Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.

远程检测和监测植被对压力的反应变得与可持续农业相关。光学遥感技术的持续发展为增加我们对与压力相关的生理过程的理解提供了工具。因此，本研究旨在概述用于检测农业作物胁迫的主要光谱技术和检索方法。首先，我们提出了以下综合观点：i) 生物和非生物胁迫因素、胁迫阶段和各自的植物响应，以及 ii) 受影响的性状、适当的光谱域和相应的远程测量性状的方法。其次，突出了系统文献分析的代表性结果，确定了压力检测和监测的现状和可能的未来趋势。由于特定的光相互作用过程，例如在反射辐射中表现出的吸收和散射，即可见光（VIS）、近红外光（ NIR)、短波红外和发射辐射，即太阳诱导荧光和热红外 (TIR)。通过对 96 篇研究论文的分析，可以观察到以下趋势：卫星和无人机数据的使用增加，同时方法从更简单的参数方法转向更先进的基于物理和混合模型。大多数研究设计主要受传感器可用性和实际经济原因驱动，导致 VIS-NIR-TIR 传感器组合的普遍使用。大多数审查的研究比较了从单源传感器域计算的压力代理，而不是以协同方式使用数据。我们确定了新的前进方向，作为改进光谱域协同使用压力检测的指导：（1）从多个传感器联合采集数据，同时分析多个压力响应（整体视图）；(2) 结合多域辐射传递模型和机器学习方法的植物性状同时检索；(3) 将来自不同光谱域的估计植物性状同化到综合作物生长模型中。作为未来展望，我们建议将多个遥感数据流结合到作物模型同化方案中，以建立农业生态系统的数字孪生，