Abstract Planned satellite launches will provide open access and operational L-band radar data streams at space-time resolutions not previously available. To further prepare, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) platform was used to observe cropland sites across the southern United States to support the development of L-band (24 cm) prototype science products. Time series flights flew over four independent areas during the growing season of 2019 while crop ground measurements were collected. Major crops include corn, cotton, pasture, peanut, rice, and soybean. A suite of cropland classification experiments applied a set of machine learning (ML) algorithms (random forest, feedforward fully connected neural network, support vector machine), the recently developed Multi-temporal Binary Tree Classification (MBTC), and a phenology (Coefficient of Variation; CoV) approach to synergistically assess performance, scattering mechanisms, and limitations. Specific objectives of this research application included 1.) evaluation of L-band mapping performance across multiple independent agricultural production areas with field scale training data, and 2.) assessment of the CoV approach for the generation of prototype NISAR Level 2 science products. Collectively, SAR terms with sensitivity to volume scattering performed well and consistently across CoV mapping experiments achieving accuracy greater than 80% for cropland vs not cropland. Dynamic phenology classes, such as herbaceous wetlands, had some confusion with CoV agriculture requiring further regionalized training optimization. Volume scattering and cross-pol terms were most useful across the different ML techniques with overall accuracy and Kappa consistently over 90% and 0.85, respectively, for crop type by late growth stages for L-band observations. As expected, time series information was more valuable compared to any single ML technique, site, or crop schema. Ultimately, as more SAR platforms launch, the user community should leverage physical contributions of different wavelengths and polarizations along with growing open access time series for efficient and meaningful agricultural products.

中文翻译：

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使用 L 波段 UAVSAR 进行农田测绘和 NISAR 产品的开发

摘要 计划中的卫星发射将以前所未有的时空分辨率提供开放访问和可操作的 L 波段雷达数据流。为了进一步准备，无人机合成孔径雷达 (UAVSAR) 平台用于观察美国南部的农田地点，以支持 L 波段（24 厘米）原型科学产品的开发。在 2019 年的生长季节期间，时间序列航班飞越了四个独立的区域，同时收集了作物地面测量数据。主要农作物有玉米、棉花、牧草、花生、水稻和大豆。一套农田分类实验应用了一组机器学习（ML）算法（随机森林、前馈全连接神经网络、支持向量机），最近开发的多时态二叉树分类（MBTC），和物候学（变异系数；CoV）方法来协同评估性能、散射机制和局限性。本研究应用的具体目标包括 1.) 使用田间规模训练数据评估多个独立农业生产区的 L 波段绘图性能，以及 2.) 评估用于生成原型 NISAR 2 级科学产品的 CoV 方法。总的来说，对体积散射敏感的 SAR 项在 CoV 映射实验中表现良好且一致，农田与非农田的准确度超过 80%。动态物候学课程，例如草本湿地，与需要进一步区域化培训优化的 CoV 农业存在一些混淆。体积散射和交叉极化项在不同的 ML 技术中最有用，对于 L 波段观察的后期生长阶段的作物类型，总体准确度和 Kappa 分别一致超过 90% 和 0.85。正如预期的那样，与任何单一的 ML 技术、站点或作物模式相比，时间序列信息更有价值。最终，随着更多 SAR 平台的推出，用户社区应该利用不同波长和极化的物理贡献以及不断增长的开放获取时间序列来开发高效和有意义的农产品。