather, which limits the number of scenes available each year and impedes analysis. As a combined effort between the USDA's Agricultural Research Service (ARS), National Agricultural Statistics Service (NASS), and NASA DEVELOP, this project explored the effectiveness of Synthetic Aperture Radar (SAR) to accurately classify crops. SAR has both day and night time capabilities as well as the ability to pierce cloud cover, which will increase data availability when compiling the CDL. For the 2016 and 2017 growing seasons, multispectral and SAR imagery were compared for test sites near Tifton, GA. A time series of SAR imagery was created to show that different crop types exhibit distinct backscatter signatures, allowing for more refined crop classification.