ee_extra.TimeSeries.core.getTimeSeriesByRegions#

ee_extra.TimeSeries.core.getTimeSeriesByRegions(x, reducer, collection, bands=None, scale=None, crs=None, crsTransform=None, tileScale=1, dateColumn='date', dateFormat='ISO', naValue=-9999)[source]#

Gets the time series by regions for the given image collection and feature collection according to the specified reducer (or reducers).

Parameters:

  • x (ImageCollection) – Image collection to get the time series from.

  • reducer (Any) – Reducer or list of reducers to use for region reduction.

  • collection (FeatureCollection) – Feature Collection to perform the reductions on. Image reductions are applied to each feature in the collection.

  • bands (Union[str, List[str], None]) – Selection of bands to get the time series from. Defaults to all bands in the image collection.

  • scale (Union[int, float, None]) – Nomical scale in meters.

  • crs (Optional[Any]) – The projection to work in. If unspecified, the projection of the image’s first band is used. If specified in addition to scale, rescaled to the specified scale.

  • crsTransform (Optional[Any]) – The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with ‘scale’, and replaces any transform already set on the projection.

  • tileScale (int) – A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default.

  • dateColumn (str) – Output name of the date column.

  • dateFormat (str) – Output format of the date column. Defaults to ISO. Available options: ‘ms’ (for milliseconds), ‘ISO’ (for ISO Standard Format) or a custom format pattern.

  • naValue (Union[int, float]) – Value to use as NA when the region reduction doesn’t retrieve a value due to masked pixels.

Returns:

Time series by regions retrieved as a Feature Collection.

Examples

>>> import ee
>>> from ee_extra.TimeSeries.core import getTimeSeriesByRegions
>>> ee.Initialize()
>>> f1 = ee.Feature(ee.Geometry.Point([3.984770,48.767221]).buffer(50),{'ID':'A'})
>>> f2 = ee.Feature(ee.Geometry.Point([4.101367,48.748076]).buffer(50),{'ID':'B'})
>>> fc = ee.FeatureCollection([f1,f2])
>>> S2 = (ee.ImageCollection('COPERNICUS/S2_SR')
...      .filterBounds(fc)
...      .filterDate('2020-01-01','2021-01-01'))
>>> ts = getTimeSeriesByRegions(S2,
...     reducer = [ee.Reducer.mean(),ee.Reducer.median()],
...     collection = fc,
...     bands = ['B3','B8'],
...     scale = 10)