Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones. <br><br> Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in <i>R</i>² and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT (RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can explain these differences as the portion of vegetation observed by MODIS is larger than when using proximal sensing including the spectral response from scattered trees and its shadows. CWC was better predicted than the other two water content metrics, probably because CWC depends on LAI, that shows a notable seasonal variation in this ecosystem. Strong statistical relationship was found between empirical models using indices sensible to chlorophyll activity (NDVI or EVI which are not directly related to water content) due to the close relationship between LAI, water content and chlorophyll activity in grassland cover, which is not true for other types of vegetation such as forest or shrubs. The empirical methods tested outperformed FMC and CWC products based on radiative transfer model inversion

Evolución del comportamiento espectral y la composición química en el dosel arbóreo de una dehesa

Revista oficial de la Asociación Española de Teledetección[EN] In the context of the BIOSPEC and FLUXPEC projects (http://www.lineas.cchs.csic.es/fluxpec/), spectral and biophysical variables measurements at leaf level have been conducted in the tree canopy of a holm oak dehesa (Quercus ilex) ecosystem during four vegetative periods. Measurements of bi-conical reflectance factor of intact leaf (ASD Fieldspec 3® spectroradiometer), specific leaf mass (SLM), leaf water content (LWC), nutrient (N, P, K, Ca, Mg, Mn, Fe, and Zn) and chlorophyll concentration were performed. The spectral measurements have been related with the biophysical variables by stepwise and partial least squares regression analyses. These analyses allowed to identify the spectral bands and regions that best explain the evolution of the biophysical variables and to estimate the nutrient contents during the leaf maturation process. Statistically significant estimates of the majority of the variables studied were obtained. Wavelengths that had the highest contributions explaining the chemical composition of the forest canopy were located in spectral regions of the red edge, the green visible region, and the shortwave infrared.[ES] En el contexto de los proyectos BIOSPEC y FLUXPEC (http://www.lineas.cchs.csic.es/fluxpec/), se han rea-lizado mediciones espectrales y de variables biofísicas a nivel de hoja en el dosel arbóreo de una dehesa de encina (Quercus ilex) durante cuatro períodos vegetativos. Se han llevado a cabo mediciones de reflectividad bi-cónica de hoja intacta (ASD Fieldspec 3®spectroradiometer), masa foliar específica (SLM), contenido de agua (LWC), concen-traciones de nutrientes (N, P, K, Ca, Mg, Mn, Fe, y Zn) y clorofilas. Las mediciones espectrales se han relacionado con las variables biofísicas mediante análisis de regresión múltiple por pasos (SWR) y regresión de mínimos cuadrados parciales (PLSR). Estos análisis han permitido identificar las bandas y regiones espectrales que explican la evolución de las variables biofísicas y estimar los contenidos de nutrientes a lo largo del proceso de maduración de las hojas en la copa. Se han obtenido modelos estadísticamente significativos para la mayoría de las variables foliares estudiadas. Las longitudes de onda que aportan mayor información sobre la composición química del dosel, se encuentran en las regiones espectrales del límite del rojo, la región verde del visible y el infrarrojo medio de onda corta (SWIR).Este trabajo ha sido financiado por los proyectos BIOSPEC (CGL2008-02301/CLI, Ministerio de Ciencia e innovación) y FLUXPEC (CGL-2012 34383, Ministerio de Economía y Competitividad).González-Cascón, R.; Pacheco-Labrador, J.; Martín, MP. (2016). Evolution of spectral behavior and chemical composition in the tree canopy of a dehesa ecosystem. Revista de Teledetección. (46):31-43. https://doi.org/10.4995/raet.2016.5688SWORD31434

Performance of singular spectrum analysis in separating seasonal and fast physiological dynamics of solar-induced chlorophyll fluorescence and PRI optical signals

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 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.Peer reviewe

Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones. Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in R2 and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT (RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can explain these differences as the portion of vegetation observed by MODIS is larger than when using proximal sensing including the spectral response from scattered trees and its shadows. CWC was better predicted than the other two water content metrics, probably because CWC depends on LAI, that shows a notable seasonal variation in this ecosystem. Strong statistical relationship was found between empirical models using indices sensible to chlorophyll activity (NDVI or EVI which are not directly related to water content) due to the close relationship between LAI, water content and chlorophyll activity in grassland cover, which is not true for other types of vegetation such as forest or shrubs. The empirical methods tested outperformed FMC and CWC products based on radiative transfer model inversion

Estimation of real evapotranspiration (ETR) and potential evapotranspiration (ETP) in the southwest of the Buenos Aires Province (Argentina) using MODIS images

[EN] Using regression analysis between actual evapotranspiration (ETR) and potential evapotranspiration (ETP) values obtained in seven meteorological observatories and remote sensing derived data from MODIS images (Surface temperature and Normalized Difference Vegetation Index - NDVI) models for estimating ETR and ETP in the southwest of the Buenos Aires Province (Argentina) were developed for the 2000–2014 period. Both models were satisfactorily evaluated in the meteorological observatories used. A regression model was adjusted for ETR with a determination coefficient of 0,6959. Regression model was nonlinear in the case of the ETP variable with a determination coefficient of 0,8409. The individual regression analysis for each meteorological observatories explicate the behavior of the regression for the total data set of ETR and ETP. According to these results, the utility of remote sensing in determination of ETR and ETP in areas without meteorological data was confirmed.[ES] Se han elaborado modelos para el cálculo de evapotranspiración real (ETR) y de evapotranspiración poten-cial (ETP) en base a un análisis de regresión múltiple entre dichos parámetros estimados en siete estaciones meteoro-lógicas y dos variables derivadas de imágenes satelitales MODIS: Temperatura de Superficie (TS) e Índice Normalizado de Diferencia de Vegetación (Normalized Difference Vegetation Index -NDVI). Dichos modelos permitieron estimar ETR y ETP en el sudoeste de la provincia de Buenos Aires (Argentina) en base al análisis del período 2000/2014. Ambos fueron calibrados satisfactoriamente en cada una de las estaciones meteorológicas utilizadas. Se ajustó un modelo de regresión múltiple lineal a la variable ETR, con un coeficiente de determinación de 0,6959. En el caso de la variable ETP el modelo de regresión ajustado fue no lineal y su coeficiente de determinación de 0,8409. El análisis de regresión individual de cada una de las estaciones meteorológicas permitió explicar el comportamiento de la regresión basada en el conjunto completo de datos, tanto para la variable ETR como para la variable ETP. Los resultados refuerzan la ventaja de la teledetección en la estimación de ETR y ETP en zonas en donde no se dispone de datos meteorológicos.Marini, F.; Santamaría, M.; Oricchio, P.; Di Bella, CM.; Basualdo, A. (2017). Estimación de evapotranspiración real (ETR) y de evapotranspiración potencial (ETP) en el sudoeste bonaerense (Argentina) a partir de imágenes MODIS. Revista de Teledetección. (48):29-41. doi:10.4995/raet.2017.6743.SWORD294148Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998). A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. 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Estimation of grassland biophysical parameters in a “dehesa” ecosystem from field spectroscopy and airborne hyperspectral imagery

[EN] The aim of this paper is the estimation of biophysical vegetation parameters from its optical properties. The variables Fuel Moisture Content (FMC), Canopy Water Content (CWC), Leaf Area Index (LAI), dry matter (Cm) and AboveGround Biomass (AGB) were estimated in the laboratory from vegetation samples collected simultaneously with the acquisition of spectral data from the Compact Airborne Spectrographic Imager (CASI) sensor and the field spectroradiometer ASD FieldSpec® 3. Spectral vegetation indices found in the literature were computed from hyperspectral data. Their linear relationships with the biophysical variables measured in the field were analysed. Results show consistent relationships between analysed biophysical parameters and spectral indices, mainly those using SWIR and red-egde bands which reveal the importance of these spectral regions for the estimation of biophysical variables in herbaceous covers. Determination coefficients (R2) above 0.91 and RRMSE of 21.4% have been obtained for the spectral indexes calculated whit ASD data, and 0.91 R2 and RRMSE of 15.5% for the spectral indexes calculated whit CASI data.[ES] Este trabajo aborda la estimación de variables biofísicas de un pastizal de dehesa a partir de información óptica generada por sensores próximos y remotos. Las variables de contenido de humedad del combustible (FMC), contenido de agua del dosel (CWC), índice de área foliar (LAI), materia seca (Cm) y biomasa superficial (AGB) fueron estimadas en laboratorio a partir de muestras de vegetación tomadas simultáneamente a la adquisición de datos hiperespectrales del sensor Compact Airbone Spectrographic Imager (CASI) y del espectro-radiómetro de campo ASD FieldSpec®3. A partir de la información espectral se han calculado diversos índices extraídos de la literatura y se han analizado las relaciones lineales existentes con las variables biofísicas medidas en campo. Los resultados muestran relaciones consistentes entre las variables biofísicas y los índices espectrales, especialmente en el caso de los índices basados en bandas del infrarrojo medio de onda corta (SWIR) y del red-edge, poniendo de manifiesto la importancia de estas regiones en la estimación de variables biofísicas en cubiertas de pastizal. Se han obteniendo coeficientes de determinación (R2) superiores a 0,91 y un error cuadrático medio relativo (RRMSE) de 21,4%, para los índices espectra-les calculados con datos ASD; yR2 de 0,91 y RRMSE de 15,5% para los índices espectrales calculados con datos CASI.Este trabajo se ha realizado en el contexto de los proyectos BIOSPEC (CGL2008-02301/CLI) financiado por el Ministerio e Innovación y FLUχPEC (CGL2012-34383) financiado por el Ministerio de Economía y Competitividad. Agradecemos al Ministerio de Educación, Cultura y Deporte la financiación recibida a través del programa de becas FPU del investigador predoctoral José Ramón Melendo. Nuestro agradecimiento al personal de SpecLab-CSIC, Universidad de Alcalá e Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria que ha participado en la recogida y procesamiento de datos.Melendo-Vega, JR.; Martín, MP.; Vilar Del Hoyo, L.; Pacheco-Labrador, J.; Echavarría, P.; Martínez-Vega, J. (2017). Estimación de variables biofísicas del pastizal en un ecosistema de dehesa a partir de espectro-radiometría de campo e imágenes hiperespectrales aeroportadas. Revista de Teledetección. (48):13-28. https://doi.org/10.4995/raet.2017.7481SWORD132848Haboudane, D. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3), 337-352. doi:10.1016/j.rse.2003.12.013Hardisky, M.A., Klemas, V., Smart, R.M. 1983. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrametry Engineering and Remote Sensing, 49, 77-83Hernández-Clemente, R., Navarro-Cerrillo, R. M., Suárez, L., Morales, F., & Zarco-Tejada, P. J. (2011). Assessing structural effects on PRI for stress detection in conifer forests. Remote Sensing of Environment, 115(9), 2360-2375. doi:10.1016/j.rse.2011.04.036Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V., & Bonfil, D. J. (2011). LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sensing of Environment, 115(8), 2141-2151. doi:10.1016/j.rse.2011.04.018Hilker, T., Coops, N. C., Hall, F. G., Black, T. A., Wulder, M. A., Nesic, Z., & Krishnan, P. (2008). Separating physiologically and directionally induced changes in PRI using BRDF models. Remote Sensing of Environment, 112(6), 2777-2788. doi:10.1016/j.rse.2008.01.011Hill, M.J., Hanan, N.P., Hoffmann, W., Scholes, R., Prince, S., Ferwerda, J., Lucas, R.M., Baker, I., Arneth, A., Higgings, S.I., Barret, D.J., Disney, M., Hutley, L. 2011. Remote sensing and modeling of savannas: The state of the dis-union. 34th International Symposium on Remote Sensing of Environment. Sydney, 1-6.HongRui, R., GuangSheng, Z., Feng, Z., XinShi, Z. 2011. Evaluating cellulose absorption index (CAI) for non-photosynthetic biomass estimation in the desert steppe of Inner Mongolia. Chinese Science Bulletin, 57, 1716-1722.Huete, A. . (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295-309. doi:10.1016/0034-4257(88)90106-xKuusk, A. (1995). A fast, invertible canopy reflectance model. Remote Sensing of Environment, 51(3), 342-350. doi:10.1016/0034-4257(94)00059-vLee, K.-S., Cohen, W. B., Kennedy, R. E., Maiersperger, T. K., & Gower, S. T. (2004). Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sensing of Environment, 91(3-4), 508-520. doi:10.1016/j.rse.2004.04.010Li, W., Niu, Z., Liang, X., Li, Z., Huang, N., Gao, S., … Muhammad, S. (2015). Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling. International Journal of Applied Earth Observation and Geoinformation, 41, 88-98. doi:10.1016/j.jag.2015.04.020Liu, J., Miller, J.R., Haboudane, D., Pattey, E. 2004. Exploring the relationship between red edge parameters and crop variables for precision agriculture. 2004 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Anchorage, 1276-1279.Mahalanobis, P.C. 1936. On the generalised distance in statistics. Proceedings National Institute of Science, India, 49-55Nagler, P. L., Inoue, Y., Glenn, E. ., Russ, A. ., & Daughtry, C. S. . (2003). Cellulose absorption index (CAI) to quantify mixed soil–plant litter scenes. Remote Sensing of Environment, 87(2-3), 310-325. doi:10.1016/j.rse.2003.06.001Pacheco-Labrador, J., González-Cascón, R., Martín, M. P., & Riaño, D. (2014). Understanding the optical responses of leaf nitrogen in Mediterranean Holm oak (Quercus ilex) using field spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 26, 105-118. doi:10.1016/j.jag.2013.05.013Perez-Priego, O., Guan, J., Rossini, M., Fava, F., Wutzler, T., Moreno, G., … Migliavacca, M. (2015). Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remote-sensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem. Biogeosciences, 12(21), 6351-6367. doi:10.5194/bg-12-6351-2015Pinty, B., & Verstraete, M. M. (1992). GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio, 101(1), 15-20. doi:10.1007/bf00031911Privette, J. ., Myneni, R. ., Knyazikhin, Y., Mukelabai, M., Roberts, G., Tian, Y., … Leblanc, S. . (2002). Early spatial and temporal validation of MODIS LAI product in the Southern Africa Kalahari. 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Proceeding, 3rd Earth Resource Technology Satellite (ERTS) Symposium, NASA, Washington DC, 1, 48-62SCHMIDTLEIN, S. (2004). Mapping of continuous floristic gradients in grasslands using hyperspectral imagery. Remote Sensing of Environment, 92(1), 126-138. doi:10.1016/j.rse.2004.05.004Serrano, L., Peñuelas, J., & Ustin, S. L. (2002). Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data. Remote Sensing of Environment, 81(2-3), 355-364. doi:10.1016/s0034-4257(02)00011-1SHAPIRO, S. S., & WILK, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3-4), 591-611. doi:10.1093/biomet/52.3-4.591Smith, G. M., & Milton, E. J. (1999). The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal of Remote Sensing, 20(13), 2653-2662. doi:10.1080/014311699211994Wieneke, S., Ahrends, H., Damm, A., Pinto, F., Stadler, A., Rossini, M., & Rascher, U. (2016). 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Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85(1), 109-124. doi:10.1016/s0034-4257(02)00197-

Evalutating the potential of desis to infer plant taxonomical and functional diversities in europwean forests

Abstract. Tackling the accelerated human-induced biodiversity loss requires tools able to map biodiversity and its changes globally. Remote sensing (RS) offers unique capabilities of characterizing Earth surfaces; therefore, it could map plant biodiversity continuously and globally. This approach is supported by the Spectral Variation Hypothesis (SVH), which states that spectra and species (taxonomic and trait) diversities are linked through environmental heterogeneity. In this work, we evaluate the capability of the DESIS hyperspectral imager to capture plant diversity patterns as measured in dedicated plots of the network FunDivEUROPE. We computed functional and taxonomical diversity metrics from field taxonomic, structural, and foliar measurements in vegetation plots sampled in Spain and Romania. In addition, we also computed functional diversity metrics both from the DESIS reflectance factors and from vegetation parameters estimated via inversion of a radiative transfer model. Results showed that only metrics computed from spectral reflectance were able to capture taxonomic variability in the area. However, the lack of sensitivity was related to the insufficient plot size and the lack of spatial match between remote sensing and field data, but also the differences between the information contained in the field traits and remote sensing data, and the potential uncertainties in the remote estimates of vegetation parameters. Thus, while DESIS showed some sensitivity to plant diversity, further efforts are needed to deploy suitable biodiversity evaluation and validation plots and networks that support the development of biodiversity remote sensing products

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

[ES] Los pastos arbolados y arbustivos son vitales para la producción ganadera extensiva y sostenible, la conservación de la biodiversidad y la provisión de servicios ecosistémicos y se localizan en áreas que serán previsiblemente más afectadas por el cambio climático. Sin embargo, las características estructurales, fenológicas, y las propiedades ópticas de la vegetación en estos ecosistemas mixtos, como los ecosistemas adehesados en la Península Ibérica que combinan un estrato herbáceo y/o arbustivo con un dosel arbóreo disperso, constituyen un serio desafío para su estudio mediante teledetección. Este trabajo combina métodos físicos y empíricos para la estimación de variables de la vegetación esenciales para la modelización de su funcionamiento: índice de área foliar (LAI, m2 /m2 ), contenido en clorofila a nivel de hoja (Cab,leaf, μg/cm2 ) y dosel (Cab,canopy, g/m2 ) y contenido en materia seca a nivel de hoja (Cm,leaf, g/cm2 ) y dosel (Cm,canopy, g/m2), en un ecosistema de dehesa. Para este propósito se construyó una base de datos espectral simulada considerando las cuatro principales etapas fenológicas del estrato herbáceo, el más dinámico del ecosistema, (rebrote otoñal, máximo verdor, inicio de la senescencia y senescencia estival) mediante la combinación de los modelos de transferencia radiativa PROSAIL y FLIGHT. Esta base de datos se empleó para ajustar diferentes modelos predictivos basados en índices de vegetación (IV) propuestos en la literatura y en Partial Least Squares Regression (PLSR). PLSR permitió obtener los modelos con mayor poder de predicción (R2  ≥ 0,93, RRMSE ≤ 10,77 %), tanto para las variables a nivel de hoja como a nivel de dosel. Los resultados sugieren que los efectos direccionales y geométricos controlan las relaciones entre los factores de reflectividad (R) simulados y los parámetros foliares. Se observa una alta variabilidad estacional en la relación entre variables biofísicas e IVs, especialmente para LAI y Cab que se confirma en el análisis PLSR. Los modelos desarrollados deben ser aún validados con datos espectrales medidos con sensores próximos o remotos.[EN] 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.ste estudio se ha llevado a cabo en el contexto de los proyectos FLUXPEC (CGL2012-34383) y SynerTGE (CGL2015-69095-R, MINECO/FEDER,UE) financiados por el Ministerio de Economía y Competitividad. Agradecemos el apoyo de los proyectos IB16185 de la Junta de Extremadura, MoReDEHESHyReS (No. 50EE1621, Agencia Espacial Alemana (DLR) y Ministerio Alemán de Asuntos Económicos y Energía) y el premio de la fundación Alexander von Humboldt vía Premio Max-Planck a Markus ReichsteinMartín, MP.; Pacheco-Labrador, J.; González-Cascón, R.; Moreno, G.; Migliavacca, M.; García, M.; Yebra, M.... (2020). Estimación de variables esenciales de la vegetación en un ecosistema de dehesa utilizando factores de reflectividad simulados estacionalmente. Revista de Teledetección. 0(55):31-48. https://doi.org/10.4995/raet.2020.13394OJS3148055Alonso, M., Rozados, M.J., Vega, J.A., Pérez- Gorostiaga, P., Cuiñas, P., Fontúrbel, M.T., Fernández, C. 2002. Biochemical Responses of Pinus pinaster Trees to Fire-Induced Trunk Girdling and Crown Scorch: Secondary Metabolites and Pigments as Needle Chemical Indicators. 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