Best Available Pixel (Bap) Composite using the Python API of Google Earth Engine (Gee)

This code is based on Pixel-Based Image Compositing for Large-Area Dense Time Series Applications and Science. (White et al., 2014) http://www.tandfonline.com/doi/full/10.1080/07038992.2014.945827

It uses a series of pixel based scores to generate a composite with the Best Available Pixel, assuming it is the one that has better score.

Installation

To use this package you must have installed and running Google Earth Engine Python API: https://developers.google.com/earth-engine/python_install

Once you have that, proceed

pip install geebap

Available Collections

• Serie Landsat

  • Landsat 1,2 and 3: Raw collections
  • Landsat 4: TOA collection
  • Landsat 5: TOA, SR (Ledaps and USGS) collections
  • Landsat 7: TOA, SR (Ledaps and USGS) collections
  • Landsat 8: TOA, SR (USGS) collections

• Sentinel 2

• Modis Series (experimental)

  • Modis Aqua
  • Modis Terra

Available Scores

• Satellite
• Distance to clouds and shadows masks
• Atmospheric Opacity
• Day of the year (doy)
• Masked pixels percentage
• Outliers
• Absolute value of a vegetation index

Available Indices

• ndvi
• evi
• nbr

Some considerations

• Sites size must not be too big. Works with 300 km2 tiles
• There is a module (sites.py) that has the avility to read a list of fusion table sites from a csv file

Basic Usage

from geebap import bap, season, filters, masks, \
                   scores, satcol, functions

import ee
ee.Initialize()

# COLLECTION
col_group = satcol.ColGroup.Landsat()

# SEASON
a_season = season.Season.Growing_North()

# MASKS
cld_mask = masks.Clouds()
equiv_mask = masks.Equivalent()

# Combine masks in a tuple
masks = (cld_mask, equiv_mask)

# FILTERS
filt_cld = filters.CloudsPercent()
filt_mask = filters.MaskPercent()

# Combine filters in a tuple
filters = (filt_cld, filt_mask)

# SCORES
doy = scores.Doy()
sat = scores.Satellite()
op = scores.AtmosOpacity()
out = scores.Outliers(("ndvi",))
ind = scores.Index("ndvi")
mascpor = scores.MaskPercent()
dist = scores.CloudDist()

# Combine scores in a tuple
scores = (doy, sat, op, out, ind, mascpor, dist)

# BAP OBJECT
bap = bap.Bap(year=2010, range=(0, 0),
              season=a_season,
              colgroup=col_group,
              masks=masks,
              scores=scores,
              filters=filters)

# SITE
site = ee.Geometry.Polygon([[-71,-42],
                            [-71,-43],
                            [-72,-43],
                            [-72,-42]])

# COMPOSITE
composite = bap.bestpixel(site=site,
                          indices=("ndvi",))

# The result (composite) is a namedtuple, so
image = composite.image

# image is a ee.Image object, so you can do anything
# from here..

one_value = functions.get_value(
                        image,
                        ee.Geometry.Point([-71.9, -38.9]),
                        30)

print(one_value)

Prints:

{u'BLUE': 0.018400000408291817,
 u'bandID': 10.0,
 u'date': 14592.0,
 u'score': 0.4800287335965901,
 u'psat': 0.8500000238418579,
 u'poutlier': 1.0,
 u'pdoy': 0.010760011453995735,
 u'pop': 0.01338691782766488,
 u'NIR': 0.365200012922287,
 u'pindice': 0.934493362903595,
 u'GREEN': 0.041200000792741776,
 u'pdist': 5.749522023787777e-19,
 u'pmascpor': 0.5515608191490173,
 u'ATM_OP': 93.0,
 u'ndvi': 0.8689867258071899,
 u'RED': 0.025599999353289604,
 u'SWIR': 0.13779999315738678}