Working With Time Series

In this example notebook, we’ll introduce the TimeSeries class, which is the basis of most of the functionality in wxee. Any ImageCollection can be turned into a TimeSeries, allowing you to access additional time series-specific methods and properties.

Setup

!pip install wxee

import ee
import wxee

ee.Authenticate()
wxee.Initialize()

Creating a Time Series

There are two ways to create a TimeSeries.

From an ID

You can create a TimeSeries the same way you create an ImageCollection, by using an ID from the Earth Engine catalog. Let’s create a TimeSeries from the Sentinel-2 dataset.

ts = wxee.TimeSeries("COPERNICUS/S2_SR")

From an Existing Collection

You can also turn an existing ImageCollection into a TimeSeries, which may be helpful if you decide you want time series functionality after doing other processing. Just use the wx accessor and the to_time_series method.

col = ee.ImageCollection("COPERNICUS/S2_SR")
ts = col.wx.to_time_series()

Earth Engine Methods

A TimeSeries has all the methods of an Earth Engine ImageCollection. For example, we can use filterMetadata, filterBounds, and size to see how many cloudless (or at least mostly cloudless) images there are over a given location (Svalbard, Norway).

svalbard = ee.Geometry.Point([15.5861, 78.1969])

cloudless = ts.filterBounds(svalbard).filterMetadata("CLOUDY_PIXEL_PERCENTAGE", "less_than", 5)
cloudless.size().getInfo()

80

wxee Methods

A time series also has additional methods added by wxee to help describe and process time series data. Let’s start by looking at the describe, dataframe, and timeline methods that provide info on how many images are in the time series and when they were acquired.

describe

First, describe provides a brief summary of the time series including the number of images, the system:start_time of the first and last chronological image, and the mean interval between images.

cloudless.describe()

COPERNICUS/S2_SR
        Images: 80
        Start date: 2018-03-27 12:27:55 UTC
        End date: 2021-09-03 11:59:18 UTC
        Mean interval: 15.90 days

As you can see, there have been 80 mostly cloudless images captured over this area–on average, about one every 16 days.

dataframe

We can summarize the ID and start and end dates of those 80 images as a pandas.DataFrame using dataframe.

df = cloudless.dataframe()
df.head()

	id	time_start	time_end
0	COPERNICUS/S2_SR/20180327T122729_20180327T1227...	2018-03-27 12:27:55	2018-03-27 12:27:55
1	COPERNICUS/S2_SR/20180405T120651_20180405T1206...	2018-04-05 12:06:45	2018-04-05 12:06:45
2	COPERNICUS/S2_SR/20180405T125659_20180405T1257...	2018-04-05 12:57:02	2018-04-05 12:57:02
3	COPERNICUS/S2_SR/20180406T122649_20180406T1226...	2018-04-06 12:26:49	2018-04-06 12:26:49
4	COPERNICUS/S2_SR/20180407T115639_20180407T1156...	2018-04-07 11:56:39	2018-04-07 11:56:39

timeline

Finally, we can visualize when those cloudless images were collected with an interactive plot using timeline.

cloudless.timeline()

Now we can see that most of the cloudless days occur between spring and summer!

Other Methods

The TimeSeries class contains many other methods for processing such as interpolate_time, aggregate_time, climatology_anomaly, and others. Detailed example notebooks can be found here.

Note

This page was auto-generated from a Jupyter notebook. For full functionality, download the notebook from Github and run it in a local Python environment.