This page was generated from a Jupyter notebook. To run and interact with it, you can download it here.

Landsat-7 cloud/shadow-free composite

This example aims to create a Landsat-7 cloud/shadow free composite on, or as close as possible to 22-23 November 2016. The area of interest covers a range of natural, agricultural and urban areas around Stellenbosch, South Africa.


geemap is required to run the notebook. You can uncomment the cell below to install it, if it isn’t installed already.

# geemap should be installed if it isn't already.
# import sys
# if 'conda' in sys.prefix:
#     # install into the conda environment the notebook is being run from
#     !conda install --yes --prefix {sys.prefix} -c conda-forge geemap
# else:
#     # install into the python environment the notebook is being run from
#     !{sys.executable} -m pip install geemap
import ee
import geedim as gd
import geemap.foliumap as geemap

# initialise earth engine with the high-volume endpoint

Create and search a Landsat-7 collection

# geojson search polygon
region = {
    'type': 'Polygon', 'coordinates': [[
        (19.075, -34.115), (19.075, -33.731), (18.723, -33.731),
        (18.723, -34.115), (19.075, -34.115)

# create and search the Landsat-7 collection
coll = gd.MaskedCollection.from_name('LANDSAT/LE07/C02/T1_L2')
filt_coll =
    '2016-11-01', '2016-12-19', region, cloudless_portion=40

# print the search results
print(filt_coll.schema_table, end='\n\n')

ABBREV     NAME                  DESCRIPTION
---------  --------------------  ----------------------------------------------
ID         system:id             Earth Engine image id
DATE       system:time_start     Image capture date/time (UTC)
FILL       FILL_PORTION          Portion of region pixels that are valid (%)
CLOUDLESS  CLOUDLESS_PORTION     Portion of filled pixels that are cloud/shadow
                                 free (%)
GRMSE      GEOMETRIC_RMSE_MODEL  Orthorectification RMSE (m)
SAA        SUN_AZIMUTH           Solar azimuth angle (deg)
SEA        SUN_ELEVATION         Solar elevation angle (deg)

ID                                          DATE              FILL CLOUDLESS GRMSE   SAA   SEA
------------------------------------------- ---------------- ----- --------- ----- ----- -----
LANDSAT/LE07/C02/T1_L2/LE07_175083_20161116 2016-11-16 08:37 65.43     80.93  4.89 67.06 61.31
LANDSAT/LE07/C02/T1_L2/LE07_175084_20161116 2016-11-16 08:38 67.79     72.44  6.74 65.06 60.50
LANDSAT/LE07/C02/T1_L2/LE07_175083_20161202 2016-12-02 08:37 65.36     99.99  4.90 73.95 62.07
LANDSAT/LE07/C02/T1_L2/LE07_175084_20161202 2016-12-02 08:38 67.02     99.98  7.48 71.70 61.41
LANDSAT/LE07/C02/T1_L2/LE07_175083_20161218 2016-12-18 08:37 66.27    100.00  4.95 78.16 61.24
LANDSAT/LE07/C02/T1_L2/LE07_175084_20161218 2016-12-18 08:38 67.05     99.98  7.13 75.91 60.67

Notes on search results

  • The 2016-11-16 images are closest to the target dates, but have some cloud in them.

  • No single image has full coverage (FILL=100) of the search area. In part, this is just due to the footprint of the images, but is also a result of the Landsat-7 SLC failure.

Find composite images

Here, we find cloud/shadow-free mosaic, and q-mosaic composite images, prioritising images closest to 2016-11-22 by specifying the date parameter.

mosaic_im = filt_coll.composite(
    method=gd.CompositeMethod.mosaic, date='2016-11-22'
q_mosaic_im = filt_coll.composite(
    method=gd.CompositeMethod.q_mosaic, date='2016-11-22'

Visualise search result and composite images

You can select which images to show/hide by clicking the layer button on the top right.


You need to download and run the notebook to view the images.

l7_vis_params = dict(min=7300, max=13000, bands=[
                     'SR_B3', 'SR_B2', 'SR_B1'], gamma=1.5)
map = geemap.Map()

map.centerObject(ee.Geometry(region), 11)
for im_id in
    im = gd.MaskedImage.from_id(im_id, mask=False)
    map.addLayer(im.ee_image.clip(region), l7_vis_params, im_id[-20:])

    mosaic_im.ee_image.clip(region), l7_vis_params, 'Mosaic composite'
    q_mosaic_im.ee_image.clip(region), l7_vis_params, 'Q-mosaic composite'

region_im = ee.Image().byte().paint(
    featureCollection=ee.Geometry(region), width=2, color=1
map.addLayer(region_im, dict(palette=['FF0000']), 'Region')


Make this Notebook Trusted to load map: File -> Trust Notebook

Visualisation notes

  • The mosaic method image contains some artefacts due to remnant cloud in the masked input images.

  • By prioritising pixels with the highest distance to cloud, the q-mosaic method is robust to imperfect cloud/shadow masking, and produces a composite free of cloud artefacts.


Lastly, we download the q-mosaic composite. crs, scale and region parameters must be specified, as the composite has no fixed projection.

# download the q_mosaic composite image, specifying crs, scale and region as
# it has no fixed projection
    'l7_q_mosaic_im.tif', crs='EPSG:3857', scale=30, region=region,
    dtype='uint16', overwrite=True