statgis.gee.time_series_analysis#

Functions to process image collections as time series

Module Contents#

Functions#

extract_dates(→ pandas.DatetimeIndex)

Extract dates from all images in an image collection.

resample(→ ee.ImageCollection)

Function to resample an Image Collection to a fixed timestamp.

detrend(→ ee.ImageCollection)

Calculate the linear trend and seasonality of an image collection.

seasonal_decompose(→ ee.ImageCollection)

Seasonal decomposition using the linear trend. This function take an image collection
statgis.gee.time_series_analysis.extract_dates(image_collection: ee.ImageCollection) → pandas.DatetimeIndex#

Extract dates from all images in an image collection.

Parameters:

image_collection (ee.ImageCollection) – Collection of image.

Returns:

dates – Series with the dates of the images.

Return type:

pd.Series

Example

>>> import ee
>>> from statgis.gee import time_series_analysis
>>> ee.Initialize()
>>> chirps = ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY").filterDate("1985-01-01", "2022-12-31")
>>> dates = time_series_analysis.extract_dates(chirps)
statgis.gee.time_series_analysis.resample(image_collection: ee.ImageCollection, reducer: ee.Reducer, scale: str) → ee.ImageCollection#

Function to resample an Image Collection to a fixed timestamp.

Parameters:

  • image_collection (ee.ImageCollection) – Image Collection to resample.

  • reducer (ee.Reducer) – To aggregate the images

  • scale (str) – Time scale to aggregate the image, must be one from: - annual. - monthly. - monthly-stat. - monthly-stat-repeated.

Returns:

final_collection – Collection resampled.

Return type:

ee.ImageCollection

Example

>>> import ee
>>> from statgis.gee import time_series_analysis
>>> ee.Initialize()
>>> chirps = ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY").filterDate("1985-01-01", "2022-12-31")
>>> annual_precipitation = time_series_analysis.resample(chirps, ee.Reducer.sum(), "annual")
statgis.gee.time_series_analysis.detrend(image_collection: ee.ImageCollection, band: str, restore_mean: bool = True) → ee.ImageCollection#

Calculate the linear trend and seasonality of an image collection.

Parameters:

  • image_collection (ee.ImageCollection) – Image collection to remove its trend.

  • band (str) – Band of interest.

  • restore_mean (bool = True) – If True, the data mean wil lbe restored in the seasonality.

Returns:

image_collection – Image collection with linear trend and seasonality.

Return type:

ee.ImageCollection

Example

>>> import ee
>>> from statgis.gee import time_series_analysis
>>> ee.Initialize()
>>> chirps = ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY").filterDate("1985-01-01", "2022-12-31")
>>> annual_precipitation = time_series_analysis.resample(chirps, ee.Reducer.sum(), "annual")
>>> seasonal_precipitation = time_series_analysis.detrend(annual_precipitation, "precipitation")
statgis.gee.time_series_analysis.seasonal_decompose(image_collection: ee.ImageCollection, band: str, restore_mean: bool = True) → ee.ImageCollection#

Seasonal decomposition using the linear trend. This function take an image collection and extract the time series components in the specified band.

Parameters:

  • image_collection (ee.ImageCollection) – Image collection of interest.

  • band (str) – Band of interest.

  • restore_mean (bool = True) – If True, the data mean wil lbe restored in the seasonality.

Returns:

final_collection – Image collection with time series components.

Return type:

ee.ImageCollection

Example

>>> import ee
>>> from statgis.gee import time_series_analysis
>>> ee.Initialize()
>>> chirps = ee.ImageCollection("UCSB-CHG/CHIRPS/DAILY").filterDate("1985-01-01", "2022-12-31")
>>> monthly_precipitation = time_series_analysis.resample(chirps, ee.Reducer.sum(), "monthly")
>>> precipitation_ts = time_series_analysis.seasonal_decompose(monthly_precipitation, "precipitation")