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.

Remote sensing (RS) of sun-induced fluorescence (SIF) has emerged as a promising indicator of photosynthetic activity and related stress from the leaf to the ecosystem level. The implementation of modern RS technology on SIF is highly motivated by the direct link of SIF to the core of photosynthetic machinery. In the last few decades, a lot of studies have been conducted on SIF measurement techniques, retrieval algorithms, modeling, application, validation, and radiative transfer processes, incorporating different RS observations (i.e., ground, unmanned aerial vehicle (UAV), airborne, and spaceborne). These studies have made a significant contribution to the enrichment of SIF science over time. However, to realize the potential of SIF and to explore its full spectrum using different RS observations, a complete document of existing SIF studies is needed. Considering this gap, we have performed a detailed review of current SIF studies from the ground, UAV, airborne, and spaceborne observations. In this review, we have discussed the in-depth interpretation of each SIF study using four RS platforms. The limitations and challenges of SIF studies have also been discussed to motivate future research and subsequently overcome them. This detailed review of SIF studies will help, support, and inspire the researchers and application-based users to consider SIF science with confidence.

The increasing demand for tropical timber from natural forests has reduced the population sizes of native species such as Cedrela spp. because of their high economic value. To prevent the decline of population sizes of the species, all Cedrela species have been incorporated into Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). The study presents information about the modeled distribution of the genus Cedrela in Peru that aims to identify potential habitat distribution of the genus, its availability in areas protected by national service of protected areas, and highlighted some areas because of their conservation relevance and the potential need for restoration. We modeled the distribution of the genus Cedrela in Peru using 947 occurrence records that included 10 species (C. odorata, C. montana, C. fissilis, C. longipetiolulata, C. angustifolia, C. nebulosa, C. kuelapensis, C. saltensis, C. weberbaueri, and C. molinensis). We aim to identify areas environmentally suitable for the occurrence of Cedrela that are legally protected by the National Service of Protected Areas (PAs) and those that are ideal for research and restoration projects. We used various environmental variables (19 bioclimatic variables, 3 topographic factors, 9 edaphic factors, solar radiation, and relative humidity) and the maximum entropy model (MaxEnt) to predict the probability of occurrence. We observed that 6.7% (86,916.2 km²) of Peru presents a high distribution probability of occurrence of Cedrela, distributed in 17 departments, with 4.4% (10,171.03 km²) of the area protected by PAs mainly under the category of protection forests. Another 11.65% (21,345.16 km²) of distribution covers areas highly prone to degradation, distributed mainly in the departments Ucayali, Loreto, and Madre de Dios, and needs immediate attention for its protection and restoration. We believe that the study will contribute significantly to conserve Cedrela and other endangered species, as well as to promote the sustainable use and management of timber species as a whole.

Hyperspectral remote sensing (RS) provides unique possibilities to monitor peatland vegetation traits and their temporal dynamics at a fine spatial scale. Peatlands provide a vital contribution to ecosystem services by their massive carbon storage and wide heterogeneity. However, monitoring, understanding, and disentangling the diverse vegetation traits from a heterogeneous landscape using complex RS signal is challenging, due to its wide biodiversity and distinctive plant species composition. In this work, we aim to demonstrate, for the first time, the large heterogeneity of peatland vegetation traits using well-established vegetation indices (VIs) and Sun-Induced Fluorescence (SIF) for describing the spatial heterogeneity of the signals which may correspond to spatial diversity of biochemical and structural traits. SIF originates from the initial reactions in photosystems and is emitted at wavelengths between 650–780 nm, with the first peak at around 687 nm and the second peak around 760 nm. We used the first HyPlant airborne data set recorded over a heterogeneous peatland area and its surrounding ecosystems (i.e., forest, grassland) in Poland. We deployed a comparative analysis of SIF and VIs obtained from differently managed and natural vegetation ecosystems, as well as from diverse small-scale peatland plant communities. Furthermore, spatial relationships between SIF and VIs from large-scale vegetation ecosystems to small-scale peatland plant communities were examined. Apart from signal variations, we observed a positive correlation between SIF and greenness-sensitive VIs, whereas a negative correlation between SIF and a VI sensitive to photosynthesis was observed for large-scale vegetation ecosystems. In general, higher values of SIF were associated with higher biomass of vascular plants (associated with higher Leaf Area Index (LAI)). SIF signals, especially SIF₇₆₀, were strongly associated with the functional diversity of the peatland vegetation. At the peatland area, higher values of SIF₇₆₀ were associated with plant communities of high perennials, whereas, lower values of SIF₇₆₀ indicated peatland patches dominated by Sphagnum. In general, SIF₇₆₀ reflected the productivity gradient on the fen peatland, from Sphagnum-dominated patches with the lowest SIF and fAPAR values indicating lowest productivity to the Carex-dominated patches with the highest SIF and fAPAR values indicating highest productivity.