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

Villa, Paolo; Bolpagni, Rossano; Pinardi, Monica; Tóth, Viktor R.

doi:10.1186/s13007-021-00816-4

Cited by 16 publications

(11 citation statements)

References 104 publications

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“…The hyperspectral data of the cotton canopy were obtained using an SR-3500 portable full-spectrum ground object spectrometer (Spectral Evolution Company, Lawrence, MA, USA) [21,22], and the parameters are shown in Table 3; we used the difference method to make the bandwidth of the instrument spectrum consistent, which was 1 nm. The canopy spectra of drip-irrigated cotton were measured on sunny, cloudless, windless days or on days with low wind speed (≤grade 2), and the time range of the measurement day was controlled from 12:00 to 16:00.…”

Section: Acquisition Of Hyperspectral Data Of the Cotton Canopymentioning

confidence: 99%

Estimation of Nitrogen Content Based on the Hyperspectral Vegetation Indexes of Interannual and Multi-Temporal in Cotton

Chen

Zhang

et al. 2022

Agronomy

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Crop nitrogen is an efficient index for estimating crop yield. Using hyperspectral information to monitor nitrogen in cotton information in real time can help guide cotton cultivation. In this study, we used drip-irrigation cotton in Xinjiang as the research object and employed various nitrogen treatments to explore the correlation between hyperspectral vegetation indexes and leaf nitrogen concentration (LNC) and the canopy nitrogen density (CND) of cotton in different growth periods and interannual. We employed 30 published hyperspectral vegetation indexes obtained through spectral monitoring in 2019 and 2020 to screen for hyperspectral vegetation indexes highly correlated with the nitrogen in cotton indexes. Based on the same group of hyperspectral vegetation indexes, interannual and multi-temporal nitrogen estimation models of cotton were established using three modeling methods: simple multiple linear regression (MLR), partial least-squares regression (PLSR), and support vector regression (SVR). The results showed the following: (1) The correlations between LNC and CND and vegetation index in individual growth periods of cotton were lower than those for the entire growth period. The correlations between hyperspectral vegetation indexes and cotton LNC, CND, leaf area index (LAI), and aboveground biomass (AGB), were significantly different between years and varieties. The relatively stable indexes between vegetation and LNC were TCARI, PRI, CCRI, and SRI-2, and the absolute values of correlation were 0.251~0.387, 0.239~0.422, 0.245~0.387, and 0.357~0.533. In addition, the correlation between CIred-edge and REIlinear and group indicators (CND, AGB, and LAI) was more stable. (2) In the models established by MLR, PLSR, and SVR, the R2 value from the SVR method was higher in the estimation model based on the entire growth period data and LNC and CND. (3) Using the same group of selected hyperspectral vegetation indexes to estimate nitrogen in cotton in different growth stages, the accuracy of the estimation model of canopy nitrogen density (CND) was higher than that of the estimation model for leaf nitrogen concentration. The canopy nitrogen density most stable model was established by MLR at the flowering and boll stages and the full-boll stage with R2 = 0.532~0.665. This study explored the application potential of hyperspectral vegetation indexes to the nitrogen of drip-irrigated cotton, and the results provide a theoretical basis for hyperspectral monitoring for crop nutrients and canopy structure.

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Section: Acquisition Of Hyperspectral Data Of the Cotton Canopymentioning

confidence: 99%

Estimation of Nitrogen Content Based on the Hyperspectral Vegetation Indexes of Interannual and Multi-Temporal in Cotton

Chen

Zhang

et al. 2022

Agronomy

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“…the mean reflectance within 510 and 520 nm, linked to absorbance band for carotenoids and anthocyanins (Féret et al 2017, Peters and Noble 2020); 3) r625, i.e. the mean reflectance within 610 and 640 nm, linked to chlorophyll‐a and ‐b content and their balance (Villa et al 2021); and iv) r800, i.e. the mean reflectance within 780 and 820 nm, linked to mesophyll structure complexity (Féret et al 2017).…”

Section: Methodsmentioning

confidence: 99%

“…In addition to typically measured leaf traits, reflectance spectroscopy has been increasingly employed over the last few years as a high-throughput tool to quantify foliar spectral properties, which can be related to structural, biochemical (e.g. pigments) and physiological characteristics of plants (Klančnik et al 2018, Jacquemoud and Ustin 2019, Villa et al 2021, providing integrative descriptions of plant phenotypes (Kothari and Schweiger 2022).…”

mentioning

confidence: 99%

Multidimensional trait variability in a widespread, Paleoarctic macrophyte: functional, spectral and genetic drivers

Dalla Vecchia,

Coppi,

Beatrice Castellani

et al. 2023

Oikos

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Understanding how environmental conditions and plant functional variation are mutually related is critical to improving our comprehension of plant adaptations. In this context, our knowledge of the interlinks between plant functional, spectral and genetic traits and environmental filters is still very limited, especially for wetland species. To gain new insights on this topic, a multidimensional dataset, centred on the widespread macrophyte species Nuphar lutea, was assembled by collecting data on functional traits (including spectral traits), genetic metrics and environmental determinants from 28 plots spanning north‐central Italy. A strong environmental filter acts on all traits (morphological, biochemical, spectral and the genetic diversity metrics) resulting in significant local control over trait patterns, exemplified by the discrimination value of water electrical conductivity. This is further reinforced by the key contribution of sediment variables in explaining traits variation. Site‐specific environmental conditions were reflected in different patterns of genetic diversity, suggesting a long‐term effect of environmental filters on genotypes as well. High water conductivity – in our study sites indicative of long‐term hydrogeological settings – is linked to more acquisitive behaviour in N. lutea and a progressive reduction in its genetic diversity, while high nutrients availability in sediments promotes higher leaf traits performance. This study better explores how high variability in leaf traits reinforces current genetic and mechanistic knowledge about competitive strategies in the key aquatic plant N. lutea, by testing the effectiveness of a novel integrative approach to assess multiple sources of plant functional variation.

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“…Spectral indices known for their sensitivity to vegetation conditions at canopy scale were derived from Sentinel-2 scenes at 10m resolution as surrogates of specific reed functional traits, or spectro-functional traits: i) the Water Adjusted Vegetation Index (WAVI), a proxy of aquatic vegetation canopy density and fractional cover (Villa et al, 2014); ii) the Green Leaf Index (GLI), a proxy of canopy greenness and fraction of absorbed PAR (Hunt et al, 2011); and iii) the Normalized Difference Spectral Index for LMA (NDSI LMA ), a proxy of leaf mass per area in macrophytes, aggregated at canopy scale (Villa et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Assessing the haplotype and spectro-functional traits interactions to explore the intraspecific diversity of common reed in Central Italy

Castellani

Coppi

Bolpagni

et al. 2022

Preprint

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As reflectance measured via remote sensing is connected to plant light use and morpho-structural features, it can be used to derive spectral proxies of functional traits, or spectro-functional traits. Focusing on disentangling intraspecific trait variability in nature, we evaluated the links between haplotype and spectro-functional traits in Phragmites australis populations. Haplotypes sequencing and multi-seasonal satellite data were used to evaluate the temporal dynamics of spectro-functional traits for reed stands sampled from seven wetlands in Central Italy, investigating meteo-climatic drivers, the differences across ecological statuses, sites, and haplotypes, and quantifying intraspecific variability due to haplotype or phenotypic plasticity. Five haplotypes were identified, including an unedited one, which explained a substantial portion of intraspecific variability in canopy traits, differing for aquatic and terrestrial stands. We found that meteo-climatic factors impact on aquatic reeds traits (not over terrestrial ones) and a dualism between most and less common haplotypes, pointing to different evolutionary strategies. Dynamics in reed canopy traits were linked to ecological status, site and haplotype, with signs of haplotype-variable effects of dieback on aquatic stands. Evaluating the spectro-functional variability over reed haplotypes may provide a straightforward approach for monitoring the genotype-phenotype relations across scales and assessing their ecological drivers.

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

Cited by 16 publications

References 104 publications

Estimation of Nitrogen Content Based on the Hyperspectral Vegetation Indexes of Interannual and Multi-Temporal in Cotton

Estimation of Nitrogen Content Based on the Hyperspectral Vegetation Indexes of Interannual and Multi-Temporal in Cotton

Multidimensional trait variability in a widespread, Paleoarctic macrophyte: functional, spectral and genetic drivers

Assessing the haplotype and spectro-functional traits interactions to explore the intraspecific diversity of common reed in Central Italy

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