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Modeling essential climate variables (Biophysical parameters)
(Responsible: F.J. García-Haro)

The main purpose is to take full advantage of remotely sensed data to measure Essential Climate Variables (ECVs): LAI (Leaf Area Index) and the FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) PAR (Photosynthetically Active Radiation)), fAPAR (Fraction of Absorbed PAR), LAI (Leaf Area Index), canopy water content (CWC), biomass and surface composition (land cover and/or ecosystem functional type, EFT).
These ECVs will find primarily applications in climate models, meteorology and Land Biosphere Applications such as agriculture and forestry, natural hazards monitoring and management, drought conditions and carbon cycle models.
Maps of these ECVs are produced from EO reflectance and other auxiliary data using available physically-based radiative transfer models (RTM) exploiting non-linear inversion methods calibrated with existing ground measurements of canopy attributes. The maps include consistent quality control information, as uncertainty bounds.


Carbon flux estimation from satellite images
(Responsible: M.A. Gilabert)

Carbon fluxes are obtained using an ecosystem functional model with its inputs derived from remote sensed data and adapted for the enviromental conditions of the study area. According to this approach, the carbon fixed by vegetation through photosynthesis depends on the LUE (light use efficiency).
Model inputs are related to most of the ECVs obtained in line of modelization of essential climatic variables. Thus, LUE depends on the vegetation cover and it is affected by different environmental stresses, being the water stress the most important in Mediterranean areas.


Vegetation dynamics. Time series analysis and change detection
(Responsible: F.J. García-Haro)

This area is aimed to develop methodologies for the processing and analysis of time series using variables related with vegetation (over several decades) derived from the research areas 1 and 2. In the first case, the time series processing includes the development of procedures for time series gap filling and noise reduction. In the second case, the time series analysis encloses the application of new methodologies for vegetation change detection due to inter- and intra-annual variations. Different approaches have been also developed to localize and quantify abrupt changes from disturbance events (e.g. forest fires) or gradual changes over a long period (e.g. land degradation or desertification processes). In addition, a variety of approaches have been also developed to characterize key phenological parameters, such as vegetation green up or start of season, peak LAI, and length of season.

The overall analysis of these time series and climatic data allows the study of vegetation dynamics as well as to monitor sensitive areas to water stress.


Field spectroscopy
(Responsible: M.A. Gilabert)

The quatitative measurement of irradiance and radiance in the field by means of spectrorradiometers to obtain reflectance. Typically, the most widely used is the bidirectional (or biconical) reflectance factor, which also needs the radiance measurement of a reference, ideal, white panel.

Field spectroscopy is used: (I) For in situ calibration of surface reflectance for terrestrial imaging spectroscopy studies (for calibration and validation of satellite/airborne measurements); (II) to better understand the nature of the interaction of electromagnetic radiation with Earth surface objects; (III) to provide data for input into radiative transfer models; (IV) to build spectral libraries; (V) to establish the optimum spectral, temporal and spatial resolution, as well as the optimum sun/target geometry to detect and characterize a target.


Vegetation indices
(Responsible: M.A. Gilabert)

VIs combine reflectance data from different wavebands in the solar region -often red (R) and near-infrared (NIR) wavelengths- to enhance the green vegetation signal from the measured signal and to produce sensitive indicators of both spatial and temporal variations in vegetation photosynthetic activity and canopy structural variations. Certainly, most VIs exploit the fact that green vegetation strongly absorbs solar radiation in the visible region (due to the presence of chlorophyll and other pigments) and strongly reflects solar radiation in the NIR. They capitalize then the strong reflectance gradient exhibited by live green vegetation around 0.7 μm (the so called red edge). The design of new vegetation indices relies on the use of canopy reflectance models as well as on field and laboratory spectroscopy.


Integration of remotely sensed data from new eo programs
(Responsible: F.J. García-Haro)

The goal is to combine different satellite data, particularly to exploit a synergistic use of actual remotely sensing programs. By combining imagery from different sensors, a synthetic fused product is generated which contains more information than the individual input images. This offers a great potential for updating the production of vegetation parameters in quasi-real-time for applications that require a frequent update of the surface parameters (e.g. agriculture/forestry, food management).  Data fusion methods use information about pixel composition provided by HR data to disaggregate to a suitable scale moderate and coarse resolution EO data (such as reflectance, land cover or biophysical products). This group has been involved in several validation networks and exploitation programs of satellite missions (LSA SAF, ENVISAT, GMES/Sentinel).


Calibration and validation (Cal/Val) of remote sensing satellite derived products
(Responsible: F.J. García-Haro)

The calibration and validation of products allows quantifying the accuracy and quality of the satellite-derived products in order to provide us a temporal and spatial consistency against reference values and existing satellite products. This process is achieved by means of the direct validation and indirect validation. The direct validation allows quantifying the accuracy of the products by direct comparison with reference measurements (e.g. in-situ values) over a small number of sites representative of different land cover types (grassland, cropland, different forest types, etc.)..... The indirect validation consists of the spatial and temporal comparison with similar satellite products.

In the frame of several projects, the UV-ERS group has contributed in some important aspects such as: (I) the design of protocols for estimation and spatial sampling at field; (II) the development efficient approaches for up-scaling the in-situ measurements to match satellite products; (III) to establish criteria for the inter-comparison between products.