Leaf Area Index (open loop)

Produced by
CNRM
Data type
Gridded
Horizontal coverage
Europe (west=25.0° W, east=45.0° E, south=30.0° N, north=70.0° N)
Horizontal resolution
0.1° x 0.1°
Temporal resolution
Daily
Temporal coverage
2018-2022
Model
SURFEX land surface model
File format
Zarr

Download the data

Time-chunked

https://data.seedsproject.eu/seeds_lai_cnrm_20180101-20221231_surfex_ol_v1/slices.zarr

Geo-chunked

https://data.seedsproject.eu/seeds_lai_cnrm_20180101-20221231_surfex_ol_v1/dices.zarr
How to download the data?

Variables

Click on a variable to see it on the viewer.

Leaf area index
lai_isba

Description

Leaf area index (LAI) is unitless measure of the total area of leaves per unit ground area (e.g., an LAI of 1 equates to 1 m2 of leaf area above 1 m2 of ground area), which can be a vital variable for predicting primary production of plants and evapotranspiration. The LAI data in SEEDS are calculated by the SURFEX land surface model. The seasonal changes in vegetation growth and senescence are simulated in a dynamic way by the model taking into account vegetation type, sunlight, and meteorological conditions that affect the growing season. The LAI is calculated over different vegetation types separately and are then spatially averaged to combine them into a single LAI for all natural surface types in the SURFEX model.

Download instructions

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This sample Python code shows how to download this dataset, create subsets of it and save them as NetCDF, using the Xarray library.

import xarray as xr import fsspec # Open remote dataset ds = xr.open_zarr( store=fsspec.get_mapper("https://data.seedsproject.eu/seeds_lai_cnrm_20180101-20221231_surfex_ol_v1/slices.zarr") ) # Subset a time range, save as NetCDF time_subset = ds.lai_isba.sel(time=slice('2019-06-01', '2019-07-01')) time_subset.to_netcdf('./lai_isba_time_subset.nc') # Subset a time and geographical range, save as NetCDF time_geo_subset = ds.lai_isba.sel(time=slice('2019-06-01', '2019-07-01'), latitude=slice(40, 43), longitude=slice(0,3.5)) time_geo_subset.to_netcdf('./lai_isba_time_geo_subset.nc')