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Imaging and non-imaging spectroscopy employed in the field and from aircraft is frequently used to assess biochemical, structural, and functional plant traits, as well as their dynamics in an environmental matrix. With the increasing availability of high-resolution spectroradiometers, it has become feasible to measure fine spectral features, such as those needed to estimate sun-induced chlorophyll fluorescence (F), which is a signal related to the photosynthetic process of plants. The measurement of F requires highly accurate and precise radiance measurements in combination with very sophisticated measurement protocols. Additionally, because F has a highly dynamic nature (compared with other vegetation information derived from spectral data) and low signal intensity, several environmental, physiological, and experimental aspects have to be considered during signal acquisition and are key for its reliable interpretation. The European Cooperation in Science and Technology (COST) Action ES1309 OPTIMISE has produced three articles addressing the main challenges in the field of F measurements. In this paper, which is the second of three, we review approaches that are available to measure F from the leaf to the canopy scale using ground-based and airborne platforms. We put specific emphasis on instrumental aspects, measurement setups, protocols, quality checks, and data processing strategies. Furthermore, we review existing techniques that account for atmospheric influences on F retrieval, address spatial scaling effects, and assess quality checks and the metadata and ancillary data required to reliably interpret retrieved F signals.

Imaging and non-imaging spectroscopy employed in the field and from aircraft is frequently used to assess biochemical, structural, and functional plant traits, as well as their dynamics in an environmental matrix. With the increasing availability of high-resolution spectroradiometers, it has become feasible to measure fine spectral features, such as those needed to estimate sun-induced chlorophyll fluorescence (F), which is a signal related to the photosynthetic process of plants. The measurement of F requires highly accurate and precise radiance measurements in combination with very sophisticated measurement protocols. Additionally, because F has a highly dynamic nature (compared with other vegetation information derived from spectral data) and low signal intensity, several environmental, physiological, and experimental aspects have to be considered during signal acquisition and are key for its reliable interpretation. The European Cooperation in Science and Technology (COST) Action ES1309 OPTIMISE has produced three articles addressing the main challenges in the field of F measurements. In this paper, which is the second of three, we review approaches that are available to measure F from the leaf to the canopy scale using ground-based and airborne platforms. We put specific emphasis on instrumental aspects, measurement setups, protocols, quality checks, and data processing strategies. Furthermore, we review existing techniques that account for atmospheric influences on F retrieval, address spatial scaling effects, and assess quality checks and the metadata and ancillary data required to reliably interpret retrieved F signals. ISSN:2072-4292

Se analizan los efectos de la agregación espacial de variables espectrales y temporal de flujos de carbono sobre la estimación de la productividad primaria bruta en un ecosistema de dehesa. Para ello se hace uso de una serie temporal de imágenes hiperespectrales de alta resolución espacial sobre el área de influencia de varias torres de flujo. Los diferentes modelos empleados revelan diferencias relacionadas con la agregación temporal de los datos de flujos, mientras que las relacionadas con la agregación espacial de los datos espectrales son menos relevantes., Este trabajo ha sido financiado por los proyectos BIOSPEC (CGL2008-02301/CLI, Ministerio de Ciencia en Innovación) y FLUXPEC (CGL2012-34383, Ministerio de Economía y Competitividad). Agradecemos a la COST Action ES1309 OPTIMISE la financiación de una STMS. Agradecemos también la colaboración del personal de SpecLab-CSIC, CEAM, Universidad de Alcalá, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Universidad de Zaragoza, Universidad de Milano- Bicocca y Max Planck Institute for Biogeochemistry.

[EN] In this study we analyzed the potential of airborne hyperspectral images acquired by the CASI sensor (Compact Airborne Spectrographic Imager) to estimate water content in the grass layer of a dehesa ecosystem. It was estimated by means of biophysical variables as Canopy Water Content (CWC), Fuel Moisture Content (FMC) and Equivalent Water Thickness (EWT) calculated from vegetation samples collected simultaneously to image acquisition. Two different field sampling strategies have been tested. First method collects all plants in 25x25cm quadrants and selects a fraction of the sample to be processed in the laboratory. In the second method the worker selects a small sample in the field. This subsample should be representative of the main one and will be completely processed in the laboratory. Afterwards linear relationships between the biophysical variables and twenty spectral indices from literature and from Normalized Difference Indices (NDIs-combination all sensor bands) are analyzed. The results show that the combination of specific bands in the form of NDI performs better than the spectral indices proposed in the literature. The regression models showed maximum correlation values (R2max) of 0.78 in the estimation of water content from NDIs. This value decrease to 0.53 for indices proposed in the literature. Clear differences were found in the correlation values when comparing the different sampling strategies. In general, lower correlation values were found when sampling methods require subjective decision on the selection in the field of plants that composes the sample. Despite of the limited outreach of the method used, the results suggest that this could be successfully applied in circumstances when information is limited and variables are not physically related., [ES] En este trabajo se analiza el potencial del sensor aeroportado CASI (Compact Airborne Spectrographic Imager) para estimar el contenido en humedad de la cubierta herbácea de un área de dehesa. Éste fue cuantificado mediante las variables Canopy Water Content (CWC), Fuel Moisture Content (FMC) y Equivalent Water Thickness (EWT), calculadas a partir de muestras de vegetación recolectadas en campo de forma simultánea a la adquisición de datos por parte del sensor. Se comparan dos protocolos diferentes para la estimación de estas variables mediante muestreos destructivos: en el primero se muestrea toda la vegetación en un cuadrante de 25x25 cm y posteriormente se selecciona una parte de la muestra para su procesado en laboratorio; en el segundo el operador selecciona en campo una muestra más pequeña que considera representativa de la muestra anterior y que es procesada en su totalidad. Posteriormente se analizan las relaciones lineales entre estas variables y veinte índices espectrales extraídos de la literatura así como todos los índices de diferencia normalizados (NDIs) posibles resultantes de combinar pares de bandas del sensor CASI. Los resultados indican que la exploración de todas las posibles combinaciones de bandas en forma de NDI permite obtener coeficientes de correlación más elevados con las variables biofísicas medidas en terreno (R2máx= 0.78) que los índices extraídos de la literatura (R2máx= 0.53). Los valores de ajuste obtenidos fueron diferentes en función del tipo de muestreo realizado; siendo por lo general inferiores cuando el muestreo incluye decisiones subjetivas por parte del operador respecto a la selección en campo de los individuos que integran la muestra. A pesar de la limitación del enfoque empleado, los resultados sugieren su aplicabilidad a estudios con información limitada y no relacionada mediante procesos físicos.

En el contexto de los proyectos BIOSPEC y FLUXPEC (http://www.lineas.cchs.csic.es/fluxpec/), se han realizado 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, LMA, contenido de agua, nutrientes (N, P, K, Ca, Mg, Mn, Fe, y Zn) y pigmentos. Las mediciones espectrales se han relacionado con las variables biofísicas mediante análisis de regresión múltiple stepwise y regresión de mínimos cuadrados parciales. Estos análisis han permitido identificar las bandas 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 estimaciones significativas para la mayoría de las variables foliares estudiadas. Las longitudes de onda con mayor contribución aparecen en la zona del eje rojo, el SWIR y la región verde del visible.

Sun induced fluorescence (SIF), the radiation flux emitted by plant chlorophylls molecules in the 650-800 nm spectral window, is considered an indicator of photosynthetic performance. Recently it has been shown that SIF can track changes in light use efficiency (LUE), and therefore it is a good predictor of gross primary production (GPP) at various scales, from leaves and ecosystem to regional and global scale. Although SIF has been successfully used to predict GPP in various ecosystems, the mechanistic link between GPP and SIF remains not fully understood, and especially the effect of function and structure on SIF at the canopy scale remains an active area of research. SIF is emitted by the whole canopy, but only a fraction of the total emission is observed with remote sensing techniques. The escape probability of SIF (Fesc) controls the amount of SIF scattered by the canopy and is integral to separate the effect of canopy structure and function on the fluorescence signal. In this contribution we make use of data collected at the research site Majadas del Tietar, a Mediterranean grassland manipulated with Nitrogen and Phosphorus. Using the SCOPE model (Soil Canopy Observation Photochemistry and Energy fluxes) we obtain Fesc and we analyse how Top of canopy SIF and emitted SIF vary in response to the fertilization. With a combination of processes-based modelling and data driven analysis, such as relative importance analysis and structural equation modelling, we unravel the processes and causal relationship that are at the base of the GPP - SIF relationship. We show that the nutrient fertilization had an effect on plant composition, and therefore canopy structure, but also plant functioning. Nitrogen induced changes in biodiversity mainly affect leaf angle distribution of the canopy and therefore scattering properties such as Fesc. The nitrogen fertilization is also responsible for a change in plant functioning, with altered SIF emission. The simultaneous change of both canopy and structure causes the fertilization effect to be visible mainly at the emission level, but not at top of canopy, as the variation in canopy structure masks the change observed at leaf level. This contribution advances the knowledge of the highly complex dynamics involved in the GPP-SIF relationship. In depth understanding of the mechanistic processes is required to fully take advantage of the increasingly prevalent SIF data streams.

Mediterranean ecosystems constitute a potential “hot spot†for assessing impact of climate change; but they are also characterized for a large heterogeneity which complicates the use of remote sensing tools for monitoring carbon fluxes in these particular ecosystems. This is the case of the wooded grasslands or savannas, which combine vegetation types structurally and physiologically very different. We have simulated the estimation of Gross Primary Production (GPP) in a Mediterranean savanna (dehesa) through up-scaling spectral information from leaf/canopy to ecosystem level by using Radiative Transfer Models (RTM). Different predictive models have been tested by simulating Landsat 5 TM and Sentinel-2 spectral bands., Field works were carried out in Majadas del Tiétar, (Cáceres, Spain) between May 2009 and April 2011. In the study site grazed grasslands mix up with scattered Holm oaks (tree fraction cover about 20%). Daily GPP and meteorological variables have been measured using Eddy Covariance technique from a flux tower operated by CEAM; moreover, ground spectral data were taken during 20 field campaigns simultaneous to Landsat 5 TM over-passes. In each campaign, an ASD Fieldspec® spectroradiometer (ASD inc., Boulder, CO, USA) was used to measure canopy reflectance from 12 grassland plots of 25 m x 25 m and leaf reflectance from up to 10 trees surrounding the flux tower. Current and previous year Holm oak leaves (up to 12 of each type per tree and sampling date) were measured using a plant probe and a leaf clip of the same spectroradiometer. The coupled PROSPECT-5 and SAIL models were used to simulate ecosystem reflectance as a linear combination of the measured grassland and the simulated tree canopy reflectance’s fraction cover. Simulated reflectance was resampled to the Landsat 5TM and Sentinel-2 spectral bands and was validated using Landsat 5 TM images simultaneously acquired and radiometrically corrected using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) system. Eventually, three different models based on Vegetation Indices (VI) computed from each sensor´s bands and the Photosynthetically Active Radiation (PAR) measured in the flux tower were used to estimate GPP., Results show a high agreement between the simulated ecosystem reflectance and the Landsat 5 TM imagery (r2 ranged between 0.94 and 1.00 for the optical bands). GPP models exclusively based on VI showed the least predictive capabilities (r2 ≤ 0.36; RRMSE Є [65.00%, 74.29%]).whereas those models where GPP is a linear function of the product VI x PAR achieved larger correlation coefficients (r2 Є [0.74, 0.88] and lower errors (RMSE Є [36.40%, 58.79%]). The most accurate predictions were achieved by models where GPP is a linear combination of a VI that is multiplied by PAR (r2 Є [0.78, 0.88]; RMSE Є [34.83%, 57.30%]). When PAR was included, the model based in the green Normalized Difference Vegetation Index (NDVI) calculated from the Sentinel-2’s 4th and 10th bands casted the least reliable estimates (r2 = 0.80; RRMSE = 58.79%). On the contrary, the best predictions were achieved using the NDVI computed from the Sentinel-2’s 5th and 6th bands (r2 = 0.88; RRMSE = 34.83%)., We conclude that a wooded grassland ecosystem level reflectance can be simulated from ground hyperspectral measurements using RTM; at least when spectral resolution is degraded to the coarser features of multispectral remote sensors such as Landsat 5 TM. Moreover, daily GPP can be estimated with an acceptable accuracy (RRMSE ~35%) from Sentinel-2 imagery using vegetation spectral indices; however incident PAR must be taken into account. Since the revisit time of this Sentinel-2 over the Mediterranean basin would be lower than 5 or 10 days; Sentinel-2 could largely improve the estimation of GPP in Mediterranean ecosystems, providing acceptable information in terms of temporal and spatial resolution and uncertainty.

Hyperspectral sensors are increasingly being used to continuously collect optical data that can be related to carbon and water ecosystem exchanges. Automated proximal sensing can solve the temporal mismatch existing between the periodic remote observations and the continuous acquisition of the Eddy Covariance systems, and can provide also tools for the up-scaling. However, characterization of spectroradiometers used continuously outdoors is necessary to assure data quality; because environmental conditions can influence the instrumentation performance, but also are drivers of the vegetation physiology estimated through the optical measurements. We describe the laboratory characterization previous to field deployment of a Unispec-DC, a dual channel spectroradiometer integrated in an automated multi-angular system (AMSPEC-MED). The instrument operates in a savanna ecosystem in Majadas del Tiétar, Cáceres, Spain, under a wide range of temperatures, radiation levels, illumination angles and internal settings., Laboratory experiments were conducted in order to characterize several features of the spectroradiometer and to estimate correction models. Dark current, the signal produced by thermally generated electrons, was modelled as a function of the temperature and the integration time set. Thermal sensitivity, the sensor’s responsivity dependence on temperature, and spectral calibration were also modeled as a function of temperature. Moreover, non-linearities, deviations from a linear relationship between the input radiance and the output signal, were characterized as a function of the grey level and (innovatively) of the integration time. Since a cosine receptor is used to sample irradiance; we also modeled the diffuser directional response deviations from the ideal response, the cosine of the incident angle of illumination., Calibrated models are used to correct the Hemispherical-Conical Reflectance Factors (HCRF) measured in a continuous mode in the field, and the influence of each model is discussed. Results suggest that effects of thermal sensitivity and non-linearity partially cancel out when reflectance is computed using channel A (irradiance) and B (radiance) ratio, however, in the case of non-linearities, this may not occur when signals are very different in each channel. Dark measurements showed a bias inversely dependent on temperature that was added to dark current. Wavelength calibration showed a dependency on temperature; however, this was small considering the spectral features of the instrument (Full Width at Half Maximum~10nm, interval sampling ~3.3nm). Finally, the cosine directional response correction model produced the largest differences between he corrected and the non-corrected HCRF. This correction requires accounting for the diffuse-to-global radiation ratios. Differences between corrected and non-corrected reflectances were larger in the near infrared region than in the visible. We conclude that characterization of spectroradiometers installed outdoors in automated continuous systems is necessary to ensure comparability and quality of data. Thermal insulation of the instruments could reduce errors related with dark current, thermal sensitivity and wavelength calibration; however these still should have to be known in order to compare with data from other instruments. Moreover, non-linearities and directional response of the cosine receptors would have to still be corrected in order to achieve reliable measurements under different ranges of irradiance and sun elevation.