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 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.

Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging

Hyperspectral cameras onboard unmanned aerial vehicles (UAVs) have recently emerged for monitoring crop traits at the sub-field scale. Different physical, statistical, and hybrid methods for crop trait retrieval have been developed. However, spectra collected from UAVs can be confounded by various issues, including illumination variation throughout the crop growing season, the effect of which on the retrieval performance is not well understood at present. In this study, four retrieval methods are compared, in terms of retrieving the leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC) of potato plants over an agricultural field for six dates during the growing season. We analyzed: (1) The standard look-up table method (LUTstd), (2) an improved (regularized) LUT method that involves variable correlation (LUTreg), (3) hybrid methods, and (4) random forest regression without (RF) and with (RFexp) the exposure time as an additional explanatory variable. The Soil–Leaf–Canopy (SLC) model was used in association with the LUT-based inversion and hybrid methods, while the statistical modelling methods (RF and RFexp) relied entirely on in situ data. The results revealed that RFexp was the best-performing method, yielding the highest accuracies, in terms of the normalized root mean square error (NRMSE), for LAI (5.36%), fCover (5.87%), and CCC (15.01%). RFexp was able to reduce the effects of illumination variability and cloud shadows. LUTreg outperformed the other two retrieval methods (hybrid methods and LUTstd), with an NRMSE of 9.18% for LAI, 10.46% for fCover, and 12.16% for CCC. Conversely, LUTreg led to lower accuracies than those derived from RF for LAI (5.51%) and for fCover (6.23%), but not for CCC (16.21%). Therefore, the machine learning approaches—in particular, RF—appear to be the most promising retrieval methods for application to UAV-based hyperspectral data.

A Satellite-Based Imaging Instrumentation Concept for Hyperspectral Thermal Remote Sensing

This paper describes the concept of the hyperspectral Earth-observing thermal infrared (TIR) satellite mission HiTeSEM (High-resolution Temperature and Spectral Emissivity Mapping). The scientific goal is to measure specific key variables from the biosphere, hydrosphere, pedosphere, and geosphere related to two global problems of significant societal relevance: food security and human health. The key variables comprise land and sea surface radiation temperature and emissivity, surface moisture, thermal inertia, evapotranspiration, soil minerals and grain size components, soil organic carbon, plant physiological variables, and heat fluxes. The retrieval of this information requires a TIR imaging system with adequate spatial and spectral resolutions and with day-night following observation capability. Another challenge is the monitoring of temporally high dynamic features like energy fluxes, which require adequate revisit time. The suggested solution is a sensor pointing concept to allow high revisit times for selected target regions (1-5 days at off-nadir). At the same time, global observations in the nadir direction are guaranteed with a lower temporal repeat cycle (>1 month). To account for the demand of a high spatial resolution for complex targets, it is suggested to combine in one optic (1) a hyperspectral TIR system with ~75 bands at 7.2-12.5 µm (instrument NEDT 0.05 K-0.1 K) and a ground sampling distance (GSD) of 60 m, and (2) a panchromatic high-resolution TIR-imager with two channels (8.0-10.25 µm and 10.25-12.5 µm) and a GSD of 20 m. The identified science case requires a good correlation of the instrument orbit with Sentinel-2 (maximum delay of 1-3 days) to combine data from the visible and near infrared (VNIR), the shortwave infrared (SWIR) and TIR spectral regions and to refine parameter retrieval.

Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation

Canopy chlorophyll content (CCC) is an essential ecophysiological variable for photosynthetic functioning. Remote sensing of CCC is vital for a wide range of ecological and agricultural applications. The objectives of this study were to explore simple and robust algorithms for spectral assessment of CCC. Hyperspectral datasets for six vegetation types (rice, wheat, corn, soybean, sugar beet and natural grass) acquired in four locations (Japan, France, Italy and USA) were analysed. To explore the best predictive model, spectral index approaches using the entire wavebands and multivariable regression approaches were employed. The comprehensive analysis elucidated the accuracy, linearity, sensitivity and applicability of various spectral models. Multivariable regression models using many wavebands proved inferior in applicability to different datasets. A simple model using the ratio spectral index (RSI; R815, R704) with the reflectance at 815 and 704 nm showed the highest accuracy and applicability. Simulation analysis using a physically based reflectance model suggested the biophysical soundness of the results. The model would work as a robust algorithm for canopy-chlorophyll-metre and/or remote sensing of CCC in ecosystem and regional scales. The predictive-ability maps using hyperspectral data allow not only evaluation of the relative significance of wavebands in various sensors but also selection of the optimal wavelengths and effective bandwidths.

Eutrophication of mangroves linked to depletion of foliar and soil base cations

There is growing concern that increasing eutrophication causes degradation of coastal ecosystems. Studies in terrestrial ecosystems have shown that increasing the concentration of nitrogen in soils contributes to the acidification process, which leads to leaching of base cations. To test the effects of eutrophication on the availability of base cations in mangroves, we compared paired leaf and soil nutrient levels sampled in Nypa fruticans and Rhizophora spp. on a severely disturbed, i.e. nutrient loaded, site (Mahakam delta) with samples from an undisturbed, near-pristine site (Berau delta) in East Kalimantan, Indonesia. The findings indicate that under pristine conditions, the availability of base cations in mangrove soils is determined largely by salinity. Anthropogenic disturbances on the Mahakam site have resulted in eutrophication, which is related to lower levels of foliar and soil base cations. Path analysis suggests that increasing soil nitrogen reduces soil pH, which in turn reduces the levels of foliar and soil base cations in mangroves.

Shrimp pond effluent dominates foliar nitrogen in disturbed mangroves as mapped using hyperspectral imagery

Conversion of mangroves to shrimp ponds creates fragmentation and eutrophication. Detection of the spatial variation of foliar nitrogen is essential for understanding the effect of eutrophication on mangroves. We aim (i) to estimate nitrogen variability across mangrove landscapes of the Mahakam delta using airborne hyperspectral remote sensing (HyMap) and (ii) to investigate links between the variation of foliar nitrogen mapped and local environmental variables. In this study, multivariate prediction models achieved a higher level of accuracy than narrow-band vegetation indices, making multivariate modeling the best choice for mapping. The variation of foliar nitrogen concentration in mangroves was significantly influenced by the local environment: (1) position of mangroves (seaward/landward), (2) distance to the shrimp ponds, and (3) predominant mangrove species. The findings suggest that anthropogenic disturbances, in this case shrimp ponds, influence nitrogen variation in mangroves. Mangroves closer to the shrimp ponds had higher foliar nitrogen concentrations.

Soil biotic impact on plant species shoot chemistry and hyperspectral reflectance patterns

Recent studies revealed that plant-soil biotic interactions may cause changes in above-ground plant chemistry. It would be a new step in below-ground-above-ground interaction research if such above-ground chemistry changes could be efficiently detected. Here we test how hyperspectral reflectance may be used to study such plant-soil biotic interactions in a nondestructive and rapid way. The native plant species Jacobaea vulgaris and Jacobaea erucifolius, and the exotic invader Senecio inaequidens were grown in different soil biotic conditions. Biomass, chemical content and shoot reflectance between 400 and 2500 nm wavelengths were determined. The data were analysed with multivariate statistics. Exposing the plants to soil biota enhanced the content of defence compounds. The highest increase (400%) was observed for the exotic invader S. inaequidens. Chemical and spectral data enabled plant species to be classified with an accuracy > 85%. Plants grown in different soil conditions were classified with 50-60% correctness. Our data suggest that soil microorganisms can affect plant chemistry and spectral reflectance. Further studies should test the potential to study plant-soil biotic interactions in the field. Such techniques could help to monitor, among other things, where invasive exotic plant species develop biotic resistance or the development of hotspots of crop soil diseases.

An accurate retrieval of leaf water content from mid to thermal infrared spectra using continuous wavelet analysis

Leaf water content determines plant health, vitality, photosynthetic efficiency and is an important indicator of drought assessment. The retrieval of leaf water content from the visible to shortwave infrared spectra is well known. Here for the first time, we estimated leaf water content from the mid to thermal infrared (2.5-14.0 μm) spectra, based on continuous wavelet analysis. The dataset comprised 394 spectra from nine plant species, with different water contents achieved through progressive drying. To identify the spectral feature most sensitive to the variations in leaf water content, first the Directional Hemispherical Reflectance (DHR) spectra were transformed into a wavelet power scalogram, and then linear relations were established between the wavelet power scalogram and leaf water content. The six individual wavelet features identified in the mid infrared yielded high correlations with leaf water content (R(2)=0.86 maximum, 0.83 minimum), as well as low RMSE (minimum 8.56%, maximum 9.27%). The combination of four wavelet features produced the most accurate model (R(2)=0.88, RMSE=8.00%). The models were consistent in terms of accuracy estimation for both calibration and validation datasets, indicating that leaf water content can be accurately retrieved from the mid to thermal infrared domain of the electromagnetic radiation.

Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 μm) to discriminate vegetation species

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.