Estimación de variables esenciales de la vegetación en un ecosistema de dehesa utilizando factores de reflectividad simulados estacionalmente

Martín, M. Pilar; Pacheco‐Labrador, Javier; González-Cascón, Rosario; Moreno, Gerardo; Migliavacca, Mirco; Garcı́a, Mariano; Yebra, Marta; Riaño, David

doi:10.4995/raet.2020.13394

Cited by 5 publications

(3 citation statements)

References 71 publications

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“…y algunos alcornoques (Quercus suber L.) y quejigos aislados (Quercus faginea Lam.) (Martín et al, 2020). El estrato herbáceo cubre prácticamente la totalidad de la zona de estudio y está conformado por una gran variedad de especies, en su mayoría anuales con un ciclo fenológico muy dinámico.…”

Section: áRea De Estudio Y Diseño Experimentalunclassified

Análisis de la diversidad funcional del estrato herbáceo en un ecosistema de dehesa a partir de datos hiperespectrales "in situ"

Gonzalo

Burchard-Levine

Rolo

et al. 2022

CIG

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The aim of this paper is the estimation of functional diversity (FD) of the herbaceous stratum in a ‘dehesa’ ecosystem using hyperspectral data obtained with an ASD FieldSpec® 3 (Analytical Spectral Devices Inc., Boulder, CO, EE. UU.) spectroradiometer. Optical data were correlated with biophysical variables (specific leaf area (SLA), above-ground biomass (AGB), leaf area index (LAI) and nitrogen content (N%)) and traditional diversity indices (Shannon and Evenness) and functional diversity indices (FDis) using as predictor variables: a) vegetation indices (VIs) and simple regression methods, and b) spectral bands and Partial Least Squares Regression (PLSR). Correlations, especially with biophysical variables, improved substantially when using hyperspectral information (R2 0.6, rRMSE 0.1), which confirms the interest of the spectral dimension to estimate the functional diversity of a complex ecosystem such as semi-arid grasslands.

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Section: áRea De Estudio Y Diseño Experimentalunclassified

Análisis de la diversidad funcional del estrato herbáceo en un ecosistema de dehesa a partir de datos hiperespectrales "in situ"

Gonzalo

Burchard-Levine

Rolo

et al. 2022

CIG

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“…Therefore, these parameters varied daily. LAI was derived from an empirical relationship with NDVI (Martín et al, 2020) (roughly three times NDVI). C ab was predicted using a model fit from field spectral measurements and pigment content determined from destructive samples of 25 × 25 cm grass patches, sampled in several campaigns between 2017 and 2019 (Martín et al, 2020;Melendo-Vega et al, 2018).…”

Section: Journal Of Geophysical Research: Biogeosciencesmentioning

confidence: 99%

“…LAI was derived from an empirical relationship with NDVI (Martín et al, 2020) (roughly three times NDVI). C ab was predicted using a model fit from field spectral measurements and pigment content determined from destructive samples of 25 × 25 cm grass patches, sampled in several campaigns between 2017 and 2019 (Martín et al, 2020;Melendo-Vega et al, 2018). C ab was estimated as C ab = (0.007 − (0.0001/NDVI) • log (1 + (NDVI/0.0001))) • 4,443, while C ca was predicted as a function of C ab , according to the linear model C ca = 0.24 • C ab + 0.67 using field information from the same data set.…”

Section: Journal Of Geophysical Research: Biogeosciencesmentioning

confidence: 99%

Performance of Singular Spectrum Analysis in Separating Seasonal and Fast Physiological Dynamics of Solar‐Induced Chlorophyll Fluorescence and PRI Optical Signals

et al. 2021

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High temporal resolution measurements of solar-induced chlorophyll fluorescence (F) and the Photochemical Reflectance Index (PRI) encode vegetation functioning. However, these signals are modulated by time-dependent processes. We tested the applicability of the Singular Spectrum Analysis (SSA) for disentangling fast components (physiology-driven) and slow components (controlled by structural and biochemical properties) from PRI, far-red F (F 760 ), and far-red apparent fluorescence yield (Fy * 760 ). The proof of concept was developed on spectral and flux time series simulated with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. This allowed the evaluation of SSA decomposition against variables that are independent of physiology or are modified by it. Slow SSA-decomposed components of PRI and Fy * 760 showed high correlations with the reference variables (R 2 = 0.97 and 0.96, respectively). Fast SSA-decomposed components of PRI and Fy * 760 were better related to the physiological reference variables than the original signals during periods when leaf area index (LAI) was above 1 m 2 m −2 . The method was also successfully applied to predict light-use efficiency (LUE) from the fast SSA-decomposed components of PRI (R 2 = 0.70) and Fy * 760 (R 2 = 0.68) when discarding data modeled with LAI < 1 m 2 m −2 and short-wave radiation R in < 250 W m −2 . The method was then tested on data acquired in a Mediterranean grassland. In this case, the fast SSA-decomposed component of apparent LUE * showed a stronger correlation with the fast SSA-decomposed component of Fy * 760 (R 2 = 0.42) than with original Fy * 760 (R 2 = 0.01). SSA-based approach is a promising tool for decoupling physiological information from measurements acquired with automated proximal sensing systems.Plain Language Summary A fraction of the solar light, which is absorbed by leaves but is not used during photosynthesis, is released through heat or as chlorophyll fluorescence (F), a small emission of energy. Recently, it became possible to indirectly estimate the heat and F by measuring the solar light incoming and reflected from leaves using high-resolution optical instruments. Heat release can be monitored with the Photochemical Reflectance Index (PRI). While both PRI and F are theoretically linked to the processes associated with photosynthesis, there is a need to remove the disturbing effects from these signals. We tested whether the Singular Spectrum Analysis (SSA) method can identify at which temporal scale (e.g., seasonal, diurnal) physiological processes (i.e., photosynthesis) and vegetation biophysical changes (e.g., phenology) drive variability in PRI and F. We applied the method on artificial time series of PRI and F simulated with a model (Soil Canopy Observation of Photochemistry and Energy fluxes [SCOPE]) and found that SSA can successfully split these signals into several components recognized as slow (seasonally changing structure and pigments) and fast (physiological response to stress) processes. The method...

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senSCOPE: Modeling radiative transfer and biochemical processes in mixed canopies combining green and senescent leaves with SCOPE

Pacheco‐Labrador

El-Madany

Tol

et al. 2020

Preprint

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Semi-arid grasslands and other ecosystems combine green and senescent leaves featuring different biochemical and optical properties, as well as functional traits. Knowing how these properties vary is necessary to understand the functioning of these ecosystems. However, differences between green and senescent leaves are not considered in recent models representing radiative transfer, heat, water and CO2 exchange such as the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE). Neglecting the contribution of senescent leaves to the optical and thermal signal of vegetation limits the possibilities to use remote sensing information for studying these ecosystems; as well as neglecting their lack of photosynthetic activity increases uncertainty in the representation of ecosystem fluxes. In this manuscript we present senSCOPE as a step towards a more realistic representation of mixed green and senescent canopies. senSCOPE is a modified version of SCOPE model that describes a canopy combining green and senescent leaves with different properties and function. The model relies on the same numerical solutions than SCOPE, but exploits the linear nature of the scattering coefficients to combine optical properties of both types of leaf. Photosynthesis and transpiration only take place in green leaves; and different green and senescent leaf temperatures are used to close the energy balance. Radiative transfer of sun-induced fluorescence (SIF) and absorptance changes induced by the xanthophyll cycle action are also simulated. senSCOPE is evaluated against SCOPE both using synthetic simulations, forward simulations based on observations in a Mediterranean tree-grass ecosystem, and inverting dataset of ground measurements of reflectance factors, SIF, thermal radiance and gross primary production on a heterogeneous and partly senescent Mediterranean grassland. Results show that senSCOPE outputs vary quite linearly with the fraction of green leaf area, whereas SCOPE does not respond linearly to the effective leaf properties, calculated as the weighted average of green and senescent leaf parameters. Inversion results and pattern-oriented model evaluation show that senSCOPE improves the estimation of some parameters, especially chlorophyll content, with respect SCOPE retrievals during the dry season. Nonetheless, inaccurate knowledge of the optical properties of senescent matter still complicates model inversion. senSCOPE brings new opportunities for the monitoring of canopies mixing green and senescent leaves, and for improving the characterization of the optical properties of senescent material.

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Estimación de variables esenciales de la vegetación en un ecosistema de dehesa utilizando factores de reflectividad simulados estacionalmente

Cited by 5 publications

References 71 publications

Análisis de la diversidad funcional del estrato herbáceo en un ecosistema de dehesa a partir de datos hiperespectrales "in situ"

Análisis de la diversidad funcional del estrato herbáceo en un ecosistema de dehesa a partir de datos hiperespectrales "in situ"

Performance of Singular Spectrum Analysis in Separating Seasonal and Fast Physiological Dynamics of Solar‐Induced Chlorophyll Fluorescence and PRI Optical Signals

senSCOPE: Modeling radiative transfer and biochemical processes in mixed canopies combining green and senescent leaves with SCOPE

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