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 (CV_thr) 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 CV_thr value of 0.5 yielded suboptimal accuracy (65%) at this site and that optimal CV_thr values monotonically decreased with decreasing spatial resolution.

中文翻译：

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密苏里州斯托克维尔市农作物/非作物分类的UAVSAR和模拟NISAR数据的性能评估

合成孔径雷达（SAR）数据具有很高的时空分辨率以及对土壤和植被的敏感性，因此非常适合用于农田的变化检测。这项工作的目的是使用NASA机载无人飞行器SAR（UAVSAR）平台收集的数据以及从中得出的模拟NISAR数据，评估NASA ISRO SAR（NISAR）农田产品的科学算法。本研究使用模式129，该模式将用于全局规模映射。该模式由分别具有20 MHz和5 MHz带宽的上频带（129A）和下频带（129B）组成。这项工作使用129A数据，因为它的量程分辨率是129B的四倍。NISAR算法使用变异系数（CV）在100 m处执行农作物/非农作物分类。_thr）从0到1，以0.01为增量。除10 m 129A产品外，所有产品均超过NISAR的任务精度要求80％。UAVSAR 10 m数据表现最佳，实现了最大的总体准确性，J统计量和Kappa值分别为85％，0.62和0.60。129A产品的相同指标分别为：10 m时为77％，0.40、0.36；30 m时为81％，0.55、0.49；在100 m处为80％，0.58、0.50。我们发现，使用文献推荐的0.5的CV _thr值在该位置产生次优的准确性（65％），并且最佳的CV _thr值随着空间分辨率的降低而单调降低。