import ee
import numpy as np
import warnings
import requests
from .common import _index
from .common import _maskClouds
from .common import _get_scale_params
from .common import _get_offset_params
from .common import _scale_STAC
from .common import _preprocess
from .common import _getSTAC
from .common import _getDOI
from .common import _getCitation
from .extending import extend
[docs]@extend(ee.imagecollection.ImageCollection)
def closest(self, date, tolerance=1, unit="month"):
"""Gets the closest image (or set of images if the collection intersects a region that requires multiple scenes) to the specified date.
Tip
----------
Check more info about getting the closest image to a specific date in the :ref:`User Guide<Closest Image to a Specific Date>`.
Parameters
----------
self : ee.ImageCollection [this]
Image Collection from which to get the closest image to the specified date.
date : ee.Date | string
Date of interest. The method will look for images closest to this date.
tolerance : float, default = 1
Filter the collection to [date - tolerance, date + tolerance) before searching the closest image. This speeds up the searching process for collections
with a high temporal resolution.
unit : string, default = 'month'
Units for tolerance. Available units: 'year', 'month', 'week', 'day', 'hour', 'minute' or 'second'.
Returns
-------
ee.ImageCollection
Closest images to the specified date.
Examples
--------
>>> import ee, eemont
>>> ee.Initialize()
>>> S2 = ee.ImageCollection('COPERNICUS/S2_SR').closest('2020-10-15')
"""
if not isinstance(date, ee.ee_date.Date):
date = ee.Date(date)
startDate = date.advance(-tolerance, unit)
endDate = date.advance(tolerance, unit)
self = self.filterDate(startDate, endDate)
def setProperties(img):
img = img.set(
"dateDist",
ee.Number(img.get("system:time_start")).subtract(date.millis()).abs(),
)
img = img.set("day", ee.Date(img.get("system:time_start")).get("day"))
img = img.set("month", ee.Date(img.get("system:time_start")).get("month"))
img = img.set("year", ee.Date(img.get("system:time_start")).get("year"))
return img
self = self.map(setProperties).sort("dateDist")
closestImageTime = self.limit(1).first().get("system:time_start")
dayToFilter = ee.Filter.eq("day", ee.Date(closestImageTime).get("day"))
monthToFilter = ee.Filter.eq("month", ee.Date(closestImageTime).get("month"))
yearToFilter = ee.Filter.eq("year", ee.Date(closestImageTime).get("year"))
self = self.filter(ee.Filter.And(dayToFilter, monthToFilter, yearToFilter))
return self
[docs]@extend(ee.imagecollection.ImageCollection)
def getTimeSeriesByRegion(
self,
reducer,
bands=None,
geometry=None,
scale=None,
crs=None,
crsTransform=None,
bestEffort=False,
maxPixels=1e12,
tileScale=1,
dateColumn="date",
dateFormat="ISO",
naValue=-9999,
):
"""Gets the time series by region for the given image collection and geometry (feature or feature collection are also supported) according to the specified reducer (or reducers).
Tip
----------
Check more info about time series in the :ref:`User Guide<Time Series By Regions>`.
Parameters
----------
self : ee.ImageCollection (this)
Image collection to get the time series from.
reducer : ee.Reducer | list[ee.Reducer]
Reducer or list of reducers to use for region reduction.
bands : str | list[str], default = None
Selection of bands to get the time series from. Defaults to all bands in the image collection.
geometry : ee.Geometry | ee.Feature | ee.FeatureCollection, default = None
Geometry to perform the region reduction. If ee.Feature or ee.FeatureCollection, the geometry() method is called. In order to get reductions by each feature please see
the getTimeSeriesByRegions() method. Defaults to the footprint of the first band for each image in the collection.
scale : numeric, default = None
Nomical scale in meters.
crs : Projection, default = None
The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.
crsTransform : list, default = None
The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix.
This option is mutually exclusive with 'scale', and replaces any transform already set on the projection.
bestEffort : boolean, default = False
If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed.
maxPixels : numeric, default = 1e12
The maximum number of pixels to reduce.
tileScale : numeric, default = 1
A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default.
dateColumn : str, default = 'date'
Output name of the date column.
dateFormat : str, default = 'ISO'
Output format of the date column. Defaults to ISO. Available options: 'ms' (for milliseconds), 'ISO' (for ISO Standard Format) or a custom format pattern.
naValue : numeric, default = -9999
Value to use as NA when the region reduction doesn't retrieve a value due to masked pixels.
Returns
-------
ee.FeatureCollection
Time series by region retrieved as a Feature Collection.
See Also
--------
getTimeSeriesByRegions : Gets the time series by regions for the given image collection and feature collection according to the specified reducer (or reducers).
Examples
--------
>>> import ee, eemont
>>> ee.Initialize()
>>> f1 = ee.Feature(ee.Geometry.Point([3.984770,48.767221]).buffer(50),{'ID':'A'})
>>> f2 = ee.Feature(ee.Geometry.Point([4.101367,48.748076]).buffer(50),{'ID':'B'})
>>> fc = ee.FeatureCollection([f1,f2])
>>> S2 = (ee.ImageCollection('COPERNICUS/S2_SR')
... .filterBounds(fc)
... .filterDate('2020-01-01','2021-01-01')
... .maskClouds()
... .scaleAndOffset()
... .spectralIndices(['EVI','NDVI']))
>>> ts = S2.getTimeSeriesByRegion(reducer = [ee.Reducer.mean(),ee.Reducer.median()],
... geometry = fc,
... bands = ['EVI','NDVI'],
... scale = 10)
"""
if bands != None:
if not isinstance(bands, list):
bands = [bands]
self = self.select(bands)
if not isinstance(reducer, list):
reducer = [reducer]
if not isinstance(geometry, ee.geometry.Geometry):
geometry = geometry.geometry()
collections = []
for red in reducer:
reducerName = red.getOutputs().get(0)
def reduceImageCollectionByRegion(img):
dictionary = img.reduceRegion(
red,
geometry,
scale,
crs,
crsTransform,
bestEffort,
maxPixels,
tileScale,
)
if dateFormat == "ms":
date = ee.Date(img.get("system:time_start")).millis()
elif dateFormat == "ISO":
date = ee.Date(img.get("system:time_start")).format()
else:
date = ee.Date(img.get("system:time_start")).format(dateFormat)
return ee.Feature(None, dictionary).set(
{dateColumn: date, "reducer": reducerName}
)
collections.append(
ee.FeatureCollection(self.map(reduceImageCollectionByRegion))
)
flattenfc = ee.FeatureCollection(collections).flatten()
def setNA(feature):
feature = ee.Algorithms.If(
condition=feature.propertyNames().size().eq(3),
trueCase=feature.set(
ee.Dictionary.fromLists(bands, [naValue] * len(bands))
),
falseCase=feature,
)
feature = ee.Feature(feature)
return feature
return flattenfc.map(setNA)
[docs]@extend(ee.imagecollection.ImageCollection)
def getTimeSeriesByRegions(
self,
reducer,
collection,
bands=None,
scale=None,
crs=None,
crsTransform=None,
tileScale=1,
dateColumn="date",
dateFormat="ISO",
naValue=-9999,
):
"""Gets the time series by regions for the given image collection and feature collection according to the specified reducer (or reducers).
Tip
----------
Check more info about time series in the :ref:`User Guide<Time Series By Regions>`.
Parameters
----------
self : ee.ImageCollection (this)
Image collection to get the time series from.
reducer : ee.Reducer | list[ee.Reducer]
Reducer or list of reducers to use for region reduction.
collection : ee.FeatureCollection
Feature Collection to perform the reductions on. Image reductions are applied to each feature in the collection.
bands : str | list[str], default = None
Selection of bands to get the time series from. Defaults to all bands in the image collection.
scale : numeric, default = None
Nomical scale in meters.
crs : Projection, default = None
The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.
crsTransform : list, default = None
The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix.
This option is mutually exclusive with 'scale', and replaces any transform already set on the projection.
tileScale : numeric, default = 1
A scaling factor used to reduce aggregation tile size; using a larger tileScale (e.g. 2 or 4) may enable computations that run out of memory with the default.
dateColumn : str, default = 'date'
Output name of the date column.
dateFormat : str, default = 'ISO'
Output format of the date column. Defaults to ISO. Available options: 'ms' (for milliseconds), 'ISO' (for ISO Standard Format) or a custom format pattern.
naValue : numeric, default = -9999
Value to use as NA when the region reduction doesn't retrieve a value due to masked pixels.
Returns
-------
ee.FeatureCollection
Time series by regions retrieved as a Feature Collection.
See Also
--------
getTimeSeriesByRegion : Gets the time series by region for the given image collection and geometry (feature or feature collection are also supported)
according to the specified reducer (or reducers).
Examples
--------
>>> import ee, eemont
>>> ee.Initialize()
>>> f1 = ee.Feature(ee.Geometry.Point([3.984770,48.767221]).buffer(50),{'ID':'A'})
>>> f2 = ee.Feature(ee.Geometry.Point([4.101367,48.748076]).buffer(50),{'ID':'B'})
>>> fc = ee.FeatureCollection([f1,f2])
>>> S2 = (ee.ImageCollection('COPERNICUS/S2_SR')
... .filterBounds(fc)
... .filterDate('2020-01-01','2021-01-01')
... .maskClouds()
... .scaleAndOffset()
... .spectralIndices(['EVI','NDVI']))
>>> ts = S2.getTimeSeriesByRegions(reducer = [ee.Reducer.mean(),ee.Reducer.median()],
... collection = fc,
... bands = ['EVI','NDVI'],
... scale = 10)
"""
if bands != None:
if not isinstance(bands, list):
bands = [bands]
self = self.select(bands)
if not isinstance(reducer, list):
reducer = [reducer]
if not isinstance(collection, ee.featurecollection.FeatureCollection):
raise Exception("Parameter collection must be an ee.FeatureCollection!")
props = collection.first().propertyNames()
collections = []
imgList = self.toList(self.size())
for red in reducer:
reducerName = red.getOutputs().get(0)
def reduceImageCollectionByRegions(img):
img = ee.Image(img)
fc = img.reduceRegions(collection, red, scale, crs, crsTransform, tileScale)
if isinstance(bands, list):
if len(bands) == 1:
fc = ee.Algorithms.If(
condition=fc.first().propertyNames().size().eq(props.size()),
trueCase=fc,
falseCase=fc.select(
props.add(reducerName), props.add(bands[0])
),
)
fc = ee.FeatureCollection(fc)
if dateFormat == "ms":
date = ee.Date(img.get("system:time_start")).millis()
elif dateFormat == "ISO":
date = ee.Date(img.get("system:time_start")).format()
else:
date = ee.Date(img.get("system:time_start")).format(dateFormat)
def setProperties(feature):
return feature.set({dateColumn: date, "reducer": reducerName})
return fc.map(setProperties)
collections.append(self.map(reduceImageCollectionByRegions).flatten())
flattenfc = ee.FeatureCollection(collections).flatten()
def setNA(feature):
feature = ee.Algorithms.If(
condition=feature.propertyNames().size().eq(props.size().add(2)),
trueCase=feature.set(
ee.Dictionary.fromLists(bands, [naValue] * len(bands))
),
falseCase=feature,
)
feature = ee.Feature(feature)
return feature
return flattenfc.map(setNA)
[docs]@extend(ee.imagecollection.ImageCollection)
def index(
self,
index="NDVI",
G=2.5,
C1=6.0,
C2=7.5,
L=1.0,
cexp=1.16,
nexp=2.0,
alpha=0.1,
slope=1.0,
intercept=0.0,
kernel="RBF",
sigma="0.5 * (a + b)",
p=2.0,
c=1.0,
online=False,
):
"""Computes one or more spectral indices (indices are added as bands) for an image collection.
Warning
-------------
**Pending Deprecation:** The :code:`index()` method will no longer be available for future versions. Please use :code:`spectralIndices()` instead.
Tip
----------
Check more info about the supported platforms and spectral indices in the :ref:`User Guide<Spectral Indices Computation>`.
Parameters
----------
self : ee.ImageCollection
Image collection to compute indices on. Must be scaled to [0,1].
index : string | list[string], default = 'NDVI'
Index or list of indices to compute.\n
Available options:
- 'vegetation' : Compute all vegetation indices.
- 'burn' : Compute all burn indices.
- 'water' : Compute all water indices.
- 'snow' : Compute all snow indices.
- 'drought' : Compute all drought indices.
- 'kernel' : Compute all kernel indices.
- 'all' : Compute all indices listed below.
Vegetation indices:
- 'BNDVI' : Blue Normalized Difference Vegetation Index.
- 'CIG' : Chlorophyll Index - Green.
- 'CVI' : Chlorophyll Vegetation Index.
- 'EVI' : Enhanced Vegetation Index.
- 'EVI2' : Two-Band Enhanced Vegetation Index.
- 'GARI' : Green Atmospherically Resistant Vegetation Index.
- 'GBNDVI' : Green-Blue Normalized Difference Vegetation Index.
- 'GEMI' : Global Environment Monitoring Index.
- 'GLI' : Green Leaf Index.
- 'GNDVI' : Green Normalized Difference Vegetation Index.
- 'GRNDVI' : Green-Red Normalized Difference Vegetation Index.
- 'GVMI' : Global Vegetation Moisture Index.
- 'MNDVI' : Modified Normalized Difference Vegetation Index.
- 'NDVI' : Normalized Difference Vegetation Index.
- 'NGRDI' : Normalized Green Red Difference Index.
- 'RVI' : Ratio Vegetation Index.
- 'SAVI' : Soil-Adjusted Vegetation Index.
- 'VARI' : Visible Atmospherically Resistant Index.
Burn and fire indices:
- 'BAI' : Burned Area Index.
- 'BAIS2' : Burned Area Index for Sentinel 2.
- 'CSIT' : Char Soil Index Thermal.
- 'NBR' : Normalized Burn Ratio.
- 'NBRT' : Normalized Burn Ratio Thermal.
- 'NDVIT' : Normalized Difference Vegetation Index Thermal
- 'SAVIT' : Soil-Adjusted Vegetation Index Thermal.
Water indices:
- 'MNDWI' : Modified Normalized Difference Water Index.
- 'NDWI' : Normalized Difference Water Index.
Snow indices:
- 'NDSI' : Normalized Difference Snow Index.
Drought indices:
- 'NDDI' : Normalized Difference Drought Index.
Kernel indices:
- 'kEVI' : Kernel Enhanced Vegetation Index.
- 'kNDVI' : Kernel Normalized Difference Vegetation Index.
- 'kRVI' : Kernel Ratio Vegetation Index.
- 'kVARI' : Kernel Visible Atmospherically Resistant Index.
G : float, default = 2.5
Gain factor. Used just for index = 'EVI'.
C1 : float, default = 6.0
Coefficient 1 for the aerosol resistance term. Used just for index = 'EVI'.
C2 : float, default = 7.5
Coefficient 2 for the aerosol resistance term. Used just for index = 'EVI'.
L : float, default = 1.0
Canopy background adjustment. Used just for index = ['EVI','SAVI'].
cexp : float, default = 1.16
Exponent used for OCVI.
nexp : float, default = 2.0
Exponent used for GDVI.
alpha : float, default = 0.1
Weighting coefficient used for WDRVI.
slope : float, default = 1.0
Soil line slope.
intercept : float, default = 0.0
Soil line intercept.
kernel : str, default = 'RBF'
Kernel used for kernel indices.\n
Available options:
- 'linear' : Linear Kernel.
- 'RBF' : Radial Basis Function (RBF) Kernel.
- 'poly' : Polynomial Kernel.
sigma : str | float, default = '0.5 * (a + b)'
Length-scale parameter. Used for kernel = 'RBF'. If str, this must be an expression including 'a' and 'b'. If numeric, this must be positive.
p : float, default = 2.0
Kernel degree. Used for kernel = 'poly'.
c : float, default = 1.0
Free parameter that trades off the influence of higher-order versus lower-order terms in the polynomial kernel.
Used for kernel = 'poly'. This must be greater than or equal to 0.
online : boolean, default = False
Wheter to retrieve the most recent list of indices directly from the GitHub repository and not from the local copy.
.. versionadded:: 0.2.0
Returns
-------
ee.ImageCollection
Image collection with the computed spectral index, or indices, as new bands.
See Also
--------
scale : Scales bands on an image collection.
Examples
--------
>>> import ee, eemont
>>> ee.Authenticate()
>>> ee.Initialize()
>>> S2 = ee.ImageCollection('COPERNICUS/S2_SR').scale()
- Computing one spectral index:
>>> S2.index('NDVI')
- Computing indices with different parameters:
>>> S2.index('SAVI',L = 0.5)
- Computing multiple indices:
>>> S2.index(['NDVI','EVI','GNDVI'])
- Computing a specific group of indices:
>>> S2.index('vegetation')
- Computing kernel indices:
>>> S2.index(['kNDVI'],kernel = 'poly',p = 5)
- Computing all indices:
>>> S2.index('all')
"""
warnings.warn(
"index() will be deprecated in future versions, please use spectralIndices() instead",
PendingDeprecationWarning,
)
return _index(
self,
index,
G,
C1,
C2,
L,
cexp,
nexp,
alpha,
slope,
intercept,
kernel,
sigma,
p,
c,
online,
)
[docs]@extend(ee.imagecollection.ImageCollection)
def spectralIndices(
self,
index="NDVI",
G=2.5,
C1=6.0,
C2=7.5,
L=1.0,
cexp=1.16,
nexp=2.0,
alpha=0.1,
slope=1.0,
intercept=0.0,
kernel="RBF",
sigma="0.5 * (a + b)",
p=2.0,
c=1.0,
online=False,
):
"""Computes one or more spectral indices (indices are added as bands) for an image collection from the Awesome List of Spectral Indices.
Tip
----------
Check more info about the supported platforms and spectral indices in the :ref:`User Guide<Spectral Indices Computation>`.
Parameters
----------
self : ee.ImageCollection
Image collection to compute indices on. Must be scaled to [0,1].
index : string | list[string], default = 'NDVI'
Index or list of indices to compute.\n
Available options:
- 'vegetation' : Compute all vegetation indices.
- 'burn' : Compute all burn indices.
- 'water' : Compute all water indices.
- 'snow' : Compute all snow indices.
- 'drought' : Compute all drought indices.
- 'kernel' : Compute all kernel indices.
- 'all' : Compute all indices listed below.
Vegetation indices:
- 'BNDVI' : Blue Normalized Difference Vegetation Index.
- 'CIG' : Chlorophyll Index - Green.
- 'CVI' : Chlorophyll Vegetation Index.
- 'EVI' : Enhanced Vegetation Index.
- 'EVI2' : Two-Band Enhanced Vegetation Index.
- 'GARI' : Green Atmospherically Resistant Vegetation Index.
- 'GBNDVI' : Green-Blue Normalized Difference Vegetation Index.
- 'GEMI' : Global Environment Monitoring Index.
- 'GLI' : Green Leaf Index.
- 'GNDVI' : Green Normalized Difference Vegetation Index.
- 'GRNDVI' : Green-Red Normalized Difference Vegetation Index.
- 'GVMI' : Global Vegetation Moisture Index.
- 'MNDVI' : Modified Normalized Difference Vegetation Index.
- 'NDVI' : Normalized Difference Vegetation Index.
- 'NGRDI' : Normalized Green Red Difference Index.
- 'RVI' : Ratio Vegetation Index.
- 'SAVI' : Soil-Adjusted Vegetation Index.
- 'VARI' : Visible Atmospherically Resistant Index.
Burn and fire indices:
- 'BAI' : Burned Area Index.
- 'BAIS2' : Burned Area Index for Sentinel 2.
- 'CSIT' : Char Soil Index Thermal.
- 'NBR' : Normalized Burn Ratio.
- 'NBRT' : Normalized Burn Ratio Thermal.
- 'NDVIT' : Normalized Difference Vegetation Index Thermal
- 'SAVIT' : Soil-Adjusted Vegetation Index Thermal.
Water indices:
- 'MNDWI' : Modified Normalized Difference Water Index.
- 'NDWI' : Normalized Difference Water Index.
Snow indices:
- 'NDSI' : Normalized Difference Snow Index.
Drought indices:
- 'NDDI' : Normalized Difference Drought Index.
Kernel indices:
- 'kEVI' : Kernel Enhanced Vegetation Index.
- 'kNDVI' : Kernel Normalized Difference Vegetation Index.
- 'kRVI' : Kernel Ratio Vegetation Index.
- 'kVARI' : Kernel Visible Atmospherically Resistant Index.
G : float, default = 2.5
Gain factor. Used just for index = 'EVI'.
C1 : float, default = 6.0
Coefficient 1 for the aerosol resistance term. Used just for index = 'EVI'.
C2 : float, default = 7.5
Coefficient 2 for the aerosol resistance term. Used just for index = 'EVI'.
L : float, default = 1.0
Canopy background adjustment. Used just for index = ['EVI','SAVI'].
cexp : float, default = 1.16
Exponent used for OCVI.
nexp : float, default = 2.0
Exponent used for GDVI.
alpha : float, default = 0.1
Weighting coefficient used for WDRVI.
slope : float, default = 1.0
Soil line slope.
intercept : float, default = 0.0
Soil line intercept.
kernel : str, default = 'RBF'
Kernel used for kernel indices.\n
Available options:
- 'linear' : Linear Kernel.
- 'RBF' : Radial Basis Function (RBF) Kernel.
- 'poly' : Polynomial Kernel.
sigma : str | float, default = '0.5 * (a + b)'
Length-scale parameter. Used for kernel = 'RBF'. If str, this must be an expression including 'a' and 'b'. If numeric, this must be positive.
p : float, default = 2.0
Kernel degree. Used for kernel = 'poly'.
c : float, default = 1.0
Free parameter that trades off the influence of higher-order versus lower-order terms in the polynomial kernel.
Used for kernel = 'poly'. This must be greater than or equal to 0.
online : boolean, default = False
Wheter to retrieve the most recent list of indices directly from the GitHub repository and not from the local copy.
Returns
-------
ee.ImageCollection
Image collection with the computed spectral index, or indices, as new bands.
See Also
--------
scaleAndOffset : Scales bands on an image collection.
Examples
--------
>>> import ee, eemont
>>> ee.Authenticate()
>>> ee.Initialize()
>>> S2 = ee.ImageCollection('COPERNICUS/S2_SR').scaleAndOffset()
- Computing one spectral index:
>>> S2.spectralIndices('NDVI')
- Computing indices with different parameters:
>>> S2.spectralIndices('SAVI',L = 0.5)
- Computing multiple indices:
>>> S2.spectralIndices(['NDVI','EVI','GNDVI'])
- Computing a specific group of indices:
>>> S2.spectralIndices('vegetation')
- Computing kernel indices:
>>> S2.spectralIndices(['kNDVI'],kernel = 'poly',p = 5)
- Computing all indices:
>>> S2.spectralIndices('all')
"""
return _index(
self,
index,
G,
C1,
C2,
L,
cexp,
nexp,
alpha,
slope,
intercept,
kernel,
sigma,
p,
c,
online,
)
[docs]@extend(ee.imagecollection.ImageCollection)
def maskClouds(
self,
method="cloud_prob",
prob=60,
maskCirrus=True,
maskShadows=True,
scaledImage=False,
dark=0.15,
cloudDist=1000,
buffer=250,
cdi=None,
):
"""Masks clouds and shadows in an image collection (valid just for Surface Reflectance products).
Tip
----------
Check more info about the supported platforms and clouds masking in the :ref:`User Guide<Masking Clouds and Shadows>`.
Parameters
----------
self : ee.ImageCollection [this]
Image collection to mask.
method : string, default = 'cloud_prob'
Method used to mask clouds.\n
Available options:
- 'cloud_prob' : Use cloud probability.
- 'qa' : Use Quality Assessment band.
This parameter is ignored for Landsat products.
prob : numeric [0, 100], default = 60
Cloud probability threshold. Valid just for method = 'prob'. This parameter is ignored for Landsat products.
maskCirrus : boolean, default = True
Whether to mask cirrus clouds. Valid just for method = 'qa'. This parameter is ignored for Landsat products.
maskShadows : boolean, default = True
Whether to mask cloud shadows. For more info see 'Braaten, J. 2020. Sentinel-2 Cloud Masking with s2cloudless. Google Earth Engine, Community Tutorials'.
scaledImage : boolean, default = False
Whether the pixel values are scaled to the range [0,1] (reflectance values). This parameter is ignored for Landsat products.
dark : float [0,1], default = 0.15
NIR threshold. NIR values below this threshold are potential cloud shadows. This parameter is ignored for Landsat products.
cloudDist : int, default = 1000
Maximum distance in meters (m) to look for cloud shadows from cloud edges. This parameter is ignored for Landsat products.
buffer : int, default = 250
Distance in meters (m) to dilate cloud and cloud shadows objects. This parameter is ignored for Landsat products.
cdi : float [-1,1], default = None
Cloud Displacement Index threshold. Values below this threshold are considered potential clouds.
A cdi = None means that the index is not used. For more info see
'Frantz, D., HaS, E., Uhl, A., Stoffels, J., Hill, J. 2018. Improvement of the Fmask algorithm for Sentinel-2 images:
Separating clouds from bright surfaces based on parallax effects. Remote Sensing of Environment 2015: 471-481'.
This parameter is ignored for Landsat products.
Returns
-------
ee.ImageCollection
Cloud-shadow masked image collection.
Notes
-----
This method may mask water as well as clouds for the Sentinel-3 Radiance product.
Examples
--------
>>> import ee, eemont
>>> ee.Authenticate()
>>> ee.Initialize()
>>> S2 = ee.ImageCollection('COPERNICUS/S2_SR').maskClouds(prob = 75,buffer = 300,cdi = -0.5)
"""
return _maskClouds(
self,
method,
prob,
maskCirrus,
maskShadows,
scaledImage,
dark,
cloudDist,
buffer,
cdi,
)
[docs]@extend(ee.imagecollection.ImageCollection)
def scale(self):
"""Scales bands on an image collection.
Warning
-------------
**Pending Deprecation:** The :code:`scale()` method will no longer be available for future versions. Please use :code:`scaleAndOffset()` instead.
Tip
----------
Check more info about the supported platforms and image scaling the :ref:`User Guide<Image Scaling>`.
Parameters
----------
self : ee.ImageCollection (this)
Image collection to scale.
Returns
-------
ee.ImageCollection
Scaled image collection.
Examples
--------
>>> import ee, eemont
>>> ee.Authenticate()
>>> ee.Initialize()
>>> S2 = ee.ImageCollection('COPERNICUS/S2_SR').scale()
"""
warnings.warn(
"scale() will be deprecated in future versions, please use scaleAndOffset() instead",
PendingDeprecationWarning,
)
return _scale_STAC(self)
[docs]@extend(ee.imagecollection.ImageCollection)
def getScaleParams(self):
"""Gets the scale parameters for each band of the image collection.
Parameters
----------
self : ee.ImageCollection (this)
Image collection to get the scale parameters from.
Returns
-------
dict
Dictionary with the scale parameters for each band.
See Also
--------
getOffsetParams : Gets the offset parameters for each band of the image collection.
scaleAndOffset : Scales bands on an image collection according to their scale and offset parameters.
Examples
--------
>>> import ee, eemont
>>> ee.Authenticate()
>>> ee.Initialize()
>>> ee.ImageCollection('MODIS/006/MOD11A2').getScaleParams()
{'Clear_sky_days': 1.0,
'Clear_sky_nights': 1.0,
'Day_view_angl': 1.0,
'Day_view_time': 0.1,
'Emis_31': 0.002,
'Emis_32': 0.002,
'LST_Day_1km': 0.02,
'LST_Night_1km': 0.02,
'Night_view_angl': 1.0,
'Night_view_time': 0.1,
'QC_Day': 1.0,
'QC_Night': 1.0}
"""
return _get_scale_params(self)
[docs]@extend(ee.imagecollection.ImageCollection)
def getOffsetParams(self):
"""Gets the offset parameters for each band of the image collection.
Parameters
----------
self : ee.ImageCollection (this)
Image collection to get the offset parameters from.
Returns
-------
dict
Dictionary with the offset parameters for each band.
See Also
--------
getScaleParams : Gets the scale parameters for each band of the image collection.
scaleAndOffset : Scales bands on an image collection according to their scale and offset parameters.
Examples
--------
>>> import ee, eemont
>>> ee.Authenticate()
>>> ee.Initialize()
>>> ee.ImageCollection('MODIS/006/MOD11A2').getOffsetParams()
{'Clear_sky_days': 0.0,
'Clear_sky_nights': 0.0,
'Day_view_angl': -65.0,
'Day_view_time': 0.0,
'Emis_31': 0.49,
'Emis_32': 0.49,
'LST_Day_1km': 0.0,
'LST_Night_1km': 0.0,
'Night_view_angl': -65.0,
'Night_view_time': 0.0,
'QC_Day': 0.0,
'QC_Night': 0.0}
"""
return _get_offset_params(self)
[docs]@extend(ee.imagecollection.ImageCollection)
def scaleAndOffset(self):
"""Scales bands on an image collection according to their scale and offset parameters.
Tip
----------
Check more info about the supported platforms and image scaling the :ref:`User Guide<Image Scaling>`.
Parameters
----------
self : ee.ImageCollection (this)
Image collection to scale.
Returns
-------
ee.ImageCollection
Scaled image collection.
See Also
--------
getOffsetParams : Gets the offset parameters for each band of the image collection.
getScaleParams : Gets the scale parameters for each band of the image collection.
Examples
--------
>>> import ee, eemont
>>> ee.Authenticate()
>>> ee.Initialize()
>>> S2 = ee.ImageCollection('COPERNICUS/S2_SR').scaleAndOffset()
"""
return _scale_STAC(self)
[docs]@extend(ee.imagecollection.ImageCollection)
def preprocess(self, **kwargs):
"""Pre-processes the image collection: masks clouds and shadows, and scales and offsets the image collection.
Tip
----------
Check more info here about the supported platforms, :ref:`Image Scaling<Image Scaling>` and :ref:`Masking Clouds and Shadows<Masking Clouds and Shadows>`.
Parameters
----------
self : ee.ImageCollection [this]
Image Collection to pre-process.
**kwargs :
Keywords arguments for maskClouds().
Returns
-------
ee.ImageCollection
Pre-processed image collection.
See Also
--------
getScaleParams : Gets the scale parameters for each band of the image collection.
getOffsetParams : Gets the offset parameters for each band of the image collection.
scaleAndOffset : Scales bands on an image collection according to their scale and offset parameters.
maskClouds : Masks clouds and shadows in an image collection.
Examples
--------
>>> import ee, eemont
>>> ee.Authenticate()
>>> ee.Initialize()
>>> S2 = ee.ImageCollection('COPERNICUS/S2_SR').preprocess()
"""
return _preprocess(self, **kwargs)
[docs]@extend(ee.imagecollection.ImageCollection)
def getSTAC(self):
"""Gets the STAC of the image collection.
Parameters
----------
self : ee.ImageCollection [this]
Image Collection to get the STAC from.
Returns
-------
dict
STAC of the image collection.
Examples
--------
>>> import ee, eemont
>>> ee.Authenticate()
>>> ee.Initialize()
>>> ee.ImageCollection('COPERNICUS/S2_SR').getSTAC()
{'stac_version': '1.0.0-rc.2',
'type': 'Collection',
'stac_extensions': ['https://stac-extensions.github.io/eo/v1.0.0/schema.json'],
'id': 'COPERNICUS/S2_SR',
'title': 'Sentinel-2 MSI: MultiSpectral Instrument, Level-2A',
'gee:type': 'image_collection',
...}
"""
return _getSTAC(self)
[docs]@extend(ee.imagecollection.ImageCollection)
def getDOI(self):
"""Gets the DOI of the image collection, if available.
Parameters
----------
self : ee.ImageCollection [this]
Image Collection to get the DOI from.
Returns
-------
str
DOI of the ee.ImageCollection dataset.
See Also
--------
getCitation : Gets the citation of the image collection, if available.
Examples
--------
>>> import ee, eemont
>>> ee.Authenticate()
>>> ee.Initialize()
>>> ee.ImageCollection('NASA/GPM_L3/IMERG_V06').getDOI()
'10.5067/GPM/IMERG/3B-HH/06'
"""
return _getDOI(self)
[docs]@extend(ee.imagecollection.ImageCollection)
def getCitation(self):
"""Gets the citation of the image collection, if available.
Parameters
----------
self : ee.ImageCollection [this]
Image Collection to get the citation from.
Returns
-------
str
Citation of the ee.ImageCollection dataset.
See Also
--------
getDOI : Gets the DOI of the image collection, if available.
Examples
--------
>>> import ee, eemont
>>> ee.Authenticate()
>>> ee.Initialize()
>>> ee.ImageCollection('NASA/GPM_L3/IMERG_V06').getCitation()
'Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2019),
GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06, Greenbelt,
MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date],
[doi:10.5067/GPM/IMERG/3B-HH/06](https://doi.org/10.5067/GPM/IMERG/3B-HH/06)'
"""
return _getCitation(self)