Huang Xiaodong, Runkle Benjamin R K, Isbell Mark, Moreno-García Beatriz, McNairn Heather, Reba Michele L, Torbick Nathan
Applied Geosolutions LLC Durham NH USA.
Department of Biological & Agricultural Engineering University of Arkansas Fayetteville AR USA.
Earth Space Sci. 2021 Mar;8(3):e2020EA001554. doi: 10.1029/2020EA001554. Epub 2021 Mar 9.
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, -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 -band availability should enhance cropland inundation metrics beyond open water that are required for tracking water quantity at field scale over large areas.
在典型的农艺实践中，灌溉水稻需要严格的水分管理。开发具有成本效益的工具来提高用水效率和评估用水情况，是行业和资源管理者扩大生态系统服务规模的关键需求。在本研究中，我们推进了基于模型的分解和机器学习方法，利用时间序列极化、多波段无人飞行器合成孔径雷达（UAVSAR）观测数据绘制淹没水稻田的地图。同时，通过美国阿肯色州研究区域内配备仪器的农田，在2019年作物生长季记录了水深淹没情况的地面实况观测数据。基于三分量模型的分解生成了代表表面散射、双次散射和体散射的指标，以及形状因子、随机性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的淹没状态，与生长阶段无关，包括在茂密冠层覆盖下的情况。机器学习（ML）比较采用随机森林（RF）算法，利用UAVSAR推导的参数识别整个区域农田的淹没状态。结果表明，RVI、双次散射在总散射中所占比例以及双次散射与体散射之间的相对比较，具有中等至较强的机制能力来识别水稻淹没状态，总体精度（OA）达到75%。使用相对比率进一步有助于减轻远距离入射角的影响。RF方法需要训练数据，在利用多个SAR参数时，分别实现了更高的OA和Kappa值，分别为88%和7%。因此，物理特征描述和ML的结合为获取冠层下农田淹没情况提供了一种强大的方法。极化多波段数据可用性的增加，应能增强除开阔水域外的农田淹没指标，这些指标是在大面积田间尺度上跟踪水量所必需的。
