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The thermal region of the electromagnetic spectrum might provide valuable information for assessing plant water status. Nevertheless, the plant's physiology and the scale of measurement, (e.g. sensor viewing geometry and the canopy aggregation) are critical for quantifiying and monitoring water stress. This study compares the Crop Water Stress Index (CWSI) of a peach orchard obtained using on-the-ground, and airborne-based canopy temperature (T). The temporal evolution of CWSI under mild water stress conditions was assessed for three different irrigation strategies (over-irrigation, OI; farmer irrigation, FI; and non-irrigation, NI). Two aerial campaigns per irrigation season (2017–2018) were performed with an airborne thermal sensor: a first flight under well-watered conditions, and a second flight once mild water stress was developed. At the time of the flights, T and net photosynthesis (P), stomatal conductance (g) and stem water potential (Ψ) were measured on the ground with a hand-held thermal camera, a portable gas exchange system and a pressure chamber, respectively. The canopy temperature obtained from the hand-held thermal camera, averaging the sunlit and shaded parts of the canopy, agreed with that derived from the airborne measurements (Y=1.00X; RMSE= 1.97 K). The CWSI values calculated from both approaches detected peach water status under different irrigation strategies. In general, Ψ was better predicted from the aircraft (R² up to 0.72 for CWSI obtained from the aircraft versus R =0.51 for T ground measurements), whereas the use of ground measurements was preferred for estimating g and P (R² up to 0.73 and 0.74 for T ground measurements versus R =45 and 0.56 for T and CWSI derived from the aircraft). Regardless the approach used for deriving T, and due to the consideration of different meteorological conditions (i.e different dates), CWSI provided a better relationship with Ψ than T, whereas the latter was more closely related with g and P., This research was funded in the frame of the project PRECIRIEGO RTC-2017-6365-2 financed by Agencia Estatal de Investigación (AEI) with European Regional Development Fund co-funds. J.M. Ramírez-Cuesta acknowledges the postdoctoral financial support received from AEI within the Juan de la Cierva Spanish Postdoctoral Program (IJC2020-043601-I)

Trabajo presentado en la 13th European Conference on Precision Agriculture (ECPA 2021), celebrada en Budapest del 19 al 22 de julio de 2021.

Accurate, spatially extensive, and frequent assessments of plant nitrogen (N) enabled by remote sensing allow growers to optimize fertilizer applications and reduce environmental impacts. Standard remote sensing methods for N assessment typically involve the use of chlorophyll-sensitive vegetation indices calculated from multispectral or hyperspectral reflectance data. However, the chlorophyll a + b derived from spectral indices is indirectly related to leaf N and saturates at high leaf N levels, dramatically reducing the sensitivity with leaf N under these conditions. Furthermore, these relationships are heavily influenced by canopy structure, variability in leaf area density, proportion of sunlit-shaded tree-crown components, soil background, and understory. Recent studies in uniform crops have demonstrated that estimation of plant N can be improved by considering leaf biochemical constituents derived from radiative transfer model (RTM) and solar-induced fluorescence (SIF). However, it is unclear whether these methods are transferable to tree crops due to their intrinsic physiological differences, structural complexity, and within-tree crown heterogeneity. We investigated how various hyperspectrally derived proxies for leaf N, including RTM-based traits and SIF, could be combined to assess N status on a 1200-ha almond orchard across two growing seasons. RTM-based chlorophyll a + b content (Cab) and SIF were found to be the most important and consistent predictors for leaf N compared to other leaf biochemical and biophysical traits. Cab alone was a modest predictor of leaf N variability (r2 = 0.49, RMSE = 0.16%, p-value

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021., Remote sensing efforts made as part of European initiatives via POnTE, XF-ACTORS and the JRC, as well as through regional programs, have focused, among others, on the development of algorithms for the early detection of Xylella fastidiosa (Xf)-induced symptoms. Airborne campaigns carried out between 2016 and 2019 collected high-resolution hyperspectral and thermal images from infected areas in the Apulia region (Italy), in the province of Alicante and on the island of Mallorca (Spain). The remote sensing imagery collections were performed alongside field surveys and laboratory analyses to assess the presence of Xf, and the severity and incidence of disease in olive and almond trees. Radiative transfer models and machine learning algorithms were used to quantify spectral plant traits for each individual infected tree, assessing their importance as pre visual indicators of Xf-induced stress. These studies conducted across species have demonstrated that specific spectral plant traits successfully revealed Xf induced symptoms at early stages, i.e., before visual symptoms appear. The results show that spectral plant traits contribute differently to symptom detection across host species (olive vs. almond), and that abiotic-induced stress affects the performance of the algorithms used for detecting infected trees. Together, the different European initiatives studying the use of remote sensing to support the monitoring of landscapes for Xylella fastidiosa detection lead us to conclude that the early detection of Xf-induced symptoms is feasible when high-resolution hyperspectral imagery and physically-based plant trait retrievals are used, obtaining accuracies exceeding 92% (kappa>0.8). These results are essential to enable the implementation of effective control and management of plant diseases using airborne- droneand satellite-based remote sensing technologies. Moreover, these large-scale hyperspectral and thermal imaging methods greatly contribute to the future operational monitoring of infected areas at large scales, well beyond what is possible from field surveys and laboratory analyses alone.

Solar-induced chlorophyll fluorescence (SIF) can be used as an indicator of crop photosynthetic activity and a proxy for vegetation stress in plant phenotyping and precision agriculture applications. SIF quantification is sensitive to the spectral resolution (SR), and its accurate retrieval requires sensors with sub-nanometer resolutions. However, for accurate SIF quantification from imaging sensors onboard airborne platforms, sub-nanometer imagers are costly and more difficult to operate than the commonly available narrow-band imagers (i.e., 4- to 6-nm bandwidths), which can also be installed on drones and lightweight aircraft. Although a few theoretical and experimental studies have evaluated narrow-band spectra for SIF quantification, there is a lack of research focused on comparing the effects of the SR on SIF from airborne hyperspectral imagers in practical applications. This study investigates the effects of SR and sensor altitude on SIF accuracy, comparing SIF quantified at the 760-nm O2-A band (SIF760) from two hyperspectral imagers with different spectral configurations (full width at half-maximum resolutions of 0.1–0.2 nm and 5.8 nm) flown in tandem on board an aircraft. SIF760 retrievals were compared from two different wheat and maize phenotyping trials grown under different nitrogen fertilizer application rates over the 2019–2021 growing seasons. SIF760 from the two sensors were correlated (R2 = 0.77–0.9, p < 0.01), with the narrow-band imager producing larger SIF760 estimates than the sub-nanometer imager (root mean square error (RMSE) 3.28–4.69 mW/m2/nm/sr). Ground-level SIF760 showed strong relationships with both sub-nanometer (R2 = 0.90, p < 0.001, RMSE = 0.07 mW/m2/nm/sr) and narrow-band (R2 = 0.88, p < 0.001, RMSE = 3.26 mW/m2/nm/sr) airborne retrievals. Simulation-based assessments of SIF760 for SRs ranging from 1 to 5.8 nm using the SCOPE model were consistent with experimental results showing significant relationships among SIF760 quantified at different SRs. Predictive algorithms of leaf nitrogen concentration using SIF760 from either the narrow-band or sub-nanometer sensor yielded similar performance, supporting the use of narrow-band resolution imagery for assessing the spatial variability of SIF in plant phenotyping, vegetation stress detection and precision agriculture contexts.

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021., Xylella fastidiosa (Xf) causes plant diseases that lead to massive economic losses in agricultural crops, making it one of the pathogens of greatest concern to agriculture nowadays. Detecting Xf at early stages of infection is crucial to prevent and manage outbreaks of this vector-borne bacterium. Recent remote sensing (RS) studies at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, RS-based forecasting of Xf outbreaks requires tools that account for their spatiotemporal dynamics. Here, we show how coupling a spatial Xf-spread model with the probability of Xf-infection predicted by an RS-driven modeling algorithm based on a Support Vector Machine (RS-SVM) helps detecting the spatial Xf distribution in a landscape. To optimize such model, we investigated which RS plant traits (i.e., pigments, structural or leaf protein content) derived from high-resolution hyperspectral imagery and biophysical modelling are most responsive to Xf infection and damage. For that, we combined a field campaign in almond orchards in Alicante province (Spain) affected by Xf (n=1,426 trees), with an airborne campaign over the same area to acquire high-resolution thermal and hyperspectral images in the visible-near-infrared (400-850 nm) and short-wave infrared regions (SWIR, 950-1700 nm). We found that coupling the epidemic spread model and the RS-based model increased accuracy by around 5% (OA = 80%, kappa = 0.48 and AUC = 0.81); compared to the best performing RS-SVM model (OA = 75%; kappa = 0.50) that included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator, alongside pigments and structural parameters. The parameters with the greatest explanatory power of the RS model were leaf protein content together with NI (28%), followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. In the subset of almond trees where the presence of Xf was tested by qPCR (n=318 tress), the combined RS-spread model yielded the best performance (OA of 71% and kappa = 0.33). Conversely, the best-performing RS-SVM model and visual inspections produced OA and kappa values of 65% and 0.31, respectively. This study shows for the first time the potential of combining spatial epidemiological models and remote sensing to monitor Xf-disease distribution in almond trees.

Trabajo presentado en la 2019 ASA-CSSA-SSSA International Annual Meeting, celebrada en San Antonio (Texas) del 10 al 13 de noviembre de 2019., The prospects of water scarcity are increasing in many areas in the world, adding more pressure for irrigated agriculture to minimize water losses and maximize water productivity. The improvements in irrigation system design and operation do not address the issues of soil and plant heterogeneity and associated variations in water demands within irrigation management units. There has been a tendency in recent decades towards increasing the size of management units to reduce labor inputs and simplify management; this approach increases the risk of widening the spatial variability within the management units. This variation relies upon static sources, such as soil properties, while others are dynamic, such as irrigation dose, plant size, and plant health. Traditional approaches proposed to manage the variability in the system design have primarily minimized differences in soil type and topography within irrigation units (map-based approach). However, multiple sources of variation are difficult to address using this approach because they are not static. Sensor-based approaches meeting the required resolutions and turnaround times provide an efficient tool to assess dynamic variations within agricultural fields. Thermal imaging acquired from UAVs or manned aircraft fulfill the requested accuracy and flexibility to deal with such dynamic variability due to the close link to transpiration. Water stress induces stomatal closure, reduces evaporative cooling and increases leaf temperature. The influence of evaporative demand on canopy temperature via air temperature and the vapor pressure deficit requires its normalization, which can be carried out using the Crop Water Stress Index (CWSI). This thermal-based approach allows conducting comparative studies in the spatial domain, but more interestingly, temporally. The opportunities derived from the analysis of the temporal and spatial variability will be reviewed, highlighting the main avenues to optimize water use in irrigated agriculture.

Trabajo presentado en la 2nd European conference on Xylella fastidiosa (how research can support solutions), celebrada en Ajaccio el 29 y 30 de octubre de 2019., Official surveillance of the Xylella fastidiosa(Xf) epidemic in Apulia shows how its front has moved northwards since 2013. However, assessing and modelling the impact of Xf remains difficult because little is known about the damage to the landscape behind the front lines.Sentinel-2 andother satellite sensors provide images of the Earth in wavelengths that are sensitive to the vegetation condition. To test their suitability for Xf damage mapping, we created a model that relies on the shift that Xf-affected olive orchards undergo from a tree-dominated landscape to one dominated by shallow-rooted, short-statured vegetation. This transition changes the way gross primary productivity, as approximated by vegetation indices, responds to meteorological conditions. Insituestimates made by plant pathologists in olive orchards confirmed that a new method based on satellite and meteorological data detected Xf damage severity (R2 = 0.6). Furthermore, this method can be used to map Xf damage every year across Apulia.We then tallied the individual olive trees lost since the first detection of Xf in 2013. We used aerial photographs from the summer of 2013 to delineate each olive tree in the region and photos of 2015 and 2018 to determine how many of them are now crownless, or gone altogether. We trained a Mask Region Convoluted Neural Network to delineate individual olive tree crowns in the 4-band photographs and it proved capable of accurately identifying crowns, irrespective of their size, shapes, proximity to neighbouring trees, or image resolution.Our results provide a unique view on the spread of Xf in Apulia over the past five years, a means of systematically monitoring Xf damage across orchards, and an estimate of the number of olive trees lost thus far. It illustrates how remote sensing can provide a quantitative baseline for addressing the environmental and economic damage of Xf.

[Purpose of Review] We provide a comprehensive review of the empirical and modelling approaches used to quantify the radiation–vegetation interactions related to vegetation temperature, leaf optical properties linked to pigment absorption and chlorophyll fluorescence emission, and of their capability to monitor vegetation health. Part 1 provides an overview of the main physiological indicators (PIs) applied in remote sensing to detect alterations in plant functioning linked to vegetation diseases and decline processes. Part 2 reviews the recent advances in the development of quantitative methods to assess PI through hyperspectral and thermal images. [Recent Findings] In recent years, the availability of high-resolution hyperspectral and thermal images has increased due to the extraordinary progress made in sensor technology, including the miniaturization of advanced cameras designed for unmanned aerial vehicle (UAV) systems and lightweight aircrafts. This technological revolution has contributed to the wider use of hyperspectral imaging sensors by the scientific community and industry; it has led to better modelling and understanding of the sensitivity of different ranges of the electromagnetic spectrum to detect biophysical alterations used as early warning indicators of vegetation health. [Summary] The review deals with the capability of PIs such as vegetation temperature, chlorophyll fluorescence, photosynthetic energy downregulation and photosynthetic pigments detected through remote sensing to monitor the early responses of plants to different stressors. Various methods for the detection of PI alterations have recently been proposed and validated to monitor vegetation health. The greatest challenges for the remote sensing community today are (i) the availability of high spatial, spectral and temporal resolution image data; (ii) the empirical validation of radiation–vegetation interactions; (iii) the upscaling of physiological alterations from the leaf to the canopy, mainly in complex heterogeneous vegetation landscapes; and (iv) the temporal dynamics of the PIs and the interaction between physiological changes. The authors received funding provided by the FluorFLIGHT (GGR801) Marie Curie Fellowship, the QUERCUSAT and ESPECTRAMED projects (Spanish Ministry of Economy and Competitiveness), the Academy of Finland (grants 266152, 317387) and the European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE-P.

Trabajo presentado en el IX International Symposium On Irrigation Of Horticultural Crops, celebrado en Matera (Italia) del 17 al 20 de junio de 2019., Almond growing is increasing throughout the Mediterranean area, and especially in Spain, because of the high prices fetched by this commodity in recent years. This has led to the establishment of new, intensive almond orchards in many Spanish irrigation schemes, even though traditionally, almonds have been grown in Spain in marginal soils under low-input conditions. The expansion of irrigated almonds has increased irrigation demand, which for Western Andalusia has been quantified in recent research as high as 7,000 m3/ha. Considering the societal requirements to decrease the share of fresh water diverted in agriculture, it remains essential to optimize almond water productivity in irrigated schemes which may be achieved through precision irrigation. To do so, we need an accurate estimation of the spatial distribution of water requirements within irrigated orchards. This work proposes a methodology to map water use by almond trees based on their canopy temperature and its relationship with crop transpiration. For this purpose, we have developed the Non-water Stress Baseline for the crop and implemented a methodology to obtain the Crop Water Stress Index using information acquired with infrared thermometers installed over selected trees. After that, this information was combined with high-resolution airborne thermal imagery acquired over the whole experimental area to derive a transpiration map. This new approach enables the segmentation of the area according to their needs, providing relevant information for precision irrigation management and system re-engineering.

Trabajo presentado en el II Spanish Symposium on Physiology and Breeding of Cereals (II SEFiMeC), celebrado en Córdoba (España) el 6 y 7 de marzo de 2019.-- Organized by excellence network AGL2016-81855-REDT., Wheat is one of the most important cereal crops worldwide as a staple food. It possesses high phenotypic plasticity, due to the presence of a large, polyploid and complex genome with three homoeologous genomes (A, B and D). This success facilitates its adaptation to most agricultural conditions across temperate regions, as Mediterranean environments, where thermo-pluviometrical factors are highly erratic and unpredictable. As a consequence of these changeable conditions, drought can be a major stress which affects the growth, development and grain filling stages of wheat crops at the molecular and physiological levels. Nevertheless, the mechanisms and gene networks involved in wheat field drought stress responses, are still largely unknown. We have integrated different agronomic and genomic approaches to assess stress-responses in several wheat panels and lines, grown under field conditions in different environments of Andalusia. Physiological measures, together with hyperspectral and thermal imagery analyses, were used to determine the nutritional, photosynthetic and water status in the field. These data, when evaluated together with transcriptomic analyses, can be used to assess the molecular basis of wheat drought-responses under field conditions. As result, transcriptional changes were found related to alterations at the physiological level. At the molecular level, a complex structure for drought-responses was found, comprising the presence of gene clusters with differential expression patterns, the influence on biosynthetic pathways such as carotenoids or the implication of gene families such as dehydrins. In addition, the genome-wide association (GWAS) approach was used for the assessment of marker-trait associations (MTA) in agronomic performance and quality traits. Transcriptomic data were used to inform the candidate genes selection process. These results and approaches can be useful for current wheat breeding programs in Andalusia focused on wheat adaption to stress conditions and quality improvement., Funding from Junta de Andalucía project P12-AGR0482 is gratefully acknowledged.

Trabajo presentado en el PAG Asia 2019 (The International conference on the Status of Plant & Animal Genome Research), celebrado en Futian Shangri-La Shenzhen (China) del 6 al 8 de junio de 2019., Wheat can adapt to most agricultural conditions across temperate regions. Although drought is a major cause of yield and quality losses, the adaptive mechanisms and gene networks underlying drought responses in the field remain largely unknown. Interdisciplinary studies involving field water status phenotyping now provide a basis for interpreting gene expression analyses and show that changes at the transcriptional level were reflected in the remote sensing physiological data from the field. The dehydrin family of genes provided a focus for the study and indicated that the genes located on the group 6 chromosomes were particularly important in drought response.

Trabajo presentado en la 2nd European conference on Xylella fastidiosa (how research can support solutions), celebrada en Ajaccio el 29 y 30 de octubre de 2019., Detecting and monitoring the spatial and temporal dynamics of the symptoms and the severity of the damage caused by Xylella fastidiosa(Xf) is a key priority to prevent its expansion. This study evaluates the use of Sentinel-2 imagery together with a radiative transfer (RT) approach to monitor epidemics caused by Xf in olive trees. A time series of Sentinel-2a imagery collected over two years was used to describe the temporal dynamics of Xf-infected olive orchards located in the region of Apulia (southern Italy). Airborne hyperspectral acquisitions were used for validation along with field visual surveys carried out for more than three thousand trees with different disease incidence (DI) and severity (DS) levels. A careful evaluation of the sensitivity of Sentinel-2 imagery to canopy alterations produced by a progressive Xf infection in olive orchards has been accomplished based on model simulations and field observations.Our results demonstrate that the assessment of Xf infection monitoring based on Sentinel-2 data requires the use of self-corrected vegetation indices (VIs) and RT modelling. Among the tested VIs, those that minimise the atmospheric and background effects such as ARVI, ATSAVI and OSAVI performed better than traditional vegetation indices usedas a quantitative proxy measure of the fractional cover (FC) of green and healthy vegetation such as NDVI, RDVI or MSR. Model simulations and field observations showed that the background effects have a significant impact on the temporal variation of DI levels detected with Sentinel-2a imagery. The use of 3-D RT modelling improved the DI estimates by 25% when accounting for the background effects, and by 32% when its heterogeneity was also considered. Therefore, the methodology proposed using a 3-D RT and Sentinel-2 data can provide useful spatiotemporal indicators to track the damage caused by Xfinfections across large areas.

The paper arises from the Workshop on “Exploring the Earth’s Ecosystems on a Global Scale: Requirements, Capabilities and Directions in Spaceborne Imaging Spectroscopy”, held at the International Space Science Institute (ISSI), Bern—Switzerland, on 21–25 November 2016. Agriculture faces the challenge of providing food, fibre and energy from limited land resources to satisfy the changing needs of a growing world population. Global megatrends, e.g., climate change, influence environmental production factors; production and consumption thus must be continuously adjusted to maintain the producer–consumer-equilibrium in the global food system. While, in some parts of the world, smallholder farming still is the dominant form of agricultural production, the use of digital information for the highly efficient cultivation of large areas has become part of agricultural practice in developed countries. Thereby, the use of satellite data to support site-specific management is a major trend. Although the most prominent use of satellite technology in farming still is navigation, Earth Observation is increasingly applied. Some operational services have been established, which provide farmers with decision-supporting spatial information. These services have mostly been boosted by the increased availability of multispectral imagery from NASA and ESA, such as the Landsat or Copernicus programs, respectively. Using multispectral data has arrived in the agricultural commodity chain. Compared to multispectral data, spectrally continuous narrow-band sampling, often referred to as hyperspectral sensing, can potentially provide additional information and/or increased sampling accuracy. However, due to the lack of hyperspectral satellite systems with high spatial resolution, these advantages mostly are not yet used in practical farming. This paper summarizes where hyperspectral data provide additional value and information in an agricultural context. It lists the variables of interest and highlights the contribution of hyperspectral sensing for information-driven agriculture, preparing the application of future operational spaceborne hyperspectral missions.

Trabahjo presentado en el 10th EARSeL SIG Imaging Spectroscopy Workshop, celebrado en Zurich del 19 al 21 de abril de 2017.

Trabajo presentado en la European Conference on Xylella 2017 (Finding answers to a global problem), celebrada en Palma de Mallorca del 13 al 15 de noviembre de 2017., A remote sensing campaign carried out in Puglia in summer 2016 collected hyperspectral and thermal images of ca 200,000 olive trees at sub-meter resolution. In the 1200 ha study area within the X. fastidiosa infected zone, 3500 trees were simultaneously evaluated in the field for severity of X. fastidiosa symptoms. The hyperspectral sensor used in this experiment acquired data in the visible and near-infrared spectral region (400-885 nm) with 260 bands of 6.5 nm FWHM at 1.85 nm/pixel and 12-bit radiometric resolution. The sensor was radiometrically calibrated in the laboratory, and images atmospherically corrected to obtain surface reflectance using total incoming irradiance measured in the field. The high resolution hyperspectral and thermal imagery acquired over the orchards allowed the delineation of individual trees using object-based crown detection algorithms. Crown temperature and hyperspectral indices were calculated for each tree to classify disease severity levels using different machine learning algorithms, including linear discriminant analysis, support vector machines and neural networks. The success and applicability of these early detection methods to other areas will be discussed in the context of a new airborne campaign planned in July 2017 over X. fastidiosa infected zones in Mallorca. In this new airborne campaign the impact of X. fastidiosa symptoms on crown reflectance will also be evaluated in the 800-1700 nm spectral region., This work was supported by JRC and has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N. 635646 “Pest Organisms Threatening Europe POnTE” and grant agreement N. 727987 “Xylella fastidiosa Active Containment Through a multidisciplinary-Oriented Research Strategy XF-ACTORS”.

La infección temprana por Vertícíllium dahliae en olivar y la discriminación entre niveles de severidad de la Verticilosis es viable mediante la utilización de imágenes térmicas, multiespectrales e hiperespectrales adquiridas con vehículos aéreos no tripulados a escala de parcela o tripulados a escala de comarca. El indicador relacionado con la temperatura de copa (CWSI), rndices de reflectancia (B, BG1, BR1) y la fluorescencia clorofOica (FLD3) se identifican como buenos indicadores para detectar la Verticilosis en etapas tempranas del desarrollo de la enfermedad, mientras que NDVI, PRI515, RIG, Hl e índices de estimación de clorofila y carotenos demuestran ser buenos indicadores para la cuantificación de daño moderado o severo causada por Verticilosis, lo que puede ser de utilidad para el diseño de estrategias de control de la Verticilosis a escala de parcela y de comarca., Las investigaciones descritas en este artículo han sido financiadas por el proyecto POB-AGR-03528 de la Consejerfa de Economía, Innovación y Ciencia de la Junta de Andalucía y el Fondo Social Europeo, y los proyectos AGL-2012-37521 y AGL2012-40053-C03-01 del Ministerio de Economía y Competitividad y el Fondo Social Europeo.