hydromt_wflow.workflows.create_lulc_lai_mapping_table#

hydromt_wflow.workflows.create_lulc_lai_mapping_table(da_lulc: ~xarray.core.dataarray.DataArray, da_lai: ~xarray.core.dataarray.DataArray, sampling_method: str = 'any', lulc_zero_classes: ~typing.List[int] = [], logger=<Logger hydromt_wflow.workflows.landuse (WARNING)>) → DataFrame[source]#

Derive LAI values per landuse class.

Parameters:

da_lulc (xr.DataArray) – Landuse map.
da_lai (xr.DataArray) – Cyclic LAI map.
sampling_method (str, optional) –
Resampling method for the LULC data to the LAI resolution. Two methods are supported:
- ’any’ (default): if any cell of the desired landuse class is present in the
  resampling window (even just one), it will be used to derive LAI values. This method is less exact but will provide LAI values for all landuse classes for the high resolution landuse map.
- ’mode’: the most frequent value in the resampling window is
  used. This method is less precise as for cells with a lot of different landuse classes, the most frequent value might still be only a small fraction of the cell. More landuse classes should however be covered and it can always be used with the landuse map of the wflow model instead of the original high resolution one.
- ’q3’: only cells with the most frequent value (mode) and that cover 75%
  (q3) of the resampling window will be used. This method is more exact but for small basins, you may have less or no samples to derive LAI values for some classes.
lulc_zero_classes (list of int, optional) – List of landuse classes that should have zero for leaf area index values for example waterbodies, open ocean etc. For very high resolution landuse maps, urban surfaces and bare areas can be included here as well. By default empty.

Returns:

df_lai_mapping – Mapping table with LAI values per landuse class. One column for each month and one line per landuse class. The number of samples used to derive the mapping values is also added to a samples column in the dataframe.

Return type:

pd.DataFrame