Accurate knowledge of the distribution, breadth and change in agricultural activity is important to food security and the related trade and policy mechanisms. Routine observations afforded by spaceborne Synthetic Aperture Radar (SAR) allows for high-fidelity monitoring of agricultural parameters at the field scale. Here we evaluate the approach to be used for generating NASA's upcoming NASA ISRO SAR (NISAR) mission's L-band cropland product using Sentinel-1C-band data. This study uses all ascending Sentinel-1A/B data collected over the conterminous United States in 2017 to compute the coefficient of variation (CV) at 150 m × 150 m resolution and evaluates the overall accuracy (OA) of CV-based crop/non-crop classifications at 100 one-by-one degree tiles. We calculate accuracies using two approaches: (a) using a literature-recommended constant CV threshold of 0.5 (CV_{thr_0.5}) and (b) determining optimal CV thresholds for every tile using Youden's J statistic (YJS), CV_{thr_YJS}. These accuracy comparisons are important for determining (1) the viability of using a computationally inexpensive and straightforward approach for cropland classification over large spatial scales/diverse land covers (i.e., can accuracies ≥80% be routinely achieved?), (2) how closely OA_0.5 compares to the performance ceiling (OA_YJS). This information will help determine whether approach (a) is appropriate and how much potential room of improvement there could be in modifying it. Results for OA_0.5 and OA_YJS are 81.5% and 86.8%, respectively. A breakdown by census geographic region, showed that OA_0.5 (OA_YJS) exceeded 80% (90%) in the South and Midwest, but was only 76.1% (73.5%) in the West. The improvement in OA_YJS mainly stems from tiles with >40% crop prevalence having about 10% greater OA values. To better examine the potential of the approach for land cover classification, results of approach (b) were also stratified by crop. Approach (b) accurately detected most non-crop classes as non-crop (>80%), but with low OA_YJS values for grasslands/pasture, especially in the West. CV values for crop were distinct from non-crop indicating that the approach is suitable for crop/non-crop classifications. Because results CV values have substantial overlap within crop/non-crop classes, indicating the approach is poorly suited for land cover classifications. We also detected a strong geographic dependence of CV_{thr_YJS}: values ranged from about 0.2 at the coasts and gradually increase to about 0.6 in the Central United States, most often falling close to 0.3 and 0.5.

准确了解农业活动的分布，广度和变化对粮食安全以及相关的贸易和政策机制很重要。星载合成孔径雷达（SAR）提供的常规观测值可以在田间范围内高保真地监测农业参数。在这里，我们评估了使用Sentinel-1C波段数据生成NASA即将到来的NASA ISRO SAR（NISAR）任务的L波段农田产品的方法。这项研究使用2017年美国全境收集的所有Sentinel-1A / B上升数据来计算150 m×150 m分辨率下的变异系数（CV），并评估基于CV的作物/非作物的总体准确度（OA） -以100个一对一度的瓦片进行作物分类。我们使用两种方法来计算精度：_{thr_0.5}）和（b）使用Youden的J统计量（YJS）CV _{thr_YJS}确定每个图块的最佳CV阈值。这些准确性比较对于确定（1）在大型空间尺度/多样的土地覆被上使用计算便宜且简单易行的耕地分类方法的可行性（即，是否可以常规达到80％以上的精度？），（2）紧密程度如何非常重要。 OA _0.5与性能上限（OA _YJS）相比较。这些信息将有助于确定方法（a）是否合适，以及修改方法有多少潜在的改进空间。OA _0.5和OA _YJS的结果分别是81.5％和86.8％。对人口普查地理区域的细分显示，南部和中西部的OA _0.5（OA _YJS）超过80％（90％），而在西部仅为76.1％（73.5％）。OA _YJS的改善主要源于作物流行率> 40％的瓷砖，其OA值提高了约10％。为了更好地检验该方法用于土地覆被分类的潜力，方法（b）的结果也按作物进行了分层。方法（b）将大多数非农作物类别准确地检测为非农作物（> 80％），但是OA _YJS较低草原/牧草的价值，尤其是在西方。作物的CV值不同于非作物的CV值，表明该方法适用于作物/非作物分类。由于结果的CV值在农作物/非农作物类别中有很大的重叠，因此表明该方法不太适合土地覆被分类。我们还发现CV _{thr_YJS}具有很强的地理依赖性：海岸的值从0.2左右开始，在美国中部逐渐增加到0.6左右，最常见的是接近0.3和0.5。