import ee
import numpy as np
import warnings
from .common import _get_indices
from .common import _get_platform
from .common import _get_scale_method
from .common import _get_expression_map
def _extend_eeImageCollection():
"""Decorator. Extends the ee.ImageCollection class."""
return lambda f: (setattr(ee.imagecollection.ImageCollection,f.__name__,f) or f)
[docs]@_extend_eeImageCollection()
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.
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_eeImageCollection()
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).
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.
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()
... .scale()
... .index(['EVI','NDVI']))
>>> ts = S2.getTimeSeriesByRegion(reducer = [ee.Reducer.mean(),ee.Reducer.median()],
... geometry = fc,
... bands = ['EVI','NDVI'],
... scale = 10)
See Also
--------
getTimeSeriesByRegions : Gets the time series by regions for the given image collection and feature collection according to the specified reducer (or reducers).
'''
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_eeImageCollection()
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).
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.
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()
... .scale()
... .index(['EVI','NDVI']))
>>> ts = S2.getTimeSeriesByRegions(reducer = [ee.Reducer.mean(),ee.Reducer.median()],
... collection = fc,
... bands = ['EVI','NDVI'],
... scale = 10)
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).
'''
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_eeImageCollection()
def index(self,index = 'NDVI',G = 2.5,C1 = 6.0,C2 = 7.5,L = 1.0):
'''Computes one or more spectral indices (indices are added as bands) for an image collection.
Parameters
----------
self : ee.ImageCollection
Image collection to compute indices on. Must be scaled to [0,1]. Check the supported platforms in User Guide > Spectral Indices > Supported Platforms.
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.
- '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.
- 'GBNDVI' : Green-Blue Normalized Difference Vegetation Index.
- 'GNDVI' : Green Normalized Difference Vegetation Index.
- 'GRNDVI' : Green-Red Normalized Difference Vegetation 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.
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.
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'].
Returns
-------
ee.ImageCollection
Image collection with the computed spectral index, or indices, as new bands.
Examples
--------
>>> import ee, eemont
>>> ee.Initialize()
>>> S2 = ee.ImageCollection('COPERNICUS/S2_SR').scale().index(['NDVI','EVI','GNDVI'])
See Also
--------
scale : Scales bands on an image collection.
'''
platformDict = _get_platform(self)
additionalParameters = {
'g': float(G),
'C1': float(C1),
'C2': float(C2),
'L': float(L),
}
spectralIndices = _get_indices()
indicesNames = list(spectralIndices.keys())
if not isinstance(index, list):
if index == 'all':
index = list(spectralIndices.keys())
elif index in ['vegetation','burn','water','snow']:
temporalListOfIndices = []
for idx in indicesNames:
if spectralIndices[idx]['type'] == index:
temporalListOfIndices.append(idx)
index = temporalListOfIndices
else:
index = [index]
for idx in index:
if idx not in list(spectralIndices.keys()):
warnings.warn("Index " + idx + " is not a built-in index and it won't be computed!",Warning)
else:
def temporalIndex(img):
lookupDic = _get_expression_map(img, platformDict)
lookupDic = {**lookupDic, **additionalParameters}
if all(band in list(lookupDic.keys()) for band in spectralIndices[idx]['requires']):
return img.addBands(img.expression(spectralIndices[idx]['formula'],lookupDic).rename(idx))
else:
warnings.warn("This platform doesn't have the required bands for " + idx + " computation!",Warning)
return img
self = self.map(temporalIndex)
return self
[docs]@_extend_eeImageCollection()
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).
Parameters
----------
self : ee.ImageCollection [this]
Image collection to mask. Check the supported platforms in User Guide > Masking Clouds and Shadows > Supported Platforms.
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.
Examples
--------
>>> import ee, eemont
>>> ee.Initialize()
>>> S2 = ee.ImageCollection('COPERNICUS/S2_SR').maskClouds(prob = 75,buffer = 300,cdi = -0.5)
Notes
-----
This method may mask water as well as clouds for the Sentinel-3 Radiance product.
'''
def S3(args):
qa = args.select('quality_flags')
notCloud = qa.bitwiseAnd(1 << 27).eq(0);
return args.updateMask(notCloud)
def S2(args):
def cloud_prob(img):
clouds = ee.Image(img.get('cloud_mask')).select('probability')
isCloud = clouds.gte(prob).rename('CLOUD_MASK')
return img.addBands(isCloud)
def QA(img):
qa = img.select('QA60')
cloudBitMask = 1 << 10
isCloud = qa.bitwiseAnd(cloudBitMask).eq(0)
if maskCirrus:
cirrusBitMask = 1 << 11
isCloud = isCloud.And(qa.bitwiseAnd(cirrusBitMask).eq(0))
isCloud = isCloud.Not().rename('CLOUD_MASK')
return img.addBands(isCloud)
def CDI(img):
idx = img.get('system:index')
S2TOA = ee.ImageCollection('COPERNICUS/S2').filter(ee.Filter.eq('system:index',idx)).first()
CloudDisplacementIndex = ee.Algorithms.Sentinel2.CDI(S2TOA)
isCloud = CloudDisplacementIndex.lt(cdi).rename('CLOUD_MASK_CDI')
return img.addBands(isCloud)
def get_shadows(img):
notWater = img.select('SCL').neq(6)
if not scaledImage:
darkPixels = img.select('B8').lt(dark * 1e4).multiply(notWater)
else:
darkPixels = img.select('B8').lt(dark).multiply(notWater)
shadowAzimuth = ee.Number(90).subtract(ee.Number(img.get('MEAN_SOLAR_AZIMUTH_ANGLE')))
cloudProjection = img.select('CLOUD_MASK').directionalDistanceTransform(shadowAzimuth,cloudDist/10)
cloudProjection = cloudProjection.reproject(crs = img.select(0).projection(),scale = 10).select('distance').mask()
isShadow = cloudProjection.multiply(darkPixels).rename('SHADOW_MASK')
return img.addBands(isShadow)
def clean_dilate(img):
isCloudShadow = img.select('CLOUD_MASK')
if cdi != None:
isCloudShadow = isCloudShadow.And(img.select('CLOUD_MASK_CDI'))
if maskShadows:
isCloudShadow = isCloudShadow.add(img.select('SHADOW_MASK')).gt(0)
isCloudShadow = isCloudShadow.focal_min(20,units = 'meters').focal_max(buffer*2/10,units = 'meters').rename('CLOUD_SHADOW_MASK')
return img.addBands(isCloudShadow)
def apply_mask(img):
return img.updateMask(img.select('CLOUD_SHADOW_MASK').Not())
if method == 'cloud_prob':
S2Clouds = ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')
fil = ee.Filter.equals(leftField = 'system:index',rightField = 'system:index')
S2WithCloudMask = ee.Join.saveFirst('cloud_mask').apply(args,S2Clouds,fil)
S2Masked = ee.ImageCollection(S2WithCloudMask).map(cloud_prob)
elif method == 'qa':
S2Masked = args.map(QA)
if cdi != None:
S2Masked = S2Masked.map(CDI)
if maskShadows:
S2Masked = S2Masked.map(get_shadows)
S2Masked = S2Masked.map(clean_dilate).map(apply_mask)
return S2Masked
def L8(args):
cloudsBitMask = (1 << 5)
qa = args.select('pixel_qa')
mask = qa.bitwiseAnd(cloudsBitMask).eq(0)
if maskShadows:
cloudShadowBitMask = (1 << 3)
mask = mask.And(qa.bitwiseAnd(cloudShadowBitMask).eq(0))
return args.updateMask(mask)
def L457(args):
qa = args.select('pixel_qa')
cloud = qa.bitwiseAnd(1 << 5).And(qa.bitwiseAnd(1 << 7))
if maskShadows:
cloud = cloud.Or(qa.bitwiseAnd(1 << 3))
mask2 = args.mask().reduce(ee.Reducer.min());
return args.updateMask(cloud.Not()).updateMask(mask2);
def MOD09GA(args):
qa = args.select('state_1km')
notCloud = qa.bitwiseAnd(1 << 0).eq(0)
if maskShadows:
notCloud = notCloud.And(qa.bitwiseAnd(1 << 2).eq(0))
if maskCirrus:
notCloud = notCloud.And(qa.bitwiseAnd(1 << 8).eq(0))
return args.updateMask(notCloud)
def MCD15A3H(args):
qa = args.select('FparExtra_QC')
notCloud = qa.bitwiseAnd(1 << 5).eq(0)
if maskShadows:
notCloud = notCloud.And(qa.bitwiseAnd(1 << 6).eq(0))
if maskCirrus:
notCloud = notCloud.And(qa.bitwiseAnd(1 << 4).eq(0))
return args.updateMask(notCloud)
def MOD09Q1(args):
qa = args.select('State')
notCloud = qa.bitwiseAnd(1 << 0).eq(0)
if maskShadows:
notCloud = notCloud.And(qa.bitwiseAnd(1 << 2).eq(0))
if maskCirrus:
notCloud = notCloud.And(qa.bitwiseAnd(1 << 8).eq(0))
return args.updateMask(notCloud)
def MOD09A1(args):
qa = args.select('StateQA')
notCloud = qa.bitwiseAnd(1 << 0).eq(0)
if maskShadows:
notCloud = notCloud.And(qa.bitwiseAnd(1 << 2).eq(0))
if maskCirrus:
notCloud = notCloud.And(qa.bitwiseAnd(1 << 8).eq(0))
return args.updateMask(notCloud)
def MOD17A2H(args):
qa = args.select('Psn_QC')
notCloud = qa.bitwiseAnd(1 << 3).eq(0)
return args.updateMask(notCloud)
def MOD16A2(args):
qa = args.select('ET_QC')
notCloud = qa.bitwiseAnd(1 << 3).eq(0)
return args.updateMask(notCloud)
def MOD13Q1A1(args):
qa = args.select('SummaryQA')
notCloud = qa.bitwiseAnd(1 << 0).eq(0)
return args.updateMask(notCloud)
def MOD13A2(args):
qa = args.select('SummaryQA')
notCloud = qa.eq(0)
return args.updateMask(notCloud)
lookup = {
'COPERNICUS/S3': S3,
'COPERNICUS/S2': S2,
'LANDSAT/LC08': L8,
'LANDSAT/LE07': L457,
'LANDSAT/LT05': L457,
'LANDSAT/LT04': L457,
'MODIS/006/MOD09GA': MOD09GA,
'MODIS/006/MCD15A3H': MCD15A3H,
'MODIS/006/MOD09Q1': MOD09Q1,
'MODIS/006/MOD09A1': MOD09A1,
'MODIS/006/MOD17A2H': MOD17A2H,
'MODIS/006/MOD16A2': MOD16A2,
'MODIS/006/MOD13Q1': MOD13Q1A1,
'MODIS/006/MOD13A1': MOD13Q1A1,
'MODIS/006/MOD13A2': MOD13A2,
'MODIS/006/MYD09GA': MOD09GA,
'MODIS/006/MYD09Q1': MOD09Q1,
'MODIS/006/MYD09A1': MOD09A1,
'MODIS/006/MYD17A2H': MOD17A2H,
'MODIS/006/MYD16A2': MOD16A2,
'MODIS/006/MYD13Q1': MOD13Q1A1,
'MODIS/006/MYD13A1': MOD13Q1A1,
'MODIS/006/MYD13A2': MOD13A2
}
platformDict = _get_platform(self)
if platformDict['platform'] not in list(lookup.keys()):
raise Exception("Sorry, satellite platform not supported for cloud masking!")
if platformDict['platform'] == 'COPERNICUS/S2':
maskedImageCollection = lookup[platformDict['platform']](self)
else:
maskedImageCollection = self.map(lookup[platformDict['platform']])
return maskedImageCollection
[docs]@_extend_eeImageCollection()
def scale(self):
'''Scales bands on an image collection.
Parameters
----------
self : ee.ImageCollection (this)
Image collection to scale. Check the supported platforms in User Guide > Image Scaling > Supported Platforms.
Returns
-------
ee.ImageCollection
Scaled image collection.
Examples
--------
>>> import ee, eemont
>>> ee.Initialize()
>>> S2 = ee.ImageCollection('COPERNICUS/S2_SR').scale()
'''
platformDict = _get_platform(self)
lookup = _get_scale_method(platformDict)
if platformDict['platform'] not in list(lookup.keys()):
raise Exception("Sorry, satellite platform not supported for scaling!")
scaledImageCollection = self.map(lookup[platformDict['platform']])
return scaledImageCollection