Statistical Comparison of Classifiers Applied to the Interferential Tear Film Lipid Layer Automatic Classification

The tear film lipid layer is heterogeneous among the population. Its classification depends on its thickness and can be done using the interference pattern categories proposed by Guillon. The interference phenomena can be characterised as a colour texture pattern, which can be automatically classified into one of these categories. From a photography of the eye, a region of interest is detected and its low-level features are extracted, generating a feature vector that describes it, to be finally classified in one of the target categories. This paper presents an exhaustive study about the problem at hand using different texture analysis methods in three colour spaces and different machine learning algorithms. All these methods and classifiers have been tested on a dataset composed of 105 images from healthy subjects and the results have been statistically analysed. As a result, the manual process done by experts can be automated with the benefits of being faster and unaffected by subjective factors, with maximum accuracy over 95%

Index of T-wave variation as a predictor of sudden cardiac death in chronic heart failure patients with atrial fibrillation

Chronic heart failure (CHF) and atrial fibrillation (AF) are worldwide leading causes of morbidity and mortality in elders, a large part due to sudden cardiac deaths (SCD). The high irregularity of ventricular response in AF patients makes the use of standard SCD-risk markers inappropriate in this target population. The aim of this study was twofold: i) to propose a new index, suitable for AF patients, able to quantify ventricular repolarization changes; and ii) to evaluate its prognostic value in a CHF population with AF. Holter ECG recordings from 176 consecutive CHF patients with AF (22 SCD) were analyzed. The index of T-wave variation (ITV), quantifying the average T-wave changes in pairs of consecutive beats under stable rhythm conditions, was computed using a fully-automatic method. Survival analysis was performed considering SCD as an independent endpoint. ITV was higher for SCD than non-SCD victims (median (Q1;Q3): 24.9 (14.4;85.4) µV vs 17.1 (11.3;28.2) µV, p=0.06). In a survival analysis where the threshold was set on the third quartile of ITV values, ITV (+) outcome was successfully associated to SCD (Hazard Ratio (CI):3.22 (1.36, 7.58)per µV, p=0.008). In conclusion, we show in this work that Ijy stratifies CHF patients with AF according to their risk of SCD, with larger ITV associated to lower survival probability

Index of T-wave variation as a predictor of sudden cardiac death in chronic heart failure patients with atrial fibrillation

Chronic heart failure (CHF) and atrial fibrillation (AF) are worldwide leading causes of morbidity and mortality in elders, a large part due to sudden cardiac deaths (SCD). The high irregularity of ventricular response in AF patients makes the use of standard SCD-risk markers inappropriate in this target population. The aim of this study was twofold: i) to propose a new index, suitable for AF patients, able to quantify ventricular repolarization changes; and ii) to evaluate its prognostic value in a CHF population with AF. Holter ECG recordings from 176 consecutive CHF patients with AF (22 SCD) were analyzed. The index of T-wave variation (ITV), quantifying the average T-wave changes in pairs of consecutive beats under stable rhythm conditions, was computed using a fully-automatic method. Survival analysis was performed considering SCD as an independent endpoint. ITVwas higher for SCD than non-SCD victims (median (Q1;Q3): 24.9 (14.4;85.4) μV vs 17.1 (11.3;28.2) μV, p=0.06). In a survival analysis where the threshold was set on the third quartile of ITVvalues, ITV(+) outcome was successfully associated to SCD (Hazard Ratio (CI):3.22 (1.36, 7.58)per μV, p=0.008). In conclusion, we show in this work that Ijy stratifies CHF patients with AF according to their risk of SCD, with larger ITVassociated to lower survival probability

XANES and EXAFS study of the local order in nanocrystalline yttria-stabilized zirconia

The local order around Zr and Y atoms of nanocrystalline yttria-stabilized zirconia (YSZ) powders with different grain sizes has been investigated by x-ray absorption spectroscopies. The samples were prepared by means of mechanical alloying with or without subsequent sintering treatment and also by milling commercial YSZ. Our study is motivated by the interest in the electrical properties of grain boundaries and the controversy about the level of disorder in the intergrain regions in nanocrystalline YSZ. The x-ray absorption near edge structure (XANES) analysis indicates that the local order of all the sintered samples is independent of the grain size. This is confirmed by the analysis of the extended x-ray absorption fine structure, which points out also that, in contrast to that found in sintered samples, the local order around the cation in the samples milled without further sintering treatment extends only to the first coordination shell. Finally, the results of ab initio Zr K-edge XANES calculations lead us to conclude that the observed changes of the shape of the white line are not related to a phase transformation but reflects the short-range order present in the as-milled samples

Tree species explain only half of explained spatial variability in plant water sensitivity

Spatiotemporal patterns of plant water uptake, loss, and storage exert a first-order control on photosynthesis and evapotranspiration. Many studies of plant responses to water stress have focused on differences between species because of their different stomatal closure, xylem conductance, and root traits. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density. Here, we seek to understand the relative importance of the dominant species for regional-scale variations in woody plant responses to water stress. We map plant water sensitivity (PWS) based on the response of remotely sensed live fuel moisture content to variations in hydrometeorology using an auto-regressive model. Live fuel moisture content dynamics are informative of PWS because they directly reflect vegetation water content and therefore patterns of plant water uptake and evapotranspiration. The PWS is studied using 21,455 wooded locations containing U.S. Forest Service Forest Inventory and Analysis plots across the western United States, where species cover is known and where a single species is locally dominant. Using a species-specific mean PWS value explains 23% of observed PWS variability. By contrast, a random forest driven by mean vegetation density, mean climate, soil properties, and topographic descriptors explains 43% of observed PWS variability. Thus, the dominant species explains only 53% (23% compared to 43%) of explainable variations in PWS. Mean climate and mean NDVI also exert significant influence on PWS. Our results suggest that studies of differences between species should explicitly consider the environments (climate, soil, topography) in which observations for each species are made, and whether those environments are representative of the entire species range

The molecular epidemiology of HIV-1 in the Comunidad Valenciana (Spain): analysis of transmission clusters

HIV infections are still a very serious concern for public heath worldwide. We have applied molecular evolution methods to study the HIV-1 epidemics in the Comunidad Valenciana (CV, Spain) from a public health surveillance perspective. For this, we analysed 1804 HIV-1 sequences comprising protease and reverse transcriptase (PR/RT) coding regions, sampled between 2004 and 2014. These sequences were subtyped and subjected to phylogenetic analyses in order to detect transmission clusters. In addition, univariate and multinomial comparisons were performed to detect epidemiological differences between HIV-1 subtypes, and risk groups. The HIV epidemic in the CV is dominated by subtype B infections among local men who have sex with men (MSM). 270 transmission clusters were identified (>57% of the dataset), 12 of which included ≥10 patients; 11 of subtype B (9 affecting MSMs) and one (n = 21) of CRF14, affecting predominately intravenous drug users (IDUs). Dated phylogenies revealed these large clusters to have originated from the mid-80s to the early 00 s. Subtype B is more likely to form transmission clusters than non-B variants and MSMs to cluster than other risk groups. Multinomial analyses revealed an association between non-B variants, which are not established in the local population yet, and different foreign groups

Origin of the inverse spin-switch behavior in manganite/cuprate/manganite trilayers

We studied ferromagnet/superconductor/ferromagnet trilayers based on La_(0.7)Ca_(0.3)MnO_(3) manganite and YBa_(2)Cu_(3)O_(7−δ) (YBCO) high-T_(c) cuprate with magnetoresistance and magnetization measurements. We find an inverse superconducting spin-switch behavior, where superconductivity is favored for parallel alignment of the magnetization in the ferromagnetic layers. We argue that this inverse superconducting spin switch originates from the transmission of spin-polarized carriers into the superconductor. In this picture, the thickness dependence of the magnetoresistance yields the spin-diffusion length in YBCO as 13 nm. A comparison of bilayers and trilayers allows ruling out the effect of the stray fields of the domain structure of the ferromagnet as the source of the inverse superconducting spin switch

Oxygen mobility in A_(2)Ti_(2y)Zr_(y)O_(7) (A: Y, Gd) ionic conductors

© Sociedad Española de Cerámica y Vidrio. Los autores del CINVESTAV-IPN agradecen a CONACYT el apoyo económico prestado para la realización de este trabajo (Proyecto 31198U). K. J. Moreno agradece al CINVESTAV-IPN el apoyo económico concedido para su estancia en la Universidad Complutense. Los autores de la Universidad Complutense agradecen el apoyo económico del MCYT (MAT 2001-3713-C04).Presentamos un estudio de la conductividad iónica en las series Y_(2)Ti_(2-y)Zr_(y)O_(7) y Gd_(2)Ti_(2-y)Zr_(y)O_(7) (0≤y≤2) obtenidas por síntesis mecanoquímica. Se presenta un estudio de la dinámica de iones oxígeno en estos materiales mediante la técnica de Espectroscopia de Admitancias. La variación con el contenido en Zr de la conductividad dc y de su energía de activación se interpreta en términos del aumento tanto del número de vacantes de oxígeno como del desorden en la estructura al aumentar el contenido en Zr.We report a study of ionic conductivity in the series Y_(2)Ti_(2-y)Zr_(y)O_(7) and Gd_(2)Ti_(2-y)Zr_(y)O_(7) (0≤y≤2) obtained by mechanochemical synthesis. We present a study of oxygen ion dynamics in these materials by Impedance Spectroscopy. The change in dc conductivity and activation energy with Zr content is interpreted in terms of the increase in the number of oxygen vacancies and of structural disorder when increasing Zr content.Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUECONACYTCINVESTAV-IPNMCYTpu

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. Journal of Chemical Ecology, 28(4), 687-700. https://doi.org/10.1023/A:1015276423880Armah, F., Odoi, J., Yengoh, G., Obiri, S., Yawson, D., Afrifa, E. 2011. Food security and climate change in drought-sensitive savanna zones of Ghana. Mitigation and Adaptation Strategies for Global Change, 16, 291-306. https://doi.org/10.1007/s11027-010-9263-9Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholcyzk, P., Smets, B. 2013. GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment, 137, 299-309. https://doi.org/10.1016/j.rse.2012.12.027Béland, M., Widlowski, J.L., Fournier, R.A. 2014. A model for deriving voxel-level tree leaf area density estimates from ground-based LiDAR. Environmental Modelling & Software, 51(0), 184- 189. https://doi.org/10.1016/j.envsoft.2013.09.034Chadwick, K.D., Asner, G.P. 2016. Organismic- Scale Remote Sensing of Canopy Foliar Traits in Lowland Tropical Forests. Remote Sensing, 8(2), 87. https://doi.org/10.3390/rs8020087Cleugh, H.A., Leuning, R., Mu, Q., Running, S.W. 2007. Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment, 106(3), 285-304. https://doi.org/10.1016/j.rse.2006.07.007Croft, H., Chen, J.M. 2017. Remote Sensing of Leaf Pigments. En S. Liang (Ed.), Comprehensive Remote Sensing (pp. 117-142). Oxford: Elsevier. https://doi.org/10.1016/B978-0-12-409548-9.10547-0Croft, H., Chen, J.M., Froelich, N.J., Chen, B., Staebler, R.M. 2015. Seasonal controls of canopy chlorophyll content on forest carbon uptake: Implications for GPP modeling. Journal of Geophysical Research: Biogeosciences, 120(8), 1576-1586. https://doi.org/10.1002/2015JG002980Croft, H., Chen, J.M., Luo, X., Bartlett, P., Chen, B., Staebler, R.M. 2017. Leaf chlorophyll content as a proxy for leaf photosynthetic capacity. Global Change Biology, 23(9), 3513-3524. https://doi.org/10.1111/gcb.13599Croft, H., Chen, J.M., Wang, R., Mo, G., Luo, S., Luo, X., He, L., Gonsamo, A., Arabian, J., Zhang, Y., Simic-Milas, A., Noland, T.L., He, Y., Homolová, L., Malenovský, Z., Yi, Q., Beringer, J., Amiri, R., Hutley, L., Arellano, P., Stahl, C., Bonal, D. 2020. The global distribution of leaf chlorophyll content. Remote Sensing of Environment, 236, 111479. https://doi.org/10.1016/j.rse.2019.111479Dash, J., Curran, P.J. 2007. Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Advances in Space Research, 39(1), 100-104. https://doi.org/10.1016/j.asr.2006.02.034Dorigo, W.A., Zurita-Milla, R., de Wit, A.J.W., Brazile, J., Singh, R., Schaepman, M.E. 2007. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation, 9(2), 165-193. https://doi.org/10.1016/j.jag.2006.05.003Doughty, C.E., Goulden, M.L. 2008. Seasonal patterns of tropical forest leaf area index and CO2 exchange. Journal of Geophysical Research: Biogeosciences, 113(G1). https://doi.org/10.1029/2007JG000590Fan, L., Gao, Y., Brück, H., Bernhofer, C. 2009. Investigating the relationship between NDVI and LAI in semi-arid grassland in Inner Mongolia using in-situ measurements. Theoretical and Applied Climatology, 95(1), 151-156. https://doi.org/10.1007/s00704-007-0369-2Fava, F., Colombo, R., Bocchi, S., Meroni, M., Sitzia, M., Fois, N., Zucca, C. 2009. Identification of hyperspectral vegetation indices for Mediterranean pasture characterization. International Journal of Applied Earth Observation and Geoinformation, 11(4), 233-243. https://doi.org/10.1016/j.jag.2009.02.003Feret, J.-B., François, C., Asner, G.P., Gitelson, A.A., Martin, R.E., Bidel, L.P.R., Ustin, S.L., le Maire, G., Jacquemoud, S. 2008. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment, 112(6), 3030-3043. https://doi.org/10.1016/j.rse.2008.02.012Fortunel, C., Garnier, E., Joffre, R., Kazakou, E., Quested, H., Grigulis, K., Lavorel, S., Ansquer, P., Castro, H., Cruz, P., DoleŽal, J., Eriksson, O., Freitas, H., Golodets, C., Jouany, C., Kigel, J., Kleyer, M., Lehsten, V., Lepš, J., Meier, T., Pakeman, R., Papadimitriou, M., Papanastasis, V.P., Quétier, F., Robson, M., Sternberg, M., Theau, J.P., Thébault, A., Zarovali, M. 2009. Leaf traits capture the effects of land use changes and climate on litter decomposability of grasslands across Europe. Ecology, 90(3), 598- 611. https://doi.org/10.1890/08-0418.1Fourty, T., Baret, F. 1997. Vegetation water and dry matter contents estimated from top-of-the-atmosphere reflectance data: A simulation study. Remote Sensing of Environment, 61(1), 34-45. https://doi.org/10.1016/S0034-4257(96)00238-6Galvão, L.S., Formaggio, A.R., Tisot, D.A. 2005. Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sensing of Environment, 94(4), 523-534. https://doi.org/10.1016/j.rse.2004.11.012García, M., Popescu, S., Riaño, D., Zhao, K., Neuenschwander, A., Agca, M., Chuvieco, E. 2012. Characterization of canopy fuels using ICESat/ GLAS data. Remote Sensing of Environment, 123(0), 81-89. https://doi.org/10.1016/j.rse.2012.03.018Gitelson, A.A., Buschmann, C., Lichtenthaler, H.K. 1999. The Chlorophyll Fluorescence Ratio F735/F700 as an Accurate Measure of the Chlorophyll Content in Plants. Remote Sensing of Environment, 69(3), 296-302. https://doi.org/10.1016/S0034-4257(99)00023-1Gitelson, A.A., Peng, Y., Viña, A., Arkebauer, T., Schepers, J.S. 2016. Efficiency of chlorophyll in gross primary productivity: A proof of concept and application in crops. Journal of Plant Physiology, 201, 101-110. https://doi.org/10.1016/j.jplph.2016.05.019Gitelson, A.A., Viña, A., Arkebauer, T.J., Rundquist, D.C., Keydan, G., Leavitt, B. 2003. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 30(5). https://doi.org/10.1029/2002GL016450González-Cascón, R., Martín, M.P. 2018. Protocol for pigment content quantification in herbaceous covers: sampling and analysis. https://doi.org/10.17504/protocols.io.qs6dwheGuillen-Climent, M., Zarco-Tejada, P., Berni, J.A.J., North, P.R.J., Villalobos, F. 2012. Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV. Precision Agriculture, 13, 473-500. https://doi.org/10.1007/s11119-012-9263-8Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2), 416-426. https://doi.org/10.1016/S0034-4257(02)00018-4Haldimann, P., Gallé, A., Feller, U. 2008. Impact of an exceptionally hot dry summer on photosynthetic traits in oak (Quercus pubescens) leaves. Tree Physiology, 28(5), 785-795. https://doi.org/10.1093/ treephys/28.5.785Hernández-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. https://doi.org/10.1016/j.rse.2011.04.036Hernández-Clemente, R., North, P.R.J., Hornero, A., Zarco-Tejada, P.J. 2017. Assessing the effects of forest health on sun-induced chlorophyll fluorescence using the FluorFLIGHT 3-D radiative transfer model to account for forest structure. Remote Sensing of Environment, 193, 165-179. https://doi.org/10.1016/j.rse.2017.02.012Hill, M.J., Hanan, N.P., Hoffmann, W., Scholes, R., Prince, S., Ferwerda, J., Lucas, R.M., Baker, I., Arneth, A., Higgins, S.I., Barrett, D.J., Disney, M., Hutley, L. 2011. Remote sensing and modeling of savannas: The state of the dis-union.Inoue, Y., Guérif, M., Baret, F., Skidmore, A., Gitelson, A., Schlerf, M., Darvishzadeh, R., Olioso, A. 2016. Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation. Plant, Cell & Environment, 39(12), 2609-2623. https://doi.org/10.1111/pce.12815Inoue, Y., Peñuelas, J., Miyata, A., Mano, M. 2008. Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice. Remote Sensing of Environment, 112(1), 156-172. https://doi.org/10.1016/j.rse.2007.04.011Jacquemoud, S., Baret, F. 1990. PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, 34(2), 75-91. https://doi.org/10.1016/0034-4257(90)90100-ZJacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., François, C., Ustin, S.L. 2009. PROSPECT+SAIL models: A review of use for vegetation characterization. Remote Sensing of Environment, 113, S56-S66. https://doi.org/10.1016/j.rse.2008.01.026Jin, J., Wang, Q. 2019. Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance. Remote Sensing, 11(2), 197. https://doi.org/10.3390/rs11020197Korhonen, L., Korpela, I., Heiskanen, J., Maltamo, M. 2011. Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index. Remote Sensing of Environment, 115(4), 1065-1080. https://doi.org/10.1016/j.rse.2010.12.011le Maire, G., François, C., Dufrêne, E. 2004. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89(1), 1-28. https://doi.org/10.1016/j.rse.2003.09.004le Maire, G., François, C., Soudani, K., Berveiller, D., Pontailler, J.-Y., Bréda, N., Genet, H., Davi, H., Dufrêne, E. 2008. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sensing of Environment, 112(10), 3846- 3864. https://doi.org/10.1016/j.rse.2008.06.005Leonenko, G., Los, S.O., North, P.R.J. 2013. Retrieval of leaf area index from MODIS surface reflectance by model inversion using different minimization criteria. Remote Sensing of Environment, 139, 257-270. https://doi.org/10.1016/j.rse.2013.07.012Li, Q., Lu, X., Wang, Y., Huang, X., Cox, P.M., Luo, Y. 2018. Leaf area index identified as a major source of variability in modeled CO2 fertilization. Biogeosciences, 15(22), 6909-6925. https://doi.org/10.5194/bg-15-6909-2018LI-COR. 2019. LAI 2200-C Plant Canopy Analyzer instruction manual. Último acceso 5 de Junio, 2020, de https://licor.app.boxenterprise.net/s/ fqjn5mlu8c1a7zir5qelLichtenthaler, H.K., Buschmann, C. 2001. Chlorophylls and Carotenoids: Measurement and Characterization by UV-VIS Spectroscopy. Current Protocols in Food Analytical Chemistry, 1(1), F4.3.1-F4.3.8. https://doi.org/10.1002/0471142913.faf0403s01Luo, T., Pan, Y., Ouyang, H., Shi, P., Ji, L., Yu, Z., Lu, Q. 2004. Leaf area index and net primary productivity along subtropical to alpine gradients in the Tibetan Plateau. Global Ecology and Biogeography, 13, 345-358. https://doi.org/10.1111/j.1466-822X.2004.00094.xMaccioni, A., Agati, G., Mazzinghi, P. 2001. New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. Journal of Photochemistry and Photobiology B: Biology, 61(1), 52-61. https://doi.org/10.1016/S1011-1344(01)00145-2Melendo-Vega, J.R., Martín, M.P., Pacheco- Labrador, J., González-Cascón, R., Moreno, G., Pérez, F., Migliavacca, M., García, M., North, P., Riaño, D. 2018. Improving the Performance of 3-D Radiative Transfer Model FLIGHT to Simulate Optical Properties of a Tree-Grass Ecosystem. Remote Sensing, 10(12), 2061. https://doi.org/10.3390/rs10122061Metternicht, G. 2003. Vegetation indices derived from high-resolution airborne videography for precision crop management. International Journal of Remote Sensing, 24(14), 2855-2877. https://doi.org/10.1080/01431160210163074Miraglio, T., Adeline, K., Huesca, M., Ustin, S., Briottet, X. 2020. Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling. Remote Sensing, 12(1), 28. https://doi.org/10.3390/rs12010028Moreno, G., Rolo, V. 2019. Agroforestry practices: silvopastorism. En M.R. Mosquera-Losada & R. Prabhu (Eds.), Agroforestry for sustainable agriculture (pp. 119-164): Burleigh Dodds Science Publishing Limited.Myneni, R.B., Hoffman, S., Knyazikhin, Y., Privette, J.L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G.R., Lotsch, A., Friedl, M., Morisette, J.T., Votava, P., Nemani, R.R., Running, S.W. 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83(1), 214-231. https://doi.org/10.1016/S0034-4257(02)00074-3North, P.R.J. 1996. Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Transactions on Geoscience and Remote Sensing, 34(4), 946-956. https://doi.org/10.1109/36.508411Novara, A., Rühl, J., La Mantia, T., Gristina, L., La Bella, S., Tuttolomondo, T. 2015. Litter contribution to soil organic carbon in the processes of agriculture abandon. Solid Earth, 6, 425-432. https://doi.org/10.5194/se-6-425-2015Pacheco-Labrador, J., El-Madany, T.S., van der Tol, C., Martín, M.P., Gonzalez-Cascon, R., Perez-Priego, O., Guan, J., Moreno, G., Carrara, A., Reichstein, M., Migliavacca, M. 2020. senSCOPE: Modeling radiative transfer and biochemical processes in mixed canopies combining green and senescent leaves with SCOPE. bioRxiv, 2020.2002.2005.935064. https://doi.org/10.1101/2020.02.05.935064Pacheco-Labrador, J., González-Cascón, R., Martín, M.P., Melendo-Vega, J.R., Hernández-Clemente, R., Zarco-Tejada, P. 2017. Impact of trichomes in the application of radiative transfer models in leaves of Quercus ilex. En: VII Congreso forestal español, Plasencia, España. 26-30 Junio 2017.Pacheco-Labrador, J., Martín, M., Riaño, D., Hilker, T., Carrara, A. 2016. New approaches in multi-angular proximal sensing of vegetation: Accounting for spatial heterogeneity and diffuse radiation in directional reflectance distribution models. Remote Sensing of Environment, 187. https://doi.org/10.1016/j.rse.2016.10.051Pacheco-Labrador, J., Perez-Priego, O., El-Madany, T.S., Julitta, T., Rossini, M., Guan, J., Moreno, G., Carvalhais, N., Martín, M.P., Gonzalez-Cascon, R., Kolle, O., Reischtein, M., van der Tol, C., Carrara, A., Martini, D., Hammer, T.W., Moossen, H., Migliavacca, M. 2019. Multiple-constraint inversion of SCOPE. Evaluating the potential of GPP and SIF for the retrieval of plant functional traits. Remote Sensing of Environment, 234, 111362. https://doi.org/10.1016/j.rse.2019.111362Polley, H.W., Yang, C., Wilsey, B.J., Fay, P.A. 2019. Spectrally derived values of community leaf dry matter content link shifts in grassland composition with change in biomass production. Remote Sensing in Ecology and Conservation, n/a(n/a). https://doi.org/10.1002/rse2.145Pulido, F., Picardo, A., Campos, P., Carranza, J., Coleto, J., Díaz, M., Diéguez, E., Escudero, A., Ezquerra, F., Fernández, P., Solla, A. 2010. Libro Verde de la Dehesa. Consejería de Medio Ambiente, Junta Castilla La Mancha.Qiao, K., Zhu, W., Zhiying, X., Li, P. 2019. Estimating the Seasonal Dynamics of the Leaf Area Index Using Piecewise LAI-VI Relationships Based on Phenophases. Remote Sensing, 11(6), 689. https://doi.org/10.3390/rs11060689Reichstein, M., Bahn, M., Mahecha, M.D., Kattge, J., Baldocchi, D.D. 2014. Linking plant and ecosystem functional biogeography. Proceedings of the National Academy of Sciences, 111(38), 13697- 13702. https://doi.org/10.1073/pnas.1216065111Riaño, D., Valladares, F., Condes, S., Chuvieco, E. 2004. Estimation of leaf area index and covered ground from airborne laser scanner (LiDAR) in two contrasting forests. Agricultural and Forest Meteorology, 124(3-4), 269-275. https://doi.org/10.1016/j.agrformet.2004.02.005Riaño, D., Vaughan, P., Chuvieco, E., Zarco-Tejada, P., Ustin, S.L. 2005. Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: analysis at leaf and canopy level. IEEE Transactions on Geoscience and Remote Sensing, 43(4), 819-8