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Semeena, V.S.; Taylor, C.M.; Folwell, S.S.; Hartley, A.

Global gridded monthly mean Leaf Area Index (LAI) for five Plant Functional Types (PFTs) derived from the Global LAnd Surface Satellite (GLASS) products for the period 2000-2014

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This dataset is available under the terms of the Open Government Licence

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https://doi.org/10.5285/6d07d60a-4cb9-44e4-be39-89ea40365236
A new monthly long term average (climatology) of Leaf Area Index (LAI) has been developed for use as ancillary data with the Joint UK Land Environment Simulator (JULES) Land Surface Model and the UK Met Office Unified Model. It is derived from an improved version of long time series of LAI from the original Global LAnd Surface Satellite (GLASS) products (http://www.glass.umd.edu/LAI/MODIS/0.05D/). The GLASS data consists of a time series of LAI from Moderate Resolution Imaging Spectroradiometer (MODIS) surface-reflectance data for the period 2000-2014. The MODIS data was provided in a spatial resolution of 1km in a sinusoidal projection and is interpolated into 0.5deg on a geographic latitude/longitude projection in this dataset. The total LAI from MODIS is segregated into five different Plant Functional Types (PFTs) using the fractional coverage of each PFT from the Climate Change Initiative (CCI) Land Cover data. For this reason this new LAI climatology should be used in combination with the CCI PFT data, which is also provided here. Two variables are provided with the dataset containing LAI, each covering the same spatial and time extent. The PFT data provided with this dataset covers a time span of only one year, 2010.

- Leaf Area Index (LAI) - LAI is an important parameter in land-surface models, influencing the surface roughness, transpiration rate and the soil water content and temperature. Numerous outputs of vegetation models such as net primary productivity (NPP), evapotranspiration (ET), light absorption by plants (FAPAR), nutrient dynamics etc., are influenced by LAI where it is a key variable in energy and water balance calculations.
- Vegetation Canopy Height (H) - H plays an important role in the interface between the atmosphere and land surface and it impacts weather and climate at local to global scales by modulating aerodynamic conductance and vegetation dynamics. Therefore, H is fundamentally needed for the calculation of turbulent exchanges of energy and mass between the atmosphere and the terrestrial ecosystem.

One variable is provided with the dataset containing CCI PFTs:
- Fractional coverage of 5 PFTS or vegetation classes and 4 land use classes – The 5 PFTs are Broad Leaf, Needle Leaf, C3 Grass, C4 Grass and Shrub. The 4 land use classes are Urban area, Inland Water, Bare Soil and Snow/Ice.
Publication date: 2021-06-22
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More information

View numbers valid from 01 June 2023 Download numbers valid from 20 June 2024 (information prior to this was not collected)

Format

NetCDF

Spatial information

Study area
Spatial representation type
Raster
Spatial reference system
WGS 84

Temporal information

Temporal extent
2000-01-01    to    2014-12-31

Provenance & quality

This data set is derived from an improved version of long time series of LAI - http://www.glass.umd.edu/LAI/MODIS/0.05D/ - from the original Global LAnd Surface Satellite (GLASS) products. The GLASS LAI product spans the period 1981 to 2014 and has a temporal resolution of 8 days. The data is based on Advanced Very High Resolution Radiometer (AVHRR) reflectance data during the period 1981-1999 at a spatial resolution of 0.05 deg on a geographic latitude/longitude projection. Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data provided in a sinusoidal projection at a spatial resolution of 1km is used to derive the GLASS LAI product during 2000-2014. Even though the GLASS product has been validated thoroughly over the entire 1981-2014 period, due to issues of instrumental homogeneity, only data from MODIS is used here. Hence, this LAI ancillary product is a climatology of 15 years covering 2000-2014.

Each grid box in JULES is divided into 9 land classes where the first 5 classes are for vegetation and are known as Plant Functional Types (PFTs). The 5 PFTs are Broad Leaf, Needle Leaf, C3Grass, C4Grass and Shrub. The other four land classes are: Urban, Inland water, Bare soil and Snow/Ice. For use in the model, in an ancillary file, JULES requires monthly distribution of LAI for different plant functional types (PFTs) for each grid box. This requires partitioning of the observed GLASS LAI per grid box into different PFTs. The GLASS LAI product is thus segregated into five Plant Functional Types (PFTs) based on the vegetation fractional distribution in Climate Change Initiative (CCI) data which is available for the year 2010.

Licensing and constraints

This dataset is available under the terms of the Open Government Licence

Cite this dataset as:
Semeena, V.S.; Taylor, C.M.; Folwell, S.S.; Hartley, A. (2021). Global gridded monthly mean Leaf Area Index (LAI) for five Plant Functional Types (PFTs) derived from the Global LAnd Surface Satellite (GLASS) products for the period 2000-2014. NERC Environmental Information Data Centre. https://doi.org/10.5285/6d07d60a-4cb9-44e4-be39-89ea40365236

Correspondence/contact details

Semeena, V.S.
UK Centre for Ecology & Hydrology
Maclean Building, Benson Lane, Crowmarsh Gifford
Wallingford
Oxfordshire
OX10 8BB
UNITED KINGDOM
 enquiries@ceh.ac.uk

Authors

Semeena, V.S.
UK Centre for Ecology & Hydrology
Taylor, C.M.
UK Centre for Ecology & Hydrology
Folwell, S.S.
UK Centre for Ecology & Hydrology

Other contacts

Rights holder
UK Centre for Ecology & Hydrology
Custodian
NERC EDS Environmental Information Data Centre
 info@eidc.ac.uk
Publisher
NERC Environmental Information Data Centre
 info@eidc.ac.uk

Additional metadata

Topic categories
biota
climatologyMeteorologyAtmosphere
INSPIRE theme
Environmental Monitoring Facilities
Keywords
Climatology , JULES , LAI , Land cover , Land use , Leaf Area Index , MODIS , PFT , Plant Functional Type
Funding
Natural Environment Research Council Award: NE/M003574/1
Last updated
08 February 2024 17:34