Extract Landsat surface reflectance time-series at given location from google earth engine
Google Earth Engine data extraction tool. Quickly obtain Landsat multispectral time-series for exploratory analysis and algorithm testing
Online documentation available at https://loicdtx.github.io/landsat-extract-gee
A python library (API + command lines) to extract Landsat time-series from the Google Earth Engine platform. Can query single pixels or spatially aggregated values over polygons. When used via the command line, extracted time-series are written to a sqlite database.
The idea is to provide quick access to Landsat time-series for exploratory analysis or algorithm testing. Instead of downloading the whole stack of Landsat scenes, preparing the data locally and extracting the time-series of interest, which may take several days, geextract allows to get time-series in a few seconds.
Compatible with python 2.7 and 3.
The principal function of the API is ts_extract
from geextract import ts_extract from datetime import datetime # Extract a Landsat 7 time-series for a 500m radius circular buffer around # a location in Yucatan lon = -89.8107197 lat = 20.4159611 LE7_dict_list = ts_extract(lon=lon, lat=lat, sensor='LE7', start=datetime(1999, 1, 1), radius=500)
geextract comes with two command lines, for extracting Landsat time-series directly from the command line.
gee_extract.py: Extract a Landsat multispectral time-series for a single site. Extracted data are automatically added to a sqlite database.
gee_extract_batch.py: Batch order Landsat multispectral time-series for multiple locations.
gee_extract.py --help # Extract all the LT5 bands for a location in Yucatan for the entire Landsat period, with a 500m radius gee_extract.py -s LT5 -b 1980-01-01 -lon -89.8107 -lat 20.4159 -r 500 -db /tmp/gee_db.sqlite -site uxmal -table col_1 gee_extract.py -s LE7 -b 1980-01-01 -lon -89.8107 -lat 20.4159 -r 500 -db /tmp/gee_db.sqlite -site uxmal -table col_1 gee_extract.py -s LC8 -b 1980-01-01 -lon -89.8107 -lat 20.4159 -r 500 -db /tmp/gee_db.sqlite -site uxmal -table col_1
gee_extract_batch.py --help # Extract all the LC8 bands in a 500 meters for two locations between 2012 and now echo "4.7174,44.7814,rompon\n-149.4260,-17.6509,tahiti" > site_list.txt gee_extract_batch.py site_list.txt -b 1984-01-01 -s LT5 -r 500 -db /tmp/gee_db.sqlite -table landsat_ts gee_extract_batch.py site_list.txt -b 1984-01-01 -s LE7 -r 500 -db /tmp/gee_db.sqlite -table landsat_ts gee_extract_batch.py site_list.txt -b 1984-01-01 -s LC8 -r 500 -db /tmp/gee_db.sqlite -table landsat_ts
You must have a Google Earth Engine account to use the package.
Then, in a vitual environment run:
pip install geextract earthengine authenticate
This will open a google authentication page in your browser, and will give you an authentication token to paste back in the terminal.
You can check that the authentication process was successful by running.
python -c "import ee; ee.Initialize()"
If nothing happens… it’s working.
A quick benchmark of the extraction speed, using a 500 m buffer.
import time from datetime import datetime from pprint import pprint import geextract lon = -89.8107197 lat = 20.4159611 for sensor in ['LT5', 'LE7', 'LT4', 'LC8']: start = time.time() out = geextract.ts_extract(lon=lon, lat=lat, sensor=sensor, start=datetime(1980, 1, 1, 0, 0), end=datetime.today(), radius=500) end = time.time() pprint('%s. Extracted %d records in %.1f seconds' % (sensor, len(out), end - start))
# 'LT5. Extracted 142 records in 1.9 seconds' # 'LE7. Extracted 249 records in 5.8 seconds' # 'LT4. Extracted 7 records in 1.0 seconds' # 'LC8. Extracted 72 records in 2.4 seconds'
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