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Accurate information on the spatial distribution of weeds is the key to effective site-specific weed management and the efficient and sustainable use of weed control measures. This work focuses on the early detection of johnsongrass, common cocklebur and velvetleaf present in a corn field using high resolution airborne hyperspectral imagery acquired when corn plants were in a four to six leaf growth stage. Following the appropriate radiometric and geometric corrections, two supervised classification techniques, such as spectral angle mapper (SAM) and spectral mixture analysis (SMA) were applied. Two different procedures were compared for endmember selections: field spectral measurements and automatic methods to identify pure pixels in the image. Maps for both, overall weeds and for each of the three weed species, were obtained with the different classification methods and endmember sources. The best results were achieved by defining the endmembers through spectral information collected with a field spectroradiometer. Overall accuracies ranged between 60% and 80% using SAM for maps that do not differentiate the weed species while it decreased to 52% when the three weed species were individually classified. In this case, the SMA classification technique clearly improved the SAM results. The proposed methodology shows it to be a promising prospect to be applicable to low cost images acquired by the new generation of hyperspectral sensors onboard unmanned aerial vehicles (UAVs).

Understanding the impact of land use and land cover change on surface energy and water budgets is increasingly important in the context of climate change research. Eddy covariance (EC) methods are the gold standard for high temporal resolution measurements of water and energy fluxes, but cannot resolve spatial heterogeneity and are limited in scope to the tower footprint (few hundred meter range). Satellite remote sensing methods have excellent coverage, but lack spatial and temporal resolution. Long-range unmanned aerial systems (UAS) can complement these other methods with high spatial resolution over larger areas. Here we use UAS thermography and multispectral data as inputs to two variants of the Two Source Energy Balance Model to accurately map surface energy and water fluxes over a nutrient manipulation experiment in a managed semi-natural oak savanna from peak growing season to senescence. We use energy flux measurements from 6 EC stations to evaluate the performance of our method and achieve good accuracy (RMSD ≈ 60 W m− 2 for latent heat flux). We use the best performing latent heat estimates to produce very high-resolution evapotranspiration (ET) maps, and investigate the drivers of ET change over the transition to the senescence period. We find that nitrogen and nitrogen plus phosphorus treatments lead to significant increases in ET (P < 0.001) for both trees (4 and 6%, respectively) and grass (12 and 9%, respectively) compared to the control. These results highlight that the high sensitivity and spatial and temporal resolution of a UAS system allows the precise estimation of relative water and energy fluxes over heterogeneous vegetation cover.

Remote sensing capabilities to monitor evergreen broadleaved vegetation are limited by the low temporal variability in the greenness signal. With canopy greenness computed from digital repeat photography (PhenoCam), we investigated how canopy greenness related to seasonal changes in leaf age and traits as well as variation of trees’ water fluxes (characterized by sap flow and canopy conductance). The results showed that sprouting leaves are mainly responsible for the rapid increase in canopy green chromatic coordinate (GCC) in spring. We found statistically significantly differences in leaf traits and spectral properties among leaves of different leaf ages. Specifically, mean GCC of young leaves was 0.385 ± 0.010 (mean ± SD), while for mature and old leaves was 0.369 ± 0.003, and 0.376 ± 0.004, respectively. Thus, the temporal dynamics of canopy GCC can be explained by changes in leaf spectral properties and leaf age. Sap flow and canopy conductance are both well explained by a combination of environmental drivers and greenness (96% and 87% of the variance explained, respectively). In particular, air temperature and vapor pressure deficit (VPD) explained most of sap flow and canopy conductance variance, respectively. Besides, GCC is an important explanatory variable for variation of canopy conductance may because GCC can represent the leaf ontogeny information. We conclude that PhenoCam GCC can be used to identify the leaf flushing for evergreen broadleaved trees, which carries important information about leaf ontogeny and traits. Thus, it can be helpful for better estimating canopy conductance which constraints water fluxes.

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

High temporal resolution measurements of solar-induced chlorophyll fluorescence (F) and the Photochemical Reflectance Index (PRI) encode vegetation functioning. However, these signals are modulated by time-dependent processes. We tested the applicability of the Singular Spectrum Analysis (SSA) for disentangling fast components (physiology-driven) and slow components (controlled by structural and biochemical properties) from PRI, far-red F (F-760), and far-red apparent fluorescence yield (Fy*(760)). The proof of concept was developed on spectral and flux time series simulated with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. This allowed the evaluation of SSA decomposition against variables that are independent of physiology or are modified by it. Slow SSA-decomposed components of PRI and Fy*(760) showed high correlations with the reference variables (R-2 = 0.97 and 0.96, respectively). Fast SSA-decomposed components of PRI and Fy*(760) were better related to the physiological reference variables than the original signals during periods when leaf area index (LAI) was above 1 m(2) m(-2). The method was also successfully applied to predict light-use efficiency (LUE) from the fast SSA-decomposed components of PRI (R-2 = 0.70) and Fy*(760) (R-2 = 0.68) when discarding data modeled with LAI R-in < 250 W m(-2). The method was then tested on data acquired in a Mediterranean grassland. In this case, the fast SSA-decomposed component of apparent LUE* showed a stronger correlation with the fast SSA-decomposed component of Fy*(760) (R-2 = 0.42) than with original Fy*(760) (R-2 = 0.01). SSA-based approach is a promising tool for decoupling physiological information from measurements acquired with automated proximal sensing systems.

Sun-induced fluorescence in the far-red region (SIF) is increasingly used as a remote and proximal-sensing tool capable of tracking vegetation gross primary production (GPP). However, the use of SIF to probe changes in GPP is challenged during extreme climatic events, such as heatwaves. Here, we examined how the 2018 European heatwave (HW) affected the GPP–SIF relationship in evergreen broadleaved trees with a relatively invariant canopy structure. To do so, we combined canopy-scale SIF measurements, GPP estimated from an eddy covariance tower, and active pulse amplitude modulation fluorescence. The HW caused an inversion of the photosynthesis–fluorescence relationship at both the canopy and leaf scales. The highly nonlinear relationship was strongly shaped by nonphotochemical quenching (NPQ), that is, a dissipation mechanism to protect from the adverse effects of high light intensity. During the extreme heat stress, plants experienced a saturation of NPQ, causing a change in the allocation of energy dissipation pathways towards SIF. Our results show the complex modulation of the NPQ–SIF–GPP relationship at an extreme level of heat stress, which is not completely represented in state-of-the-art coupled radiative transfer and photosynthesis models.

Many satellite missions rely on modeling approaches to acquire global or regional evapotranspiration (ET) products. However, a current challenge in ET modeling lies in dealing with sub-pixel heterogeneity, as models often assume homogeneous conditions at the pixel level. This is particularly an issue for heterogeneous landscapes, such as tree-grass ecosystems (TGE). In these areas, while appearing homogeneous at larger spatial scales pertaining to a single land cover type, the separation of the spectral signals of the main landscape features (e.g. trees and grasses) may not be achieved at the conventional satellite sensor resolution (e.g. 10–1000 m). This leads to important heterogeneity within the pixel grid that may not be accounted for in traditional modeling frameworks. This study examined the effect of pixel heterogeneity on ET simulations over a complex TGE in central Spain. High resolution hyperspectral imagery from five airborne campaigns forced the two-source energy balance (TSEB) model at 1.5–1000 m spatial resolutions. Along with this, the sharpened (20 m) and original (1000 m) Sentinels for Evapotranspiration (Sen-ET) products were evaluated over the study site for 2017. Results indicated that TSEB accurately simulated ET (RMSD: ~60 W/m2) when the pixel scale was able to robustly discriminate between grass and tree pixels (<5 m). However, model uncertainty drastically increased at spatial resolution greater than 10 m (RMSD: ~115 W/m2). Model performance remains relatively constant between 30 and 1000 m spatial resolutions, with within pixel heterogeneity being similar at all these scales. For mixed pixels (≥30 m), forcing an effective landscape roughness into TSEB (RMSD: ~80 W/m2) or applying a seasonally changing TSEB (TSEB-2S; RMSD: ~65 W/m2) improved the modeling performance. The Sen-ET products behaved similarly at both scales with RMSD of ET roughly 80 W/m2. The non-linear relationship between input parameters and flux output, along with the poor re

It is well documented that energy balance and other remote sensing-based evapotranspiration (ET) models face greater uncertainty over water-limited tree-grass ecosystems (TGEs), representing nearly 1/6th of the global land surface. Their dual vegetation strata, the grass-dominated understory and tree-dominated overstory, make for distinct structural, physiological and phenological characteristics, which challenge models compared to more homogeneous and energy-limited ecosystems. Along with this, the contribution of grasses and trees to total transpiration (T), along with their different climatic drivers, is still largely unknown nor quantified in TGEs. This study proposes a thermal-based three-source energy balance (3SEB) model, accommodating an additional vegetation source within the well-known two-source energy balance (TSEB) model. The model was implemented at both tower and continental scales using eddy-covariance (EC) TGE sites, with variable tree canopy cover and rainfall (P) regimes and Meteosat Second Generation (MSG) images. 3SEB robustly simulated latent heat (LE) and related energy fluxes in all sites (Tower: LE RMSD ~60 W/m2; MSG: LE RMSD ~90 W/m2), improving over both TSEB and seasonally changing TSEB (TSEB-2S) models. In addition, 3SEB inherently partitions water fluxes between the tree, grass and soil sources. The modelled T correlated well with EC T estimates (r > .76), derived from a machine learning ET partitioning method. The T/ET was found positively related to both P and leaf area index, especially compared to the decomposed grass understory T/ET. However, trees and grasses had contrasting relations with respect to monthly P. These results demonstrate the importance in decomposing total ET into the different vegetation sources, as they have distinct climatic drivers, and hence, different relations to seasonal water availability. These promising results improved ET and energy flux estimations over complex TGEs, which may contribute to enhance glo

Nutrient availability, especially of nitrogen (N) and phosphorus (P), is of major importance for every organism and at a larger scale for ecosystem functioning and productivity. Changes in nutrient availability and potential stoichiometric imbalance due to anthropogenic nitrogen deposition might lead to nutrient deficiency or alter ecosystem functioning in various ways. In this study, we present 6 years (2014–2020) of flux-, plant-, and remote sensing data from a large-scale nutrient manipulation experiment conducted in a Mediterranean savanna-type ecosystem with an emphasis on the effects of N and P treatments on ecosystem-scale water-use efficiency (WUE) and related mechanisms. Two plots were fertilized with N (NT, 16.9 Ha) and N + P (NPT, 21.5 Ha), and a third unfertilized plot served as a control (CT). Fertilization had a strong impact on leaf nutrient stoichiometry only within the herbaceous layer with increased leaf N in both fertilized treatments and increased leaf P in NPT. Following fertilization, WUE in NT and NPT increased during the peak of growing season. While gross primary productivity similarly increased in NT and NPT, transpiration and surface conductance increased more in NT than in NPT. The results show that the NPT plot with higher nutrient availability, but more balanced N:P leaf stoichiometry had the highest WUE. On average, higher N availability resulted in a 40% increased leaf area index (LAI) in both fertilized treatments in the spring. Increased LAI reduced aerodynamic conductance and thus evaporation at both fertilized plots in the spring. Despite reduced evaporation, annual evapotranspiration increased by 10% (48.6 ± 28.3 kg H2O m−2), in the NT plot, while NPT remained similar to CT (−1%, −6.7 ± 12.2 kgH2O m−2). Potential causes for increased transpiration at NT could be increased root biomass and thus higher water uptake or rhizosphere priming to increase P-mobilization through microbes. The annual net ecosystem exchange shifted from a c

Digital relations are deeply transforming our lives : from the nature of political participation to the relationship between digital and non-digital environments ; from the reorganization of the public sphere to the ethics of responsibility, transparency or inclusiveness. We are witnessing fundamental changes in the infrastructures of democracy and the emergence of new forms of digital citizenship.

Wildfire occurrence is expected to increase in future climate and Land Use Land Cover (LULC) change scenarios, especially in vulnerable areas as the European Mediterranean Basin. In this study future probability of wildfire occurrence was estimated for a 20-year time period (2041–2060, centered on 2050) by applying a statistically-based regression model using LULC-derived contact areas with the forest cover (interfaces) as proxy for the human-related factor and a combination of Live Fuel Moisture Content and seasonal climate-related variables as predictors. Future wildfire occurrence was mapped under RCP 8.5 high emissions scenario in four Spanish regions with heterogeneous socioeconomic, LULC and natural fire-related characteristics at 1 km2 target spatial resolution. Results showed increased wildfire probability in ∼19–73% of 1 km2 cells, observing regional differences in the variable effects. This approach could be applied to other spatial scales offering tools for planning and management actions and to obtain different possible future scenarios.

Providing accurate information on fire effects is critical to understanding post-fire ecological processes and to design appropriate land management strategies. Multispectral imagery from optical passive sensors is commonly used to estimate fire damage, yet this type of data is only sensitive to the effects in the upper canopy. This paper evaluates the sensitivity of full waveform LiDAR data to estimate the severity of wildfires using a 3D radiative transfer model approach. The approach represents the first attempt to evaluate the effect of different fire impacts, i.e. changes in vegetation structure as well as soil and leaf color, on the LiDAR signal. The FLIGHT 3D radiative transfer model was employed to simulate full waveform data for 10 plots representative of Mediterranean ecosystems along with a wide range of post-fire scenarios characterized by different severity levels, as defined by the composite burn index (CBI). A new metric is proposed, the waveform area relative change (WARC), which provides a comprehensive severity assessment considering all strata and accounting for changes in structure and leaf and soil color. It showed a strong correlation with CBI values (Spearman's Rho = 0.9 ± 0.02), outperforming the relative change of LiDAR metrics commonly applied for vegetation modeling, such as the relative height of energy quantiles (Spearman's Rho = 0.56 ± 0.07, for the relative change of RH60, the second strongest correlation). Logarithmic models fitted for each plot based on the WARC yielded very good performance with R2 (± standard deviation) and RMSE (± standard deviation) of 0.8 (± 0.05) and 0.22 (± 0.03), respectively. LiDAR metrics were evaluated over the King Fire, California, U.S., for which pre- and post-fire discrete return airborne LiDAR data were available. Pseudo-waveforms were computed after radiometric normalization of the intensity data. The WARC showed again the strongest correlation with field measures of GeoCBI values (Spearman's Rho = 0

Live Fuel Moisture Content (LFMC) contributes to fire danger and behavior, as it affects fire ignition and propagation. This paper presents a two layered Landsat LFMC product based on topographically corrected relative Spectral Indices (SI) over a 2000–2011 time series, which can be integrated into fire behavior simulation models. Nine chaparral sampling sites across three Landsat-5 Thematic Mapper (TM) scenes were used to validate the product over the Western USA. The relations between field-measured LFMC and Landsat-derived SIs were strong for each individual site but worsened when pooled together. The Enhanced Vegetation Index (EVI) presented the strongest correlations (r) and the least Root Mean Square Error (RMSE), followed by the Normalized Difference Infrared Index (NDII), Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI). The relations between LFMC and the SIs for all sites improved after using their relative values and relative LFMC, increasing r from 0.44 up to 0.69 for relative EVI (relEVI), the best predictive variable. This relEVI served to estimate the herbaceous and woody LFMC based on minimum and maximum seasonal LFMC values. The understory herbaceous LFMC on the woody pixels was extrapolated from the surrounding pixels where the herbaceous vegetation is the top layer. Running simulations on the Wildfire Analyst (WFA) fire behavior model demonstrated that this LFMC product alone impacts significantly the fire spatial distribution in terms of burned probability, with average burned area differences over 21% after 8 h burning since ignition, compared to commonly carried out simulations based on constant values for each fuel model. The method could be applied to Landsat-7 and -8 and Sentinel-2A and -2B after proper sensor inter-calibration and topographic correction

This paper develops an algorithm to estimate vegetation canopy gap fraction (GF), taking advantage of the full Terrestrial Laser Scanner (TLS) resolution. After calculating the TLS angular resolution, the algorithm identifies the missing laser hits (gaps) within an angular grid in the azimuthal and zenithal directions. The algorithm was first tested on angular data simulations with random (R), cluster (C) and random and cluster together (RC) gap pattern distributions. Noise introduced in the simulations as a percentage of the resolution accounted for the effect of TLS angular uncertainty. The algorithm performs accurately if angular noise is <6% of the angular resolution. To assess the impact of the change in projection, this study compared these GF estimates from angular grid simulations to their transformation into simulated hemispherical images (SHI). SHI with C patterns perform accurately, but R and RC patterns underestimate GF, especially for GF values below 0.6. The SHI performance to estimate GF was always far below the algorithm developed here with the angular grid simulations. When applied to actual TLS data acquired over individual Quercus ilex L. trees, the algorithm rendered a GF between 0.26 and 0.40. TLS had an angular noise <6%. Converting the angular grid into simulated HI (TLS-SHI) provided a better agreement with actual HI acquired in the same location as the TLS data, since they are in the same projection. The TLS-SHI underestimated GF by an average of 4% compared to HI. HI and TLS-SHI presented 14% and 17% lower values than the GF calculated from the angular grids, respectively. Nevertheless, the results from the simulations indicate that the algorithm based on the angular grid should be closer to the actual GF of the tree canopy.

Sun-Induced fluorescence at 760 nm (F760) is increasingly being used to predict gross primary production (GPP) through light use efficiency (LUE) modeling, even though the mechanistic processes that link the two are not well understood. We analyzed the effect of nitrogen (N) and phosphorous (P) availability on the processes that link GPP and F760 in a Mediterranean grassland manipulated with nutrient addition. To do so, we used a combination of process-based modeling with Soil-Canopy Observation of Photosynthesis and Energy (SCOPE), and statistical analyses such as path modeling. With this study, we uncover the mechanisms that link the fertilization-driven changes in canopy nitrogen concentration (N%) to the observed changes in F760 and GPP. N addition changed plant community structure and increased canopy chlorophyll content, which jointly led to changes in photosynthetic active radiation (APAR), ultimately affecting both GPP and F760. Changes in the abundance of graminoids, (%graminoids) driven by N addition led to changes in structural properties of the canopy such as leaf angle distribution, that ultimately influenced observed F760 by controlling the escape probability of F760 (Fesc). In particular, we found a change in GPP¿F760 relationship between the first and the second year of the experiment that was largely driven by the effect of plant type composition on Fesc, whose best predictor is %graminoids. The P addition led to a statistically significant increase on light use efficiency of fluorescence emission (LUEf), in particular in plots also with N addition, consistent with leaf level studies. The N addition induced changes in the biophysical properties of the canopy that led to a trade-off between surface temperature (Ts), which decreased, and F760 at leaf scale (F760leaf,fw), which increased. We found that Ts is an important predictor of the light use efficiency of photosynthesis, indicating the importance of Ts in LUE modeling approaches to predict GPP

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 pro

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.

Updated and harmonized land cover (LC) data is essential for wildfire estimation in fire-prone areas as is the case in southern Europe. CORINE Land cover (CLC) and ESA Climate Change Initiative Land Cover (CCI-LC) maps have been analyzed and compared their performance in the estimation of wildfire occurrence in Europe at regional and local scales for the period 2010–2014. LC maps legends were harmonized and similarities and discrepancies were compared. Overall agreement between the two maps for the whole Europe was ˜75%. Forest and agriculture showed the largest agreement, while shrubland and grassland the lowest. Quantity and allocation disagreements were calculated including exchange and shift components (Pontius and Santacruz, 2014) which provided detailed information about the contribution of each class to the overall disagreement. Spatial discrepancies were found in areas where grassland and shrubland were the dominant classes as in United Kingdom or East Turkey. Land Use and Coverage Area frame Survey (LUCAS) was used as ground truth for validation purposes. The agreement with LUCAS was slightly higher for CCI-LC (59%) than for CLC (56%). Generalized Linear Models (GLM), based on presence-absence of wildfires, were used to estimate wildfire occurrence at 3 × 3 km grid cell resolution from both LC maps at the European scale. LC interfaces and climatic variables (temperature and precipitation) where used as explicative variables while fires from European Forest Fire Information System EFFIS (2010–2014 period) were used as response variable. Wildfire occurrence was also estimated with the two maps at local scale in a test region (Zamora, Spain) using a more precise location of the response variable (x, y fire ignition points). At the European scale models obtained within the two maps showed similar results. CCI-LC model sensitivity was 77.26%, specificity 25.89% and omission error 22.74% while CLC model sensitivity was 75.68%, specificity 29.99% and omission erro

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.

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.

To understand what is driving spatial flux variability within a savanna type ecosystem in central Spain, data of three co-located eddy covariance (EC) towers in combination with hyperspectral airborne measurements and footprint analysis were used. The three EC systems show consistent, and unbiased mass and energy fluxes. Nevertheless, instantaneous between-tower flux differences i.e. paired half hourly fluxes, showed large variability. A period of 13 days around an airborne hyperspectral campaign was analyzed and proved that between-tower differences can be associated to biophysical properties of the sampled footprint areas. At high photosynthetically active radiation (PAR) net ecosystem exchange (NEE) was mainly controlled by chlorophyll content of the vegetation (estimated through MERIS Terrestrial Chlorophyll Index (MTCI)), while sensible heat flux (H) was driven by surface temperature. The spatial variability of biophysical properties translates into flux variability depending on the location and size of footprints. For H, negative correlations were found with surface temperature for between-tower differences, and for individual towers in time, meaning that higher H was observed at lower surface temperatures. High aerodynamic conductance of tree canopies reduces the canopy surface temperature and the excess energy is relieved as H. Therefore, higher tree canopy fractions yielded to lower surface temperatures and at the same time to higher H. For NEE, flux differences between towers were correlated to differences in MTCI of the respective footprints, showing that higher chlorophyll content of the vegetation translates into more photosynthetic CO uptake, which controls NEE variability. Between-tower differences of latent heat fluxes (LE) showed no consistent correlation to any vegetation index (VI), or structural parameter e.g. tree-grass-fraction. This missing correlation is most likely caused by the large contribution of soil evaporation to ecosystem LE, which i

[EN] Land uses and their relationship with socio-economic factors are a key aspect in the management of wildfires. This study analyzes the forest fires occurrence in the period 2005-2010 using as explanatory variables the contact zones between land uses (interfaces: agricultural-forest (AFI), urban-forest (WUI) and grassland-forest (GFI)) due to its relationship with the wildfire occurrence in Spain, calculated from Climate Change Initiative-Land Cover maps, and using Max Ent to generate two predictive models (general and specific) in Zamora and Madrid. In the general model, the AFI contributed the more in Zamora, and the WUI in Madrid. The vegetation formations models generally improved the accuracy values in both provinces and the percentage of agreement in Zamora and the spatial risk discrimination, optimizing potential actions aimed at prevention., [ES] Los usos del suelo y su relación con factores socio-económicos son un aspecto clave en la gestión de incendios forestales. Este estudio analiza la ocurrencia de incendios forestales en el período 2005-2010 utilizando como variables explicativas las zonas de contacto entre usos del suelo (interfaces: agrícola-forestal (IAF), urbano-forestal (IUF) y pasto-forestal (IPF)) por su relación con la ocurrencia de incendios en España, calculadas a partir de mapas de usos del suelo de Climate Change Initiative-Land Cover. Se utilizó MaxEnt para generar dos modelos predictivos (general y específico) en Zamora y Madrid. En el modelo general, la IAF contribuyó más en Zamora y la IUF en Madrid. El modelo específico mejoró los ajustes generales en ambas provincias, los porcentajes de acierto en Zamora y la discriminación espacial del riesgo, optimizando las potenciales actuaciones dirigidas a la prevención.

The 3-D Radiative Transfer Model (RTM) FLIGHT can represent scattering in open forest or savannas featuring underlying bare soils. However, FLIGHT might not be suitable for multilayered tree-grass ecosystems (TGE), where a grass understory can dominate the reflectance factor (RF) dynamics due to strong seasonal variability and low tree fractional cover. To address this issue, we coupled FLIGHT with the 1-D RTM PROSAIL. The model is evaluated against spectral observations of proximal and remote sensing sensors: the ASD Fieldspec® 3 spectroradiometer, the Airborne Spectrographic Imager (CASI) and the MultiSpectral Instrument (MSI) onboard Sentinel-2. We tested the capability of both PROSAIL and PROSAIL+FLIGHT to reproduce the variability of different phenological stages determined by 16-year time series analysis of Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index (MODIS-NDVI). Then, we combined concomitant observations of biophysical variables and RF to test the capability of the models to reproduce observed RF. PROSAIL achieved a Relative Root Mean Square Error (RRMSE) between 6% to 32% at proximal sensing scale. PROSAIL+FLIGHT RRMSE ranged between 7% to 31% at remote sensing scales. RRMSE increased in periods when large fractions of standing dead material mixed with emergent green grasses —especially in autumn—; suggesting that the model cannot represent the spectral features of this material. PROSAIL+FLIGHT improves RF simulation especially in summer and at mid-high view angles.

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.

Aim of study We evaluated the use of dimethylsulfoxide (DMSO) for the photometric determination of chlorophyll (Chla and Chlb) and carotenoids in Quercus ilex L. leaves by comparative analysis using aqueous-acetone extraction. Area of study a Q. ilex dehesa in Las Majadas del Tietar, Cáceres, Spain Material and methods Q. ilex leaves were sampled during two vegetative periods. Field SPAD-502 Chlorophyll measurements and photometric chlorophyll determinations were performed. Two procedures were used extraction of intact foliar discs at 65ºC with DMSO and fine foliar powder with cold aqueous-acetone. Main results DMSO produced Chlb overestimation and different fitting performance for SPAD vs pigment calibrations (R2=0.64, RMSE=0.20, p<0.0001 for Chla (µg/cm2); R2=0.33, RMSE=0.23, p<0.0001 for Chlb (µg/cm2) and R2=0.50, RMSE=0.23, p<0.0001 for carotenoids (µg/cm2)). Aqueous-acetone provided more accurate predictions (R2=0.90, RMSE=0.16, p<0.0001 for Chla and R2=0.91, RMSE=0.16, p<0.0001 for Chlb, R2=0.90, RMSE=0.02, p<0.0001 for carotenoids) and mean ratio Chla/Chlb=3.6 inside the range for sun exposed leaves. Research highlights Oxidizing conditions and polyphenol concentrations in Q. ilex leaves generated brown colorations in the DMSO extraction procedure, interfering with the photometric measurements in the red-orange region. Aqueous-acetone extraction was free from interference. DMSO should be avoided for pigment determination in Q. ilex leaves or when comparing different tree species. © 2017 INIA.

Sun-induced fluorescence (SIF) in the far-red region provides a new noninvasive measurement approach that has the potential to quantify dynamic changes in light-use efficiency and gross primary production (GPP). However, the mechanistic link between GPP and SIF is not completely understood. We analyzed the structural and functional factors controlling the emission of SIF at 760 nm (F760) in a Mediterranean grassland manipulated with nutrient addition of nitrogen (N), phosphorous (P) or nitrogen¿phosphorous (NP). Using the soil¿canopy observation of photosynthesis and energy (SCOPE) model, we investigated how nutrient-induced changes in canopy structure (i.e. changes in plant forms abundance that influence leaf inclination distribution function, LIDF) and functional traits (e.g. N content in dry mass of leaves, N%, Chlorophyll a+b concentration (Cab) and maximum carboxylation capacity (Vcmax)) affected the observed linear relationship between F760 and GPP. We conclude that the addition of nutrients imposed a change in the abundance of different plant forms and biochemistry of the canopy that controls F760. Changes in canopy structure mainly control the GPP¿F760 relationship, with a secondary effect of Cab and Vcmax. In order to exploit F760 data to model GPP at the global/regional scale, canopy structural variability, biodiversity and functional traits are important factors that have to be considered.

Spatio-temporal mismatches between Remote Sensing (RS) and Eddy Covariance (EC) data as well as spatial heterogeneity jeopardize terrestrial Gross Primary Production (GPP) modeling. This article combines: (a) high spatial resolution hyperspectral imagery; (b) EC footprint climatology estimates; and (c) semi-empirical models of increasing complexity to analyze the impact of these factors on GPP estimation. Analyses are carried out in a Mediterranean Tree-Grass Ecosystem (TGE) that combines vegetation with very different physiologies and structure. Half-hourly GPP (GPP) were predicted with relative errors ~36%. Results suggest that, at EC footprint scale, the ecosystem signals are quite homogeneous, despite tree and grass mixture. Models fit using EC and RS data with high degree of spatial and temporal match did not significantly improved models performance; in fact, errors were explained by meteorological variables instead. In addition, the performance of the different models was quite similar. This suggests that none of the models accurately represented light use efficiency or the fraction of absorbed photosynthetically active radiation. This is partly due to model formulation; however, results also suggest that the mixture of the different vegetation types might contribute to hamper such modeling, and should be accounted for GPP models in TGE and other heterogeneous ecosystems.

[ES] Este trabajo aborda la estimación de variables biofísicas de un pastizal de dehesa a partir de información óptica generada por sensores próximos y remotos. Las variables de contenido de humedad del combustible (FMC), contenido de agua del dosel (CWC), índice de área foliar (LAI), materia seca (Cm) y biomasa superficial (AGB) fueron estimadas en laboratorio a partir de muestras de vegetación tomadas simultáneamente a la adquisición de datos hiperespectrales del sensor Compact Airbone Spectrographic Imager (CASI) y del espectro-radiómetro de campo ASD FieldSpec® 3. A partir de la información espectral se han calculado diversos índices extraídos de la literatura y se han analizado las relaciones lineales existentes con las variables biofísicas medidas en campo. Los resultados muestran relaciones consistentes entre las variables biofísicas y los índices espectrales, especialmente en el caso de los índices basados en bandas del infrarrojo medio de onda corta (SWIR) y del red-edge, poniendo de manifiesto la importancia de estas regiones en la estimación de variables biofísicas en cubiertas de pastizal. Se han obteniendo coeficientes de determinación (R2) superiores a 0,91 y un error cuadrático medio relativo (RRMSE) de 21,4%, para los índices espectrales calculados con datos ASD; y R2 de 0,91 y RRMSE de 15,5% para los índices espectrales calculados con datos CASI., [EN] The aim of this paper is the estimation of biophysical vegetation parameters from its optical properties. The variables Fuel Moisture Content (FMC), Canopy Water Content (CWC), Leaf Area Index (LAI), dry matter (Cm) and Above-Ground Biomass (AGB) were estimated in the laboratory from vegetation samples collected simultaneously with the acquisition of spectral data from the Compact Airborne Spectrographic Imager (CASI) sensor and the field spectroradiometer ASD FieldSpec® 3. Spectral vegetation indices found in the literature were computed from hyperspectral data. Their linear relationships with the biophysical variables measured in the field were analysed. Results show consistent relationships between analysed biophysical parameters and spectral indices, mainly those using SWIR and red-egde bands which reveal the importance of these spectral regions for the estimation of biophysical variables in herbaceous covers. Determination coefficients (R2) above 0.91 and RRMSE of 21.4% have been obtained for the spectral indexes calculated whit ASD data, and 0.91 R2 and RRMSE of 15.5% for the spectral indexes calculated whit CASI data.

In the period 1980s–2010s, 95% of wildfires in Mediterranean Europe were due to human causes. In this highly populated region, socio-economic changes occurred in the last decades have influenced wildfire activity. Socio-economic drivers are difficult to model at regional/global scales due to lack of spatial and temporal homogeneous data sources. In this paper, human caused-wildfires have been modeled using Land Use-Land Cover (LULC) interfaces based on the theoretical relationships found in previous works between human activities associated to this areas and fire occurrence. Generalized Additive Models were used to analyze human-caused fire occurrence in the European Mediterranean (EUMed) basin (Portugal, Spain, South-France and Italy) in two different time periods 1990s and 2000s. The estimated effects of the LULC interfaces on fire occurrence were significant in all periods in the whole study area and at country level, except in Portugal. Concerning trends, no significant changes were observed between periods in EUMed and in the individual countries. Nevertheless influence of the LULC interfaces on wildfire occurrence exhibited different tendencies among the studied sites.

The development of tower-mounted automated multi-angular hyperspectral systems has brought new opportunities and challenges for the characterization of the Bidirectional Reflectance Distribution Function (BRDF) on a continuous basis. This study describes the deployment of one of these systems in a Mediterranean savanna ecosystem (AMSPEC-MED), and proposes new approaches for modeling of directional effects. In this study, a Hemispherical-Directional Reflectance Distribution Function (HDRDF) was introduced in order to quantify the effect of diffuse radiation on the estimation of BRDF. The HDRDFs of the two covers of the ecosystem - trees and grasses - were un-mixed using a 3-Dimensional (3-D) model of the observed scene. Up-scaling HDRDF estimates to MODIS BRDF product. Despite the uncertainties in the estimation of diffuse irradiance and the 3-D representation of the scene, HDRDF un-mixing demonstrates the potential of automated multi-angular proximal sensing to study vegetation properties in heterogeneous ecosystems and the correction of directional effects of different sources.

This paper assesses the relative importance of socioeconomic factors linked to fire occurrence through the simulation of future land use/land cover (LULC) change scenarios in the Madrid region (Spain). This region is a clear example of the socioeconomic changes that have been occurring over recent decades in the European Mediterranean as well as their impact on LULC and fire occurrence. Using the LULC changes observed between 1990 and 2006 as a reference, future scenarios were run up to 2025 with the conversion of land use and its effects model. Simultaneously, the relationship between LULC arrangement (interfaces) and historical fire occurrence was calculated using logistic regression analysis and used to quantify changes in future fire occurrence due to projected changes in LULC interfaces. The results revealed that it is possible to explain the probability of fire occurrence using only variables obtained from LULC maps, although the explanatory power of the model is low. In this context, border areas between some LULC types are of particular interest (i.e., urban/forest, grassland/forest and agricultural/forest interfaces). Results indicated that expected LULC changes in Euro-Mediterranean regions, particularly given the foreseeable increase in the wildland¿urban interface, will substantially increase fire occurrence (up to 155 %). This underlines the importance of future LULC scenarios when planning fire prevention measures.

Resolving the spatial and temporal dynamics of gross primary productivity (GPP) of terrestrial ecosystems across different scales remains a challenge. Remote sensing is regarded as the solution to upscale point observations conducted at the ecosystem level, using the eddy covariance (EC) technique, to the landscape and global levels. In addition to traditional vegetation indices, the photochemical reflectance index (PRI) and the emission of solar-induced chlorophyll fluorescence (SIF), now measurable from space, provide a new range of opportunities to monitor the global carbon cycle using remote sensing. However, the scale mismatch between EC observations and the much coarser satellite-derived data complicate the integration of the two sources of data. The solution is to establish a network of in situ spectral measurements that can act as a bridge between EC measurements and remote-sensing data. In situ spectral measurements have already been conducted for many years at EC sites, but using variable instrumentation, setups, and measurement standards. In Europe in particular, in situ spectral measurements remain highly heterogeneous. The goal of EUROSPEC Cost Action ES0930 was to promote the development of common measuring protocols and new instruments towards establishing best practices and standardization of these measurements. In this review we describe the background and main tradeoffs of in situ spectral measurements, review the main results of EUROSPEC Cost Action, and discuss the future challenges and opportunities of in situ spectral measurements for improved estimation of local and global estimates of GPP over terrestrial ecosystems.

[EN] In this article we study forest fires in the Autonomous Community of Madrid into two periods, 1989-1995 and 2007-2010, to characterize and compare the fire regime. We also analyse the selectivity action of fire in relation to the land uses of the region. Fire data was obtained from Landsat (TM / ETM +) while the land use information was obtained from CORINE Land Cover maps (1990 and 2006). Fire regime was defined through seven indicators. Results showed a clear decreasing of fire activity between the two periods, most important in the burned area (78%) than in the number of fires (16%). The analysis of the fire selectivity on land uses was based on the Manly´s selection ratio. In the period 1989-1995 a higher incidence in coniferous forests and, less important, in shrubs was observed. In the period 2007-2010 the incidence was higher in grassland areas. The analysis showed the important role that the artificial/forest interface plays in the fire occurrence, especially in 2007-2010, [ES] En este artículo se analizan los incendios forestales ocurridos en la Comunidad Autónoma de Madrid en dos períodos, 1989-1995 y 2007-2010, con el objetivo de caracterizar y comparar el régimen de incendios en cada uno de ellos, así como la acción selectiva del fuego en relación con los usos del suelo. Los datos de incendios se generaron a partir de imágenes Landsat (TM/ETM+) mientras que la información sobre usos del suelo se obtuvo de los mapas CORINE Land Cover (1990 y 2006). El régimen de incendios fue definido a través de siete indicadores. Los resultados mostraron una clara reducción de la incidencia de incendios con un 16% menos de incendios y un 78% menos de superficie quemada en el segundo período respecto al primero. El análisis del carácter selectivo del fuego sobre los usos del suelo se basó en el cociente de selección de Manly. En el período 1989-1995 destacó una mayor incidencia en bosques de coníferas y, en menor medida, en matorrales. En el período 2007-2010, la incidencia fue mayor en las zonas de pastos. El análisis mostró el importante papel de la interfaz entre usos artificiales y forestales en la ocurrencia de incendios, especialmente en el período 2007-2010.

Las dehesas son sistemas agro-forestales en los que se producen complejos mecanismos de intercambio de carbono y agua debido a la presencia de estratos de vegetación diferenciados (pastizal, arbolado y matorral), en un marco climático caracterizado por periodos de sequía. Dichos componentes no se dan en las mismas proporciones constituyendo complejos mosaicos en función del tipo de manejo, la densidad del arbolado y el momento de año. En este contexto, una de las variables clave en la parametrización del balance energético entre el suelo y atmósfera es la temperatura de superficie (Ts). El análisis de sus patrones espacio-temporales es importante para valorar el papel de la dehesa como sumidero de carbono, por cuanto la Ts controla la velocidad de procesos metabólicos en la vegetación y la descomposición de la materia orgánica del suelo, influyendo de esta manera en la transferencia neta del carbono hacia la atmósfera. Este trabajo analiza la variación espacio-temporal de la Ts en función de la densidad y tipo de arbolado. Utilizando como referencia el SIOSE (PNOT) se analizan las diferencias espacio-temporales de los valores de Ts, calculados a partir de una serie multitemporal de imágenes Landsat-5 TM. En este trabajo se presentan los resultados cartográficos en 14 fechas a lo largo del período de 2009-2011. Se han detectado diferencias estadísticamente significativas en los valores de Ts en función de la densidad del arbolado, así que los valores de Ts más elevados se localizan en las dehesas con menor cubierta de Quercus ilex y de especies arbustivas. Debido a la mayor variabilidad eco-fisiológica del estrato herbáceo, las diferencias medias más acusadas se registran en las imágenes de verano.

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.

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.

This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones. Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in R2 and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for

[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.

Implementation of precision viticulture techniques requires the use of emerging sensing technologies to assess the vineyard spatial variability. This work shows the capability of multispectral imagery acquired from a remotely piloted aerial system (RPAS), and the derived spectral indices to assess the vegetative, productive, and berry composition spatial variability within a vineyard (Vitis vinifera L.). Multi-spectral imagery of 17 cm spatial resolution was acquired using a RPAS. Classical vegetation spectral indices and two newly defined normalised indices, NVI1 = (R802 − R531)/(R802 + R531) and NVI2 = (R802 − R570)/(R802 + R570), were computed. Their spatial distribution and relationships with grapevine vegetative, yield, and berry composition parameters were studied. Most of the spectral indices and field data varied spatially within the vineyard, as showed through the variogram parameters. While the correlations were significant but moderate among the spectral indices and the field variables, the kappa index showed that the spatial pattern of the spectral indices agreed with that of the vegetative variables (0.38–0.70) and mean cluster weight (0.40). These results proved the utility of the multi-spectral imagery acquired from a RPAS to delineate homogeneous zones within the vineyard, allowing the grapegrower to carry out a specific management of each subarea.

Field spectroradiometers integrated in automated systems at Eddy Covariance (EC) sites are a powerful tool for monitoring and upscaling vegetation physiology and carbon and water fluxes. However, exposure to varying environmental conditions can affect the functioning of these sensors, especially if these cannot be completely insulated and stabilized. This can cause inaccuracy in the spectral measurements and hinder the comparison between data acquired at different sites. This paper describes the characterization of key sensor models in a double beam spectroradiometer necessary to calculate the Hemispherical-Conical Reflectance Factor (HCRF). Dark current, temperature dependence, non-linearity, spectral calibration and cosine receptor directional responses are modeled in the laboratory as a function of temperature, instrument settings, radiation measured or illumination angle. These models are used to correct the spectral measurements acquired continuously by the same instrument integrated outdoors in an automated system (AMSPEC-MED). Results suggest that part of the instrumental issues cancel out mutually or can be controlled by the instrument configuration, so that changes induced in HCFR reached about 0.05 at maximum. However, these corrections are necessary to ensure the inter-comparison of data with other ground or remote sensors and to discriminate instrumentally induced changes in HCRF from those related with vegetation physiology and directional effects.

[ES] Según la guía de buenas prácticas del Panel Intergubernamental de Expertos en Cambio Climático (IPCC), las técnicas de teledetección son adecuadas para la estimación de los sumideros de carbono a partir de la estimación de la biomasa. En este trabajo se ha utilizado una serie temporal de imágenes Landsat para estimar la cantidad de biomasa del pasto en una zona de dehesa al NE de la provincia de Cáceres (España) en el período 1985-2010. A partir de las imágenes de biomasa obtenidas se ha analizado la evolución de los stocks de carbono en la cubierta herbácea. Se realizaron muestreos de campo de biomasa, así como de radiometría, en cuatro parcelas ubicadas dentro de la zona de estudio para la calibración del modelo. Se aplicó un modelo de regresión lineal entre los datos de campo e índices espectrales para la estimación de biomasa a partir de las imágenes Landsat. El índice EVI obtuvo los mejores ajustes (R2=0.79). Se aplicó un factor de carbono de 50% de la biomasa para la estimación de los stocks de carbono en la cubierta herbácea. Los valores promedios de biomasa mostraron sensibilidad a los cambios extremos de las series temporales de precipitación y temperatura., [EN] According to the Intergovernmental Panel on Climate Change Good Practice Guidance (IPCC GPG), remote sensing techniques are suitable for the estimation of carbon sinks, through the estimation of above ground biomass. In this work we used a Landsat time series to estimate grass biomass in a wooded grassland ecosystem located Northeast Cáceres (Spain) from 1985 to 2010. From the results of the estimation of biomass, we analyzed the evolution of carbon stocks in grasslands. Field sampling of biomass were performed, as well as field spectroscopy, in four plots located within the study site for model calibration. Linear regression models between field data and spectral indices were applied for estimating biomass from Landsat data. EVI was the best index for biomass estimation (R2=0.79). A carbon fraction of 50% of the biomass was applied for the carbon stock estimation. The average values of biomass were found to be sensitive to extreme changes in time series of precipitation and temperature.

Understanding the interaction between natural and socio-economic factors that determine fire regime is essential to make accurate projections and impact assessments. However, this requires having accurate historical, systematic, homogeneous and spatially explicit information on fire occurrence. Fire databases usually have serious limitations in this regard; therefore other sources of information, such as remote sensing, have emerged as alternatives to generate optimal fire maps on various spatial and temporal scales. Several national and international projects work in order to generate information to study the factors that determine the current fire regime and its future evolution. This work is included in the framework of the project "Forest fires under climate, social and economic Changes in Europe, the Mediterranean and other fire-affected areas of the World" (FUME http://www.fumeproject.eu), which aims to study the changes and factors related to fire regimes through time to determine the potential impacts on vegetation in Mediterranean regions and concrete steps to address future risk scenarios., We analyzed the changes in the fire regime in Madrid region (Spain) in the past three decades (1984-2010) and its relation to land use changes. We identified and mapped fires that have occurred in the region during those years using Landsat satellite images by combining digital techniques and visual analysis. The results show a clear cyclical behaviour of the fire, with years of high incidence (as 1985, 2000 and 2003, highlighted by the number of fires and the area concerned, over 2000 ha) followed by another with a clear occurrence decrease. At the same time, we analyzed the land use changes that have occurred in Madrid region between the early 80s and mid-2000s using as reference the CORINE Land-cover maps (1990, 2000 and 2006) and the Vegetation and Land Use map of the Community of Madrid, 1982., We studied the relationship between fire regimes and observed land-use and land-cover changes in the periods analyzed, it was determined that between years 1984 and 2006 most of the burned area remained pre-fire cover type (above 80% of the area). However, in areas that experienced change, the most important transitions were recorded in wooded areas, especially conifers, which became shrubs or sparsely vegetated areas, followed by non-irrigated crops, which were replaced by grasslands or industrial areas, and sparse vegetation which changed to shrubs. Finally, the analysis of land-use changes over burned areas situated shrubland as the most favored type of cover, either as a result of a vegetative degradation process after intense burning of wooded areas, especially conifers, or as stage of natural increase in areas previously covered by sparsely vegetation.

The direct estimation of nitrogen (N) in fresh vegetation is challenging due to its weak influence on leaf reflectance and the overlaps with absorption features of other compounds. Different empirical models relate in this work leaf nitrogen concentration ([N]Leaf) on Holm oak to leaf reflectance as well as derived spectral indices such as normalized difference indices (NDIs), the three bands indices (TBIs) and indices previously used to predict leaf N and chlorophyll. The models were calibrated and assessed their accuracy, robustness and the strength of relationship when other biochemicals were considered. Red edge was the spectral region most strongly correlated with [N]Leaf, whereas most of the published spectral indexes did not provide accurate estimations. NDIs and TBIs based models could achieve robust and acceptable accuracies (TBI1310,1720,730: R2 = 0.76, [0.64,0.86]; RMSE (%) = 9.36, [7.04,12.83]). These models sometimes included indices with bands close to absorption features of N bonds or nitrogenous compounds, but also of other biochemicals. Models were independently and inter-annually validated using the bootstrap method, which allowed discarding those models non-robust across different years. Partial correlation analysis revealed that spectral estimators did not strongly respond to [N]Leaf but to other leaf variables such as chlorophyll and water, even if bands close to absorption features of N bonds or compounds were present in the models.

Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global change) and FLUXPEC (Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean “dehesa” ecosystem) projects LST retrieved from Landsat data is required to integrate ground-based observations of energy, water, and carbon fluxes with multi-scale remotely-sensed data and assess water and carbon balance in ecologically fragile heterogeneous ecosystem of Mediterranean wooded grassland (dehesa). Thus, three methods based on the Radiative Transfer Equation were used to extract LST from a series of 2009–2011 Landsat-5 TM images to assess the applicability for temperature input generation to a Landsat-MODIS LST integration. When compared to surface temperatures simulated using MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) with atmospheric profiles inputs (LSTref), values from Single-Channel (SC) algorithm are the closest (root-mean-square deviation (RMSD) = 0.50 °C); procedure based on the online Radiative Transfer Equation Atmospheric Correction Parameters Calculator (RTE-ACPC) shows RMSD = 0.85 °C; Mono-Window algorithm (MW) presents the highest RMSD (2.34 °C) with systematical LST underestimation (bias = 1.81 °C). Differences between Landsat-retrieved LST and MODIS LST are in the range of 2 to 4 °C and can be explained mainly by differences in observation geometry, emissivity, and time mismatch between Landsat and MODIS overpasses. There is a seasonal bias in Landsat-MODIS LST differences due to greater variations in surface emissivity and thermal contrasts between landcover components.

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.

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.

We report a nonlinearity effect related to the integration time in a double-beam spectroradiometer equipped with two negative-module metal–oxide semiconductor (NMOS) sensors. This effect can be explained by the addition of photoelectrons produced by the radiant flux on the sensors during the readout phase to the photoelectrons produced during the measurement phase. A new method is proposed to characterize and correct both gray-level and integration-time-related nonlinearities in NMOS sensors. This method is experimentally simple and outperforms other commonly used correction procedures.

We report the characterization and correction of nonlinear responses of a commercial field portable spectroradiometer intended to be used to monitor vegetation physiology. Calibration of photoresponse allowed the successful correction of spectral data and the modeling of biases in reflectance at different levels of the dynamic range. Finally, the impact of nonlinearities on a spectral estimator of photosynthetic status, the photochemical reflectance index (PRI) is discussed. Significance of the biases proved that, although nonlinearity can potentially affect reflectance along most of the dynamic range of the instrument, experimental uncertainties can limit its impact. Nonlinearity biased PRI by affecting the reference band of the index and suggested unreal changes on plant physiology. Results show that nonlinearity could be a significant problem in field spectroscopy, especially in the case of spectroradiometers integrated in unattended systems to monitor vegetation responses to radiation. An automatic adjustment of integration time to reach only a certain level of the dynamic range may reduce nonlinearity effects, though may not always avoid them. We conclude that linearity characterization is necessary to understand impacts and correct potential biases.