BUSCADOR RECOLECTA

SynerTGE es el website del proyecto de investigación homónimo. SynerTGE members and Institutions are:
Dr. M.Pilar Martín, Dr. David Riaño, Dr. Lara Vilar, Dr. Javier Pacheco-Labrador, Mr. José Ramón Melendo Mr. Javier Becerra Corral and Mr. Vicente Burchard-Levine. Spectroscopy and Environmental Remote Sensing Laboratory (SpecLab). Institute of Economics, Geography and Demography (IEGD), Centre for Human and Social Sciences, National Research Council (CSIC): Dr. Fernando Pérez, Dr. Raquel Montorio. Department of Geography, University of Zaragoza (UZ). Dr. Alfredo Serreta. Technological College of Huesca, University of Zaragoza (UZ). Dr. Alberto García. Centro Universitario de la Defensa (CUD). Dr. M. Rosario González Cascón. National Institute for Agriculture and Food Research and Technology (INIA).
Subcontractors:
Dr. Arnaud Carrara. CEAM (Centre for Mediterranean Environmental Studies) Foundation; Mr. Jorge Angás. 3D Scanner Technologies;
International collaborations:
Dr. Rasmus Fensholt. Department of Geosciences and Natural Resource Management, University of Copenhagen. Denmark; Dr. John Gajardo. Centro de Geomatica, Universidad de Talca. Chile;
Dr. Mirco Migliavacca. Departament of Biogeochemical Integration. Max Planck Institute of Biochemistry. Germany; Dr. Dennis Baldocchi. Biometeorology Lab. University of California Berkeley. USA; Dr. Gorka Mendiguren. Geological Survey of Denmark and Greenland. Denmark; Dr. Marta Yebra. Fenner School of Environment and Society. Australian National University. Australia., Mixed tree-grass and shrub-grass vegetation associations are one of the most spatially extensive and widely distributed forms of terrestrial vegetation on Earth. They are vital for livestock production and play a pivotal role in regional and global food production and food security. SynerTGE proposes to estimate key vegetation biophysical parameters., Peer reviewed

The most recent efforts to provide remote sensing (RS) estimates of plant function rely on the combination of Radiative Transfer Models (RTM) and Soil-Vegetation-Atmosphere Transfer (SVAT) models, such as the SoilCanopy Observation Photosynthesis and Energy fluxes (SCOPE) model. In this work we used ground spectroradiometric and chamber-based CO2 flux measurements in a nutrient manipulated Mediterranean grassland in
order to: 1) develop a multiple-constraint inversion approach of SCOPE able to retrieve vegetation biochemical, structural as well as key functional traits, such as chlorophyll concentration (Cab), leaf area index (LAI), maximum carboxylation rate (Vcmax) and the Ball-Berry sensitivity parameter (m); and 2) compare the potential of the of gross primary production (GPP) and sun-induced fluorescence (SIF), together with up-welling Thermal
Infrared (TIR) radiance and optical reflectance factors (RF), to estimate such parameters. The performance of the proposed inversion method as well as of the different sets of constraints was assessed with contemporary
measurements of water and heat fluxes and leaf nitrogen content, using pattern-oriented model evaluation. The multiple-constraint inversion approach proposed together with the combination of optical RF and diel
GPP and TIR data provided reliable estimates of parameters, and improved predicted water and heat fluxes. The addition of SIF to this scheme slightly improved the estimation of m. Parameter estimates were coherent with the variability imposed by the fertilization and the seasonality of the grassland. Results revealed that fertilization had an impact on Vcmax, while no significant differences were found for m. The combination of RF, SIF and diel TIR data weakly constrained functional traits. Approaches not including GPP failed to estimate LAI; however GPP overestimated Cab in the dry period. These problems might be related to the presence of high fractions of
senescent leaves in the grassland. The proposed inversion approach together with pattern-oriented model evaluation open new perspectives for the retrieval of plant functional traits relevant for land surface models, and can be utilized at various research sites where hyperspectral remote sensing imagery and eddy covariance flux measurements are simultaneously taken, JPL, MM, and MR acknowledge the EnMAP project MoReDEHESHyReS “Modelling Responses of Dehesas with Hyperspectral Remote Sensing” (Contract No. 50EE1621, German Aerospace Center (DLR) and the German Federal Ministry of Economic Affairs and Energy). DM, MM and MR received funding from the European Union’s Horizon 2020 research and innovation programme via the TRUSTEE project under the Marie Skłodowska-Curie grant agreement No. 721995. Authors acknowledge the Alexander von Humboldt Foundation for supporting this research with the Max-Planck Prize to Markus Reichstein; SynerTGE “Landsat-8 + Sentinel-2: exploring sensor synergies for monitoring and modelling key vegetation biophysical variables in tree-grass ecosystems” (CGL2015-69095-R, Spanish Ministry of Science, Innovation and Universities); and FLUχPEC “Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean ‘dehesa’ ecosystem” (CGL2012- 34383, Spanish Ministry of Economy and Competitiveness). Authors are very thankful to Dr. Karl Segl and Prof. Dr. Luis Guanter for their support with the EnMAP end-to-end simulator; as well as the MPI-BGC Freiland Group and especially Martin Hertel as well as Ramón LópezJiménez (CEAM) for technical assistance. We are grateful to all the colleagues from MPI-BGC, University of Extremadura, University of Milano-Bicocca, SpecLab-CSIC, INIA and CEAM which have collaborated in any of the field and laboratory works. We acknowledge the Majadas de Tiétar city council for its support., Peer reviewed

18 Pág.
 Departamento de Medio Ambiente y Agronomía​, The coupling of radiative transfer, energy balance, and photosynthesis models has brought new opportunities to characterize vegetation functional properties from space. However, these models do not accurately represent processes in ecosystems characterized by mixtures of green vegetation and senescent plant material (SPM), in particular grasslands. These inaccuracies limit the retrieval of vegetation biophysical and functional properties. Green and senesced plants feature contrasting spectral properties and carry out different functions that current coupled models do not represent separately. Besides, senescent pigments' absorption features change as SPM decomposes, and neither is this process well parameterized in radiative transfer models. This manuscript aims at overcoming these limitations. On the one hand, we have developed senSCOPE, a version of the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) that separately represents light interaction and physiology of green and senesced leaves. On the other, we have characterized new specific absorption coefficients of senescent pigments (Ks) from optical measurements of SPM from a Mediterranean grassland. Sensitivity analyses revealed that compared to SCOPE, senSCOPE 1) predicts variables that respond more linearly to the faction of green leaf area; and 2) keeps high levels of absorbed photosynthetically active radiation in the green leaves, which leads to significant differences in leaf photosynthesis, non-photochemical quenching, and transpiration. Moreover, we compared SCOPE vs. senSCOPE's capability to provide estimates of functional and biophysical parameters of vegetation. We assimilated different combinations of reflectance factors (R), chlorophyll sun-induced fluorescence radiance in the O2-A band (F760), gross primary production (GPP), and thermal radiance (Lt) measured in a Mediterranean grassland. Besides, we compared the role of three different sets of Ks coefficients in the inversion of senSCOPE, two estimated from SPM. The performance of the inversions was assessed using field data and a pattern-oriented model evaluation approach. Unlike SCOPE, senSCOPE provided unbiased estimates of chlorophyll content (Cab) during the dry season. The use of SPM-specific Ks improved the representation of R in the near-infrared wavelengths; and, consequently, the estimation of leaf area index (LAI). Compared with field LAI, the coefficient of determination R2 increased from ~0.4 to ~0.6, depending on the inversion constraints. Compared with SCOPE, the new model and coefficients together reduced the root mean squared error between observed and modeled R (~40%), F760 (~30%), and GPP (~5%). Both models failed to represent Lt; in this case, senSCOPE featured larger uncertainties. The modeling approach we propose improves the simulation and retrieval of vegetation properties and function in grasslands. Further work is needed to test the applicability of senSCOPE in different ecosystems, improve the simulation of the thermal spectral domain, and better characterize the optical parameters of SPM. To do so, new databases of SPM optical and biophysical properties should be produced., JPL, MM and MR acknowledge the EnMAP project MoReDEHESHyReS “Modelling Responses of Dehesas with Hyperspectral Remote Sensing” (Contract No. 50EE1621, German Aerospace Center (DLR) and the German Federal Ministry of Economic Affairs and Energy). Authors acknowledge the Alexander von Humboldt Foundation for supporting this research with the Max-Planck Prize to Markus Reichstein; the project SynerTGE “Landsat-8+Sentinel-2: exploring sensor synergies for monitoring and modeling key vegetation biophysical variables in tree-grass ecosystems” (CGL2015-69095-R, MINECO/FEDER,UE); and the project FLUχPEC “Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean ‘dehesa’ ecosystem” (CGL2012-34383, Spanish Ministry of Economy and Competitiveness)., Peer reviewed

18 Pág., [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., [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., 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 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 Reichstein, Peer reviewed

Atmospheric correction of optical satellite imagery is an essential pre-processing for modelling biophysical variables, multi-temporal analysis, and digital classification processes. Sentinel-2 products available for users are distributed by the European Space Agency (ESA) as Top Of Atmosphere reflectance values in cartographic geometry (Level-1C product). In order to obtain Bottom Of Atmosphere reflectance images (Level-2A product) derived from this Level-1C products, ESA provides the SEN2COR module, which is implemented in the Sentinel Application Platform. Alternatively, ESA recently distributes Level-2A products processed by SEN2COR with a default configuration. On the other hand, the conversion from Level-1C to Level-2A product can be generated using alternative atmospheric correction methods, such as MAJA, 6S, or iCOR. In this context, this paper aims to evaluate the quality of Level-2A products obtained through different methods in Mediterranean shrub and grasslands by comparing data obtained from Sentinel-2 imagery with field spectrometry data. For that purpose, six plots with different land covers (asphalt, grass, shrub, pasture, and bare soil) were analyzed, by using synchronous imagery to fieldwork (from July to September 2016). The results suggest the suitability of the applied atmospheric corrections, with coefficients of determination higher than 0.90 and root mean square error lower than 0.04 achieving a relative error in bottom of atmosphere reflectance of only 2–3%. Nevertheless, minor differences were observed between the four tested methods, with slightly varying results depending on the spectral band and land cover., This work has been supported by the SynerTGE project (CGL2015-69095-R), funded by the Spanish Ministry of Economy and Competitiveness; the HyZCP project (2015-17), funded by the Centro Universitario de la Defensa de Zaragoza, and projects CGL2016-75217-R (MINECO/FEDER, EU) and PyrenEOS EFA 048/15, which has been 65% cofinanced by the European Regional Development through the Interreg V-A Spain-France-Andorra programme (POCTEFA 2014-2020).

[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., 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 Reichstein