doyBySeasons

geetools.ImageCollectionAccessor.doyBySeasons(band, region, seasonStart, seasonEnd, reducer='mean', dateProperty='system:time_start', scale=10000, crs=None, crsTransform=None, bestEffort=False, maxPixels=10**7, tileScale=1)

Aggregate for each year on a single region a single band.

This method is returning a dictionary with all the years as keys and their reduced value for each day of the season over the specified region for a specific band as value. To set the start and end of the season, use the :py:method:`ee.Date.getRelative` or time.struct_time method to get the day of the year.

{
    "year1": {"doy1": value1, "doy2": value2, ...},
    "year2": {"doy1": value1, "doy2": value2, ...},
    ...
}

Parameters:

• band (str) – The band to reduce.

• region (ee.Geometry) – The region to reduce the data on.

• seasonStart (int | ee.Number) – The day of the year that marks the start of the season.

• seasonEnd (int | ee.Number) – The day of the year that marks the end of the season.

• reducer (str | ee.Reducer) – The name of the reducer or a reducer object to use. Default is “mean”.

• dateProperty (str) – The property to use as date for each image. Default is “system:time_start”.

• scale (int) – The scale in meters to use for the reduction. default is 10000m

• crs (str | None) – 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 (list | None) – 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.

• bestEffort (bool) – If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed.

• maxPixels (int | None) – The maximum number of pixels to reduce. Defaults to 1e7.

• tileScale (float) – A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default.

Returns:

A dictionary with the reduced values for each year and each day.

Return type:

ee.Dictionary

See also

• doyByBands: Aggregate the images that occurs on the same day and then reduce each band on a single region.

• doyByRegions: Aggregate the images that occurs on the same day and then reduce a single band on multiple regions.

• doyByYears: Aggregate for each year on a single region a single band.

• plot_doy_by_bands: Plot the reduced data for each image in the collection by bands on a specific region.

• plot_doy_by_regions: Plot the reduced data for each image in the collection by regions for a single band.

• plot_doy_by_seasons: Plot the reduced data for each image in the collection by years for a single band.

• plot_doy_by_years: Plot the reduced data for each image in the collection by years for a single band.

Examples

import ee, geetools
from geetools.utils import initialize_documentation

initialize_documentation()

collection = (
    ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA")
    .filterBounds(ee.Geometry.Point(-122.262, 37.8719))
    .filter(ee.Filter.Or(
        ee.Filter.date("2022-01-01", "2022-12-31"),
        ee.Filter.date("2016-01-01", "2016-12-31"),
    ))
    .map(lambda i: ee.Image(i).addBands(
        ee.Image(i)
        .normalizedDifference(["B5", "B4"])
        .rename("NDVI")
    ))
)

reduced = collection.geetools.doyBySeasons(
    band = "NDVI",
    region = ee.Geometry.Point(-122.262, 37.8719).buffer(1000),
    seasonStart = ee.Date("2016-05-01").getRelative("day", "year"),
    seasonEnd = ee.Date("2016-10-31").getRelative("day", "year"),
    reducer = "mean",
    dateProperty = "system:time_start",
    scale = 10000
)
reduced.getInfo()

{'2016': {'130': 0.4160950775662901,
  '146': 0.39329032676766745,
  '162': 0.32798277343161575,
  '178': 0.3579929229762581,
  '194': 0.36713309680948975,
  '210': 0.339837394867634,
  '226': 0.3341378873574409,
  '242': 0.355559207281013,
  '258': 0.33714390050834026,
  '274': 0.3175092020178191,
  '290': 0.3576826300116792},
 '2022': {'130': 0.39201567981642593,
  '146': 0.07580643539212828,
  '162': 0.054009129745240976,
  '178': 0.34839430568351415,
  '194': 0.3355445848845227,
  '210': 0.10000627907209342,
  '226': 0.3393665149601866,
  '242': 0.3297845808901515,
  '258': 0.3376274785631656,
  '274': 0.2612639689943977,
  '290': 0.324367810839363}}