Sources of variability in canopy reflectance and the convergent properties of plants

Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal

PREMISE

Spectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data.

METHODS

Using over 3500 leaf-level spectral measurements, we evaluated whether foliar reflectance spectra (400-2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model.

RESULTS

Spectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species.

CONCLUSIONS

Our results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change.

Relationship between Height and Exposure in Multispectral Vegetation Index Response and Product Characteristics in a Traditional Olive Orchard

The present research had two aims. The first was to evaluate the effect of height and exposure on the vegetative response of olive canopies' vertical axis studied through a multispectral sensor and on the qualitative and quantitative product characteristics. The second was to examine the relationship between multispectral data and productive characteristics. Six olive plants were sampled, and their canopy's vertical axis was subdivided into four sectors based on two heights (Top and Low) and two exposures (West and East). A ground-vehicle-mounted multispectral proximal sensor (OptRx from AgLeader®) was used to investigate the different behaviours of the olive canopy vegetation index (VI) responses in each sector. A selective harvest was performed, in which each plant and sector were harvested separately. Product characterisation was conducted to investigate the response of the products (both olives and oils) in each sector. The results of Tukey's test (p > 0.05) showed a significant effect of height for the VI responses, with the Low sector obtaining higher values than the Top sector. The olive product showed some height and exposure effect, particularly for the olives' dimension and resistance to detachment, which was statistically higher in the upper part of the canopies. The regression studies highlighted some relationships between the VIs and product characteristics, particularly for resistance to detachments (R2 = 0.44-0.63), which can affect harvest management. In conclusion, the results showed the complexity of the olive canopies' response to multispectral data collection, highlighting the need to study the vertical axis to assess the variability of the canopy itself. The relationship between multispectral data and product characteristics must be further investigated.

Non-destructive monitoring of microalgae biofilms

Biofilm-based cultivation systems are emerging as a promising technology for microalgae production. However, efficient and non-invasive monitoring routines are still lacking. Here, a protocol to monitor microalgae biofilms based on reflectance indices (RIs) is proposed. This framework was developed using a rotating biofilm system for astaxanthin production by cultivating Haematococcus pluvialis on cotton carriers. Biofilm traits such as biomass, astaxanthin, and chlorophyll were characterized under different light and nutrient regimes. Reflectance spectra were collected to identify the spectral bands and the RIs that correlated the most with those biofilm traits. Robust linear models built on more than 170 spectra were selected and validated on an independent dataset. Astaxanthin content could be precisely predicted over a dynamic range from 0 to 4% of dry weight, regardless of the cultivation conditions. This study demonstrates the strength of reflectance spectroscopy as a non-invasive tool to improve the operational efficiency of microalgae biofilm-based technology.

Prediction of biomass accumulation and tolerance of wheat seedlings to drought and elevated temperatures using hyperspectral imaging

Early prediction of important agricultural traits in wheat opens up broad prospects for the development of approaches to accelerate the selection of genotypes for further breeding trials. This study is devoted to the search for predictors of biomass accumulation and tolerance of wheat to abiotic stressors. Hyperspectral (HS) and chlorophyll fluorescence (ChlF) parameters were analyzed as predictors under laboratory conditions. The predictive ability of reflectance and normalized difference indices (NDIs), as well as their relationship with parameters of photosynthetic activity, which is a key process influencing organic matter production and crop yields, were analyzed. HS parameters calculated using the wavelengths in Red (R) band and the spectral range next to the red edge (FR-NIR) were found to be correlated with biomass accumulation. The same ranges showed potential for predicting wheat tolerance to elevated temperatures. The relationship of HS predictors with biomass accumulation and heat tolerance were of opposite sign. A number of ChlF parameters also showed statistically significant correlation with biomass accumulation and heat tolerance. A correlation between HS and ChlF parameters, that demonstrated potential for predicting biomass accumulation and tolerance, has been shown. No predictors of drought tolerance were found among the HS and ChlF parameters analyzed.

High-throughput characterization and phenotyping of resistance and tolerance to virus infection in sweetpotato

Breeders have made important efforts to develop genotypes able to resist virus attacks in sweetpotato, a major crop providing food security and poverty alleviation to smallholder farmers in many regions of Sub-Saharan Africa, Asia and Latin America. However, a lack of accurate objective quantitative methods for this selection target in sweetpotato prevents a consistent and extensive assessment of large breeding populations. In this study, an approach to characterize and classify resistance in sweetpotato was established by assessing total yield loss and virus load after the infection of the three most common viruses (SPFMV, SPCSV, SPLCV). Twelve sweetpotato genotypes with contrasting reactions to virus infection were grown in the field under three different treatments: pre-infected by the three viruses, un-infected and protected from re-infection, and un-infected but exposed to natural infection. Virus loads were assessed using ELISA, (RT-)qPCR, and loop-mediated isothermal amplification (LAMP) methods, and also through multispectral reflectance and canopy temperature collected using an unmanned aerial vehicle. Total yield reduction compared to control and the arithmetic sum of (RT-)qPCR relative expression ratios were used to classify genotypes into four categories: resistant, tolerant, susceptible, and sensitives. Using 14 remote sensing predictors, machine learning algorithms were trained to classify all plots under the said categories. The study found that remotely sensed predictors were effective in discriminating the different virus response categories. The results suggest that using machine learning and remotely sensed data, further complemented by fast and sensitive LAMP assays to confirm results of predicted classifications could be used as a high throughput approach to support virus resistance phenotyping in sweetpotato breeding.

Fire intensity impacts on physiological performance and mortality in Pinus monticola and Pseudotsuga menziesii saplings: a dose-response analysis

Fire is a major cause of tree injury and mortality worldwide, yet our current understanding of fire effects is largely based on ocular estimates of stem charring and foliage discoloration, which are error prone and provide little information on underlying tree function. Accurate quantification of physiological performance is a research and forest management need, given that declining performance could help identify mechanisms of-and serve as an early warning sign for-mortality. Many previous efforts have been hampered by the inability to quantify the heat flux that a tree experiences during a fire, given its highly variable nature in space and time. In this study, we used a dose-response approach to elucidate fire impacts by subjecting Pinus monticola var. minima Lemmon and Pseudotsuga menziesii (Mirb.) Franco var. glauca (Beissn.) Franco saplings to surface fires of varying intensity doses and measuring short-term post-fire physiological performance in photosynthetic rate and chlorophyll fluorescence. We also evaluated the ability of spectral reflectance indices to quantify change in physiological performance at the individual tree crown and stand scales. Although physiological performance in both P. monticola and P. menziesii declined with increasing fire intensity, P. monticola maintained a greater photosynthetic rate and higher chlorophyll fluorescence at higher doses, for longer after the fire. Pinus monticola also had complete survival at lower fire intensity doses, whereas P. menziesii had some mortality at all doses, implying higher fire resistance for P. monticola at this life stage. Generally, individual-scale spectral indices were more accurate at quantifying physiological performance than those acquired at the stand-scale. The Photochemical Reflectance Index outperformed other indices at quantifying photosynthesis and chlorophyll fluorescence, highlighting its potential use to quantify crown scale physiological performance. Spectral indices that incorporated near-infrared and shortwave infrared reflectance, such as the Normalized Burn Ratio, were accurate at characterizing stand-scale mortality. The results from this study were included in a conifer cross-comparison using physiology and mortality data from other dose-response studies. The comparison highlights the close evolutionary relationship between fire and species within the Pinus genus, assessed to date, given the high survivorship of Pinus species at lower fire intensities versus other conifers.

Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications

Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.

Integrating plant physiology into simulation of fire behavior and effects

Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine-scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine-scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process-based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.

Generating 3D Multispectral Point Clouds of Plants with Fusion of Snapshot Spectral and RGB-D Images

Accurate and high-throughput plant phenotyping is important for accelerating crop breeding. Spectral imaging that can acquire both spectral and spatial information of plants related to structural, biochemical, and physiological traits becomes one of the popular phenotyping techniques. However, close-range spectral imaging of plants could be highly affected by the complex plant structure and illumination conditions, which becomes one of the main challenges for close-range plant phenotyping. In this study, we proposed a new method for generating high-quality plant 3-dimensional multispectral point clouds. Speeded-Up Robust Features and Demons was used for fusing depth and snapshot spectral images acquired at close range. A reflectance correction method for plant spectral images based on hemisphere references combined with artificial neural network was developed for eliminating the illumination effects. The proposed Speeded-Up Robust Features and Demons achieved an average structural similarity index measure of 0.931, outperforming the classic approaches with an average structural similarity index measure of 0.889 in RGB and snapshot spectral image registration. The distribution of digital number values of the references at different positions and orientations was simulated using artificial neural network with the determination coefficient (R ²) of 0.962 and root mean squared error of 0.036. Compared with the ground truth measured by ASD spectrometer, the average root mean squared error of the reflectance spectra before and after reflectance correction at different leaf positions decreased by 78.0%. For the same leaf position, the average Euclidean distances between the multiview reflectance spectra decreased by 60.7%. Our results indicate that the proposed method achieves a good performance in generating plant 3-dimensional multispectral point clouds, which is promising for close-range plant phenotyping.

Light scattering in stacked mesophyll cells results in similarity characteristic of solar spectral reflectance and transmittance of natural leaves

Solar spectral reflectance and transmittance of natural leaves exhibit dramatic similarity. To elucidate the formation mechanism and physiological significance, a radiative transfer model was constructed, and the effects of stacked mesophyll cells, chlorophyll content and leaf thickness on the visible light absorptance of the natural leaves were analyzed. Results indicated that light scattering caused by the stacked mesophyll cells is responsible for the similarity. The optical path of visible light in the natural leaves is increased with the scattering process, resulting in that the visible light transmittance is significantly reduced meanwhile the visible light reflectance is at a low level, thus the visible light absorptance tends to a maximum and the absorption of photosynthetically active radiation (PAR) by the natural leaves is significantly enhanced. Interestingly, as two key leaf functional traits affecting the absorption process of PAR, chlorophyll content and leaf thickness of the natural leaves in a certain environment show a convergent behavior, resulting in the high visible light absorptance of the natural leaves, which demonstrates the PAR utilizing strategies of the natural leaves. This work provides a new perspective for revealing the evolutionary processes and ecological strategies of natural leaves, and can be adopted to guide the improvement directions of crop photosynthesis.

Infrared spectroscopy for ploidy estimation: An example in two species of Veronica using fresh and herbarium specimens

PREMISE

Polyploidy has become a central factor in plant evolutionary biological research in recent decades. Methods such as flow cytometry have revealed the widespread occurrence of polyploidy; however, its inference relies on expensive lab equipment and is largely restricted to fresh or recently dried material.

METHODS

Here, we assess the applicability of infrared spectroscopy to infer ploidy in two related species of Veronica (Plantaginaceae). Infrared spectroscopy relies on differences in the absorbance of tissues, which could be affected by primary and secondary metabolites related to polyploidy. We sampled 33 living plants from the greenhouse and 74 herbarium specimens with ploidy known through flow cytometrical measurements and analyzed the resulting spectra using discriminant analysis of principal components (DAPC) and neural network (NNET) classifiers.

RESULTS

Living material of both species combined was classified with 70% (DAPC) to 75% (NNET) accuracy, whereas herbarium material was classified with 84% (DAPC) to 85% (NNET) accuracy. Analyzing both species separately resulted in less clear results.

DISCUSSION

Infrared spectroscopy is quite reliable but is not a certain method for assessing intraspecific ploidy level differences in two species of Veronica. More accurate inferences rely on large training data sets and herbarium material. This study demonstrates an important way to expand the field of polyploid research to herbaria.

Convolutional neural network for high-resolution wetland mapping with open data: Variable selection and the challenges of a generalizable model

Landscape scale wetland conservation requires accurate, up-to-date wetland maps. The most useful approaches to creating such maps are automated, spatially generalizable, temporally repeatable, and can be applied at large spatial scales. However, mapping wetlands with predictive models is challenging due to the highly variable characteristics of wetlands in both space and time. Currently, most approaches are limited by coarse resolution, commercial data, and geographic specificity. Here, we trained a deep learning model and evaluated its ability to automatically map wetlands at landscape scale in a variety of geographies. We trained a U-Net architecture to map wetlands at 1-meter spatial resolution with the following remotely sensed covariates: multispectral data from the National Agriculture Imagery Program and the Sentinel-2 satellite system, and two LiDAR-derived datasets, intensity and geomorphons. The full model mapped wetlands accurately (94 % accuracy, 96.5 % precision, 95.2 % AUC) at 1-meter resolution. Post hoc model evaluation showed that the model correctly predicted wetlands even in areas that had incorrect label/training data, which penalized the recall rate (90.2 %). Applying the model in a new geography resulted in poor performance (precision = ~80 %, recall = 48 %). However, limited retraining in this geography improved model performance substantially, demonstrating an effective means to create a spatially generalizable model. We demonstrate wetlands can be mapped at high-resolution (1 m) using free data and efficient deep-learning models that do not require manual feature engineering. Including LiDAR and geomorphons as input data improved model accuracy by 2 %, and where these data are unavailable a simpler model can efficiently map wetlands. Given the dynamic nature of wetlands and the important ecosystem services they provide, high-resolution mapping can be a game changer in terms of informing restoration and development decisions.

Precision Horticulture: Application of Optical Sensor Technology for Nitrogen Monitoring Status in Cocoplum, a Native Landscaping Plant

Cocoplum (Chrysobalanus icaco) is an ecologically significant native species to Southern Florida. Application of precision agriculture technologies such as optical sensors reduces the cost of over-fertilization and nutrient runoff. The aim of this work was to establish a base line sensor value for fertilizer treatment in cocoplum by monitoring chlorophyll content using the Soil Plant Analytical Development (SPAD), atLEAF, and Normalized Difference Vegetation Index (NDVI) sensors. Initial slow-released fertilizer treatment 8N-3P-9K was used at 15 g (control), 15 g (supplemented with +15 g × 2; T1), 15 g (+15 g; T2), 30 g (+15 g × 2; T3), 30 g (+15 g; T4), and 45 g (+15 g × 2; T5). Evaluations were conducted at 0 (base reading), 30, 60, 90, 120, 150, and 180 days after treatment. Growth parameters, optical non-destructive chlorophyll meters, leaf and soil total nitrogen and total carbon, and total nitrogen of leachate were analyzed. The results demonstrated that the treatment using 30 g slow-released fertilizer (8N-3P-9K) supplemented twice with 15 g in November and March after the first fertilization in October provided the least contamination through runoff while still providing adequate nutrients for plant growth compared to higher fertilizer concentrations. These results demonstrate that the highest treatment of nitrogen can cause considerable losses of N, causing extra costs to producers and environmental damage due to the flow of nutrients. Thus, techniques that help in N monitoring to avoid the excessive use of nitrogen fertilization are necessary. This study can serve as a basis for future research and for nurseries and farms, since it demonstrated from the monitoring of the chlorophyll content by optical sensors and by foliar and substrate analysis that lower treatments of nitrogen fertilization are sufficient to provide nutrients suitable for the growth of cocoplum plants.

Do Tree Size and Tree Shade Tolerance Affect the Photosynthetic Capacity of Broad-Leaved Tree Species?

(1) Background: leaf structure traits are closely related to leaf photosynthesis, reflecting the ability of trees to obtain external resources in the process of growth. (2) Methods: We studied the morphological, chemical, anatomical, stomatal traits and maximum net photosynthetic rate of six broad-leaf species in northern temperate mixed broad-leaved Korean pine (Pinus koraiensis) forest. (3) Aim: To investigate whether there are differences in leaf structural traits of trees with different shade tolerances and different sizes and the effects of these differences on leaf photosynthetic capacity. (4) Results: the effects of leaf structure traits on leaf photosynthesis were different among trees with different shade tolerances or different sizes. Under the condition of light saturation, the net photosynthetic rate, nitrogen use efficiency, phosphorus use efficiency and stomatal conductance of shade-intolerant trees or small trees were higher than those of shade-tolerant trees or large trees. (5) Conclusions: the shade tolerance of tree species or the size of trees affect the traits of leaf structure and indirectly affect the photosynthetic ability of plants. When constructing the leaf trait-photosynthesis model, the shade tolerance and tree size of tree species should be taken into account.

Comparative assessment of satellite- and drone-based vegetation indices to predict arthropod biomass in shrub-steppes

Arthropod biomass is a key element in ecosystem functionality and a basic food item for many species. It must be estimated through traditional costly field sampling, normally at just a few sampling points. Arthropod biomass and plant productivity should be narrowly related because a large majority of arthropods are herbivorous, and others depend on these. Quantifying plant productivity with satellite or aerial vehicle imagery is an easy and fast procedure already tested and implemented in agriculture and field ecology. However, the capability of satellite or aerial vehicle imagery for quantifying arthropod biomass and its relationship with plant productivity has been scarcely addressed. Here, we used unmanned aerial vehicle (UAV) and satellite Sentinel-2 (S2) imagery to establish a relationship between plant productivity and arthropod biomass estimated through ground-truth field sampling in shrub steppes. We UAV-sampled seven plots of 47.6-72.3 ha at a 4-cm pixel resolution, subsequently downscaling spatial resolution to 50 cm resolution. In parallel, we used S2 imagery from the same and other dates and locations at 10-m spatial resolution. We related several vegetation indices (VIs) with arthropod biomass (epigeous, coprophagous, and four functional consumer groups: predatory, detritivore, phytophagous, and diverse) estimated at 41-48 sampling stations for UAV flying plots and in 67-79 sampling stations for S2. VIs derived from UAV were consistently and positively related to all arthropod biomass groups. Three out of seven and six out of seven S2-derived VIs were positively related to epigeous and coprophagous arthropod biomass, respectively. The blue normalized difference VI (BNDVI) and enhanced normalized difference VI (ENDVI) showed consistent and positive relationships with arthropod biomass, regardless of the arthropod group or spatial resolution. Our results showed that UAV and S2-VI imagery data may be viable and cost-efficient alternatives for quantifying arthropod biomass at large scales in shrub steppes. The relationship between VI and arthropod biomass is probably habitat-dependent, so future research should address this relationship and include several habitats to validate VIs as proxies of arthropod biomass.

Hyperspectral remote sensing to assess weed competitiveness in maize farmland ecosystems

Weed competition causes serious economic losses to maize production. Timely and accurate assessment of pressure from competition is crucial for ecological weed management. In this work, we apply hyperspectral remote sensing (HRS) technology to conduct a competitive experiment in Harbin, China, in 2021, with 5-leaf maize as the study target. A weed competition assessment method that combines comprehensive competition indices (CCI) and deep learning is proposed. For the comprehensive competition assessment, the relationship between different weed competitive pressures (Levels 1-5) and changes in the structural and physiological information of maize was analyzed. The accumulative/transient competition indices CCI-A and CCI-T were designed for accurate quantification. The results showed that parameters such as plant height, stalk thickness and nutrient elements of maize decreased with increasing competition level. Parameters, such as stomatal conductance and transpiration rate, showed a fluctuating change of increasing and then decreasing with increasing competition level. Compared with the traditional relative competitive intensity (RCI), the standard deviation of CCI is 0.303 and 0.499. The dispersion effect of CCI is better and more suitable for quantifying the competition response. HRS images combined with 3D-CNN model were then applied to reveal the spectral response to different weed competition pressures (Levels 1-5) and to make early predictions of weed competition. The first-order derivative showed that the spectral reflectance exhibited significant differences at 520-525 nm peak, 570-655 nm trough, and near 700 nm red edge. For hyperspectral spatial-spectral features, the 3D-CNN model is proposed for prediction of competing indices CCI. In addition, the VIP method is used to select the characteristic wavelengths. The 3D-CNN model achieves a prediction accuracy of RMSE = 0.106 and 0.152 using 13 feature bands, which can accurately quantify the subtle changes in competition indices. Overall, this study shows that the combination of CCI and deep learning can provide a multivariate and comprehensive assessment of weed competition pressure.

The Spectral Species Concept in Living Color

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

Multisensor UAS mapping of Plant Species and Plant Functional Types in Midwestern Grasslands

Uncrewed aerial systems (UASs) have emerged as powerful ecological observation platforms capable of filling critical spatial and spectral observation gaps in plant physiological and phenological traits that have been difficult to measure from space-borne sensors. Despite recent technological advances, the high cost of drone-borne sensors limits the widespread application of UAS technology across scientific disciplines. Here, we evaluate the tradeoffs between off-the-shelf and sophisticated drone-borne sensors for mapping plant species and plant functional types (PFTs) within a diverse grassland. Specifically, we compared species and PFT mapping accuracies derived from hyperspectral, multispectral, and RGB imagery fused with light detection and ranging (LiDAR) or structure-for-motion (SfM)-derived canopy height models (CHM). Sensor–data fusion were used to consider either a single observation period or near-monthly observation frequencies for integration of phenological information (i.e., phenometrics). Results indicate that overall classification accuracies for plant species and PFTs were highest in hyperspectral and LiDAR-CHM fusions (78 and 89%, respectively), followed by multispectral and phenometric–SfM–CHM fusions (52 and 60%, respectively) and RGB and SfM–CHM fusions (45 and 47%, respectively). Our findings demonstrate clear tradeoffs in mapping accuracies from economical versus exorbitant sensor networks but highlight that off-the-shelf multispectral sensors may achieve accuracies comparable to those of sophisticated UAS sensors by integrating phenometrics into machine learning image classifiers.

Estimating Community-Level Plant Functional Traits in a Species-Rich Alpine Meadow Using UAV Image Spectroscopy

Plant functional traits at the community level (plant community traits hereafter) are commonly used in trait-based ecology for the study of vegetation–environment relationships. Previous studies have shown that a variety of plant functional traits at the species or community level can be successfully retrieved by airborne or spaceborne imaging spectrometer in homogeneous, species-poor ecosystems. However, findings from these studies may not apply to heterogeneous, species-rich ecosystems. Here, we aim to determine whether unmanned aerial vehicle (UAV)-based hyperspectral imaging could adequately estimate plant community traits in a species-rich alpine meadow ecosystem on the Qinghai–Tibet Plateau. To achieve this, we compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging. Our results show that chlorophyll a, chlorophyll b, carotenoid content, starch content, specific leaf area and leaf thickness were estimated with good accuracies, with the highest R2 values between 0.64 (nRMSE = 0.16) and 0.83 (nRMSE = 0.11). Meanwhile, the estimation accuracies for nitrogen content, phosphorus content, plant height and leaf dry matter content were relatively low, with the highest R2 varying from 0.3 (nRMSE = 0.24) to 0.54 (nRMSE = 0.20). Among the four tested algorithms, the GA-PLSR produced the highest accuracy, followed by PLSR and XGBoost, and RF showed the poorest performance. Overall, our study demonstrates that UAV-based visible and near-infrared hyperspectral imaging has the potential to accurately estimate multiple plant community traits for the natural grassland ecosystem at a fine scale.

Rapid quantification of alkaloids, sugar and yield of tobacco (Nicotiana tabacum L.) varieties by using Vis-NIR-SWIR spectroradiometry

Tobacco genetic improvement programs, as well as the tobacco industry, require techniques that allow the estimation of its attributes in a fast and cheap way. The use of remote sensing through visible, near infrared and short-wave spectroscopy (Vis-NIR-SWIR) has been studied aiming to meet such demand. Thus, the aim of this work was to evaluate the use of Vis-NIR-SWIR spectroradiometer as a rapid tool to estimate alkaloids, sugars and yield of tobacco varieties. For that purpose, a study was carried out in a greenhouse with plants grown in pots (18 dm-3) containing nutrient solutions. The experimental design was completely randomized, with 30 treatments (tobacco varieties) and 10 repetitions. Tobacco leaf reflectance was collected at 13, 34 and 68 days after transplantation (DAT) with a plant-probe device connected to the spectroradiometer by an optical fiber. Subsequently, leaf analysis of alkaloids, sugars and yield were performed, and such attributes were estimated by using the Partial Least Squares Regression (PLSR), combined with the following pre-processing (PP) techniques: multiplicative scatter correction (MSC), Savitzky-Golay (SG) and standard normal variate (SNV). The results showed presence of typical inflections of chemical and structural components of the plants, which allowed obtaining PLSR models with R2p and RPDp superior to 0.71 and 2.27, respectively, for all PP techniques and attributes evaluated. The most important wavelengths were well distributed within the three operating ranges of the spectroradiometer (Vis-NIR-SWIR). Thus, the methodology proposed by this research was able to simultaneously determine all the three attributes (alkaloids, sugars and yield) with excellent predictive capacity. This is a promising result for genetic improvement and processing of tobacco (as well as other crops), since it is necessary to evaluate a large number of samples within a short period and at a low cost.

Multisite and Multitemporal Grassland Yield Estimation Using UAV-Borne Hyperspectral Data

Grassland ecosystems can be hotspots of biodiversity and act as carbon sinks while at the same time providing the basis of forage production for ruminants in dairy and meat production. Annual grassland dry matter yield (DMY) is one of the most important agronomic parameters reflecting differences in usage intensity such as number of harvests and fertilization. Current methods for grassland DMY estimation are labor-intensive and prone to error due to small sample size. With the advent of unmanned aerial vehicles (UAVs) and miniaturized hyperspectral sensors, a novel tool for remote sensing of grassland with high spatial, temporal and radiometric resolution and coverage is available. The present study aimed at developing a robust model capable of estimating grassland biomass across a gradient of usage intensity throughout one growing season. Therefore, UAV-borne hyperspectral data from eight grassland sites in North Hesse, Germany, originating from different harvests, were utilized for the modeling of fresh matter yield (FMY) and DMY. Four machine learning (ML) algorithms were compared for their modeling performance. Among them, the rule-based ML method Cubist regression (CBR) performed best, delivering high prediction accuracies for both FMY (nRMSEp 7.6%, Rp2 0.87) and DMY (nRMSEp 12.9%, Rp2 0.75). The model showed a high robustness across sites and harvest dates. The best models were employed to produce maps for FMY and DMY, enabling the detailed analysis of spatial patterns. Although the complexity of the approach still restricts its practical application in agricultural management, the current study proved that biomass of grassland sites being subject to different management intensities can be modeled from UAV-borne hyperspectral data at high spatial resolution with high prediction accuracies.

Prediction of Photosynthetic, Biophysical, and Biochemical Traits in Wheat Canopies to Reduce the Phenotyping Bottleneck

To achieve food security, it is necessary to increase crop radiation use efficiency (RUE) and yield through the enhancement of canopy photosynthesis to increase the availability of assimilates for the grain, but its study in the field is constrained by low throughput and the lack of integrative measurements at canopy level. In this study, partial least squares regression (PLSR) was used with high-throughput phenotyping (HTP) data in spring wheat to build predictive models of photosynthetic, biophysical, and biochemical traits for the top, middle, and bottom layers of wheat canopies. The combined layer model predictions performed better than individual layer predictions with a significance as follows for photosynthesis R ² = 0.48, RMSE = 5.24 μmol m^-2 s^-1 and stomatal conductance: R ² = 0.36, RMSE = 0.14 mol m^-2 s^-1. The predictions of these traits from PLSR models upscaled to canopy level compared to field observations were statistically significant at initiation of booting (R ² = 0.3, p < 0.05; R ² = 0.29, p < 0.05) and at 7 days after anthesis (R ² = 0.15, p < 0.05; R ² = 0.65, p < 0.001). Using HTP allowed us to increase phenotyping capacity 30-fold compared to conventional phenotyping methods. This approach can be adapted to screen breeding progeny and genetic resources for RUE and to improve our understanding of wheat physiology by adding different layers of the canopy to physiological modeling.

New Normalized Difference Reflectance Indices for Estimation of Soil Drought Influence on Pea and Wheat

Soil drought is an important problem in plant cultivation. Remote sensing using reflectance indices (RIs) can detect early changes in plants caused by soil drought. The development of new RIs which are sensitive to these changes is an important applied task. Previously, we revealed 46 normalized difference RIs based on a spectral region of visible light which were sensitive to the action of a short-term water shortage on pea plants under controlled conditions (Remote Sens. 2021, 13, 962). In the current work, we tested the efficiency of these RIs for revealing changes in pea and wheat plants induced by the soil drought under the conditions of both a vegetation room and open ground. RI (613, 605) and RI (670, 432) based on 613 and 605 nm wavelengths and on 670 and 432 nm wavelengths, respectively, were effective for revealing the action of the soil drought on investigated objects. Particularly, RI (613, 605) and RI (670, 432) which were measured in plant canopy, were significantly increased by the strong soil drought. The correlations between these indices and relative water content in plants were strong. Revealed effects were observed in both pea and wheat plants, at the plant cultivation under controlled and open-ground conditions, and using different angles of measurement. Thus, RI (613, 605) and RI (670, 432) seem to be effective tools for the remote sensing of plant changes under soil drought.

Early detection of a tree pathogen using airborne remote sensing

Native forests of Hawai'i Island are experiencing an ecological crisis in the form of Rapid 'Ōhi'a Death (ROD), a recently characterized disease caused by two fungal pathogens in the genus Ceratocystis. Since approximately 2010, this disease has caused extensive mortality of Hawai'i's keystone endemic tree, known as 'ōhi'a (Metrosideros polymorpha). Visible symptoms of ROD include rapid browning of canopy leaves, followed by death of the tree within weeks. This quick progression leading to tree mortality makes early detection critical to understanding where the disease will move at a timescale feasible for controlling the disease. We used repeat laser-guided imaging spectroscopy (LGIS) of forests on Hawai'i Island collected by the Global Airborne Observatory (GAO) in 2018 and 2019 to derive maps of foliar trait indices previously found to be important in distinguishing between ROD-infected and healthy 'ōhi'a canopies. Data from these maps were used to develop a prognostic indicator of tree stress prior to the visible onset of browning. We identified canopies that were green in 2018, but became brown in 2019 (defined as "to become brown"; TBB), and a corresponding set of canopies that remained green. The data mapped in 2018 showed separability of foliar trait indices between TBB and green 'ōhi'a, indicating early detection of canopy stress prior to the onset of ROD. Overall, a combination of linear and non-linear analyses revealed canopy water content (CWC), foliar tannins, leaf mass per area (LMA), phenols, cellulose, and non-structural carbohydrates (NSC) are primary drivers of the prognostic spectral capability which collectively result in strong consistent changes in leaf spectral reflectance in the near-infrared (700-1300 nm) and shortwave-infrared regions (1300-2500 nm). Results provide insight into the underlying foliar traits that are indicative of physiological responses of M. polymorpha trees infected with Ceratocycstis and suggest that imaging spectroscopy is an effective tool for identifying trees likely to succumb to ROD prior to the onset of visible symptoms.

Detection of Invasive Black Locust (Robinia pseudoacacia) in Small Woody Features Using Spatiotemporal Compositing of Sentinel-2 Data

Recognition of invasive species and their distribution is key for managing and protecting native species within both natural and man-made ecosystems. Small woody features (SWF) represent fragmented patches or narrow linear tree features that are of high importance in intensively utilized agricultural landscapes. Simultaneously, they frequently serve as expansion pathways for invasive species such as black locust. In this study, Sentinel-2 products, combined with spatiotemporal compositing approaches, are used to address the challenge of broad area black locust mapping at a high granularity. This is accomplished by conducting a comprehensive analysis of the classification performance of various compositing approaches and multitemporal classification settings throughout four vegetation seasons. The annual, seasonal (bi-monthly), and monthly median values of cloud-masked Sentinel-2 reflectance products are aggregated and stacked into varied time-series datasets per given year. The random forest algorithm is trained and output classification maps validated based on field-based reference datasets across Danubian lowlands (Slovakia). The main results of the study proved the usefulness of spatiotemporal compositing of Sentinel-2 products for mapping black locust in small woody features across wide area. In particular, temporally aggregated monthly composites stacked to seasonal time series datasets yielded consistently high overall accuracies ranging from 89.10% to 91.47% with balanced producer’s and user’s accuracies for each year’s annual series. We presume that a similar approach could be used for a broader scale species distribution mapping, assuming they are spectrally or phenologically distinctive, as is often the case for many invasive species.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

A Simple and Robust Spectral Index for Identifying Lodged Maize Using Gaofen1 Satellite Data

Crop lodging is a major destructive factor for agricultural production. Developing a cost-efficient and accurate method to assess crop lodging is crucial for informing crop management decisions and reducing lodging losses. Satellite remote sensing can provide continuous data on a large scale; however, its utility in detecting lodging crops is limited due to the complexity of lodging events and the unavailability of high spatial and temporal resolution data. Gaofen1 satellite was launched in 2013. The short revisit cycle and wide orbit coverage of the Gaofen1 satellite make it suitable for lodging identification. However, few studies have explored lodging detection using Gaofen1 data, and the operational application of existing approaches over large spatial extents seems to be unrealistic. In this paper, we discuss the identification method of lodged maize and explore the potential of using Gaofen1 data. An analysis of the spectral features after maize lodging revealed that reflectance increased significantly in all bands, compared to non-lodged maize. A spectral sum index was proposed to distinguish lodged and non-lodged maize. Two study areas were considered: Zhaodong City in Heilongjiang Province and Ningjiang District in Jilin Province. The results of the identified lodged maize from the Gaofen1 data were validated based on three methods: first, ground sample points exhibited the overall accuracies of 92.86% and 88.24% for Zhaodong City and Ningjiang District, respectively; second, the cross-comparison differences of 1.01% for Zhaodong City and 1.13% for Ningjiang District were obtained, compared to the results acquired from the finer-resolution Planet data; and third, the identified results from Gaofen1 data and those from farmer survey questionnaires were found to be consistent. The validation results indicate that the proposed index is promising, and the Gaofen1 data have the potential for rapid lodging monitoring.

Place-Based Analysis of Satellite Time Series Shows Opposing Land Change Patterns in the Copperbelt Region of Zambia

The process of land degradation needs to be understood at various spatial and temporal scales in order to protect ecosystem services and communities directly dependent on it. This is especially true for regions in sub-Saharan Africa, where socio economic and political factors exacerbate ecological degradation. This study identifies spatially explicit land change dynamics in the Copperbelt province of Zambia in a local context using satellite vegetation index time series derived from the MODIS sensor. Three sets of parameters, namely, monthly series, annual peaking magnitude, and annual mean growing season were developed for the period 2000 to 2019. Trend was estimated by applying harmonic regression on monthly series and linear least square regression on annually aggregated series. Estimated spatial trends were further used as a basis to map endemic land change processes. Our observations were as follows: (a) 15% of the study area dominant in the east showed positive trends, (b) 3% of the study area dominant in the west showed negative trends, (c) natural regeneration in mosaic landscapes (post shifting cultivation) and land management in forest reserves were chiefly responsible for positive trends, and (d) degradation over intact miombo woodland and cultivation areas contributed to negative trends. Additionally, lower productivity over areas with semi-permanent agriculture and shift of new encroachment into woodlands from east to west of Copperbelt was observed. Pivot agriculture was not a main driver in land change. Although overall greening trends prevailed across the study site, the risk of intact woodlands being exposed to various disturbances remains high. The outcome of this study can provide insights about natural and assisted landscape restoration specifically addressing the miombo ecoregion.

Fractional Vegetation Cover Derived from UAV and Sentinel-2 Imagery as a Proxy for In Situ FAPAR in a Dense Mixed-Coniferous Forest?

The fraction of absorbed photosynthetic active radiation (FAPAR) is an essential climate variable for assessing the productivity of ecosystems. Satellite remote sensing provides spatially distributed FAPAR products, but their accurate and efficient validation is challenging in forest environments. As the FAPAR is linked to the canopy structure, it may be approximated by the fractional vegetation cover (FCOVER) under the assumption that incoming radiation is either absorbed or passed through gaps in the canopy. With FCOVER being easier to retrieve, FAPAR validation activities could benefit from a priori information on FCOVER. Spatially distributed FCOVER is available from satellite remote sensing or can be retrieved from imagery of Unmanned Aerial Vehicles (UAVs) at a centimetric resolution. We investigated remote sensing-derived FCOVER as a proxy for in situ FAPAR in a dense mixed-coniferous forest, considering both absolute values and spatiotemporal variability. Therefore, direct FAPAR measurements, acquired with a Wireless Sensor Network, were related to FCOVER derived from UAV and Sentinel-2 (S2) imagery at different seasons. The results indicated that spatially aggregated UAV-derived FCOVER was close (RMSE = 0.02) to in situ FAPAR during the peak vegetation period when the canopy was almost closed. The S2 FCOVER product underestimated both the in situ FAPAR and UAV-derived FCOVER (RMSE > 0.3), which we attributed to the generic nature of the retrieval algorithm and the coarser resolution of the product. We concluded that UAV-derived FCOVER may be used as a proxy for direct FAPAR measurements in dense canopies. As another key finding, the spatial variability of the FCOVER consistently surpassed that of the in situ FAPAR, which was also well-reflected in the S2 FAPAR and FCOVER products. We recommend integrating this experimental finding as consistency criteria in the context of ECV quality assessments. To facilitate the FAPAR sampling activities, we further suggest assessing the spatial variability of UAV-derived FCOVER to benchmark sampling sizes for in situ FAPAR measurements. Finally, our study contributes to refining the FAPAR sampling protocols needed for the validation and improvement of FAPAR estimates in forest environments.

Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors

A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods.

Biophysical Determinants of Shifting Tundra Vegetation Productivity in the Beaufort Delta Region of Canada

Temperature increases across the circumpolar north have driven rapid increases in vegetation productivity, often described as ‘greening’. These changes have been widespread, but spatial variation in their pattern and magnitude suggests that biophysical factors also influence the response of tundra vegetation to climate warming. In this study, we used field sampling of soils and vegetation and random forests modeling to identify the determinants of trends in Landsat-derived Enhanced Vegetation Index, a surrogate for productivity, in the Beaufort Delta region of Canada between 1984 and 2016. This region has experienced notable change, with over 71% of the Tuktoyaktuk Coastlands and over 66% of the Yukon North Slope exhibiting statistically significant greening. Using both classification and regression random forests analyses, we show that increases in productivity have been more widespread and rapid at low-to-moderate elevations and in areas dominated by till blanket and glaciofluvial deposits, suggesting that nutrient and moisture availability mediate the impact of climate warming on tundra vegetation. Rapid greening in shrub-dominated vegetation types and observed increases in the cover of low and tall shrub cover (4.8% and 6.0%) also indicate that regional changes have been driven by shifts in the abundance of these functional groups. Our findings demonstrate the utility of random forests models for identifying regional drivers of tundra vegetation change. To obtain additional fine-grained insights on drivers of increased tundra productivity, we recommend future research combine spatially comprehensive time series satellite data (as used herein) with samples of high spatial resolution imagery and integrated field investigations.

Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

Environmental conditions are very changeable; fluctuations in temperature, precipitation, illumination intensity, and other factors can decrease a plant productivity and crop. The remote sensing of plants under these conditions is the basis for the protection of plants and increases their survivability. This problem can be solved through measurements of plant reflectance and calculation of reflectance indices. Reflectance indices are related to the vegetation biomass, specific physiological processes, and biochemical compositions in plants; the indices can be used for both short-term and long-term plant monitoring. In our review, we considered the applications of reflectance indices in plant remote sensing. In Optical Methods and Platforms of Remote Sensing of Plants, we briefly discussed multi- and hyperspectral imaging, including descriptions of multispectral and hyperspectral cameras with different principles and their efficiency for the remote sensing of plants. In Main Reflectance Indices, we described the main reflectance indices, including vegetation, water, and pigment reflectance indices, as well as the photochemical reflectance index and its modifications. We focused on the relationships of leaf reflectance and reflectance indices to plant biomass, development, and physiological and biochemical characteristics. In Problems of Measurement and Analysis of Reflectance Indices, we discussed the methods of the correction of the reflectance indices that can be used for decreasing the influence of environmental conditions (mainly illumination, air, and soil) and plant characteristics (orientation of leaves, their thickness, and others) on their measurements and the analysis of the plant remote sensing. Additionally, the variability of plants was also considered as an important factor that influences the results of measurement and analysis.

Investigating the Correlation between Multisource Remote Sensing Data for Predicting Potential Spread of Ips typographus L. Spots in Healthy Trees

In the last decade, thousands of hectares of forests have been lost in the Czech Republic, primarily related to European spruce bark beetle (Ips typographus L.), while more than 50% of the remaining Czech forests are in great danger, thus posing severe threats to the resilience, stability, and functionality of those forests. The role of remote sensing in monitoring dynamic structural changes caused by pests is essential to understand and sustainably manage these forests. This study hypothesized a possible correlation between tree health status and multisource time series remote sensing data using different processed layers to predict the potential spread of attack by European spruce bark beetle in healthy trees. For this purpose, we used WorldView-2, Pléiades 1B, and SPOT-6 images for the period of April to September from 2018 to 2020; unmanned aerial vehicle (UAV) imagery data were also collected for use as a reference data source. Our results revealed that spectral resolution is crucial for the early detection of infestation. We observed a significant difference in the reflectance of different health statuses, which can lead to the early detection of infestation as much as two years in advance. More specifically, several bands from two different satellites in 2018 perfectly predicted the health status classes from 2020. This method could be used to evaluate health status classes in the early stage of infestation over large forested areas, which would provide a better understanding of the current situation and information for decision making and planning for the future.

Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery

This study aimed to identify the optimal sets of spectral bands for monitoring multiple grapevine nutrients in vineyards. We used spectral data spanning 400–2500 nm and leaf samples from 100 Concord grapevine canopies, lab-analyzed for six key nutrient values, to select the optimal bands for the nutrient regression models. The canopy spectral data were obtained with unmanned aerial systems (UAS), using push-broom imaging spectrometers (hyperspectral sensors). The novel use of UAS-based hyperspectral imagery to assess the grapevine nutrient status fills the gap between in situ spectral sampling and UAS-based multispectral imaging, avoiding their inherent trade-offs between spatial and spectral resolution. We found that an ensemble feature ranking method, utilizing six different machine learning feature selection methods, produced similar regression results as the standard PLSR feature selection and regression while generally selecting fewer wavelengths. We identified a set of biochemically consistent bands (606, 641, and 1494 nm) to predict the nitrogen content with an RMSE of 0.17% (using leave-one-out cross-validation) in samples with nitrogen contents ranging between 2.4 and 3.6%. Further studying is needed to confirm the relevance and consistency of the wavelengths selected for each nutrient model, but ensemble feature selection showed promise in identifying stable sets of wavelengths for assessing grapevine nutrient contents from canopy spectra.

Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery

Global food security is critical to eliminating hunger and malnutrition. In the changing climate, farmers in developing countries must adopt technologies and farming practices such as precision agriculture (PA). PA-based approaches enable farmers to cope with frequent and intensified droughts and heatwaves, optimising yields, increasing efficiencies, and reducing operational costs. Biophysical parameters such as Leaf Area Index (LAI), Leaf Chlorophyll Content (LCab), and Canopy Chlorophyll Content (CCC) are essential for characterising field-level spatial variability and thus are necessary for enabling variable rate application technologies, precision irrigation, and crop monitoring. Moreover, robust machine learning algorithms offer prospects for improving the estimation of biophysical parameters due to their capability to deal with non-linear data, small samples, and noisy variables. This study compared the predictive performance of sparse Partial Least Squares (sPLS), Random Forest (RF), and Gradient Boosting Machines (GBM) for estimating LAI, LCab, and CCC with Sentinel-2 imagery in Bothaville, South Africa and identified, using variable importance measures, the most influential bands for estimating crop biophysical parameters. The results showed that RF was superior in estimating all three biophysical parameters, followed by GBM which was better in estimating LAI and CCC, but not LCab, where sPLS was relatively better. Since all biophysical parameters could be achieved with RF, it can be considered a good contender for operationalisation. Overall, the findings in this study are significant for future biophysical product development using RF to reduce reliance on many algorithms for specific parameters, thus facilitating the rapid extraction of actionable information to support PA and crop monitoring activities.

Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations

Annually, over 100 million tons of nitrogen fertilizer are applied in wheat fields to ensure maximum productivity. This amount is often more than needed for optimal yield and can potentially have negative economic and environmental consequences. Monitoring crop nitrogen levels can inform managers of input requirements and potentially avoid excessive fertilization. Standard methods assessing plant nitrogen content, however, are time-consuming, destructive, and expensive. Therefore, the development of approaches estimating leaf nitrogen content in vivo and in situ could benefit fertilization management programs as well as breeding programs for nitrogen use efficiency (NUE). This study examined the ability of hyperspectral data to estimate leaf nitrogen concentrations and nitrogen uptake efficiency (NUpE) at the leaf and canopy levels in multiple winter wheat lines across two seasons. We collected spectral profiles of wheat foliage and canopies using full-range (350–2500 nm) spectroradiometers in combination with leaf tissue collection for standard analytical determination of nitrogen. We then applied partial least-squares regression, using spectral and reference nitrogen measurements, to build predictive models of leaf and canopy nitrogen concentrations. External validation of data from a multi-year model demonstrated effective nitrogen estimation at leaf and canopy level (R2 = 0.72, 0.67; root-mean-square error (RMSE) = 0.42, 0.46; normalized RMSE = 12, 13; bias = −0.06, 0.04, respectively). While NUpE was not directly well predicted using spectral data, NUpE values calculated from predicted leaf and canopy nitrogen levels were well correlated with NUpE determined using traditional methods, suggesting the potential of the approach in possibly replacing standard determination of plant nitrogen in assessing NUE. The results of our research reinforce the ability of hyperspectral data for the retrieval of nitrogen status and expand the utility of hyperspectral data in winter wheat lines to the application of nitrogen management practices and breeding programs.

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (V_c,max ), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-V_c,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (V_c,max ), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between V_c,max and the four morphological and biochemical traits (R²  ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-V_c,max relationships across three forest biomes into a single robust model for V_c,max (R²  = 0.77), and also accurately estimated the four traits (R²  = 0.75-0.94). These findings challenge the traditional use of the empirical trait-V_c,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising V_c,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

Coupling spectral and resource-use complementarity in experimental grassland and forest communities

Reflectance spectra provide integrative measures of plant phenotypes by capturing chemical, morphological, anatomical and architectural trait information. Here, we investigate the linkages between plant spectral variation, and spectral and resource-use complementarity that contribute to ecosystem productivity. In both a forest and prairie grassland diversity experiment, we delineated n-dimensional hypervolumes using wavelength bands of reflectance spectra to test the association between the spectral space occupied by individual plants and their growth, as well as between the spectral space occupied by plant communities and ecosystem productivity. We show that the spectral space occupied by individuals increased with their growth, and the spectral space occupied by plant communities increased with ecosystem productivity. Furthermore, ecosystem productivity was better explained by inter-individual spectral complementarity than by the large spectral space occupied by productive individuals. Our results indicate that spectral hypervolumes of plants can reflect ecological strategies that shape community composition and ecosystem function, and that spectral complementarity can reveal resource-use complementarity.

Source:sink imbalance detected with leaf- and canopy-level spectroscopy in a field-grown crop

The finely tuned balance between sources and sinks determines plant resource partitioning and regulates growth and development. Understanding and measuring metabolic indicators of source or sink limitation forms a vital part of global efforts to increase crop yield for future food security. We measured metabolic profiles of Cucurbita pepo (zucchini) grown in the field under carbon sink limitation and control conditions. We demonstrate that these profiles can be measured non-destructively using hyperspectral reflectance at both leaf and canopy scales. Total non-structural carbohydrates (TNC) increased 82% in sink-limited plants; leaf mass per unit area (LMA) increased 38% and free amino acids increased 22%. Partial least-squares regression (PLSR) models link these measured functional traits with reflectance data, enabling high-throughput estimation of traits comprising the sink limitation response. Leaf- and canopy-scale models for TNC had R² values of 0.93 and 0.64 and %RMSE of 13 and 38%, respectively. For LMA, R² values were 0.91 and 0.60 and %RMSE 7 and 14%; for free amino acids, R² was 0.53 and 0.21 with %RMSE 20 and 26%. Remote sensing can enable accurate, rapid detection of sink limitation in the field at the leaf and canopy scale, greatly expanding our ability to understand and measure metabolic responses to stress.

The Optical Properties of Leaf Structural Elements and Their Contribution to Photosynthetic Performance and Photoprotection

Leaves have evolved to effectively harvest light, and, in parallel, to balance photosynthetic CO2 assimilation with water losses. At times, leaves must operate under light limiting conditions while at other instances (temporally distant or even within seconds), the same leaves must modulate light capture to avoid photoinhibition and achieve a uniform internal light gradient. The light-harvesting capacity and the photosynthetic performance of a given leaf are both determined by the organization and the properties of its structural elements, with some of these having evolved as adaptations to stressful environments. In this respect, the present review focuses on the optical roles of particular leaf structural elements (the light capture module) while integrating their involvement in other important functional modules. Superficial leaf tissues (epidermis including cuticle) and structures (epidermal appendages such as trichomes) play a crucial role against light interception. The epidermis, together with the cuticle, behaves as a reflector, as a selective UV filter and, in some cases, each epidermal cell acts as a lens focusing light to the interior. Non glandular trichomes reflect a considerable part of the solar radiation and absorb mainly in the UV spectral band. Mesophyll photosynthetic tissues and biominerals are involved in the efficient propagation of light within the mesophyll. Bundle sheath extensions and sclereids transfer light to internal layers of the mesophyll, particularly important in thick and compact leaves or in leaves with a flutter habit. All of the aforementioned structural elements have been typically optimized during evolution for multiple functions, thus offering adaptive advantages in challenging environments. Hence, each particular leaf design incorporates suitable optical traits advantageously and cost-effectively with the other fundamental functions of the leaf.

Using Hybrid Artificial Intelligence and Evolutionary Optimization Algorithms for Estimating Soybean Yield and Fresh Biomass Using Hyperspectral Vegetation Indices

Recent advanced high-throughput field phenotyping combined with sophisticated big data analysis methods have provided plant breeders with unprecedented tools for a better prediction of important agronomic traits, such as yield and fresh biomass (FBIO), at early growth stages. This study aimed to demonstrate the potential use of 35 selected hyperspectral vegetation indices (HVI), collected at the R5 growth stage, for predicting soybean seed yield and FBIO. Two artificial intelligence algorithms, ensemble-bagging (EB) and deep neural network (DNN), were used to predict soybean seed yield and FBIO using HVI. Considering HVI as input variables, the coefficients of determination (R2) of 0.76 and 0.77 for yield and 0.91 and 0.89 for FBIO were obtained using DNN and EB, respectively. In this study, we also used hybrid DNN-SPEA2 to estimate the optimum HVI values in soybeans with maximized yield and FBIO productions. In addition, to identify the most informative HVI in predicting yield and FBIO, the feature recursive elimination wrapper method was used and the top ranking HVI were determined to be associated with red, 670 nm and near-infrared, 800 nm, regions. Overall, this study introduced hybrid DNN-SPEA2 as a robust mathematical tool for optimizing and using informative HVI for estimating soybean seed yield and FBIO at early growth stages, which can be employed by soybean breeders for discriminating superior genotypes in large breeding populations.

Predicting photosynthetic capacity in tobacco using shortwave infrared spectral reflectance

Plateauing yield and stressful environmental conditions necessitate selecting crops for superior physiological traits with untapped potential to enhance crop performance. Plant productivity is often limited by carbon fixation rates that could be improved by increasing maximum photosynthetic carboxylation capacity (Vcmax). However, Vcmax measurements using gas exchange and biochemical assays are slow and laborious, prohibiting selection in breeding programs. Rapid hyperspectral reflectance measurements show potential for predicting Vcmax using regression models. While several hyperspectral models have been developed, contributions from different spectral regions to predictions of Vcmax have not been clearly identified or linked to biochemical variation contributing to Vcmax. In this study, hyperspectral reflectance data from 350-2500 nm were used to build partial least squares regression models predicting in vivo and in vitro Vcmax. Wild-type and transgenic tobacco plants with antisense reductions in Rubisco content were used to alter Vcmax independent from chlorophyll, carbon, and nitrogen content. Different spectral regions were used to independently build partial least squares regression models and identify key regions linked to Vcmax and other leaf traits. The greatest Vcmax prediction accuracy used a portion of the shortwave infrared region from 2070 nm to 2470 nm, where the inclusion of fewer spectral regions resulted in more accurate models.

Retrieval of Arctic Vegetation Biophysical and Biochemical Properties from CHRIS/PROBA Multi-Angle Imagery Using Empirical and Physical Modelling

Mapping and monitoring of Arctic vegetation biochemical and biophysical properties is gaining importance as global climate change is disproportionately affecting this region. Previous studies using remote sensing to model Arctic vegetation biochemical and biophysical properties have generally involved empirical modelling with nadir looking broadband sensors and have typically been conducted at the field scale in one study area. Satellite hyperspectral remote sensing has not been previously investigated for retrieving leaf and canopy biochemical and biophysical properties of Arctic vegetation across multiple sites using either empirical or physically-based modelling approaches. Furthermore, multi-angle hyperspectral sensors (CHRIS/PROBA), which can provide insight into vegetation reflectance anisotropy and potentially improve vegetation parameter estimation, have also not been investigated for this purpose. In this study, three modelling approaches previously investigated with field spectroscopy data (Kennedy et al., 2020) were used with CHRIS Mode-1 imagery to predict leaf chlorophyll content, plant area index and canopy chlorophyll content across a bioclimatic gradient in the Western Canadian Arctic. Modelling approaches included: parametric linear regression based on vegetation indices (VI), non-parametric machine learning Gaussian processes regression (GPR) and inversion of the PROSAIL radiative transfer model using a look-up table approach (LUT). CHRIS imagery was acquired with −55°, −36°, 0°, +36°, +55° view zenith angles (VZA) between 2011 and 2014 over three field sites extending from the Richardson Mountains in central Yukon, Canada to the north end of Banks Island, Northwest Territories, Canada. Field measurements were acquired within several weeks of satellite acquisitions. GPR had the best model fit (mean cross-validated (cv) coefficient of determination, r2cv = 0.61 across all vegetation variables, sites and VZAs vs. 0.59 for the simple ratio, SR) and predictive performance (normalized root mean square error, NRMSEcv = 0.13 vs. 0.14 for SR). The revised optimized soil adjusted VI (ROSAVI) performance was slightly poorer (r2cv = 0.51; NRMSEcv = 0.15). The physically-based PROSAIL model performed poorer than all empirical models (r2 = 0.50; NRMSE = 0.18). This ranking of model performance is similar to that found in the previous field spectroscopy study, where empirical model fits and predictive performance were only slightly worse. With respect to view angle performance, NRMSE varied only slightly, indicating no distinct advantage for any one VZA. Overall, strong potential has been demonstrated for empirical modelling of Arctic vegetation chlorophyll and plant area index using hyperspectral data combined with band selection/optimization procedures in the Arctic. Recently launched and future hyperspectral satellites, including next generation airborne sensors, will likely provide improvements to the model performance reported here.

Field-based remote sensing models predict radiation use efficiency in wheat

Wheat yields are stagnating or declining in many regions, requiring efforts to improve the light conversion efficiency, known as radiation use efficiency (RUE). RUE is a key trait in plant physiology because it links light capture and primary metabolism with biomass accumulation and yield, but its measurement is time consuming and this has limited its use in fundamental research and large-scale physiological breeding. In this study, high-throughput plant phenotyping (HTPP) approaches were used among a population of field-grown wheat with variation in RUE and photosynthetic traits to build predictive models of RUE, biomass, and intercepted photosynthetically active radiation (IPAR). Three approaches were used: best combination of sensors; canopy vegetation indices; and partial least squares regression. The use of remote sensing models predicted RUE with up to 70% accuracy compared with ground truth data. Water indices and canopy greenness indices [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI)] are the better option to predict RUE, biomass, and IPAR, and indices related to gas exchange, non-photochemical quenching [photochemical reflectance index (PRI)] and senescence [structural-insensitive pigment index (SIPI)] are better predictors for these traits at the vegetative and grain-filling stages, respectively. These models will be instrumental to explain canopy processes, improve crop growth and yield modelling, and potentially be used to predict RUE in different crops or ecosystems.