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 shortterm, 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.

High-quality bathymetric maps of inland water bodies are a common requirement for hydraulic engineering and hydrological science applications. Remote sensing methods, such as space-borne and airborne multispectral imaging or lidar, have been developed to estimate water depth, but are ineffective for most inland water bodies, because of the attenuation of electromagnetic radiation in water, especially under turbid conditions. Surveys conducted with boats equipped with sonars can retrieve accurate water depths, but are expensive, time-consuming, and unsuitable for unnavigable water bodies. We develop and assess a novel approach to retrieve accurate and high-resolution bathymetry maps. We measured accurate water depths using a tethered floating sonar controlled by an unmanned aerial vehicle (UAV) in a lake and in two different rivers located in Denmark. The developed technique combines the advantages of remote sensing with the potential of bathymetric sonars. UAV surveys can be conducted also in unnavigable, inaccessible, or remote water bodies. The tethered sonar can measure bathymetry with an accuracy of ∼2.1 % of the actual depth for observations up to 35 m, without being significantly affected by water turbidity, bed form, or bed material.