Wurster Patrick M, Maneta Marco, Kimball John S, Endsley K Arthur, Beguería Santiago
Regional Hydrology Lab, Geosciences Department, University of Montana, Missoula, MT, United States.
Department of Ecosystem and Conservation Sciences, W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, United States.
Front Big Data. 2021 Jan 18;3:597720. doi: 10.3389/fdata.2020.597720. eCollection 2020.
Accurate monitoring of crop condition is critical to detect anomalies that may threaten the economic viability of agriculture and to understand how crops respond to climatic variability. Retrievals of soil moisture and vegetation information from satellite-based remote-sensing products offer an opportunity for continuous and affordable crop condition monitoring. This study compared weekly anomalies in accumulated gross primary production (GPP) from the SMAP Level-4 Carbon (L4C) product to anomalies calculated from a state-scale weekly crop condition index (CCI) and also to crop yield anomalies calculated from county-level yield data reported at the end of the season. We focused on barley, spring wheat, corn, and soybeans cultivated in the continental United States from 2000 to 2018. We found that consistencies between SMAP L4C GPP anomalies and both crop condition and yield anomalies increased as crops developed from the emergence stage (r: 0.4-0.7) and matured (r: 0.6-0.9) and that the agreement was better in drier regions (r: 0.4-0.9) than in wetter regions (r: -0.8-0.4). The L4C provides weekly GPP estimates at a 1-km scale, permitting the evaluation and tracking of anomalies in crop status at higher spatial detail than metrics based on the state-level CCI or county-level crop yields. We demonstrate that the L4C GPP product can be used operationally to monitor crop condition with the potential to become an important tool to inform decision-making and research.
准确监测作物状况对于发现可能威胁农业经济可行性的异常情况以及了解作物如何应对气候变化至关重要。从基于卫星的遥感产品中获取土壤水分和植被信息为持续且经济实惠的作物状况监测提供了机会。本研究将SMAP四级碳（L4C）产品中累积总初级生产力（GPP）的每周异常值与根据州级每周作物状况指数（CCI）计算出的异常值以及根据季节末报告的县级产量数据计算出的作物产量异常值进行了比较。我们重点关注了2000年至2018年在美国大陆种植的大麦、春小麦、玉米和大豆。我们发现，随着作物从出苗期（r：0.4 - 0.7）发育到成熟期（r：0.6 - 0.9），SMAP L4C GPP异常值与作物状况和产量异常值之间的一致性增加，并且在较干旱地区（r：0.4 - 0.9）的一致性优于较湿润地区（r： - 0.8 - 0.4）。L4C以1公里尺度提供每周GPP估计值，与基于州级CCI或县级作物产量的指标相比，能够以更高的空间细节评估和跟踪作物状况异常。我们证明，L4C GPP产品可用于实际监测作物状况，有潜力成为为决策和研究提供信息的重要工具。
