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. (Dataset). https://doi.org/10.5285/6d07d60a-4cb9-44e4-be39-89ea40365236
Import this citation into your reference management software:
BibTeX | Reference Manager (RIS) | Endnote
This dataset is available under the terms of the Open Government Licence
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
- 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.
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
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.
Correspondence/contact details
Wallingford
Oxfordshire
OX10 8BB
UNITED KINGDOM
Authors
Other contacts
- Rights holder
-
UK Centre for Ecology & Hydrology
- Custodian
-
NERC EDS Environmental Information Data Centreinfo@eidc.ac.uk
- Publisher
-
NERC Environmental Information Data Centreinfo@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
More information about these numbers
Get the data
By accessing or using this dataset, you agree to the terms of the relevant licence agreement(s). You will ensure that this dataset is cited in any publication that describes research in which the data have been used.
This dataset is available under the terms of the Open Government Licence
CITE 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