Synthetic Aperture Radar (SAR) data are well-suited for change detection over agricultural fields, owing to high spatiotemporal resolution and sensitivity to soil and vegetation. The goal of this work is to evaluate the science algorithm for the NASA ISRO SAR (NISAR) Cropland Area product using UAVSAR and simulated NISAR data (129A). The NISAR algorithm uses the coefficient of variation (CV) to perform crop/non-crop classification at 100 m. We evaluate classifications using three accuracy metrics (overall accuracy, J-statistic, Cohen’s Kappa) and spatial resolutions (10, 30 and 100 m) for crop/non-crop delineating CV thresholds (CVthr) ranging from 0 to 1 in 0.01 increments. All but the 10 m 129A product exceeded the mission accuracy requirement of 80%. The UAVSAR 10 m data performed best, achieving maximum overall accuracy, J-statistic, and Kappa values of 85%, 0.62 and 0.60. The same metrics for the 129A product respectively are: 77%, 0.40, 0.36 at 10 m; 81%, 0.55, 0.49 at 30 m; 80%, 0.58, 0.50 at 100 m. We found that using a literature recommended CVthr value of 0.5 was suboptimal (65%) and that optimal CVthr values monotonically decreased with decreasing spatial resolution.