Estimation of essential vegetation variables in a dehesa ecosystem using reflectance factors simulated at different phenological stages




radiative transfer models, PROSAIL FLIGHT, vegetation indices, PLSR, biophysical variables, tree-grass ecosystems, phenophases


Mixed vegetation systems such as wood pastures and shrubby pastures are vital for extensive and sustainable livestock production as well as for the conservation of biodiversity and provision of ecosystem services, and are mostly located in areas that are expected to be more strongly affected by climate change. However, the structural characteristics, phenology, and the optical properties of the vegetation in these mixed -ecosystems such as savanna-like ecosystems in the Iberian Peninsula which combines herbaceous and/or shrubby understory with a low density tree cover, constitute a serious challenge for the remote sensing studies. This work combines physical and empirical methods to improve the estimation of essential vegetation variables: leaf area index (LAI, m2 / m2 ), leaf (Cab,leaf, μg / cm2 ) and canopy(Cab,canopy, g / m2 ) chlorophyll content, and leaf (Cm, leaf, g / cm2 ) and canopy (Cm,canopy, g / m2 ) dry matter content in a dehesa ecosystem. For this purpose, a spectral simulated database for the four main phenological stages of the highly dynamic herbaceous layer (summer senescence, autumn regrowth, greenness peak and beginning of senescence), was built by coupling PROSAIL and FLIGHT radiative transfer models. This database was used to calibrate different predictive models based on vegetation indices (VI) proposed in the literature which combine different spectral bands; as well as Partial Least Squares Regression (PLSR) using all bands in the simulated spectral range (400-2500 nm). PLSR models offered greater predictive power (R2 ≥ 0.93, RRMSE ≤ 10.77 %) both for the leaf and canopy- level variables. The results suggest that directional and geometric effects control the relationships between simulated reflectance factors and the foliar parameters. High seasonal variability is observed in the relationship between biophysical variables and IVs, especially for LAI and Cab, which is confirmed in the PLSR analysis. The models developed need to be validated with spectral data obtained either with proximal or remote sensors.


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Author Biographies

M.P. Martín, Consejo Superior de Investigaciones Científicas (CSIC)

Investigadora científica y responsable del Laboratorio de Espectro-radiometria y Teldetección Ambiental (SpecLab) del CSIC

J. Pacheco-Labrador, Max Planck Institute for Biogeochemistry

Post-doc researcher (Biogeochemical Integration Department)

R. González-Cascón, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)

Científica Titular. Departamento de Medio Ambiente

G. Moreno, Universidad de Extremadura

Profesor titular de universidad. Departamento de Biología vegetal, ecología y ciencias de la tierra

M. Migliavacca, Max Planck Institute for Biogeochemistry

Scientist/Group Leader(Biogeochemical Integration Department)

M. García, Universidad de Alcalá

Profesor ayudante doctor. Dept. de Geología, Geografía y Medio Ambiente

M. Yebra, The Australian National University

Senior Lecturer in Environment and Engineeringat the Fenner School of Environment & Society and Research School of Aerospace, Mechanical, and Environmental Engineering ),Mission Specialist of theANU Institute for Space

Fenner School of Environment and Society. Australian National University
Bushfire & Natural Hazards Cooperative Research Centre

D. Riaño-Arribas, Consejo Superior de Investigaciones Científicas (CSIC); Unversity of California

Consejo Superior de Investigaciones Científicas
Center for Spatial Technologies and Remote Sensing (CSTARS), University of California


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