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 data collected by NASA's airborne Uninhabited Aerial Vehicle SAR (UAVSAR) platform and the simulated NISAR data derived from it. This study uses mode 129, which is to be used for global-scale mapping. The mode consists of an upper (129A) and lower band (129B), respectively having bandwidths of 20 and 5 MHz. This work uses 129A data because it has a four times finer range resolution compared to 129B. The NISAR algorithm uses the coefficient of variation (CV) to perform crop/noncrop 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/noncrop delineating CV thresholds (CVthr) ranging from 0 to 1 in 0.01 increments. All but the 10 m 129A product exceeded NISAR's 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 yielded suboptimal accuracy (65%) at this site and that optimal CVthr values monotonically decreased with decreasing spatial resolution.