scholarly journals Leaf reflectance can surrogate foliar economics better than physiological traits across macrophyte species

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Paolo Villa ◽  
Rossano Bolpagni ◽  
Monica Pinardi ◽  
Viktor R. Tóth
Keyword(s):  
Remote Sensing ◽  
Plant Traits ◽  
Spectral Band ◽  
Matter Content ◽  
Hyperspectral Data ◽  
Leaf Mass Per Area ◽  
Dry Matter Content ◽  
Terrestrial Plants ◽  
Leaf Reflectance ◽  
Macrophyte Species

Abstract Background Macrophytes are key players in aquatic ecosystems diversity, but knowledge on variability of their functional traits, among and within species, is still limited. Remote sensing is a high-throughput, feasible option for characterizing plant traits at different scales, provided that reliable spectroscopy models are calibrated with congruous empirical data, but existing applications are biased towards terrestrial plants. We sampled leaves from six floating and emergent macrophyte species common in temperate areas, covering different phenological stages, seasons, and environmental conditions, and measured leaf reflectance (400–2500 nm) and leaf traits (dealing with photophysiology, pigments, and structure). We explored optimal spectral band combinations and established non-parametric reflectance-based models for selected traits, eventually showing how airborne hyperspectral data could capture spatial–temporal macrophyte variability. Results Our key finding is that structural—leaf dry matter content, leaf mass per area—and biochemical—chlorophyll-a content and chlorophylls to carotenoids ratio—traits can be surrogated by leaf reflectance with normalized error under 17% across macrophyte species. On the other hand, the performance of reflectance-based models for photophysiological traits substantively varies, depending on macrophyte species and target parameters. Conclusions Our main results show the link between leaf reflectance and leaf economics (structure and biochemistry) for aquatic plants, thus envisioning a crucial role for remote sensing in enhancing the level of detail of macrophyte functional diversity analysis to intra-site and intra-species scales. At the same time, we highlighted some difficulties in establishing a general link between reflectance and photosynthetic performance under high environmental heterogeneity, potentially opening further investigation directions.

scholarly journals Leaf reflectance can surrogate foliar economics better than physiological traits across macrophyte species

2020 ◽  
Author(s):  
Paolo Villa ◽  
Rossano Bolpagni ◽  
Monica Pinardi ◽  
Viktor R. Tóth
Keyword(s):  
Remote Sensing ◽  
Plant Traits ◽  
Leaf Traits ◽  
Matter Content ◽  
Hyperspectral Data ◽  
Leaf Mass Per Area ◽  
Dry Matter Content ◽  
Leaf Reflectance ◽  
Macrophyte Species ◽  
Selected Traits

AbstractMacrophytes are key players in aquatic ecosystems diversity, but knowledge on variability of their functional traits, among and within species, is still limited. Remote sensing is a high-throughput, feasible option for characterizing plant traits at different scales, provided that reliable spectroscopy models are calibrated with congruous empirical data.We sampled leaves from six floating and emergent macrophyte species common in temperate areas, covering different phenological stages, seasons, and environmental conditions, and measured leaf reflectance (400-2500 nm) and leaf traits (dealing with photophysiology, pigments and structure). We explored optimal spectral bands combinations and established non-parametric reflectance-based models for selected traits, eventually showing how airborne hyperspectral data can capture spatial-temporal macrophyte variability.Our key finding is that structural - leaf dry matter content, leaf mass per area - and biochemical - chlorophyll-a content and chlorophylls to carotenoids ratio - traits can be surrogated by leaf reflectance with relative error under 20% across macrophyte species, while performance of reflectance-based models for photophysiological traits depends on species.This finding shows the link between leaf reflectance and leaf economics (structure and biochemistry) for aquatic plants, thus supporting the use of remote sensing for enhancing the level of detail of macrophyte functional diversity analysis, to intra-site and intra-species scales.

scholarly journals Early prediction of biomass in hybrid rye based on hyperspectral data surpasses genomic predictability in less-related breeding material

2021 ◽  
Vol 134 (5) ◽  
pp. 1409-1422
Author(s):  
Rodrigo José Galán ◽  
Angela-Maria Bernal-Vasquez ◽  
Christian Jebsen ◽  
Hans-Peter Piepho ◽  
Patrick Thorwarth ◽  
...  
Keyword(s):  
Dry Matter ◽  
Genetic Relatedness ◽  
Snp Array ◽  
Matter Content ◽  
Hyperspectral Data ◽  
Winter Rye ◽  
Dry Matter Content ◽  
Early Prediction ◽  
Bivariate Model ◽  
Fresh Matter

Abstract Key message Hyperspectral data is a promising complement to genomic data to predict biomass under scenarios of low genetic relatedness. Sufficient environmental connectivity between data used for model training and validation is required. Abstract The demand for sustainable sources of biomass is increasing worldwide. The early prediction of biomass via indirect selection of dry matter yield (DMY) based on hyperspectral and/or genomic prediction is crucial to affordably untap the potential of winter rye (Secale cereale L.) as a dual-purpose crop. However, this estimation involves multiple genetic backgrounds and genetic relatedness is a crucial factor in genomic selection (GS). To assess the prospect of prediction using reflectance data as a suitable complement to GS for biomass breeding, the influence of trait heritability ($$H^{2}$$ H 2 ) and genetic relatedness were compared. Models were based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices to predict DMY and other biomass-related traits such as dry matter content (DMC) and fresh matter yield (FMY). For this, 270 elite rye lines from nine interconnected bi-parental families were genotyped using a 10 k-SNP array and phenotyped as testcrosses at four locations in two years (eight environments). From 400 discrete narrow bands (410 nm–993 nm) collected by an uncrewed aerial vehicle (UAV) on two dates in each environment, 32 hyperspectral bands previously selected by Lasso were incorporated into a prediction model. HBLUP showed higher prediction abilities (0.41 – 0.61) than GBLUP (0.14 – 0.28) under a decreased genetic relationship, especially for mid-heritable traits (FMY and DMY), suggesting that HBLUP is much less affected by relatedness and $$H^{2}$$ H 2 . However, the predictive power of both models was largely affected by environmental variances. Prediction abilities for DMY were further enhanced (up to 20%) by integrating both matrices and plant height into a bivariate model. Thus, data derived from high-throughput phenotyping emerges as a suitable strategy to efficiently leverage selection gains in biomass rye breeding; however, sufficient environmental connectivity is needed.

scholarly journals Leaf reflectance and functional traits as environmental indicators of urban dust deposition

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jiyou Zhu ◽  
Jingliang Xu ◽  
Yujuan Cao ◽  
Jing Fu ◽  
Benling Li ◽  
...  
Keyword(s):  
Functional Traits ◽  
Leaf Tissue ◽  
Matter Content ◽  
Urban Environments ◽  
Dry Matter Content ◽  
Urban Dust ◽  
Dust Deposition ◽  
Leaf Reflectance ◽  
Red Edge ◽  
Simple Ratio

Abstract Background How to quickly predict and evaluate urban dust deposition is the key to the control of urban atmospheric environment. Here, we focus on changes of plant reflectance and plant functional traits due to dust deposition, and develop a prediction model of dust deposition based on these traits. Results The results showed that (1) The average dust deposition per unit area of Ligustrum quihoui leaves was significantly different among urban environments (street (18.1001 g/m2), community (14.5597 g/m2) and park (9.7661 g/m2)). Among different urban environments, leaf reflectance curves tends to be consistent, but there were significant differences in leaf reflectance values (park (0.052–0.585) > community (0.028–0.477) > street (0.025–0.203)). (2) There were five major reflection peaks and five major absorption valleys. (3) The spectral reflectances before and after dust removal were significantly different (clean leaves > dust-stagnant leaves). 695 ~ 1400 nm was the sensitive range of spectral response. (4) Dust deposition has significant influence on slope and position of red edge. Red edge slope was park > community > street. After dust deposition, the red edge position has obviously “blue shift”. The moving distance of the red edge position increases with the increase of dust deposition. The forecast model of dust deposition amount established by simple ratio index (y = 2.517x + 0.381, R2 = 0.787, RMSE (root-mean-square error) = 0.187. In the model, y refers to dust retention, x refers to simple ratio index.) has an average accuracy of 99.98%. (5) With the increase of dust deposition, the specific leaf area and chlorophyll content index decreased gradually. The leaf dry matter content, leaf tissue density and leaf thickness increased gradually. Conclusion In the dust-polluted environment, L. quihoui generally presents a combination of characters with lower specific leaf area, chlorophyll content index, and higher leaf dry matter content, leaf tissue density and leaf thickness. Leaf reflectance spectroscopy and functional traits have been proved to be effective in evaluating the changes of urban dust deposition.

scholarly journals Predicting Key Grassland Characteristics from Hyperspectral Data

2021 ◽  
Vol 3 (2) ◽  
pp. 313-322
Author(s):  
Patrick Jackman ◽  
Thomas Lee ◽  
Michael French ◽  
Jayadeep Sasikumar ◽  
Patricia O’Byrne ◽  
...  
Keyword(s):  
Dry Matter ◽  
Sugar Content ◽  
Ground Truth ◽  
Matter Content ◽  
Hyperspectral Data ◽  
Chemistry Laboratory ◽  
Dry Matter Content ◽  
Least Squares Regression ◽  
Circular Spot ◽  
East Midlands

A series of experiments were conducted to measure and quantify the yield, dry matter content, sugars content, and nitrates content of grass intended for ensilement. These experiments took place in the East Midlands of Ireland during the Spring, Summer, and Autumn of 2019. A bespoke sensor rig was constructed; included in this rig was a hyperspectral radiometer that measured a broad spectrum of reflected natural light from a circular spot approximately 1.2 m in area. Grass inside a 50 cm square quadrat was manually collected from the centre of the circular spot for ground truth estimation of the grass qualities. Up to 25 spots were recorded and sampled each day. The radiometer readings for each spot were automatically recorded onto a laptop that controlled the sensor rig, and ground truth measurements were made either on-site or within 24 h in a wet chemistry laboratory. The collected data was used to build Partial Least Squares Regression (PLSR) predictive models of grass qualities from the hyperspectral dataset, and it was found that substantial relationships exist between the spectral reflectance from the grass and yield (r2 = 0.62), dry matter % (r2 = 0.54), sugar content (r2 = 0.54) and nitrates (r2 = 0.50). This shows that hyperspectral reflectance data contains substantial information about key grass qualities and can form part of a broader holistic data-driven approach to provide accurate and rapid predictions to farmers, agronomists, and agricultural contractors.

scholarly journals Visible-NIR reflectance spectroscopy as a potential tool for management of soil phosphorus fertility in legume-based pasture in New Zealand

2006 ◽  
pp. 75-80
Author(s):  
K. Kawamura ◽  
A.D. Mackay ◽  
M. Tuohy ◽  
K. Betteridge ◽  
I.D. Sanches
Keyword(s):  
Remote Sensing ◽  
Pilot Study ◽  
Soil Fertility ◽  
Spectral Data ◽  
Nutrient Management ◽  
Matter Content ◽  
Dry Matter Content ◽  
Soil P ◽  
Olsen P ◽  
P Content

Increasing the current precision of nutrient management will need analytical tools that aid in collecting site specific data. A technology with potential is hyperspectral remote sensing. Modern, portable spectroradiometers permit reflectance data in the spectral region between 350 and 2500 nm to be collected quickly. With the limited sampling, handling, and processing required the technology also offers rapid turn around times, if calibrations can be developed. In this paper the findings of a pilot study examining the use of hyperspectral reflectance spectra of pasture to indirectly assess the phosphorus (P) status of the soil are presented and discussed. Spectral data were collected in spring 2004 and again in summer 2006 from a small area of each of 30 legume-based sheep grazed pasture plots that varied in soil P fertility (Olsen P 6-68 μg/ml). Significant (P0.80). In an exploratory analysis using all the spectral waveband data, several paired-bands with high coefficients of determination (R2) were detected for pasture P and K content, but not for pasture N content, pasture growth rate or pasture dry matter content. The differences detected in pasture P content were consistent with the differences in soil P fertility measured by the Olsen P soil test, as indicated by the relationship between pasture P content and soil Olsen P in both 2004 (R2 = 0.90) and 2006 (R2 = 0.86). This pilot study needs to be broadened to examine other methodologies for interpreting the spectral data and extended to other pasture types and soil groups of varying soil fertility. Keywords: Olsen P, soil fertility, remote sensing, hyperspectral imaging, spatial variability, soil phosphate, plant phosphate

scholarly journals Validating the Predictive Power of Statistical Models in Retrieving Leaf Dry Matter Content of a Coastal Wetland from a Sentinel-2 Image

2019 ◽  
Vol 11 (16) ◽  
pp. 1936 ◽  
Author(s):  
Abebe Mohammed Ali ◽  
Roshanak Darvishzadeh ◽  
Kasra Rafiezadeh Shahi ◽  
Andrew Skidmore
Keyword(s):  
Remote Sensing ◽  
Dry Matter ◽  
Vegetation Index ◽  
Coastal Wetland ◽  
Vegetation Indices ◽  
Matter Content ◽  
Process Models ◽  
Dry Matter Content ◽  
Leaf Dry Matter Content ◽  
Sentinel 2

Leaf dry matter content (LDMC), the ratio of leaf dry mass to its fresh mass, is a key plant trait, which is an indicator for many critical aspects of plant growth and survival. Accurate and fast detection of the spatiotemporal dynamics of LDMC would help understanding plants’ carbon assimilation and relative growth rate, and may then be used as an input for vegetation process models to monitor ecosystems. Satellite remote sensing is an effective tool for predicting such plant traits non-destructively. However, studies on the applicability of remote sensing for LDMC retrieval are scarce. Only a few studies have looked into the practicality of using remotely sensed data for the prediction of LDMC in a forest ecosystem. In this study, we assessed the performance of partial least squares regression (PLSR) plus 11 widely used vegetation indices (VIs), calculated based on different combinations of Sentinel-2 bands, in predicting LDMC in a coastal wetland. The accuracy of the selected methods was validated using LDMC, destructively measured in 50 randomly distributed sample plots at the study site in Schiermonnikoog, the Netherlands. The PLSR applied to canopy reflectance of Sentinel-2 bands resulted in accurate prediction of LDMC (coefficient of determination (R2) = 0.71, RMSE = 0.033). PLSR applied to the studied VIs provided an R2 of 0.70 and RMSE of 0.033. Four vegetation indices (enhanced vegetation index(EVI), specific leaf area vegetation index (SLAVI), simple ratio vegetation index (SRVI), and visible atmospherically resistant index (VARI)) computed using band 3 (green) and band 11 of the Sentinel-2 performed equally well and achieved a good measure of accuracy (R2 = 0.67, RMSE = 0.034). Our findings demonstrate the feasibility of using Sentinel-2 surface reflectance data to map LDMC in a coastal wetland.

scholarly journals Predicting Key Grassland Characteristics from Hyperspectral Data

2021 ◽  
Author(s):  
Patrick Jackman ◽  
Thomas Lee ◽  
Michael French ◽  
Jayadeep Sasikumar ◽  
Patricia O’Byrne ◽  
...  
Keyword(s):  
Dry Matter ◽  
Sugar Content ◽  
Ground Truth ◽  
Matter Content ◽  
Hyperspectral Data ◽  
Chemistry Laboratory ◽  
Dry Matter Content ◽  
Least Squares Regression ◽  
Circular Spot ◽  
East Midlands

A series of experiments were conducted to measure and quantify the yield, dry matter content, sugars content and nitrates content of grass intended for ensilement. These experiments took place in the East Midlands of Ireland during the Spring, Summer and Autumn of 2019. A bespoke sensor rig was constructed; included in this rig was a hyperspectral radiometer that measured a broad spectrum of reflected natural light from a circular spot approximately 1.2 metres in area. Grass inside a 50cm square quadrat was manually collected from the centre of the circular spot for ground truth estimation of the grass qualities. Up to 25 spots were recorded and sampled each day. The radiometer readings for each spot were automatically recorded onto a laptop that controlled the sensor rig, and ground truth measurements were made either on site or within 24 hours in a wet chemistry laboratory. The collected data was used to build Partial Least Squares Regression (PLSR) predictive models of grass qualities from the hyperspectral dataset and it was found that substantial relationships exist between the spectral reflectance from the grass and yield (r2 = 0.62), dry matter % (r2 = 0.54), sugar content (r2 = 0.54) and nitrates (r2 = 0.50). This shows that hyperspectral reflectance data contains substantial information about key grass qualities and can form part of a broader holistic data driven approach to provide accurate and rapid predictions to farmers, agronomists and agricultural contractors.

scholarly journals Aridity drives coordinated trait shifts but not decreased trait variance across the geographic range of eight Australian trees

2020 ◽  
Author(s):  
Leander DL Anderegg ◽  
Xingwen Loy ◽  
Ian P. Markham ◽  
Christina M Elmer ◽  
Mark J Hovenden ◽  
...  
Keyword(s):  
Functional Trait ◽  
Matter Content ◽  
Complex Nature ◽  
Leaf Mass Per Area ◽  
Species Variation ◽  
Dry Matter Content ◽  
Trait Variation ◽  
Huber Value ◽  
Trait Variance ◽  
Species Trait

AbstractContextLarge intraspecific functional trait variation strongly impacts many aspects of natural communities and ecosystems, yet is inconsistent across traits and species.ApproachWe measured within-species variation in leaf mass per area (LMA), leaf dry matter content (LDMC), branch wood density (WD), and allocation to stem area vs. leaf area in branches (branch Huber value, HV) across the aridity range of seven Australian eucalypts and an Acacia species to explore how traits and their variances change with aridity.Results and ConclusionsWithin-species, we found consistent increases in LMA, LDMC and WD, and HV with increasing aridity, resulting in consistent trait coordination across tissues. However, this coordination only emerged across sites with large climate differences. Unlike trait means, patterns of trait variance with aridity were mixed across populations and species and showed limited support for constrained trait variation in dryer populations or more xeric species.SynthesisOur results highlight that climate can drive consistent within-species trait patterns, but that these patterns might often be obscured by the complex nature of morphological traits and sampling incomplete species ranges or sampling confounded stress gradients.

scholarly journals Data-Driven Methods for the Estimation of Leaf Water and Dry Matter Content: Performances, Potential and Limitations

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5394
Author(s):  
Bin Yang ◽  
Hui Lin ◽  
Yuhao He
Keyword(s):  
Remote Sensing ◽  
Dry Matter ◽  
Vegetation Indices ◽  
Matter Content ◽  
Data Driven ◽  
Support Vector ◽  
Dry Matter Content ◽  
Biochemical Constituents ◽  
Equivalent Water Thickness ◽  
Leaf Level

Leaf equivalent water thickness (EWT) and dry matter content (expressed as leaf mass per area (LMA)) are two critical traits for vegetation function monitoring, crop yield estimation, and precise agriculture management. Data-driven methods are widely used for remote sensing of leaf EWT and LMA because of their simplicity, satisfactory accuracy, and computation efficiency, such as the vegetation indices (VI)-based and machine learning (ML)-based methods. However, most of the data-driven methods are utilized at the canopy level, comparison of the performances of the data-driven methods at the leaf level has not been well documented. Moreover, the ML-based data-driven methods generally adopt leaf optical properties directly as their inputs, which may subsequently decrease their ability in remote sensing of leaf biochemical constituents. Performances of the ML-based methods cooperating with VI are rarely evaluated. Using the independent LOPEX and ANGERS datasets, we compared the performances of three data-driven methods: VI-based, ML-reflectance-based, and ML-VI-based methods, for the estimation of leaf EWT and LMA. Three sampling strategies were also utilized for evaluation of the generalization of these data-driven methods. Our results evidenced that ML-VI-based methods were the most accurate among these data-driven methods. Compared to the ML-reflectance-based and VI-based methods, the ML-VI-based model with support vector regression overall reduced errors by 5.7% (41.5%) and 1.8% (12.4%) for the estimation of leaf EWT (LMA), respectively. The ML-VI-based model inherits advantages of vegetation indices and ML techniques, which made it sensitive to changes of leaf biochemical constituents and capable of solving nonlinear tasks. It is thus recommended for the estimation of EWT and LMA at the leaf level. Moreover, its performance can further be enhanced by improving its generalization ability, such as adopting techniques on the selection of better wavelengths and definition of new vegetation indices. These results thus provided a prior knowledge of the data-driven methods and can be helpful for future studies on the remote sensing of leaf biochemical constituents.

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