Abstract The phenological dynamics of crops reflect the response and feedback of agricultural systems to climate and environmental constraints, and have significant controls on carbon and nutrient cycling across the globe. Remote monitoring of crop phenological dynamics in a consistent and systematic manner is vitally crucial for optimizing the farm management activities and evaluating the agricultural resilience to extreme weather conditions and future climate change. Yet our ability to retrieve crop growing stages with satellite time series is limited. The remotely sensed phenological transition dates may not be characteristic of crop physiological growing stages. The objective of this study is to develop a remote sensing phenological monitoring framework that can reconcile satellite-based phenological measures with ground-based crop growing observations, with corn and soybean in Illinois as a case study. The framework comprises three key components: time series phenological pre-processing, time series phenological modeling, and time series phenological characterization. As an exploratory prototype, the framework retrieved a total of 56 phenological transition dates that were subsequently evaluated with the district-level ground phenological observations. The results indicated that the devised framework can adequately retrieve a wide range of physiological growing stages for corn and soybean in Illinois, with R square greater than 0.6 and RMSE less than 1 week for most stages. The devised framework largely extends the limited satellite phenological measures to a range of phenological transition dates that are characteristic of essential crop growing stages. It paves the way for formulating standard crop phenological monitoring protocols via remote sensing. The wealth of retrieved phenological characteristics open up unique opportunities to enhance our understanding of the complex mechanisms underlying the crop growth in response to varying environmental stresses, and to make more adaptive farm management strategies towards sustained agricultural development.

中文翻译：

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表征玉米和大豆生理生长阶段的遥感物候监测框架

摘要 作物物候动态反映了农业系统对气候和环境约束的响应和反馈，对全球范围内的碳和养分循环具有重要控制作用。以一致和系统的方式远程监测作物物候动态对于优化农场管理活动和评估农业对极端天气条件和未来气候变化的适应力至关重要。然而，我们利用卫星时间序列检索作物生长阶段的能力是有限的。遥感物候转变日期可能不是作物生理生长阶段的特征。本研究的目的是以伊利诺伊州的玉米和大豆为例，开发一个遥感物候监测框架，该框架可以将基于卫星的物候测量与基于地面的作物生长观测相协调。该框架包括三个关键组件：时间序列物候预处理、时间序列物候建模和时间序列物候表征。作为探索性原型，该框架检索了总共 56 个物候过渡日期，随后使用地区级地面物候观测进行了评估。结果表明，所设计的框架可以充分检索伊利诺伊州玉米和大豆的各种生理生长阶段，大多数阶段的 R 平方大于 0.6，RMSE 小于 1 周。设计的框架在很大程度上将有限的卫星物候测量扩展到一系列物候过渡日期，这些日期是基本作物生长阶段的特征。它为通过遥感制定标准作物物候监测协议铺平了道路。大量检索到的物候特征为我们提供了独特的机会，以增强我们对作物生长对不同环境压力做出反应的复杂机制的理解，并制定更具适应性的农场管理策略以实现可持续农业发展。