Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

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

Serving the Public Interest in Several Ways: Theory and Empirics

We develop a model where people differ in their altruistic preferences and can serve the public interest in two ways: by making donations to charity and by taking a public service job and exerting effort on the job. Our theory predicts that people who are more altruistic are more likely to take a public service job and, for a given job, make higher donations to charity. Comparing equally altruistic workers, those with a regular job make higher donations to charity than those with a public service job by a simple substitution argument. We subsequently test these predictions using cross-sectional data from Germany on self-reported altruism, sector of employment, and donations to charity. In addition, we use panel data from the Netherlands on volunteering and sector of employment. We find support for most of our predictions.Hervorming Sociale Regelgevin

Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review

Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems

Thermal infrared remote sensing of vegetation: Current status and perspectives

Vegetation plays a vital role in the ecological functioning of terrestrial and coastal ecosystems. Remote sensing generally provides timely and accurate information to manage ecosystems sustainably and effectively. In this respect, thermal infrared (TIR, 3–14 µm) remote sensing data form a valuable data source for vegetation studies. The TIR data provides unique information compared to other parts of the electromagnetic spectrum. This article aims to gather and review the most relevant information obtained using TIR remote sensing data for terrestrial vegetation at leaf and canopy levels using laboratory/field-based, airborne and spaceborne platforms. We address this topic from various angles, particularly focusing on vegetation discrimination as well as the quantification of water stress by means of canopy temperature and spectral emissivity. In addition, attempts to associate TIR spectral features with vegetation biochemical compounds, as well as the retrieval of vegetation biochemical and biophysical parameters, are reviewed. Research needs and requirements for successful use of remote sensing in vegetation studies across the TIR region, as well as significant challenges, are also discussed. Our review reveals that, despite the increasing interest among remote sensing experts in using TIR data, there are still large gaps in our understanding and interpretation of TIR imagery. Some inconsistent findings and contradictory observations have come to light in different levels (i.e., leaf and canopy levels). In addition, our review shows that airborne and TIR hyperspectral-based studies are currently limited due to cost, particularly across large spatial extents. It can be concluded that TIR remote sensing of vegetation offers unique insights in understanding terrestrial vegetation (e.g., vegetation water stress and retrieval of biophysical parameters). TIR is complementary to other remote sensing data sources, with a high potential for fusing data from different parts of the spectrum. However, we highlight challenges obtaining consistent, meaningful and accurate results for land surface temperature and land surface emissivity retrieval

A Hyperspectral Thermal Infrared Imaging Instrument for Natural Resources Applications

A new instrument has been setup at the Centre de Recherche Public-Gabriel Lippmann to measure spectral emissivity values of typical earth surface samples in the 8 to 12 μm range at a spectral resolution of up to 0.25 cm−1. The instrument is based on a Hyper-Cam-LW built by Telops with a modified fore-optic for vertical measurements at ground level and a platform for airborne acquisitions. A processing chain has been developed to convert calibrated radiances into emissivity spectra. Repeat measurements taken on samples of sandstone show a high repeatability of the system with a wavelength dependent standard deviation of less than 0.01 (1.25% of the mean emissivity). Evaluation of retrieved emissivity spectra indicates good agreement with reference measurements. The new instrument facilitates the assessment of the spatial variability of emissivity spectra of material surfaces—at present still largely unknown—at various scales from ground and airborne platforms and thus will provide new opportunities in environmental remote sensing