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

  1. Martín, M. P. 1
  2. Pacheco-Labrador, J. 2
  3. González-Cascón, R. 3
  4. Moreno, G. 4
  5. Migliavacca, M. 2
  6. García, M. 5
  7. Yebra, M. 6
  8. Riaño, D. 7
  1. 1 Consejo Superior de Investigaciones Científicas
    info

    Consejo Superior de Investigaciones Científicas

    Madrid, España

    ROR https://ror.org/02gfc7t72

  2. 2 Max Planck Institute for Biogeochemistry
    info

    Max Planck Institute for Biogeochemistry

    Jena, Alemania

    ROR https://ror.org/051yxp643

  3. 3 Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA)
  4. 4 Universidad de Extremadura
    info

    Universidad de Extremadura

    Badajoz, España

    ROR https://ror.org/0174shg90

  5. 5 Universidad de Alcalá
    info

    Universidad de Alcalá

    Alcalá de Henares, España

    ROR https://ror.org/04pmn0e78

  6. 6 Fenner School of Environment and Society. Australian National University Bushfire & Natural Hazards Cooperative Research Centre
  7. 7 Consejo Superior de Investigaciones Científicas Center for Spatial Technologies and Remote Sensing (CSTARS), University of California
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Revista:
Revista de teledetección: Revista de la Asociación Española de Teledetección

ISSN: 1133-0953

Año de publicación: 2020

Número: 55

Páginas: 31-48

Tipo: Artículo

DOI: 10.4995/RAET.2020.13394 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista de teledetección: Revista de la Asociación Española de Teledetección

Resumen

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.

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Información de financiación

Este 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 Econom?a y Competitividad. Agradecemos el apoyo de los proyectos IB16185 de la Junta de Extremadura, MoReDEHESHyReS (No. 50EE1621, Agencia Espacial Alemana (DLR) y Ministerio Alem?n de Asuntos Econ?micos y Energ?a) y el premio de la fundaci?n Alexander von Humboldt v?a Premio Max-Planck a Markus Reichstein.

Financiadores

Referencias bibliográficas

  • Alonso, 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:1015276423880
  • Armah, 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/s11027-010-9263-9
  • Baret, 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.2012.12.027
  • Béland, 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.2013.09.034
  • Chadwick, 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/rs8020087
  • Cleugh, 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.2006.07.007
  • Croft, 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/B978-0-12-409548-9.10547-0
  • Croft, 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/10.1002/2015JG002980
  • Croft, 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.13599
  • Croft, 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.2019.111479
  • Dash, 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.2006.02.034
  • Dorigo, 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.2006.05.003
  • Doughty, 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/10.1029/2007JG000590
  • Fan, 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/s00704-007-0369-2
  • Fava, 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.2009.02.003
  • Feret, 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.2008.02.012
  • Fortunel, 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/10.1890/08-0418.1
  • Fourty, 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)00238-6
  • Galvão, 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.2004.11.012
  • García, 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.2012.03.018
  • Gitelson, 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)00023-1
  • Gitelson, 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.2016.05.019
  • Gitelson, 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/10.1029/2002GL016450
  • González-Cascón, R., Martín, M.P. 2018. Protocol for pigment content quantification in herbaceous covers: sampling and analysis. https://doi.org/10.17504/protocols.io.qs6dwhe
  • Guillen-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/s11119-012-9263-8
  • Haboudane, 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)00018-4
  • Haldimann, 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/28.5.785
  • Hernández-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.2011.04.036
  • Hernández-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.2017.02.012
  • Hill, 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.12815
  • Inoue, 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.2007.04.011
  • Jacquemoud, 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-Z
  • Jacquemoud, 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.2008.01.026
  • Jin, 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/rs11020197
  • Korhonen, 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.2010.12.011
  • le 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.2003.09.004
  • le 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.2008.06.005
  • Leonenko, 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.2013.07.012
  • Li, 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-15-6909-2018
  • LI-COR. 2019. LAI 2200-C Plant Canopy Analyzer instruction manual. Último acceso 5 de Junio, 2020, de https://licor.app.boxenterprise.net/s/ fqjn5mlu8c1a7zir5qel
  • Lichtenthaler, 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.faf0403s01
  • Luo, 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.x
  • Maccioni, 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)00145-2
  • Melendo-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/rs10122061
  • Metternicht, 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/10.1080/01431160210163074
  • Miraglio, 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/rs12010028
  • Moreno, 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)00074-3
  • North, 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/10.1109/36.508411
  • Novara, 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-6-425-2015
  • Pacheco-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/10.1101/2020.02.05.935064
  • Pacheco-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.2016.10.051
  • Pacheco-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.2019.111362
  • Polley, 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/rse2.145
  • Pulido, 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/rs11060689
  • Reichstein, 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.1216065111
  • Riaño, 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.2004.02.005
  • Riaño, 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), 819-826. https://doi.org/10.1109/TGRS.2005.843316
  • Ritchie, R.J. 2008. Universal chlorophyll equations for estimating chlorophylls a, b, c, and d and total chlorophylls in natural assemblages of photosynthetic organisms using acetone, methanol, or ethanol solvents. Photosynthetica, 46(1), 115- 126. https://doi.org/10.1007/s11099-008-0019-7
  • Rouse, J.W., Haas, R.H., Deering, D.W., Schell, J.A. 1973. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Greenbelt, Maryland.
  • Schlerf, M., Atzberger, C., Hill, J. 2005. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sensing of Environment, 95(2), 177-194. https://doi.org/10.1016/j.rse.2004.12.016
  • Shipley, B., Vu, T.T. 2002. Dry matter content as a measure of dry matter concentration in plants and their parts. New Phytologist, 153(2), 359-364. https://doi.org/10.1046/j.0028-646X.2001.00320.x
  • Sims, D.A., Gamon, J.A. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81(2), 337-354. https://doi.org/10.1016/S0034-4257(02)00010-X
  • Van Cleemput, E., Vanierschot, L., Fernández- Castilla, B., Honnay, O., Somers, B. 2018. The functional characterization of grass- and shrubland ecosystems using hyperspectral remote sensing: trends, accuracy and moderating variables. Remote Sensing of Environment, 209, 747-763. https://doi.org/10.1016/j.rse.2018.02.030
  • Verhoef, W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sensing of Environment, 16(2), 125- 141. https://doi.org/10.1016/0034-4257(84)90057-9
  • Verrelst, J., Malenovský, Z., Van der Tol, C., Camps- Valls, G., Gastellu-Etchegorry, J.P., Lewis, P., North, P., Moreno, J. 2019. Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods. Surveys in Geophysics, 40(3), 589-629. https://doi.org/10.1007/s10712-018-9478-y
  • Vogelmann, J.E., Rock, B.N., Moss, D.M. 1993. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14(8), 1563- 1575. https://doi.org/10.1080/01431169308953986
  • Wang, Q., Adiku, S., Tenhunen, J., Granier, A. 2005. On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sensing of Environment, 94(2), 244-255. https://doi.org/10.1016/j.rse.2004.10.006
  • Wang, S., Li, Y., Ju, W., Chen, B., Chen, J., Croft, H., Mickler, R.A., Yang, F. 2020. Estimation of Leaf Photosynthetic Capacity From Leaf Chlorophyll Content and Leaf Age in a Subtropical Evergreen Coniferous Plantation. Journal of Geophysical Research: Biogeosciences, 125(2), e2019JG005020. https://doi.org/10.1029/2019JG005020
  • Watson, D.J. 1947. Comparative physiological studies on the growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Annals of Botany, 11(1), 41-76. https://doi.org/10.1093/oxfordjournals.aob.a083148
  • Wenhan, Q. 1993. Modeling bidirectional reflectance of multicomponent vegetation canopies. Remote Sensing of Environment, 46(3), 235-245. https://doi.org/10.1016/0034-4257(93)90045-Y
  • Wright, I.J., Reich, P.B., Westoby, M., Ackerly, D.D., Baruch, Z., Bongers, F., Cavender-Bares, J., Chapin, T., Cornelissen, J.H.C., Diemer, M., Flexas, J., Garnier, E., Groom, P.K., Gulias, J., Hikosaka, K., Lamont, B.B., Lee, T., Lee, W., Lusk, C., Midgley, J.J., Navas, M.L., Niinemets, Ü., Oleksyn, J., Osada, N., Poorter, H., Poot, P., Prior, L., Pyankov, V.I., Roumet, C., Thomas, S.C., Tjoelker, M.G., Veneklaas, E.J., Villar, R. 2004. The worldwide leaf economics spectrum. Nature, 428(6985), 821-827. https://doi.org/10.1038/nature02403
  • Yebra, M., Dennison, P.E., Chuvieco, E., Riaño, D., Zylstra, P., Hunt, E.R., Danson, F.M., Qi, Y., Jurdao, S. 2013. A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products. Remote Sensing of Environment, 136, 455-468. https://doi.org/10.1016/j.rse.2013.05.029
Fuente de los datos: Dialnet