Resampling

Satpy provides multiple resampling algorithms for resampling geolocated data to uniform projected grids. The easiest way to perform resampling in Satpy is through the Scene object’s resample() method. Additional utility functions are also available to assist in resampling data. Below is more information on resampling with Satpy as well as links to the relevant API documentation for available keyword arguments.

Resampling algorithms

Available Resampling Algorithms

Resampler	Description	Related
nearest	Nearest Neighbor	KDTreeResampler
ewa	Elliptical Weighted Averaging	EWAResampler
native	Native	NativeResampler
bilinear	Bilinear	BilinearResampler

The resampling algorithm used can be specified with the resampler keyword argument and defaults to nearest:

>>> scn = Scene(...)
>>> euro_scn = global_scene.resample('euro4', resampler='nearest')

Warning

Some resampling algorithms expect certain forms of data. For example, the EWA resampling expects polar-orbiting swath data and prefers if the data can be broken in to “scan lines”. See the API documentation for a specific algorithm for more information.

Resampling for comparison and composites

While all the resamplers can be used to put datasets of different resolutions on to a common area, the ‘native’ resampler is designed to match datasets to one resolution in the dataset’s original projection. This is extremely useful when generating composites between bands of different resolutions.

>>> new_scn = scn.resample(resampler='native')

By default this resamples to the highest resolution area (smallest footprint per pixel) shared between the loaded datasets. You can easily specify the lower resolution area:

>>> new_scn = scn.resample(scn.min_area(), resampler='native')

Providing an area that is neither the minimum or maximum resolution area may work, but behavior is currently undefined.

Caching for geostationary data

Satpy will do its best to reuse calculations performed to resample datasets, but it can only do this for the current processing and will lose this information when the process/script ends. Some resampling algorithms, like nearest and bilinear, can benefit by caching intermediate data on disk in the directory specified by cache_dir and using it next time. This is most beneficial with geostationary satellite data where the locations of the source data and the target pixels don’t change over time.

>>> new_scn = scn.resample('euro4', cache_dir='/path/to/cache_dir')

See the documentation for specific algorithms to see availability and limitations of caching for that algorithm.

Create custom area definition

See pyresample.geometry.AreaDefinition for information on creating areas that can be passed to the resample method:

>>> from pyresample.geometry import AreaDefinition
>>> my_area = AreaDefinition(...)
>>> local_scene = global_scene.resample(my_area)

Create dynamic area definition

See pyresample.geometry.DynamicAreaDefinition for more information.

Examples coming soon…

Store area definitions

Area definitions can be added to a custom YAML file (see pyresample’s documentation for more information) and loaded using pyresample’s utility methods:

>>> from pyresample.utils import parse_area_file
>>> my_area = parse_area_file('my_areas.yaml', 'my_area')[0]

Examples coming soon…