doyByYears#

geetools.ee_image_collection.ImageCollectionAccessor.doyByYears(band, region, 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 over the specified region for a specific band as value.

{
    "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.

  • 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.

  • doyBySeasons: 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.doyByYears(
    band = "NDVI",
    region = ee.Geometry.Point(-122.262, 37.8719).buffer(1000),
    reducer = "mean",
    dateProperty = "system:time_start",
    scale = 10000
)
reduced.getInfo()

{'2016': {'114': 0.5602087093993884,
  '130': 0.5567390104809519,
  '146': 0.5320520800514571,
  '162': 0.38296301942341515,
  '178': 0.48876907548560816,
  '194': 0.4277642839266327,
  '2': 0.18039120971768205,
  '210': 0.4695113426976842,
  '226': 0.44273214236227504,
  '242': 0.40626011961747965,
  '258': 0.3850503530508611,
  '274': 0.44472521772378426,
  '290': 0.4008216852872101,
  '306': 0.51172048910413,
  '322': 0.47783248176314247,
  '338': 0.39035549793548036,
  '34': 0.5018829275313158,
  '354': 0.39082736909849486,
  '50': 0.4672897301155814,
  '66': 0.039181623854979704,
  '82': 0.5216778326026866,
  '98': 0.04983350141352292},
 '2022': {'114': 0.5307749085358947,
  '130': 0.4956961333546755,
  '146': 0.08797856743167884,
  '162': 0.05910096371491292,
  '178': 0.49401842744569574,
  '18': 0.42744229250463855,
  '194': 0.45398605392772073,
  '2': 0.06448681769379908,
  '210': 0.1768454152745308,
  '226': 0.49199842015256107,
  '242': 0.41926775357483387,
  '258': 0.4534184981687464,
  '274': 0.42303466742879897,
  '290': 0.492751970875086,
  '306': 0.4884940267108327,
  '322': 0.5190275017036148,
  '338': 0.019854696159657285,
  '34': 0.506291016501142,
  '354': 0.35483874214776234,
  '50': 0.13262948040207623,
  '66': 0.49592781396354657,
  '82': 0.584249248089327,
  '98': 0.5946662139123087}}