Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model‐based decomposition and machine learning to map inundated rice using time‐series polarimetric, L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) observations. Simultaneous ground truth observations recorded water depth inundation during the 2019 crop season using instrumented fields across the study site in Arkansas, USA. A three‐component model‐based decomposition generated metrics representing surface‐, double bounce‐, and volume‐scattering along with a shape factor, randomness factor, and the Radar Vegetation Index (RVI). These physically meaningful metrics characterized crop inundation status independent of growth stage including under dense canopy cover. Machine learning (ML) comparisons employed Random Forest (RF) using the UAVSAR derived parameters to identify cropland inundation status across the region. Outcomes show that RVI, proportion of the double‐bounce within total scattering, and the relative comparison between the double‐bounce and the volume scattering have moderate to strong mechanistic ability to identify rice inundation status with Overall Accuracy (OA) achieving 75%. The use of relative ratios further helped mitigate the impacts of far range incidence angles. The RF approach, which requires training data, achieved a higher OA and Kappa of 88% and 71%, respectively, when leveraging multiple SAR parameters. Thus, the combination of physical characterization and ML provides a powerful approach to retrieving cropland inundation under the canopy. The growth of polarimetric L‐band availability should enhance cropland inundation metrics beyond open water that are required for tracking water quantity at field scale over large areas.

中文翻译：

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使用极化无人机SAR数据进行水稻淹水评估


在典型的农艺实践下，灌溉水稻需要严格的水管理。提高用水效率和评估的具有成本效益的工具是行业和资源管理者扩展生态系统服务的关键需求。在这项研究中，我们推进基于模型的分解和机器学习，利用时间序列偏振、 L波段无人机合成孔径雷达 (UAVSAR) 观测来绘制被淹没的水稻地图。使用美国阿肯色州研究地点的仪器田地同步地面实况观测记录了 2019 年作物季节的水深淹没情况。基于三分量模型的分解生成了表示表面散射、双反射散射和体积散射的指标，以及形状因子、随机因子和雷达植被指数 (RVI)。这些具有物理意义的指标表征了作物淹没状态，与生长阶段无关，包括在茂密的树冠覆盖下。机器学习 (ML) 比较采用随机森林 (RF)，使用 UAVSAR 导出的参数来识别整个地区农田的淹没状况。结果表明，RVI、双反射占总散射的比例以及双反射与体积散射之间的相对比较对于识别水稻淹水状况具有中等到强的机械能力，总体准确度（OA）达到75%。相对比率的使用进一步有助于减轻远距离入射角的影响。需要训练数据的 RF 方法在利用多个 SAR 参数时分别实现了 88% 和 71% 的更高 OA 和 Kappa。因此，物理表征和机器学习的结合提供了一种强大的方法来检索树冠下的农田淹没情况。 偏振L波段可用性的增长应该会增强开放水域以外的农田淹没指标，这是跟踪大面积田间规模水量所需的。