Canopy Scale Research Articles

Multi-Model Rice Canopy Chlorophyll Content Inversion Based on UAV Hyperspectral Images

Rice is China’s main crop and its output accounts for 30% of the world’s total annual rice production. Rice growth status is closely related to chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values). The determination of a SPAD value is of great significance to the health status of rice, agricultural irrigation and regulated fertilization. The traditional SPAD value measurement method is not only time-consuming, laborious and expensive but also causes irreparable damage to vegetation. The main aim of the present study is to obtain a SPAD value through the inversion of hyperspectral remote sensing images. In order to achieve this purpose, the hyperspectral image of rice at different growth stages at the canopy scale was first acquired using a hyperspectral imaging instrument equipped with a drone; the spectral characteristics of the rice canopy at different growth stages were analyzed and combined with a ground-level measured SPAD value, the bands with high correlation between the SPAD values and the spectra of the rice canopy at different fertility stages were selected. Subsequently, we combined the spectral characteristics with the continuous projection algorithm to extract the characteristic band and used the PLS method in MATLAB software to analyze and calculate the weight of each type of spectral value and the corresponding canopy SPAD value; we then used the wavelength corresponding to the spectral value with the highest weight as the used band. Secondly, the four methods of univariate regression, partial least squares (PLS) regression, support vector machine (SVM) regression and back propagation (BP) neural network regression are integrated to establish the estimation model of the SPAD value of rice canopy. Finally, the models are used to map the SPAD values of the rice canopy. Research shows that the model with the highest decision coefficient among the four booting stage models is “booting stage-SVR” (R2 = 0.6258), and the model with the highest decision coefficient among the four dairy maturity models is “milk-ripe stage-BP” (R2 = 0.6716), all of which can meet the requirement of accurately retrieving the SPAD value of rice canopy. The above results can provide a technical reference for the accurate, rapid and non-destructive monitoring of chlorophyll content in rice leaves and provide a core band selection basis for large-scale hyperspectral remote sensing monitoring of rice.

Contributions of leaf distribution and leaf functions to photosynthesis and water-use efficiency from leaf to canopy in apple: A comparison of interstocks and cultivars.

Grafting has been widely used in horticulture to induce dwarfing and avoid stress-derived limitations on plant growth and yield by affecting plant architecture and leaf functions. However, the respective effects on plant photosynthesis and water use efficiency (WUE) of leaf distribution and functions that depend on both rootstock and scion have not been fully elucidated. This study aimed to (i) clarify the scion × interstock impacts on the variability of leaf photosynthetic traits and WUE, and (ii) decipher the respective effects of leaf distribution and functions on canopy photosynthesis and WUE (WUEc). Leaf gas exchange over light gradients and responses to light, CO2, temperature, and vapor pressure deficit were measured in two apple cultivars, 'Liquan Fuji' ('Fuji') and 'Regal Gala' ('Gala'), grafted onto rootstocks combined with interstocks: a vigorous (VV, 'Qinguan'), or a dwarf one (VD, M26). The 3D architecture-based RATP model was parameterized to estimate the canopy photosynthesis rate (Ac ), transpiration rate (E c), and WUEc. Then, virtual scenarios were used to compare the relative contributions of cultivar and interstock to canopy A c, E c, and WUE c. These scenarios changed the leaf distribution and functions of either cultivar or interstock. At the leaf scale, VD trees had significantly higher leaf nitrogen per area but a lower maximum carboxylation rate and dark respiration in both cultivars. In parallel with higher leaf stomatal conductance (gs ) and transpiration in VD 'Fuji' and similar gs in VD 'Gala', VD trees showed significantly lower leaf photosynthesis rate and WUE than VV trees. However, lower leaf photosynthetic capacities in VD trees were compensated at the canopy scale, with A c and WUE c for 'Fuji' significantly improved in VD trees under both sunny and cloudy conditions, and for 'Gala' significantly improved in VD trees under cloudy conditions compared with VV trees. Switching scenarios highlighted that 'Gala' leaf functions and distribution and VD leaf distributions enhanced A c and WUE c simultaneously, irrespective of weather conditions. Up-scaling leaf gas exchange to the canopy scale by utilizing 3D architecture-based modeling and reliable measurements of tree architecture and leaf functional traits provides insights to explore the influence of genetic materials and tree management practices.

Individual tree-based forest species diversity estimation by classification and clustering methods using UAV data

Monitoring forest species diversity is essential for biodiversity conservation and ecological management. Currently, unmanned aerial vehicle (UAV) remote sensing technology has been increasingly used in biodiversity monitoring due to its flexibility and low cost. In this study, we compared two methods for estimating forest species diversity indices, namely the spectral angle mapper (SAM) classification approach based on the established species-spectral library, and the self-adaptive Fuzzy C-Means (FCM) clustering algorithm by selected biochemical and structural features. We conducted this study in two complex subtropical forest areas, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves using UAV-borne hyperspectral and LiDAR data. The results showed that the classification method performed better with higher values of R2 than the clustering algorithm for predicting both species richness (0.62 > 0.46 for MZL and 0.55 > 0.46 for GGS) and Shannon-Wiener index (0.64 > 0.58 for MZL, 0.52 > 0.47 for GGS). However, the Simpson index estimated by the classification method correlated less with the field measurements than the clustering algorithm (R2 = 0.44 and 0.83 for MZL and R2 = 0.44 and 0.62 for GGS). Our study demonstrated that the classification method could provide more accurate monitoring of forest diversity indices but requires spectral information of all dominant tree species at individual canopy scale. By comparison, the clustering method might introduce uncertainties due to the amounts of biochemical and structural inputs derived from the hyperspectral and LiDAR data, but it could acquire forest diversity patterns rapidly without distinguishing the specific tree species. Our findings underlined the advantages of UAV remote sensing for monitoring the species diversity in complex forest ecosystems and discussed the applicability of classification and clustering methods for estimating different individual tree-based species diversity indices.

Impact of potentially toxic elements on pines in a former ore processing mine: Exploitation of hyperspectral response from needle and canopy scales

Anthropic potentially toxic element (PTE) releases can lead to persistent pollution in soil. Monitoring PTEs by their detection and quantification on large scale is of great interest. The vegetation exposed to PTEs can exhibit a reduction of physiological activities, structural damage … Such vegetation trait changes impact the spectral signature in the reflective domain 0.4–2.5 μm. The objective of this study is to characterize the impact of PTEs on the spectral signature of two pine species (Aleppo and Stone pines) in the reflective domain and ensure their assessment. The study focuses on nine PTEs: As, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Zn. The spectra are measured by an in-field spectrometer and an aerial hyperspectral instrument on a former ore processing site. They are completed by measurements related to vegetation traits at needle and tree scales (photosynthetic pigments, dry matter, morphometry …) to define the most sensitive vegetation parameter to each PTE in soil. A result of this study is that chlorophylls and carotenoids are the most correlated to PTE contents. Context-specific spectral indices are specified and used to assess metal contents in soil by regression. These new vegetation indices are compared at needle and canopy scales to literature indices. Most of the PTE contents are predicted at both scales with Pearson correlation scores between 0.6 and 0.9, depending on species and scale.

Leaf microbiome data for European beech (Fagus sylvatica) at the leaf and canopy scales collected in a gallery forest in South-West France

Key messageThe datasets describe bacterial and fungal communities of European beech (Fagus sylvatica) leaves collected along a vertical gradient in a gallery forest throughout the growing season. They also describe communities in the surrounding environment of beech trees. Dataset access is at https://doi.org/10.7910/DVN/FFHAQU, and associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/f17fe848-fc3e-4297-be11-9871b35a1be4. Both can be used to uncover the dynamics and assembly processes of phyllosphere microbial communities in forest ecosystems.

Hydrodynamics across seagrass meadows and its impacts on Indonesian coastal ecosystems: A review

Seagrass canopies are important components of the world’s coastal environments providing critical ecological services. Nearshore hydrodynamics, i.e., waves and currents, are essential in controlling the ecological processes across coastal environments. Seagrass meadows can impose more complex hydrodynamics processes by attenuating sea-swell waves and decreasing the impact of nearshore mean water level rise due to wave setup and Infragravity (IG) waves. Consequently, the seagrasses dissipate waves and reduce flows allowing sediments to settle and accrete the shorelines. However, despite their significant roles, knowledge of hydrodynamics in the Indonesian seagrass ecosystems is relatively limited compared to other coastal ecosystems such as sandy beaches, mangroves, and coral reefs. This review highlights the dynamics of waves and currents, and their interaction with sediment transport and ecological processes, including biogeochemical and dispersal processes on the seagrass ecosystem contributing to the existing seagrass research in Indonesia. The associated literature is collected from scientific databases such as Scopus and Google Scholar that range between 1965 and 2021. The result showed that most of the research on hydrodynamic in seagrass ecosystems was carried out in temperate zones. Until recently, there have been limited publications discussing the interaction between the Indonesian (tropical) seagrass ecosystem and hydrodynamics parameters, even though the region has abundant seagrass species. Moreover, Indonesia is strongly influenced by various atmospheric-oceanic forcing, including the Asian monsoon affecting the dynamic of the coastal area with seagrass ecosystems. At a canopy scale, the correlation between the nearshore (tropical) hydrodynamics and ecological processes in the system is yet to be explored. Considering the potential benefit of seagrasses to coastal ecosystems, developing future research in hydrodynamics across the ecosystem is critical to overcoming the knowledge gaps in Indonesia. The knowledge gained could support the Indonesian seagrass ecosystem services and their resilience to potential hazards and climate change.

Multi-Angle Detection of Spatial Differences in Tea Physiological Parameters

Chlorophyll and nitrogen contents were used as leaf physiological parameters. Based on multispectral images from multiple detection angles and the stoichiometric data of tea (Camellia sinensis) leaves in different positions on plants, the spatial differences in tea physiological parameters were explored, and the full channel difference vegetation index was established to effectively remove soil and shadow noise. Support vector machine, random forest (RF), partial least square, and back-propagation algorithms from the multispectral images of leaf and canopy scales were then used to train the tea physiological parameter detection model. Finally, the detection effects of the multispectral images obtained from different angles on the physiological parameters of the top, middle, and bottom tea leaves were analysed and compared. The results revealed distinct spatial differences in the physiological parameters of tea leaves in individual plants. Chlorophyll content was lowest at the top and relatively high at the middle and bottom; nitrogen content was the highest at the top and relatively low at the middle and bottom. The horizontal distribution of physiological parameters was similar, i.e., the values in the east and south were high, whereas those in the west and north were low. The multispectral detection accuracy of the physiological parameters at the leaf scale was better than that at the canopy scale; the model trained by the RF algorithm had the highest comprehensive accuracy. The coefficient of determination between the predicted and measured values of the spad-502 plus instrument was (R2) = 0.79, and the root mean square error (RMSE) was 0.11. The predicted result for the nitrogen content and the measured value was R2 = 0.36 and RMSE = 0.03. The detection accuracy of the multispectral image taken at 60° for the physiological parameters of tea was generally superior to those taken at other shooting angles. These results can guide the high-precision remote sensing detection of tea physiological parameters.

Improving the spatial and temporal estimation of ecosystem respiration using multi-source data and machine learning methods in a rainfed winter wheat cropland

The investigation of ecosystem respiration (RE) and its vital influential factors along with the timely and accurate detection of spatiotemporal variations in RE are essential for guiding agricultural production planning. RE observation in the plot region is primarily based on the laborious chamber method. However, upscaling the spatial-temporal estimates of RE at the canopy scale is still challenging. The present study conducted a field experiment to determine RE using the chamber method. A multi-rotor unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to acquire the canopy spectral data of wheat during each RE test experiment. Moreover, the agronomic indicators of aboveground plant biomass, leaf area index, leaf dry mass as well as agrometeorological and soil data were measured simultaneously. The study analyzed the potential of multi-information for estimating RE at the field scale and proposed two strategies for RE estimation. In addition, a semiempirical, yet Lloyd and Taylor-based, remote sensing model (LT1-NIRV) was developed for estimating RE observed across different growth stages with a small margin of error (coefficient of determination [R2] = 0.60–0.64, root-mean-square error [RMSE] = 285.98–316.19 mg m−2 h−1). Further, five machine learning (ML) algorithms were utilized to independently estimate RE using two different datasets. The rigorous analyses, which included statistical comparison and cross-validation for estimating RE, confirmed that the XGBoost model, with the highest R2 and lowest RMSE (R2 = 0.88 and RMSE = 172.70 mg m−2 h−1), performed the best among the evaluated ML models. The LT1-NIRV model was less effective in estimating RE compared with the other ML models. Based on this comprehensive comparison analysis, the ML model can successfully estimate variations in wheat field RE using high-resolution UAV multispectral images and environmental factors from the wheat cropland system, thereby providing a valuable reference for monitoring and upscaling RE observations.

Investigating the Diurnal Radiative, Turbulent, and Biophysical Processes in the Amazonian Canopy‐Atmosphere Interface by Combining LES Simulations and Observations

AbstractWe investigate the atmospheric diurnal variability inside and above the Amazonian rainforest for a representative day during the dry season. To this end, we combine high‐resolution large‐eddy simulations that are constrained and evaluated against a comprehensive observation set, including CO2 concentrations, gathered during GoAmazon2014/15. We design systematic numerical experiments to quantify whether a multilayer approach in solving the explicit canopy improves our canopy‐atmosphere representation. We particularly focus on the relationship between photosynthesis and plant transpiration, and their distribution at leaf and canopy scales. We found the variability of photosynthesis drivers like vapor pressure deficit and leaf temperature to be about 3 times larger for sunlit leaves compared to shaded leaves. This leads to a large spread on leaf stomatal conductance values with minimum and maximum values varying more than 100%. Regarding the turbulent structure, we find wind‐driven stripe‐like shapes at the canopy top and structures resembling convective cells at the canopy. Wind‐related variables provide the best spatiotemporal agreement between model and observations. The potential temperature and heat flux profiles agree with an observed decoupling near the canopy top interface, although with less variability and cold biases of up to 3 K. The increasing complexity on the biophysical processes leads to the largest disagreements for evaporation, CO2 plant assimilation and soil efflux. The model is able to capture the correct dependences and trends with the magnitudes still differing. We finally discuss the need to revise leaf and soil models and to complete the observations at leaf and canopy levels.

An Improved Approach to Monitoring Wheat Stripe Rust with Sun-Induced Chlorophyll Fluorescence

Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIFyield (normalized by absorbed photosynthetically active radiation), fluorescence yield (ΦF) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (ΦF-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL < 20%). The strongest correlation between NDVI and SL (R = 0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), ΦF-r (R = 0.53) and SIF (R = 0.51), and the response of ΦF (R = 0.45) and SIFyield (R = 0.34) to SL was weak. Under lightly diseased conditions, ΦF-r (R = 0.62) showed the strongest response to disease, followed by SIFyield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between ΦF and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, ΦF-r estimated by the newly proposed ‘NIRvR approach’ (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for ΦF) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of ΦF-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress.

Effects of Ground Subsidence on Vegetation Chlorophyll Content in Semi-Arid Mining Area: From Leaf Scale to Canopy Scale.

Ground subsidence is the main cause of vegetation degradation in mining areas. It is of great significance to study the effects of ground subsidence on vegetation. At present, few studies have analyzed the effects of ground subsidence on vegetation from different scales. However, the conclusions on different scales may differ. In this experiment, chlorophyll content was used as an indicator of vegetation degradation. We conducted a long-term field survey in the Lijiahao coalfield in China. Based on field survey data and remote sensing images, we analyzed the effects of ground subsidence on chlorophyll content from two scales (leaf scale and canopy scale) and summarized the similarities and differences. We found that, regardless of leaf scale or canopy scale, the effects of subsidence on chlorophyll content have the following three characteristics: (1) mining had the least effect on chlorophyll content in the neutral area, followed by the compression area, and the greatest effect on chlorophyll content in the extension area; (2) subsidence had a slight effect on chlorophyll content of Caragana korshins, but a serious effect on chlorophyll content of Stipa baicalensis; (3) chlorophyll content was not immediately affected when the ground sank. It was the cumulative subsidence that affects chlorophyll content. The difference between leaf scale and canopy scale was that the chlorophyll content at canopy scale is more affected by mining. This means that when assessing vegetation degradation, the results obtained by remote sensing were more severe than those measured in the field. We believe that this is because the canopy chlorophyll content obtained by remote sensing is also affected by the plant canopy structure. We recommend that mining and ecological restoration should be carried out concurrently, and that ground fissures should be taken as the focus of ecological restoration. In addition, Caragana korshins ought to be widely planted. Most importantly, managers should assess the effects of ground subsidence on vegetation on different scales. However, managers need to be aware of differences at different scales.

Assessing the severity of cotton Verticillium wilt disease from in situ canopy images and spectra using convolutional neural networks

Verticillium wilt (VW) is a common soilborne disease of cotton. It occurs mainly in the seedling and boll-opening stages and severely impairs the yield and quality of the fiber. Rapid and accurate identification and evaluation of VW severity (VWS) forms the basis of field cotton VW control, which has great significance to cotton production. Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses, which require abundant time and professional expertise. Remote and proximal sensing using imagery and spectrometry have great potential for this purpose. In this study, we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values, in situ images, and spectra of 361 cotton canopies. To estimate cotton VWS values at the canopy scale, we developed two deep learning approaches that use in situ images and spectra, respectively. For the imagery-based method, given the high complexity of the in situ environment, we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes (CFS) dataset with over 1000 images for each scene-unit type. We performed pretrained convolutional neural networks (CNNs) training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy. The results showed that the DarkNet-19 model achieved satisfactory performance in CFS classification and VWS values estimation (R2 = 0.91, root-mean-square error (RMSE) = 6.35%). For the spectroscopy-based method, we first designed a one-dimensional regression network (1D CNN) with four convolutional layers. After dimensionality reduction by sensitive-band selection and principal component analysis, we fitted the 1D CNN with varying numbers of principal components (PCs). The 1D CNN model with the top 20 PCs performed best (R2 = 0.93, RMSE = 5.77%). These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.

Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)

Abstract. Plant transpiration dominates terrestrial latent heat fluxes (LE) and plays a central role in regulating the water cycle and land surface energy budget. However, Earth system models (ESMs) currently disagree strongly on the amount of transpiration, and thus LE, leading to large uncertainties in simulating future climate. Therefore, it is crucial to correctly represent the mechanisms controlling the transpiration in models. At the leaf scale, transpiration is controlled by stomatal regulation, and at the canopy scale, through turbulence, which is a function of canopy structure and wind. The coupling of vegetation to the atmosphere can be characterized by the coefficient Ω. A value of Ω→0 implies a strong coupling of vegetation and the atmosphere, leaving a dominant role to stomatal conductance in regulating water (H2O) and carbon dioxide (CO2) fluxes, while Ω→1 implies a complete decoupling of leaves from the atmosphere, i.e., the transfer of H2O and CO2 is limited by aerodynamic transport. In this study, we investigated how well the land surface model (LSM) Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) (v7266) simulates the coupling of vegetation to the atmosphere by using empirical daily estimates of Ω derived from flux measurements from 90 FLUXNET sites. Our results show that ORCHIDEE generally captures the Ω in forest vegetation types (0.27 ± 0.12) compared with observation (0.26 ± 0.09) but underestimates Ω in grasslands (GRA) and croplands (CRO) (0.25 ± 0.15 for model, 0.33 ± 0.17 for observation). The good model performance in forests is due to compensation of biases in surface conductance (Gs) and aerodynamic conductance (Ga). Calibration of key parameters controlling the dependence of the stomatal conductance to the water vapor deficit (VPD) improves the simulated Gs and Ω estimates in grasslands and croplands (0.28 ± 0.20). To assess the underlying controls of Ω, we applied random forest (RF) models to both simulated and observation-based Ω. We found that large observed Ω are associated with periods of low wind speed, high temperature and low VPD; it is also related to sites with large leaf area index (LAI) and/or short vegetation. The RF models applied to ORCHIDEE output generally agree with this pattern. However, we found that the ORCHIDEE underestimated the sensitivity of Ω to VPD when the VPD is high, overestimated the impact of the LAI on Ω, and did not correctly simulate the temperature dependence of Ω when temperature is high. Our results highlight the importance of observational constraints on simulating the vegetation–atmosphere coupling strength, which can help to improve predictive accuracy of water fluxes in Earth system models.

A disease-specific spectral index tracks Magnaporthe oryzae infection in paddy rice from ground to space

Rice blast (RB, caused by the fungus Magnaporthe oryzae) is the most devastating disease in global rice production, and can cause significant yield losses and increasingly threaten global food security. Accurate detection of RB occurrence with a universal metric is crucial to facilitate early disease prevention and curtailing the disease expansion but has not been addressed to date. This study aimed to design a rice blast index (RIBI) for quantifying the disease index (DI) and tracking the smallholder rice blast dispersal over multiple spatial scales. To achieve this goal, a large dataset including leaf- and canopy-scale reflectance spectra and satellite imagery was acquired within the framework of seven independent campaigns over four years (2018–2021). Specifically, an extensive collection of Magnaporthe oryzae infected samples were analyzed to examine the specific spectral response to pathogen infection in paddy rice from leaf to near-ground canopy scales. Two variants of the RIBI were developed, which were RIBInir = (R753-R1102)/(R665 + R1102) and RIBIred = (R753-R1102)/(R665 + R1102) based on the single-band separability and exhaustive search of band combinations. They were subsequently evaluated for quantifying the RB occurrence from ground to space. Spatial cluster analysis was then integrated with the superior RIBI adjusted for Sentinel-2 imagery to explore the spatio-temporal dynamics of pathogen infection, and to reveal the within-field hotspots of potential rice blast dispersal in smallholder farms.The results demonstrated that both RIBInir and RIBIred exhibited high overall accuracies for the classification of infected and healthy samples at the leaf scale under greenhouse in 2018 (RIBInir: 81.41%; RIBIred: 84.62%) and 2019 (RIBInir: 81.30%; RIBIred: 90.37%) and field conditions in 2020 (RIBInir: 86.36%; RIBIred: 89.39%). Compared with traditional VIs (Near-ground: R2 < 0.47, satellite: R2 < 0.54), the RIBInir yielded improved R2 in quantifying the DI from in situ spectra to satellite imagery (Near-ground: R2 = 0.73, satellite: R2 = 0.78). The strongest DI-RIBInir relationship was attributed to the use of two near-infrared (NIR) bands that helped enhance the unique spectral responses in the NIR region induced by pathogen infection, in contrast to the extensively studied visible region. Multi-temporal analysis of Sentinel-2A derived RIBInir successfully captured the temporal dynamics of RB infection and recovery and yielded compelling maps showing the spatial propagation and attenuation of disease over time. This research opens new opportunities towards quantifying field disease occurrence and detecting the within-field hotspots of potential disease dispersal in a timely manner from publicly available satellite imagery.

Partitioning evapotranspiration and carbon flux in ungrazed and grazed tallgrass prairie

The effect of cattle grazing on water and carbon cycling in grasslands is not fully understood. The goal of this study was to evaluate how grazing affects carbon and water cycles in a tallgrass prairie. Eddy covariance measurements were carried out from 2003 to 2005 growing seasons (from May to September) in Manhattan, Kansas, U.S., in grazed (GR) and ungrazed (UG) tallgrass prairie paddocks. Net ecosystem CO2 exchange (NEE) was partitioned into gross primary productivity (GPP) and ecosystem respiration (Reco) using the relationship between the air temperature and nighttime NEE data. Evapotranspiration (ET) was partitioned into transpiration (T) and evaporation (E) using an approach based on the concept of underlying water use efficiency. Our results revealed that grazing reduced maximum aboveground biomass (AGB) and green leaf area index (GLAI) by 22.5% and 24.3%, respectively. Grazing also reduced GPP, NEE and T in the middle of the growing seasons but enhanced these flux components at the end of the growing seasons compared to the UG paddock. Grazing reduced the growing season cumulative NEE by 11.9% and enhanced ET by 3.3% compared to the UG paddock. The reduction in NEE at UG was associated with an increase in Reco (9.4%). In turn, changes in ET in response to grazing were driven more by the increase in E (26.6%) than by the decrease in T (5.9%). Despite the reduction in AGB and GLAI caused by grazing, similar cumulative GPP values at the canopy scale at both paddocks suggest that there are compensatory mechanisms that maintain similar CO2 assimilation at UG and GR. Our findings suggest that the grazing regime adopted in this tallgrass prairie impacts the carbon cycle more strongly than the water cycle, given the higher reduction in NEE compared to the slight increase in ET on GR compared to UG. These results can be useful to outline strategies to improve management practices aimed at improving the efficiency of rangelands.

Estimating leaf nitrogen content by coupling a nitrogen allocation model with canopy reflectance

The area-based leaf nitrogen content (LNCA) has gained increasing attention recently in ecology and plant physiology, and the mass-based nitrogen content (LNCM) has been extensively used by agronomists for making fertilization recommendations. Remote sensing has been emerging as an important tool to quantify LNCA and LNCM across multiple observation scales, such as leaf and canopy. Most models for estimating LNCA and LNCM are based on their empirical relationships with chlorophyll-related vegetation indices (VIs), which cannot capture the dynamic allocation of nitrogen into different components throughout the growing season. This study proposed a simplified nitrogen allocation model by dividing the total nitrogen into photosynthetic nitrogen (PN) and non-photosynthetic nitrogen with the purpose of estimating LNCA and LNCM in rice and winter wheat. The nitrogen allocation model was calibrated with an independent leaf-scale dataset (R2 = 0.82) and then applied to the canopy scale without the need to adjust the model coefficients. The estimated fractions of PN were 71% and 78% for rice and wheat leaves, respectively, which were close to the reported value (∼75%) for common C3 plants. Overall, the nitrogen allocation-based models were applicable for estimating both LNCA and LNCM with relative root mean square error (RRMSE) values <15% for the jointing and booting stages. In addition, our novel estimation model was generic for rice and wheat, yielding the best estimations for LNCM (R2 = 0.63) across the entire growing season compared with traditional empirical models based on VIs. This study represents a first attempt to couple nitrogen allocation theory with remote sensing data to improve the spectroscopic estimation of LNCA and LNCM.

Assessment of crop traits retrieved from airborne hyperspectral and thermal remote sensing imagery to predict wheat grain protein content

Wheat (Triticum spp.) is among the world’s most widely grown crops and receives large quantities of nitrogen (N) fertiliser. Grain protein content (GPC) is influenced by genetic, agronomic and weather conditions affecting crops’ physiological status and stress levels; accurate GPC prediction has potential to reduce N losses and improve profit. Success in GPC estimation from remotely sensed plant traits has been limited. For progress to be made, it is necessary to robustly identify imaging spectroscopy-based physiological traits most closely associated with GPC in both experimental and commercial contexts. We present results from piloted hyperspectral flights and ground campaigns at two dryland field experiments with divergent water supply and wide-ranging N treatments, and from two years’ flights over 17 commercial fields planted to either bread (T. aestivum) or durum (T. durum) wheat, in the southern Australian wheat belt. Imagery was acquired with airborne hyperspectral and thermal sensors, with spatial resolutions of approx. 0.3 m and 0.5 m for experimental plots and 1 m/1.7 m in commercial fields. Leaf clip measurements, leaf and grain samples were collected and, in commercial fields, ∼40,000 records obtained from harvester-mounted protein monitors. Crop Water Stress Index (CWSI), solar-induced fluorescence (SIF), reflectance indices and PRO4SAIL radiative transfer model inverted parameters were retrieved for each plot and GPC record location. The photochemical reflectance index (PRI) related to xanthophyll pigments was consistently associated with GPC at both leaf and canopy scale in the plots and transect. In the commercial crops, a gradient boosted machine learning algorithm (GBM) ranked CWSI as the strongest indicator of GPC under severe water stress, while SIF, PRI and inverted biochemical constituents anthocyanins and carotenoids were consistently important under more benign conditions. Structural parameters inverted from the hyperspectral reflectance imagery were not prominent except under severe drought. We attained statistically significant results estimating GPC in unseen samples, with best relationships between predicted and observed GPC of r2 = 0.80 in a model built with thermal and physiological traits obtained from the hyperspectral and thermal imagery.

The potential of crop models in simulation of barley quality traits under changing climates: A review

Most of the experimental and modeling studies that evaluate the impacts of climate change and variability on barley have been focused on grain yield. However, little is known on the effects of combined change in temperature, CO 2 concentration, and extreme events on barley grain quality and how capable are the current process-based crop models capture the signal of climate change on quality traits. Here in this review, we initially explored the response of quality traits of barley to heat, drought, and CO 2 concentration from experiential studies. Next, we reviewed the state of the art of some of the current modeling approaches to capture grain quality. Lastly, we suggested possible opportunities to improve current models for tracking the detailed quality traits of barley. Heat and drought stress increase the protein concentration which has a negative effect on malting quality. The rise of CO 2 concentration significantly reduces the grain protein, again resulting in a decline of the malting and brewing quality since the nitrogen concentration of grains needs to be kept at a specific level. The current crop models that simulate barley grain quality are limited to simulation of grain nitrogen concentration, size, and number in response to climate extremes and CO 2 . Nevertheless, crop models fail to account for the complex interactions between the conflicting effects of rising temperatures and droughts as well as increasing CO 2 concentrations on grain protein. They have mainly adapted wheat models that cannot capture barley’s protein composition and whole grain malting quality. Implementation of experiments from gene to canopy scales which are explicitly designed to detect the interactions among environmental variables on detailed quality traits and couple the remote sensing plus data-driven approaches to crop models are possible opportunities to improve modeling of barley grain quality. The development of modeling routines can capture the detailed grain quality provide valuable tools for forming climate adaptive strategies. Equally important, they can guide breeding programs to develop climate-resilient but high-quality barley genotypes. • Barley quality response to climate change and available modeling are explored. • Malting quality declines due to elevated CO2 and heat/drought effects. • Current models do not capture environmental interactions on barley quality traits. • Targeted trials and better models needed to test adaptation strategies on grain quality.

Whole-Canopy Photosynthetic Characterization of Apple Tree and the Effects Induced by Grafting on Rootstocks with Different Vigor

Leaf photosynthesis is pivotal for the synthesis of carbohydrates; however, the growth and development of horticultural crops are more closely related to canopy photosynthetic capacity. Measurements of canopy photosynthesis allow a better evaluation of the fruit tree performance at the canopy scale. Therefore, an open chamber system to determine instantaneous canopy apparent photosynthesis (CAPi) was investigated. CAPi slightly overestimated the biomass accumulation by 5.9%, which revealed that the CAPi method is valuable for quantifying canopy photosynthesis. Further, many woody horticultural plants are usually grafted for propagation, such as apples, whereas the rootstocks always influence the canopy size and photosynthesis. In this study, the effect of dwarfing, semi-dwarfing, and vigorous apple rootstocks on canopy photosynthesis was studied. Compared to vigorous rootstock, dwarfing rootstock significantly reduced the leaf net photosynthetic rate and total leaf area by 20.8% and 53.1%, respectively, and resulted in a 59.7% reduction in CAPi. Throughout the study, CAPi was an effective method that should be considered for canopy photosynthesis measurement of horticultural crops in the future.

Plant spectra as integrative measures of plant phenotypes

Abstract Spectroscopy at the leaf and canopy scales has attracted considerable interest in plant ecology over the past decades. Using reflectance spectra, ecologists can infer plant traits and strategies—and the community‐ or ecosystem‐level processes they correlate with—at individual or community levels, covering more individuals and larger areas than traditional field surveys. Because of the complex entanglement of structural and chemical factors that generate spectra, it can be tricky to understand exactly what phenotypic information they contain. We discuss common approaches to estimating plant traits from spectra—radiative transfer and empirical models—and elaborate on their strengths and limitations in terms of the causal influences of various traits on the spectrum. Many chemical traits have broad, shallow and overlapping absorption features, and we suggest that covariance among traits may have an important role in giving empirical models the flexibility to estimate such traits. While trait estimates from reflectance spectra have been used to test ecological hypotheses over the past decades, there is also a growing body of research that uses spectra directly, without estimating specific traits. By treating positions of species in multidimensional spectral space as analogous to trait space, researchers can infer processes that structure plant communities using the information content of the full spectrum, which may be greater than any standard set of traits. We illustrate this power by showing that co‐occurring grassland species are more separable in spectral space than in trait space and that the intrinsic dimensionality of spectral data is comparable to fairly comprehensive trait datasets. Nevertheless, using spectra this way may make it harder to interpret patterns in terms of specific biological processes. Synthesis. Plant spectra integrate many aspects of plant form and function. The information in the spectrum can be distilled into estimates of specific traits, or the spectrum can be used in its own right. These two approaches may be complementary—the former being most useful when specific traits of interest are known in advance and reliable models exist to estimate them, and the latter being most useful under uncertainty about which aspects of function matter most.