This notebook explores Copernicus global land data available in openEO.
This assumes that you have openEO credentials configured in your environment: https://open-eo.github.io/openeo-python-client/auth.html#config-files-and-openeo-auth-helper-tool
import openeo
session = openeo.connect("https://openeo-dev.vito.be").authenticate_oidc("egi")
Authenticated using refresh token.
session.describe_collection("CGLS_LAI300_V1_GLOBAL")
LAI300 = session.load_collection("CGLS_LAI300_V1_GLOBAL")
Downloading data for an area of interest is rather simple. In this example, we export to NetCDF, allowing us to uses xarray for further analysis.
%time LAI300.filter_temporal("2019-08-10","2019-08-30").filter_bbox([3.889160,43.984910,14.370117,47.587642]).download("LAI.nc",format="NetCDF")
CPU times: user 361 ms, sys: 128 ms, total: 489 ms Wall time: 30.2 s
import xarray as xr
lai = xr.open_dataset('LAI.nc',engine="h5netcdf")
import hvplot.xarray
lai.hvplot.hist(dynamic=False)
lai.t
<xarray.DataArray 't' (t: 2)> array(['2019-08-10T00:00:00.000000000', '2019-08-20T00:00:00.000000000'], dtype='datetime64[ns]') Coordinates: spatial_ref int64 ... * t (t) datetime64[ns] 2019-08-10 2019-08-20
import holoviews as hv
lai.sel(t="2019-08-10").hvplot(cmap='RdYlGn',dynamic=False,width=1300,height=1300)