Data standards

This variables is accumulated from the beginning of the forecast time to the end of the forecast step. By model convention, downward fluxes are positive.

Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises).

Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step.

The units of precipitation are depth in meters. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.

The value is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.

Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).

Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation.

This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are kilo-joules per square metre (KJ m-2).

To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.

It is the horizontal speed of air at a height of ten meters above the surface of the Earth, in meters per second. The value is the combination of the Eastward and Northward components of the 10m wind.

Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.

The surface is at 0 cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them.

It is assumed that there is no heat transfer out of the bottom of the lowest layer.

The surface is at 0 cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them.

It is assumed that there is no heat transfer out of the bottom of the lowest layer.

The surface is at 0 cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them.

It is assumed that there is no heat transfer out of the bottom of the lowest layer.

The surface is at 0 cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them.

It is assumed that there is no heat transfer out of the bottom of the lowest layer.

The surface is at 0 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level.

The surface is at 0 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level.

The surface is at 0 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level.

The surface is at 0 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level.

The FAPAR Anomaly indicator, that is implemented in the Copernicus European Drought Observatory (EDO), is used to detect and monitor the impacts on vegetation growth and productivity of environmental stress factors, especially plant water stress due to drought. The FAPAR Anomaly indicator is computed as deviations of the satellite-measured biophysical variable Fraction of Absorbed Photosynthetically Active Radiation (FAPAR, sometimes written as fAPAR or FPAR), composited for 10-day intervals, from its long-term mean values. FAPAR is one of the 50 so-called “Essential Climate Variables” (ECVs) that have been defined by the Global Climate Observing System (GCOS) as being both feasible for global climate observation, and important to support the work of the United Nations Framework Convention on Climate Change (UNFCCC) and the Intergovernmental Panel on Climate Change (IPCC) (Bojinski et al., 2014).

The FAPAR indicator, that is implemented in the Copernicus European Drought Observatory (EDO), is used to detect and monitor the impacts on vegetation growth and productivity of environmental stress factors, especially plant water stress due to drought. The FAPAR Anomaly indicator is computed as deviations of the satellite-measured biophysical variable Fraction of Absorbed Photosynthetically Active Radiation (FAPAR, sometimes written as fAPAR or FPAR), composited for 10-day intervals, from its long-term mean values. FAPAR is one of the 50 so-called “Essential Climate Variables” (ECVs) that have been defined by the Global Climate Observing System (GCOS) as being both feasible for global climate observation, and important to support the work of the United Nations Framework Convention on Climate Change (UNFCCC) and the Intergovernmental Panel on Climate Change (IPCC) (Bojinski et al., 2014).

The Combined Drought Indicator (CDI), that is implemented in the Copernicus European Drought Observatory (EDO), is used for drought early warning, specifically designed to monitor agricultural drought. Through the combination of spatial patterns of precipitation, soil moisture and greenness vegetation anomalies, the CDI identifies areas at risk of agricultural drought, areas where the vegetation has already been affected by drought and areas in the process of recovery to normal conditions. Accordingly, the CDI classification scheme defines three primary drought classes (Watch, Warning and Alert) and three recovery classes (Temporary Soil Moisture recovery, Temporary vegetation recovery and Recovery).

LISFLOOD is a hydrological rainfall runoff model which has been developed by the JRC of the European Commission in order to reproduce the hydrology of large and trans national European river catchments (de Roo et al., 2000; van der Knijff et al., 2008), and which currently runs operationally within the Copernicus European Flood Awareness System (EFAS, www.efas.eu/). Input data for the LISFLOOD model include daily meteorological observations for the European continent, updated with a two day delay, which are obtained from the JRC’s MARS AGRI4CAST database1 , and which are extended for seven days using numerical 1 agri4cast.jrc.ec.europa.eu/DataPortal/ Copernicus European Drought Observatory (EDO): edo.jrc.ec.europa.eu © European Commission, 2019. 3 weather forecasts produced by the European Centre for Medium Range Weather Forecasts (ECMWF). The LISFLOOD model simulates soil moisture in two surface layers (skin layer and root zone) separately for forested and other layers. These four soil moisture layers are averaged daily to derive a single mean root zone soil moisture conditions to be successively standardized to 1.

LISFLOOD is a hydrological rainfall runoff model which has been developed by the JRC of the European Commission in order to reproduce the hydrology of large and trans national European river catchments (de Roo et al., 2000; van der Knijff et al., 2008), and which currently runs operationally within the Copernicus European Flood Awareness System (EFAS, www.efas.eu/). Input data for the LISFLOOD model include daily meteorological observations for the European continent, updated with a two day delay, which are obtained from the JRC’s MARS AGRI4CAST database1 , and which are extended for seven days using numerical 1 agri4cast.jrc.ec.europa.eu/DataPortal/ Copernicus European Drought Observatory (EDO): edo.jrc.ec.europa.eu © European Commission, 2019. 3 weather forecasts produced by the European Centre for Medium Range Weather Forecasts (ECMWF). The LISFLOOD model simulates soil moisture in two surface layers (skin layer and root zone) separately for forested and other layers. These four soil moisture layers are averaged daily to derive a single mean root zone soil moisture conditions to be successively standardized to 1.

The Total Water Storage (TWS) Anomaly indicator that is implemented in the Copernicus Global Drought Observatory (GDO) is used for determining the occurrence of long-term hydrological drought conditions, which arise when the TWS reaches values lower than usual. This quantity is often used as a proxy of groundwater drought. The TWS Anomaly indicator in GDO is computed as anomalies of GRACE-derived TWS data - which are produced by the Center for Space Research (CSR) at the University of Texas at Austin, as scaled by the NASA Jet Propulsion Laboratory (JPL) (available at: podaac-tools.jpl.nasa.gov/drive/files/allData/tellus/L3/gracefo/land_mass/RL06/).

The Heat and Cold Wave Index (HCWI) that is implemented in the Copernicus European Drought Observatory (EDO) is used to detect and monitor periods of extreme-temperature anomalies (i.e. heat and cold waves) that can have strong impacts on human activities and health. The HCWI indicator is computed for each location (grid-cell), using the methodology developed by Lavaysse et al. (2018), based on the persistence for at least three consecutive days of events with both daily minimum and maximum temperatures (Tmin and Tmax) above the 90th percentile daily threshold (for heat waves) or below the 10th percentile daily threshold (for cold waves). For each location, the daily threshold values for Tmin and Tmax are derived from a 30-year climatological baseline period (1981-2010), using the JRC’s MARS AGRI4CAST database of daily meteorological observations.

The Heat and Cold Wave Index (HCWI) that is implemented in the Copernicus European Drought Observatory (EDO) is used to detect and monitor periods of extreme-temperature anomalies (i.e. heat and cold waves) that can have strong impacts on human activities and health. The HCWI indicator is computed for each location (grid-cell), using the methodology developed by Lavaysse et al. (2018), based on the persistence for at least three consecutive days of events with both daily minimum and maximum temperatures (Tmin and Tmax) above the 90th percentile daily threshold (for heat waves) or below the 10th percentile daily threshold (for cold waves). For each location, the daily threshold values for Tmin and Tmax are derived from a 30-year climatological baseline period (1981-2010), using the JRC’s MARS AGRI4CAST database of daily meteorological observations.

The Heat and Cold Wave Index (HCWI) that is implemented in the Copernicus European Drought Observatory (EDO) is used to detect and monitor periods of extreme-temperature anomalies (i.e. heat and cold waves) that can have strong impacts on human activities and health. The HCWI indicator is computed for each location (grid-cell), using the methodology developed by Lavaysse et al. (2018), based on the persistence for at least three consecutive days of events with both daily minimum and maximum temperatures (Tmin and Tmax) above the 90th percentile daily threshold (for heat waves) or below the 10th percentile daily threshold (for cold waves). For each location, the daily threshold values for Tmin and Tmax are derived from a 30-year climatological baseline period (1981-2010), using the JRC’s MARS AGRI4CAST database of daily meteorological observations.

The Heat and Cold Wave Index (HCWI) that is implemented in the Copernicus European Drought Observatory (EDO) is used to detect and monitor periods of extreme-temperature anomalies (i.e. heat and cold waves) that can have strong impacts on human activities and health. The HCWI indicator is computed for each location (grid-cell), using the methodology developed by Lavaysse et al. (2018), based on the persistence for at least three consecutive days of events with both daily minimum and maximum temperatures (Tmin and Tmax) above the 90th percentile daily threshold (for heat waves) or below the 10th percentile daily threshold (for cold waves). For each location, the daily threshold values for Tmin and Tmax are derived from a 30-year climatological baseline period (1981-2010), using the JRC’s MARS AGRI4CAST database of daily meteorological observations.

Height above sea level in meters extracted from the Digital Elevation Model (EU-DEM Copernicus Land Monitoring Service 25 meter resolution dataset). The average is calculated for the GCU area defined with the polygon of GCU boundaries. Beware that for some GCU (i) there are no shapefile available so that the average elvevation over a 5km buffer around the GCU coordinate is taken (ii) it may happen that GCU visited by the wield team (WP2) does not fall in the shapefile available on EUFGIS, this mismatch is reported.

Minimum height above sea level in meters extracted from the Digital Elevation Model (EU-DEM Copernicus Land Monitoring Service 25 meter resolution dataset). The average is calculated for the GCU area defined with the polygon of GCU boundaries. Beware that for some GCU (i) there are no shapefile available so that the average elvevation over a 5km buffer around the GCU coordinate is taken (ii) it may happen that GCU visited by the wield team (WP2) does not fall in the shapefile available on EUFGIS, this mismatch is reported.

Maximum height above sea level in meters extracted from the Digital Elevation Model (EU-DEM Copernicus Land Monitoring Service 25 meter resolution dataset). The average is calculated for the GCU area defined with the polygon of GCU boundaries. Beware that for some GCU (i) there are no shapefile available so that the average elvevation over a 5km buffer around the GCU coordinate is taken (ii) it may happen that GCU visited by the wield team (WP2) does not fall in the shapefile available on EUFGIS, this mismatch is reported.

Standard deviation of height above sea level, extracted from the Digital Elevation Model (EU-DEM Copernicus Land Monitoring Service 25 meter resolution dataset), for the referenced GCU geometry.

Slope in degrees extracted from the Digital Elevation Model (EU-DEM Copernicus Land Monitoring Service 25 meter resolution dataset). The average is calculated for the GCU area defined with the polygon of GCU boundaries. Beware that for some GCU (i) there are no shapefile available so that the average elvevation over a 5km buffer around the GCU coordinate is taken (ii) it may happen that GCU visited by the wield team (WP2) does not fall in the shapefile available on EUFGIS, this mismatch is reported.

Aspect in degrees extracted from the Digital Elevation Model (EU-DEM Copernicus Land Monitoring Service 25 meter resolution dataset). The average is calculated for the GCU area defined with the polygon of GCU boundaries. Beware that for some GCU (i) there are no shapefile available so that the average elvevation over a 5km buffer around the GCU coordinate is taken (ii) it may happen that GCU visited by the wield team (WP2) does not fall in the shapefile available on EUFGIS, this mismatch is reported.

See the WP2 protocol for more information.

See the WP2 protocol for more information.

See the WP2 protocol for more information.

Micro-topography is assessed at the scale of the representative circular plot with a radius of 15 m. with the following three categories:

- chr_MicroTopography_1: convex

- chr_MicroTopography_2: neutral

- chr_MicroTopography_3: concave

The index allows to determine whether there is more in- or outflow of water which has an influence on the soil available water capacity. For example, there is more water inflow if the topography is concave but there is more outflow if the topography is convex.

Remotely sensed monthly average land surface temperature in Kelvin. The monthly average is calculated for the GCU area defined with the polygon of GCU boundaries. Beware that for some GCU (i) there are no shapefile available so that the average Land surface temperature over a 5km buffer around the GCU coordinate is taken.

Slope of the relationship between remotely sensed annual mean land surface temperature of the GCU and time (°C/year).

Beware that for some GCUs there are no shapefile available so that the average Land surface temperature over a 5 km buffer around the GCU coordinate is taken.

Remotely sensed maximum monthly land surface temperature in °C. The average of the maximum values of the GCU area is computed for the polygon foot print of GCU boundaries. Beware that for some GCU (i) there are no shapefile available so that the average Land surface temperature over a 5km buffer around the GCU coordinate is taken.

The changes in land surface temperature per month are given in degrees and indicate whether the canopy tends to warm up or cool down over time. If the value is positive, the canopy tends to warm up. If the sign is negative, the canopy tends to cool down. The surface temperature of the tree canopy is estimated by a sensor on the MODIS satellite, which measures the thermal infrared emissions of the vegetation. The maximum canopy surface temperature can serve as an indicator of drought and heat stress in a forest, as the canopy heats up when the water supply to the trees is insufficient to cool the leaves.

Linear trend in temporal dynamics of the normalised difference vegetation index (NDVI) across the GCU, to find out whether the health of the population is declining, remaining stable or increasing. The NDVI is a widely used metric based on remote sensing and is calculated as the normalised difference of spectrometric reflectance measurements in two specific bands: one in which the leaf absorbs light (red) and one in which the leaf absorbs little light (near infrared). The spectrometric data comes from MODIS satellites. The NDVI value varies between -1 (for water) and 1 for dense vegetation. A value close to 0 indicates rocks or poorly vegetated areas.

Monthly average value for the whole GCU, to quantify the seasonal variation in the health and density of the vegetation. The normalised difference vegetation index (NDVI) is a widely used metric based on remote sensing and is calculated as the normalised difference of spectrometric reflectance measurements in two specific bands: one in which the leaf absorbs light (red) and one in which the leaf absorbs little light (near infrared). The spectrometric data comes from MODIS satellites. The NDVI value varies between -1 (for water) and 1 for dense vegetation. A value close to 0 indicates rocks or poorly vegetated areas.

Slope of the relationship between yearly mean remotely sensed NDVI over the GCU and time (NDVI/year).

Beware that for some GCUs there are no shapefile available so that the average Land surface temperature over a 5 km buffer around the GCU coordinate is taken.

Monthly maximum value for the whole GCU, to quantify the seasonal variation in the health and density of the vegetation. The normalised difference vegetation index (NDVI) is a widely used metric based on remote sensing and is calculated as the normalised difference of spectrometric reflectance measurements in two specific bands: one in which the leaf absorbs light (red) and one in which the leaf absorbs little light (near infrared). The spectrometric data comes from MODIS satellites. The NDVI value varies between -1 (for water) and 1 for dense vegetation. A value close to 0 indicates rocks or poorly vegetated areas.

Slope of the relationship between yearly maximum of monthly remotely sensed NDVI over the GCU and time (NDVI/year).

Beware that for some GCUs there are no shapefile available so that the average Land surface temperature over a 5 km buffer around the GCU coordinate is taken.

Monthly average value of the leaf area index (LAI) for the whole GCU, giving an idea of the seasonal dynamics of the vegetation. The LAI is an indicator of leaf area (one side) relative to the soil surface. I.e., a LAI of 2 represents a leaf area of 2 m2 for 1 m2 of soil. This gives an overview of the density of the plant cover and therefore the extent of energy and gas exchange between the plant and its environment. The data come from the reflectance of the top of the canopy measured with satellite data at 0.5km resolution (from MODIS).

Average variation in leaf area, giving an idea of the annual dynamics of the vegetation. If the value is positive, then there is a tendency for leaf area to increase, often a sign of improved environmental conditions. If the value is negative, this indicates a decrease in leaf area, often implying greater stress. The leaf area index (LAI) is an indicator of leaf area (one side) relative to the soil surface. I.e., a LAI of 2 represents a leaf area of 2 m2 for 1 m2 of soil. This gives an overview of the density of the plant cover and therefore the extent of energy and gas exchange between the plant and its environment.These data come from the reflectance of the top of the canopy measured with satellite data at 0.5km resolution (from MODIS).

Average maximum leaf area index (LAI) for the whole GCU (exactly the quantile 90 of yearly maximum to avoid outlier data), is a very good indicator of the state of the forest studied, mainly in terms of leaf density and forest development conditions. The LAI is an indicator of leaf area (one side) relative to the soil surface. I.e., a LAI of 2 represents a leaf area of 2 m2 for 1 m2 of soil. This gives an overview of the density of the plant cover and therefore the extent of energy and gas exchange between the plant and its environment.These data come from the reflectance of the top of the canopy measured with satellite data at 0.5km resolution (from MODIS).

Slope of the relationship between yearly maximum of monthly remotely sensed LAI over the GCU and time (LAI/year).

Beware that for some GCUs there are no shapefile available so that the average Land surface temperature over a 5 km buffer around the GCU coordinate is taken.

Monthly average gross primary productivity (GPP) for the whole GCU, to establish the seasonal variation in photosynthesis. The GPP (kg of C/m2/day) corresponds to the amount of carbon that enters the ecosystem per unit surface and time. This value is derived from the sattelite MODIS at 1 km resolution that provides the fraction of light intercepted by the canopy and a model of photosynthesis that converts the intercepted light into carbon assimilation. This makes it possible to define the amount of energy available for the plant to function (respiration) and the amount available for growth (net primary productivity). Beware that this value is known to be incorrect during drought stress periods.

Linear temporal trend in gross primary productivity (GPP) for the whole GCU, which is a key indicator of whether or not the forest is growing and storing carbon. The Gross Primary Productivity (kg of C/m2/day) corresponds to the amount of carbon produced in the ecosystem per unit surface and time. This value is derived from the satellite MODIS at 1 km resolution that provides the fraction of light intercepted by the canopy and a model of photosynthesis that converts the intercepted light in to a carbon assimilation. This makes it possible to define the amount of energy available for the plant to function (respiration) and the amount available for growth (net primary productivity).

Average annual sum of gross primary productivity (GPP) for the whole GCU, to establish the typical photosynthetic activity of vegetation. This value is obtained by summing all months and divided by the number of years in the time series (2003-2021 here). The GPP (kg of C/m2/day) corresponds to the amount of carbon that enters the ecosystem per unit surface and time. This value is derived from the sattelite MODIS at 1 km resolution that provides the fraction of light intercepted by the canopy and a model of photosynthesis that converts the intercepted light in to a carbon assimilation. This makes it possible to define the amount of energy available for the plant to function (respiration) and the amount available for growth (net primary productivity).

Slope of the relationship between yearly sum remotely sensed biomass of the GCU and time (Kg C/m2/year).

Beware that for some GCUs there are no shapefile available so that the average Land surface temperature over a 5 km buffer around the GCU coordinate is taken.

Slope of the relationship between yearly max of monthly remotely sensed biomass of the GCU and time (Kg C/m2/year).

Beware that for some GCUs there are no shapefile available so that the average Land surface temperature over a 5 km buffer around the GCU coordinate is taken.

Standard deviation of monthly average gross primary productivity (GPP) for the whole GCU, to establish the seasonal variation in photosynthesis. The GPP (kg of C/m2/day) corresponds to the amount of carbon that enters the ecosystem per unit surface and time. This value is derived from the sattelite MODIS at 500 m. resolution that provides the fraction of light intercepted by the canopy and a model of photosynthesis that converts the intercepted light into carbon assimilation. This makes it possible to define the amount of energy available for the plant to function (respiration) and the amount available for growth (net primary productivity). Beware that this value is known to be incorrect during drought stress periods.

Monthly average of normalised difference water index (NDWI) for the whole GCU, to observe seasonal variations in water stress through plant water content. NDWI provides an effective measure of moisture content. This index is calculated on the basis of the GREEN-NIR combination (visible green and near infrared spectrum) and enables the detection of water pools as well as subtle changes in the water content of vegetation. These values (from MODIS at 1 km resolution) range from -1 (no water) to 1 (water surface), and their variation is a very good indicator of plant water stress.

Linear temporal trend in normalized difference water index (NDWI) for the whole GCU, to observe long-term trends of vegetation water content. NDWI provides an effective measure of moisture content. This index is calculated on the basis of the GREEN-NIR combination (visible green and near infrared spectrum) and enables the detection of water bodies as well as subtle changes in the water content of vegetation. These values (from MODIS at 1km resolution) range from -1 (no water) to 1 (water surface), and the temporal trend is an indicator of long-term changes in canopy water status.

Remotely sensed annual maximum of the monthly GCU averaged Normalized Difference Water Index. The average is calculated for the GCU area defined with the polygon of GCU boundaries. Beware that for some GCUs there are no shapefiles available so that the average Normalised Difference Water Index over a 5 km buffer around the GCU coordinate is callculated.

Slope of the relationship between yearly maximum of monthly remotely sensed NDWI over the GCU and time (NDWI/year).

Beware that for some GCUs there are no shapefile available so that the average Land surface temperature over a 5 km buffer around the GCU coordinate is taken.

Beware that for some GCU there are no shapefile available in which case NA is reported here.

The soil depth is determined either by excavating a pit or by extracting a soil core with a soil auger to the depth at which rocks are found or the extraction of further soil is impossible.

It can be used together with soil texture to calculate the amount of water stored in the soil that can be utilised by trees.

The soil coarse elements are the individual mineral constituents greater than 2 mm and include gravel, pebbles, stones and blocks. The values are estimated in horisons of 20 cm in the soil pit inside the representative circular plot and then averaged across all horisons. It gives average share of soil corse elements in the soil of the GCU.

The Copernicus DLT raster product provides a basic land cover classification with 3 thematic classes (All non-tree covered areas / Broadleaved / Coniferous) at 10m spatial resolution and covers the full of EEA39 area. The data refers to the unit polygons and the value is obtained by a mode reducer.

In this dataset we only track Broadleaved and Coniferous, non-tree covered areas are not tracked.

Relative humidity is calculated using the temperature and the dewpoint temperature at 2 meters above surface.

The true plant area index value (PAI) is assessed at the position corresponding to and in between 10 measured adult dominant or co-dominant trees sampled either inside or in the vicinity of the representative circular plot with a radius of 15 m. Its is assessed using a digital camera (hemispherical pictures). It includes all tree species present in the plot. The effect of foliage agregation is removed.

Latitude measured in the circular plot within the GCU

Longitude measured in the circular plot within the GCU

Remotely sensed averaged to canopy length (m) of the GCU. The value come from the beam of the GEDI lidar falling inside the GCU area defined with the polygon (shapefile) of GCU boundaries. If the polygon is not available, the beam falling if a 5km buffer around the coordinate of the GCU is taken.

The fraction of absorbed photosynthetically active radiation (FPAR, sometimes also noted fAPAR or fPAR ) is the fraction of the incoming solar radiation in the photosynthetically active radiation spectral region that is absorbed by a photosynthetic organism , typically describing the light absorption across an integrated plant canopy. This biophysical variable is directly related to the primary productivity of photosynthesis and some models use it to estimate the assimilation of carbon dioxide in vegetation in conjunction with the leaf area index . FPAR can also be used as an indicator of the state and evolution of the vegetation cover; with this function, it advantageously replaces the Normalized Difference Vegetation Index (NDVI), provided it is itself properly estimated.