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Vegetation productivity 2000-2016

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Prod-ID: DAT-225-en
Created 08 Dec 2019 Published 16 Dec 2019 Last modified 22 Feb 2022
2 min read
The raster files are the above ground vegetation productivity time-series and the derived linear trend for the period 2000-2016.The data set addresses trends in land surface productivity derived from remote sensing observed time series of vegetation indices. The vegetation index used in the indicator is the Plant Phenology Index (PPI, Jin and Eklundh, 2014). PPI is based on the MODIS Nadir BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR. The product provides reflectance data for the MODIS “land” bands (1 - 7) adjusted using a bi-directional reflectance distribution function. This function models values as if they were collected from a nadir-view to remove so called cross-track illumination effects. The Plant Phenology Index (PPI) is a new vegetation index optimized for efficient monitoring of vegetation phenology. It is derived from radiative transfer solution using reflectance in visible-red (RED) and near-infrared (NIR) spectral domains. PPI is defined to have a linear relationship to the canopy green leaf area index (LAI) and its temporal pattern is strongly similar to the temporal pattern of gross primary productivity (GPP) estimated by flux towers at ground reference stations. PPI is less affected by presence of snow compared to commonly used vegetation indices such as Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI).The product is distributed with 500 m pixel size (MODIS Sinusoidal Grid) with 8-days compositing period.

European data

Annual above ground vegetation productivity time-series 2000-2016, version 1, Aug. 2018
  • Annual above ground vegetation productivity time-series 2000-2016, version 1, Aug. 2018
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  • Annual above ground vegetation productivity time-series 2000-2016, version 1, Aug. 2018 - INSPIRE Metadata
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Trends in annual above ground vegetation productivity 2000-2016, version 1, Mar. 2019
  • Trends in annual above ground vegetation productivity 2000-2016, version 1, Mar. 2019
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  • Trends in annual above ground vegetation productivity 2000-2016, version 1, Mar. 2019 - INSPIRE Metadata
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Metadata

information.png Reporting obligations (ROD)
  • Not applicable
Relation:
Parent data set:  Environmental Accounting Reference Layers - SDI
Parent data set:  Start of vegetation growing season 2000-2016
Parent data set:  Vegetation growing season length 2000-2016
Derived data set:  Start of vegetation growing season 2000-2016
Derived data set:  Vegetation growing season length 2000-2016
Derived data set:  Environmental Accounting Reference Layers - SDI
information.png Rights:
EEA standard re-use policy: unless otherwise indicated, re-use of content on the EEA website for commercial or non-commercial purposes is permitted free of charge, provided that the source is acknowledged (https://www.eea.europa.eu/legal/copyright). Copyright holder: European Environment Agency (EEA).
information.png Data sources

Plant Phenology Index

information.png Owners:
European Environment Agency (EEA)
information.png Processors:
European Topic Centre on Urban, Land and Soil Systems (ETC-ULS)
 

Related content

Based on external data

Data Environmental Accounting Reference Layers - SDI This is a collection of the geospatial Environmental Accounting Reference Layers of the Integrated Data Platform published on the website and registered in EEA`s Spatial Data Infrastructure (SDI). The link under "GIS data" points to the SDI thematic node on Environmental Accounting Reference Layers.
Data Start of vegetation growing season 2000-2016 The raster files are the time series of the start of the vegetation growing season (day of the year) and the derived linear trends (in day / year). The start of the growing season time-series is based on the time series of the Plant Phenology Index (PPI) derived from the MODIS BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR). The PPI index is optimized for efficient monitoring of vegetation phenology and is derived from the source MODIS data using radiative transfer solutions applied to the reflectance in visible-red and near infrared spectral domains. The start of season indicator is based on calculating the start of the vegetation growing season from the annual PPI temporal curve using the TIMESAT software for each year between and including 2000 and 2016.
Data Vegetation growing season length 2000-2016 The raster files are the annual above ground growing season length time-series and the derived linear trends for the period 2000-2016. The data set addresses trends in the season length of land surface vegetation derived from remote sensing observed time series of vegetation indices. The vegetation index used in the indicator is the Plant Phenology Index (PPI, Jin and Eklundh, 2014). PPI is based on the MODIS Nadir BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR. The product provides reflectance data for the MODIS “land” bands (1 - 7) adjusted using a bi-directional reflectance distribution function. This function models values as if they were collected from a nadir-view to remove so called cross-track illumination effects. The Plant Phenology Index (PPI) is a new vegetation index optimized for efficient monitoring of vegetation phenology. It is derived from radiative transfer solution using reflectance in visible-red (RED) and near-infrared (NIR) spectral domains. PPI is defined to have a linear relationship to the canopy green leaf area index (LAI) and its temporal pattern is strongly similar to the temporal pattern of gross primary productivity (GPP) estimated by flux towers at ground reference stations. PPI is less affected by presence of snow compared to commonly used vegetation indices such as Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI). The product is distributed with 500 m pixel size (MODIS Sinusoidal Grid) with 8-days compositing period.

Derived dataset

Data Environmental Accounting Reference Layers - SDI This is a collection of the geospatial Environmental Accounting Reference Layers of the Integrated Data Platform published on the website and registered in EEA`s Spatial Data Infrastructure (SDI). The link under "GIS data" points to the SDI thematic node on Environmental Accounting Reference Layers.
Data Start of vegetation growing season 2000-2016 The raster files are the time series of the start of the vegetation growing season (day of the year) and the derived linear trends (in day / year). The start of the growing season time-series is based on the time series of the Plant Phenology Index (PPI) derived from the MODIS BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR). The PPI index is optimized for efficient monitoring of vegetation phenology and is derived from the source MODIS data using radiative transfer solutions applied to the reflectance in visible-red and near infrared spectral domains. The start of season indicator is based on calculating the start of the vegetation growing season from the annual PPI temporal curve using the TIMESAT software for each year between and including 2000 and 2016.
Data Vegetation growing season length 2000-2016 The raster files are the annual above ground growing season length time-series and the derived linear trends for the period 2000-2016. The data set addresses trends in the season length of land surface vegetation derived from remote sensing observed time series of vegetation indices. The vegetation index used in the indicator is the Plant Phenology Index (PPI, Jin and Eklundh, 2014). PPI is based on the MODIS Nadir BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR. The product provides reflectance data for the MODIS “land” bands (1 - 7) adjusted using a bi-directional reflectance distribution function. This function models values as if they were collected from a nadir-view to remove so called cross-track illumination effects. The Plant Phenology Index (PPI) is a new vegetation index optimized for efficient monitoring of vegetation phenology. It is derived from radiative transfer solution using reflectance in visible-red (RED) and near-infrared (NIR) spectral domains. PPI is defined to have a linear relationship to the canopy green leaf area index (LAI) and its temporal pattern is strongly similar to the temporal pattern of gross primary productivity (GPP) estimated by flux towers at ground reference stations. PPI is less affected by presence of snow compared to commonly used vegetation indices such as Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI). The product is distributed with 500 m pixel size (MODIS Sinusoidal Grid) with 8-days compositing period.

Interactive charts

Data Visualization Productivity change due to land use (2000-2019)
Data Visualization Productivity change due to land use (2000-2019)
Data Visualization Annual area of drought impact on vegetation productivity (2000-2019)
Data Visualization Average annual drought-impacted area by land cover type, as a percentage of total country area
Dashboard (Tableau) Vegetation productivity and land use, 2000-2018 Vegetation productivity indicates the spatial distribution and change of the vegetation cover - a key characteristic of ecosystem condition. Climatic variations are important drivers of vegetation productivity, but land use changes are even stronger. Productivity in Europe increases most due to agricultural land management and converting other lands to agriculture, whereas largest decrease is caused by sprawling urban areas. The dashboard below enables the exploration of these processes during 2000-2018.
Dashboard (Tableau) Drought impact on ecosystems in Europe, 2000-2019 Yearly vegetation productivity during 2000-2019 are analysed in areas under drought pressure, measured as precipitation shortages and low soil moisture content. Vegetation productivity values are disaggregated and detailed by year and by detailed land cover categories. Strong negative values indicate strong drought intensity, with vegetation productivity values lower than the long term average normal condition. Scroll down to the More information section for further details.
Data Visualization Average annual area affected by water deficit, by land cover type and country
Data Visualization
Draft
Area of vegetation productivity decrease due to water deficit, 2000-2016
Dashboard (Tableau) Vegetation productivity loss under drought impact by land cover and countries (2000-2016, EEA-39) Yearly vegetation productivity during 2000-2016 are analyzed in areas under drought pressure, measured as precipitation shortages and low soil moisture content. Vegetation productivity values are disaggregated and detailed by year and by detailed land cover categories. Strong negative values indicate strong drought intensity, with vegetation productivity values lower than the long term average normal condition. Scroll down to the More information section for further details.

Interactive maps produced

Produced Indicators

Produced figures

Figure Long term average soil moisture and soil moisture trends, 2000-2019 The maps show the long-term average soil moisture contents (left) and the trends in soil moisture values (right), aggregated by NUTS3 regions. Soil moisture is equal to 0 when the soil is severely dry (wilting point) and equal to 1 when the soil moisture is above the field capacity. Low long-term average soil moisture values indicate areas where during the 2000-2019 period the soil moisture deficit was the biggest problem. Trends are expressed in standard deviation from the long term average. Negative trends indicate that soil moisture values show a decreasing tendency during the 2000-2019 period. Areas with lower soil moisture content together with decreasing tendency in the soil moisture are in risk of loosing their land functions of supplying ecosystem services. See also: https://www.eea.europa.eu/data-and-maps/data/data-viewers/soil-moisture and Original dataset: https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_soilmoisture.pdf
Figure Intensity of and area affected by water deficit in Europe, 2000-2016 The map shows the long-term impact of water deficit on vegetation productivity, and the area of low vegetation productivity under water deficit impact, aggregated by NUTS3 regions. Negative anomalies are expressed in standard deviation and indicate vegetation productivity conditions that are lower than the long-term average under normal, non-drought conditions.
Figure Effect of land use change on vegetation productivity The chart shows the vegetation productivity changes (%) over areas with land use change in the period 2000-2018. The values are broken down by major land use change drivers.
Figure Significant vegetation productivity trends per ecosystems. The chart shows significant trends of vegetation productivity, expressed in % change. The % change values were derived from the fitted linear trend line.
Figure Effect on vegetation productivity: precipitation The chart shows the effect of precipitation variations on vegetation productivity, expressed in standard deviation units of vegetation productivity.
Figure Effect on vegetation productivity: number of frost days The chart shows the effect of frost frequency variations on vegetation productivity, expressed in standard deviation units of vegetation productivity.
Figure Effect on vegetation productivity: temperature The chart shows the effect of temperature variations on vegetation productivity, expressed in standard deviation units of vegetation productivity.

Used in indicators

Indicator Specification Soil moisture deficit This indicator shows the annual deviation in soil moisture content of each 500-m grid cell from the long-term (1995-2019) average. Negative soil moisture anomalies indicate that the annual average availability of soil moisture to plants drops to such a level that it has the potential to affect terrestrial vegetation and, hence, cause persistent changes in ecosystem condition. Negative long-term averages and negative trends in the annual data indicate increasing pressures on vegetation and ecosystems, and thus represent a climatic driver that should be considered in EU nature restoration plans. Therefore, the indicator can inform policy action on ecosystem restoration in the EU but also on adaptation to climate change.
Indicator Specification Drought impact on ecosystems in Europe The indicator addresses anomalies and long term trends of vegetation productivity derived from remote sensing observed time series of vegetation indices in areas that are pressured by drought. Drought pressure is computed as the combination of significant Pearson correlation coefficients (r) derived between time series anomalies of yearly vegetation productivity and the anomalies of various drought hazards (dH) during the growing season. Drought impact is indicated as the most sever negative productivity anomaly under drought pressured areas as well as long term decreasing linear trends of annual vegetation productivity for areas that are pressured by drought hazards. Detailed indicators’ specifications are presented under Methodology.
Indicator Specification Vegetation productivity The indicator addresses trends in land surface productivity derived from remote sensing observed time series of vegetation indices. The vegetation index used in the indicator is the Plant Phenology Index (PPI, Jin and Eklundh, 2014). PPI is based on the MODIS Nadir BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR. The product provides reflectance data for the MODIS “land” bands (1 - 7) adjusted using a bi-directional reflectance distribution function. This function models values as if they were collected from a nadir-view to remove so called cross-track illumination effects. The Plant Phenology Index (PPI) is a new vegetation index optimized for efficient monitoring of vegetation phenology. It is derived from radiative transfer solution using reflectance in visible-red (RED) and near-infrared (NIR) spectral domains. PPI is defined to have a linear relationship to the canopy green leaf area index (LAI) and its temporal pattern is strongly similar to the temporal pattern of gross primary productivity (GPP) estimated by flux towers at ground reference stations. PPI is less affected by presence of snow compared to commonly used vegetation indices such as Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI). The product is distributed with 500 m pixel size (MODIS Sinusoidal Grid) with 8-days compositing period.  References: Jönsson P., Eklundh L., 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Computers & Geosciences 30 (2004) 833–845. Eklundh L., Jönsson P., 2015. TIMESAT: A Software Package for Time-Series Processing and Assessment of Vegetation Dynamics. In: Kuenzer C., Dech S., Wagner W. (eds) Remote Sensing Time Series. Remote Sensing and Digital Image Processing, vol 22. Springer, Cham Jin, H., Eklundh, L. 2014. A physically based vegetation index for improved monitoring of plant phenology, Remote Sensing of Environment, 152, 512 – 525. Karkauskaite, P., Tagesson, T., Fensholt, R., 2017. Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone, Remote Sensing, 9 (485), 21 pp. Jin, H.X.; Jönsson, A.M.; Bolmgren, K.; Langvall, O.; Eklundh, L., 2017. Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index. Remote Sensing of Environment 2017,198, 203-212. Abdi, A. M., N. Boke-Olén, H. Jin, L. Eklundh, T. Tagesson, V. Lehsten and J. Ardö (2019). First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems. International Journal of Applied Earth Observation and Geoinformation 78: 249-260. Jin, H., A. M. Jönsson, C. Olsson, J. Lindström, P. Jönsson and L. Eklundh (2019). New satellite-based estimates show significant trends in spring phenology and complex sensitivities to temperature and precipitation at northern European latitudes. International Journal of Biometeorology 63(6): 763-775.

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Temporal coverage

For references, please go to https://www.eea.europa.eu/data-and-maps/data/annual-above-ground-vegetation-productivity or scan the QR code.

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