'scale': 10,
'region': roi_geometry
}
task = ee.batch.Export.image(s2_median, 's2_median %d' % number,
task_config)
task.start()
return 1
listOfMarkers = ee.FeatureCollection([
ee.Feature(ee.Geometry.Point([6.8115234375, 60.88836817267309]),
{"system:index": "0"}),
ee.Feature(ee.Geometry.Point([15.1611328125, 57.469327688204295]),
{"system:index": "1"}),
ee.Feature(ee.Geometry.Point([27.0703125, 62.95584745563692]),
{"system:index": "2"})
])
listOfMarkers = listOfMarkers.sort('system:index')
#Convert FeatureCollection of marker points to list;
#maximum number of markers is 100
listOfMarkersList = listOfMarkers.toList(100)
numberOfMarkers = listOfMarkersList.size().getInfo()
#Loop through list of markers for export of the individual ROIs
i = 0
while (i < numberOfMarkers):
exportROIimages(ee.Feature(listOfMarkersList.get(i)), i)
i += 1
# %%
# Add Earth Engine dataset
# LSIB: Large Scale International Boundary Polygons, Simplified
# dataset = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017')
# styleParams = {
# 'fillColor': 'b5ffb4',
# 'color': '00909F',
# 'width': 3.0,
# }
# countries = dataset.style(**styleParams)
# Map.addLayer(countries, {}, 'USDOS/LSIB_SIMPLE/2017')
# LSIB: Large Scale International Boundary Polygons, Detailed
dataset = ee.FeatureCollection('USDOS/LSIB/2013')
visParams = {
'palette': ['f5ff64', 'b5ffb4', 'beeaff', 'ffc0e8', '8e8dff', 'adadad'],
'min': 0.0,
'max': 894.0,
'opacity': 0.8,
}
image = ee.Image().float().paint(dataset, 'iso_num')
Map.addLayer(image, visParams, 'USDOS/LSIB/2013')
# Map.addLayer(dataset, {}, 'for Inspector', False)
# %%
"""
## Display Earth Engine data layers
"""
def getHistoricalMap(self,
shape,
startYear,
endYear,
startMonth,
endMonth,
method='discrete',
climatology=True,
month=None,
defringe=True,
pcnt_perm=40,
pcnt_temp=8,
water_thresh=0.35,
ndvi_thresh=0.5,
hand_thresh=30,
cloud_thresh=10,
algorithm='SWT',
wcolor='#00008b'):
# def spatialSelect(feature):
# test = ee.Algorithms.If(geom.contains(feature.geometry()),feature,None)
# return ee.Feature(test)
#countries = landShp.filterBounds(geom).map(spatialSelect,True)
if shape:
shape = shape.replace('["', '[')
shape = shape.replace('"]', ']')
shape = shape.replace('","', ',')
shape = ee.FeatureCollection(eval(shape))
else:
shape = self.REGION
if climatology:
if month == None:
raise ValueError(
'Month needs to be defined to calculate climatology')
if algorithm == 'SWT':
iniTime = '{}-01-01'.format(startYear)
endTime = '{}-12-31'.format(endYear)
# get images
images = getLandsatCollection(geom, iniTime, endTime, climatology,
month, defringe, cloud_thresh)
# Height Above Nearest Drainage (HAND)
HAND = ee.Image('users/arjenhaag/SERVIR-Mekong/HAND_MERIT')
# get HAND mask
HAND_mask = HAND.gt(float(hand_thresh))
water = SurfaceWaterAlgorithm(geom, images, pcnt_perm, pcnt_temp,
water_thresh, ndvi_thresh,
HAND_mask).clip(countries)
waterMap = getTileLayerUrl(
water.updateMask(water.eq(2)).visualize(
min=0, max=2, palette='#ffffff,#9999ff,' + wcolor))
elif algorithm == 'JRC':
water = self.JRCAlgorithm(shape, startYear, endYear, startMonth,
endMonth, method).clip(shape)
#water = JRCAlgorithm(geom,iniTime,endTime).clip(countries)
waterMap = self.getTileLayerUrl(
water.visualize(min=0,
max=1,
bands='water',
palette='#ffffff,' + wcolor))
else:
raise NotImplementedError(
'Selected algorithm string not available. Options are: "SWT" or "JRC"'
)
return waterMap
def appendBand(current, previous):
# Rename the band
previous = ee.Image(previous)
# This dataset only has a single layer, regions encoded with a number indicating aggricultural ground.
current = current.select([0])
# Append it to the result (Note: only return current item on first element/iteration)
accum = ee.Algorithms.If(ee.Algorithms.IsEqual(previous, None), current,
previous.addBands(ee.Image(current)))
# Return the accumulation
return accum
# County shape layer in Google Earth Engine Feature Collection.
# The public instance is fine but it is important that the
county_region = ee.FeatureCollection(
'ft:1S4EB6319wWW2sWQDPhDvmSBIVrD3iEmCLYB7nMM')
imgcoll = ee.ImageCollection('MODIS/051/MCD12Q1') \
.filterBounds(ee.Geometry.Rectangle(-106.5, 50,-64, 23))\
.filterDate('2002-12-31','2016-8-4')
img = imgcoll.iterate(appendBand)
img = ee.Image(img)
img_0 = ee.Image(ee.Number(-100))
img_16000 = ee.Image(ee.Number(16000))
img = img.min(img_16000)
img = img.max(img_0)
for loc1, loc2, lat, lon in locations.values:
fname = '{}_{}'.format(int(loc1), int(loc2))
# %%
Map = emap.Map(center=[40, -100], zoom=4)
Map.add_basemap('ROADMAP') # Add Google Map
Map
# %%
"""
## Add Earth Engine Python script
"""
# %%
# Add Earth Engine dataset
Map.setCenter(-122.45, 37.75, 13)
bart = ee.FeatureCollection('ft:1xCCZkVn8DIkB7i7RVkvsYWxAxsdsQZ6SbD9PCXw')
parks = ee.FeatureCollection('ft:10KC6VfBWMUvNcuxU7mbSEg__F_4UVe9uDkCldBw')
buffered_bart = bart.map(lambda f: f.buffer(2000))
join_filter = ee.Filter.withinDistance(2000, '.geo', None, '.geo')
close_parks = ee.Join.simple().apply(parks, bart, join_filter)
Map.addLayer(buffered_bart, {'color': 'b0b0b0'}, "BART Stations")
Map.addLayer(close_parks, {'color': '008000'}, "Parks")
# %%
"""
## Display Earth Engine data layers
"""
# %%
# In[21]:
#geemap.js_snippet_to_py(js_snippet, add_new_cell=True, import_ee=True, import_geemap=True, show_map=True)
# ### Load in collections and required images
# In[3]:
# load collections and required images
collection = ee.ImageCollection('MODIS/006/MOD09A1').filterDate('2000-01-01', '2020-12-31')
forestmask = ee.Image("users/marcogirardello/phenoutils/mask_unchanged_500m")
smallgrid = ee.FeatureCollection('users/marcogirardello/phenoutils/grid_export_phenology3')
# ### <span style="color:blue">Pre-processing step 1: filtering data by quality flags and calculated NDVI.</span>
# This include snow, cloud, fire, cloud shadows and the land/water mask
# In[4]:
# end and start date of period of interest
start_date = ee.Date.fromYMD(2001, 1, 1)
end_date = ee.Date.fromYMD(2019, 12, 31)
# In[5]:
# %%
Map = folium.Map(location=[40, -100], zoom_start=4)
Map.setOptions('HYBRID')
# %%
'''
## Add Earth Engine Python script
'''
# %%
# Load input imagery: Landsat 7 5-year composite.
image = ee.Image('LANDSAT/LE7_TOA_5YEAR/2008_2012')
# Load a FeatureCollection of counties in Maine.
maineCounties = ee.FeatureCollection('TIGER/2016/Counties') \
.filter(ee.Filter.eq('STATEFP', '23'))
# Add reducer output to the Features in the collection.
maineMeansFeatures = image.reduceRegions(**{
'collection': maineCounties,
'reducer': ee.Reducer.mean(),
'scale': 30,
})
feature = ee.Feature(maineMeansFeatures.first()).select(image.bandNames())
# print(feature.getInfo())
properties = feature.propertyNames()
# Print the first feature, to illustrate the result.
print(feature.toDictionary(properties).getInfo())
## Create an interactive map
The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function.
"""
# %%
Map = geemap.Map(center=[40, -100], zoom=4)
Map
# %%
"""
## Add Earth Engine Python script
"""
# %%
# Add Earth Engine dataset
dataset = ee.FeatureCollection('TIGER/2010/Tracts_DP1')
visParams = {
'min': 0,
'max': 4000,
'opacity': 0.8,
}
# Turn the strings into numbers
dataset = dataset.map(
lambda f: f.set('shape_area', ee.Number.parse(f.get('dp0010001'))))
# Map.setCenter(-103.882, 43.036, 8)
image = ee.Image().float().paint(dataset, 'dp0010001')
Map.addLayer(image, visParams, 'TIGER/2010/Tracts_DP1')
# Map.addLayer(dataset, {}, 'for Inspector', False)
import ee
import geemap
# Create a map centered at (lat, lon).
Map = geemap.Map(center=[40, -100], zoom=4)
# get a single feature
countries = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017")
country = countries.filter(ee.Filter.eq('country_na', 'Ukraine'))
Map.addLayer(country, {'color': 'orange'}, 'feature collection layer')
# TEST: center feature on a map
Map.centerObject(country)
# Display the map.
Map
#!/usr/bin/env python
"""Buffer Example.
Display the area within 2 kilometers of any San Francisco BART station.
"""
import ee
import geemap
# Create a map centered at (lat, lon).
Map = geemap.Map(center=[40, -100], zoom=4)
Map.setCenter(-122.4, 37.7, 11)
bart_stations = ee.FeatureCollection('GOOGLE/EE/DEMOS/bart-locations')
buffered = bart_stations.map(lambda f: f.buffer(2000))
unioned = buffered.union()
Map.addLayer(unioned, {'color': '800080'}, "BART stations")
# Display the map.
Map
import ee
import config
import oauth2client
ee.Initialize(config.EE_CREDENTIALS)
IMAGE_COLLECTION = ee.ImageCollection('JRC/GSW1_0/MonthlyHistory')
watershed = ee.FeatureCollection(
"ft:1vTonxuDFs7rBkt02H3ZzFy1SSFsNPhlPlRE15pVr", "geometry")
lulc = ee.Image("users/servirmekong/california/RegionalLC")
pal = '6f6f6f,aec3d4,b1f9ff,111149,287463,152106,c3aa69,9ad2a5,7db087,486f50,387242,115420,cc0013,8dc33b,ffff00,a1843b,cec2a5,674c06,3bc3b2,f4a460,800080'
def showLandCover(mylegend):
print "legend", mylegend
mymask = ee.Image(0)
# enable all checked boxes
for value in mylegend:
print value
tempmask = lulc.eq(ee.Number(int(value)))
mymask = mymask.add(tempmask)
print "returning"
return lulc.updateMask(mymask).getMapId({
'min': '0',
'max': '20',
'palette': pal
# %%
Map = emap.Map(center=[40, -100], zoom=4)
Map.add_basemap('ROADMAP') # Add Google Map
Map
# %%
"""
## Add Earth Engine Python script
"""
# %%
# Add Earth Engine dataset
# Create a FeatureCollection from an Earth Engine Table.
# Load census roads.
roads = ee.FeatureCollection('TIGER/2016/Roads')
# Get only interstates.
interstates = roads.filter(ee.Filter.eq('rttyp', 'I'))
# Get only surface roads.
surfaceRoads = roads.filter(ee.Filter.eq('rttyp', 'M'))
# Display the roads in different colors.
Map.addLayer(surfaceRoads, {'color': 'gray'}, 'surface roads')
Map.addLayer(interstates, {'color': 'red'}, 'interstates')
# %%
"""
## Display Earth Engine data layers
"""
def main(ini_path=None,
overwrite_flag=False,
delay_time=0,
gee_key_file=None,
max_ready=-1,
reverse_flag=False):
"""Compute monthly Tcorr images by WRS2 tile
Parameters
----------
ini_path : str
Input file path.
overwrite_flag : bool, optional
If True, overwrite existing files (the default is False).
delay_time : float, optional
Delay time in seconds between starting export tasks (or checking the
number of queued tasks, see "max_ready" parameter). The default is 0.
gee_key_file : str, None, optional
Earth Engine service account JSON key file (the default is None).
max_ready: int, optional
Maximum number of queued "READY" tasks. The default is -1 which is
implies no limit to the number of tasks that will be submitted.
reverse_flag : bool, optional
If True, process WRS2 tiles in reverse order.
"""
logging.info('\nCompute monthly Tcorr images by WRS2 tile')
# TODO: Read from INI
study_area_extent = [-124, 35, -119, 42]
# study_area_extent = [-121.7, 39, -121.7, 39]
# study_area_extent = None
ini = utils.read_ini(ini_path)
model_name = 'SSEBOP'
# model_name = ini['INPUTS']['et_model'].upper()
tmax_name = ini[model_name]['tmax_source']
export_id_fmt = 'tcorr_scene_{product}_{wrs2}_month{month:02d}'
asset_id_fmt = '{coll_id}/{wrs2}_month{month:02d}'
tcorr_monthly_coll_id = '{}/{}_monthly'.format(
ini['EXPORT']['export_coll'], tmax_name.lower())
wrs2_coll_id = 'projects/earthengine-legacy/assets/' \
'projects/usgs-ssebop/wrs2_descending_custom'
wrs2_tile_field = 'WRS2_TILE'
# wrs2_path_field = 'ROW'
# wrs2_row_field = 'PATH'
try:
wrs2_tiles = str(ini['INPUTS']['wrs2_tiles'])
wrs2_tiles = [x.strip() for x in wrs2_tiles.split(',')]
wrs2_tiles = sorted([x.lower() for x in wrs2_tiles if x])
except KeyError:
wrs2_tiles = []
logging.debug(' wrs2_tiles: not set in INI, defaulting to []')
except Exception as e:
raise e
try:
study_area_extent = str(ini['INPUTS']['study_area_extent']) \
.replace('[', '').replace(']', '').split(',')
study_area_extent = [float(x.strip()) for x in study_area_extent]
except KeyError:
study_area_extent = None
logging.debug(' study_area_extent: not set in INI')
except Exception as e:
raise e
# TODO: Add try/except blocks and default values?
collections = [x.strip() for x in ini['INPUTS']['collections'].split(',')]
cloud_cover = float(ini['INPUTS']['cloud_cover'])
min_pixel_count = float(ini['TCORR']['min_pixel_count'])
min_scene_count = float(ini['TCORR']['min_scene_count'])
if (tmax_name.upper() == 'CIMIS'
and ini['INPUTS']['end_date'] < '2003-10-01'):
logging.error(
'\nCIMIS is not currently available before 2003-10-01, exiting\n')
sys.exit()
elif (tmax_name.upper() == 'DAYMET'
and ini['INPUTS']['end_date'] > '2018-12-31'):
logging.warning('\nDAYMET is not currently available past 2018-12-31, '
'using median Tmax values\n')
# sys.exit()
# elif (tmax_name.upper() == 'TOPOWX' and
# ini['INPUTS']['end_date'] > '2017-12-31'):
# logging.warning(
# '\nDAYMET is not currently available past 2017-12-31, '
# 'using median Tmax values\n')
# # sys.exit()
# Extract the model keyword arguments from the INI
# Set the property name to lower case and try to cast values to numbers
model_args = {
k.lower(): float(v) if utils.is_number(v) else v
for k, v in dict(ini[model_name]).items()
}
# et_reference_args = {
# k: model_args.pop(k)
# for k in [k for k in model_args.keys() if k.startswith('et_reference_')]}
logging.info('\nInitializing Earth Engine')
if gee_key_file:
logging.info(
' Using service account key file: {}'.format(gee_key_file))
# The "EE_ACCOUNT" parameter is not used if the key file is valid
ee.Initialize(ee.ServiceAccountCredentials('x', key_file=gee_key_file),
use_cloud_api=True)
else:
ee.Initialize(use_cloud_api=True)
logging.debug('\nTmax properties')
tmax_source = tmax_name.split('_', 1)[0]
tmax_version = tmax_name.split('_', 1)[1]
tmax_coll_id = 'projects/earthengine-legacy/assets/' \
'projects/usgs-ssebop/tmax/{}'.format(tmax_name.lower())
tmax_coll = ee.ImageCollection(tmax_coll_id)
tmax_mask = ee.Image(tmax_coll.first()).select([0]).multiply(0)
logging.debug(' Collection: {}'.format(tmax_coll_id))
logging.debug(' Source: {}'.format(tmax_source))
logging.debug(' Version: {}'.format(tmax_version))
logging.debug('\nExport properties')
export_info = utils.get_info(ee.Image(tmax_mask))
if 'daymet' in tmax_name.lower():
# Custom smaller extent for DAYMET focused on CONUS
export_extent = [-1999750, -1890500, 2500250, 1109500]
export_shape = [4500, 3000]
export_geo = [1000, 0, -1999750, 0, -1000, 1109500]
# Custom medium extent for DAYMET of CONUS, Mexico, and southern Canada
# export_extent = [-2099750, -3090500, 2900250, 1909500]
# export_shape = [5000, 5000]
# export_geo = [1000, 0, -2099750, 0, -1000, 1909500]
export_crs = export_info['bands'][0]['crs']
else:
export_crs = export_info['bands'][0]['crs']
export_geo = export_info['bands'][0]['crs_transform']
export_shape = export_info['bands'][0]['dimensions']
# export_geo = ee.Image(tmax_mask).projection().getInfo()['transform']
# export_crs = ee.Image(tmax_mask).projection().getInfo()['crs']
# export_shape = ee.Image(tmax_mask).getInfo()['bands'][0]['dimensions']
export_extent = [
export_geo[2], export_geo[5] + export_shape[1] * export_geo[4],
export_geo[2] + export_shape[0] * export_geo[0], export_geo[5]
]
export_geom = ee.Geometry.Rectangle(export_extent,
proj=export_crs,
geodesic=False)
logging.debug(' CRS: {}'.format(export_crs))
logging.debug(' Extent: {}'.format(export_extent))
logging.debug(' Geo: {}'.format(export_geo))
logging.debug(' Shape: {}'.format(export_shape))
if study_area_extent is None:
if 'daymet' in tmax_name.lower():
# CGM - For now force DAYMET to a slightly smaller "CONUS" extent
study_area_extent = [-125, 25, -65, 49]
# study_area_extent = [-125, 25, -65, 52]
elif 'cimis' in tmax_name.lower():
study_area_extent = [-124, 35, -119, 42]
else:
# TODO: Make sure output from bounds is in WGS84
study_area_extent = tmax_mask.geometry().bounds().getInfo()
logging.debug(f'\nStudy area extent not set in INI, '
f'default to {study_area_extent}')
study_area_geom = ee.Geometry.Rectangle(study_area_extent,
proj='EPSG:4326',
geodesic=False)
if not ee.data.getInfo(tcorr_monthly_coll_id):
logging.info('\nExport collection does not exist and will be built'
'\n {}'.format(tcorr_monthly_coll_id))
input('Press ENTER to continue')
ee.data.createAsset({'type': 'IMAGE_COLLECTION'},
tcorr_monthly_coll_id)
# Get current asset list
logging.debug('\nGetting GEE asset list')
asset_list = utils.get_ee_assets(tcorr_monthly_coll_id)
# if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
# pprint.pprint(asset_list[:10])
# Get current running tasks
tasks = utils.get_ee_tasks()
if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
logging.debug(' Tasks: {}\n'.format(len(tasks)))
input('ENTER')
# if cron_flag:
# # CGM - This seems like a silly way of getting the date as a datetime
# # Why am I doing this and not using the commented out line?
# end_dt = datetime.date.today().strftime('%Y-%m-%d')
# end_dt = datetime.datetime.strptime(end_dt, '%Y-%m-%d')
# end_dt = end_dt + datetime.timedelta(days=-4)
# # end_dt = datetime.datetime.today() + datetime.timedelta(days=-1)
# start_dt = end_dt + datetime.timedelta(days=-64)
# else:
start_dt = datetime.datetime.strptime(ini['INPUTS']['start_date'],
'%Y-%m-%d')
end_dt = datetime.datetime.strptime(ini['INPUTS']['end_date'], '%Y-%m-%d')
start_date = start_dt.strftime('%Y-%m-%d')
end_date = end_dt.strftime('%Y-%m-%d')
next_date = (end_dt + datetime.timedelta(days=1)).strftime('%Y-%m-%d')
logging.debug('Start Date: {}'.format(start_date))
logging.debug('End Date: {}\n'.format(end_date))
# Limit by year and month
try:
month_list = sorted(list(utils.parse_int_set(ini['TCORR']['months'])))
except:
logging.info('\nTCORR "months" parameter not set in the INI,'
'\n Defaulting to all months (1-12)\n')
month_list = list(range(1, 13))
try:
year_list = sorted(list(utils.parse_int_set(ini['TCORR']['years'])))
except:
logging.info('\nTCORR "years" parameter not set in the INI,'
'\n Defaulting to all available years\n')
year_list = []
# Get the list of WRS2 tiles that intersect the data area and study area
wrs2_coll = ee.FeatureCollection(wrs2_coll_id) \
.filterBounds(export_geom) \
.filterBounds(study_area_geom)
if wrs2_tiles:
wrs2_coll = wrs2_coll.filter(
ee.Filter.inList(wrs2_tile_field, wrs2_tiles))
wrs2_info = wrs2_coll.getInfo()['features']
for wrs2_ftr in sorted(wrs2_info,
key=lambda k: k['properties']['WRS2_TILE'],
reverse=reverse_flag):
wrs2_tile = wrs2_ftr['properties'][wrs2_tile_field]
logging.info('{}'.format(wrs2_tile))
wrs2_path = int(wrs2_tile[1:4])
wrs2_row = int(wrs2_tile[5:8])
# wrs2_path = wrs2_ftr['properites']['PATH']
# wrs2_row = wrs2_ftr['properites']['ROW']
for month in month_list:
logging.info('Month: {}'.format(month))
export_id = export_id_fmt.format(product=tmax_name.lower(),
wrs2=wrs2_tile,
month=month)
logging.debug(' Export ID: {}'.format(export_id))
asset_id = asset_id_fmt.format(coll_id=tcorr_monthly_coll_id,
wrs2=wrs2_tile,
month=month)
logging.debug(' Asset ID: {}'.format(asset_id))
if overwrite_flag:
if export_id in tasks.keys():
logging.debug(' Task already submitted, cancelling')
ee.data.cancelTask(tasks[export_id]['id'])
# This is intentionally not an "elif" so that a task can be
# cancelled and an existing image/file/asset can be removed
if asset_id in asset_list:
logging.debug(' Asset already exists, removing')
ee.data.deleteAsset(asset_id)
else:
if export_id in tasks.keys():
logging.debug(' Task already submitted, exiting')
continue
elif asset_id in asset_list:
logging.debug(' Asset already exists, skipping')
continue
# CGM: I couldn't find a way to build this from the Collection class
# TODO: Will need to be changed/updated for SR collection
# TODO: Add code to handle real time collections
landsat_coll = ee.ImageCollection([])
if 'LANDSAT/LC08/C01/T1_TOA' in collections:
l8_coll = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') \
.filterMetadata('WRS_PATH', 'equals', wrs2_path) \
.filterMetadata('WRS_ROW', 'equals', wrs2_row) \
.filterMetadata('CLOUD_COVER_LAND', 'less_than', cloud_cover) \
.filterMetadata('DATA_TYPE', 'equals', 'L1TP') \
.filter(ee.Filter.gt('system:time_start',
ee.Date('2013-03-24').millis())) \
.filter(ee.Filter.calendarRange(month, month, 'month'))
# .filterDate(start_date, next_date)
landsat_coll = landsat_coll.merge(l8_coll)
if 'LANDSAT/LE07/C01/T1_TOA' in collections:
l7_coll = ee.ImageCollection('LANDSAT/LE07/C01/T1_TOA') \
.filterMetadata('WRS_PATH', 'equals', wrs2_path) \
.filterMetadata('WRS_ROW', 'equals', wrs2_row) \
.filterMetadata('CLOUD_COVER_LAND', 'less_than', cloud_cover) \
.filterMetadata('DATA_TYPE', 'equals', 'L1TP') \
.filter(ee.Filter.calendarRange(month, month, 'month'))
# .filterDate(start_date, next_date)
landsat_coll = landsat_coll.merge(l7_coll)
if 'LANDSAT/LT05/C01/T1_TOA' in collections:
l5_coll = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA') \
.filterMetadata('WRS_PATH', 'equals', wrs2_path) \
.filterMetadata('WRS_ROW', 'equals', wrs2_row) \
.filterMetadata('CLOUD_COVER_LAND', 'less_than', cloud_cover) \
.filterMetadata('DATA_TYPE', 'equals', 'L1TP') \
.filter(ee.Filter.lt('system:time_start',
ee.Date('2011-12-31').millis())) \
.filter(ee.Filter.calendarRange(month, month, 'month'))
# .filterDate(start_date, next_date)
landsat_coll = landsat_coll.merge(l5_coll)
# if 'LANDSAT/LT04/C01/T1_TOA' in collections:
# l4_coll = ee.ImageCollection('LANDSAT/LT04/C01/T1_TOA') \
# .filterMetadata('WRS_PATH', 'equals', wrs2_path) \
# .filterMetadata('WRS_ROW', 'equals', wrs2_row) \
# .filterMetadata('CLOUD_COVER_LAND', 'less_than', cloud_cover) \
# .filterMetadata('DATA_TYPE', 'equals', 'L1TP') \
# .filter(ee.Filter.calendarRange(month, month, 'month'))
# # .filterDate(start_date, next_date)
# landsat_coll = landsat_coll.merge(l4_coll)
def tcorr_img_func(landsat_img):
# TODO: Will need to be changed for SR
t_obj = ssebop.Image.from_landsat_c1_toa(
landsat_img, **model_args)
t_stats = ee.Dictionary(t_obj.tcorr_stats) \
.combine({'tcorr_p5': 0, 'tcorr_count': 0}, overwrite=False)
tcorr = ee.Number(t_stats.get('tcorr_p5'))
count = ee.Number(t_stats.get('tcorr_count'))
return tmax_mask.add(tcorr) \
.rename(['tcorr']) \
.set({
'system:time_start': ee.Image(landsat_img).get('system:time_start'),
'tcorr_value': tcorr,
'tcorr_pixel_count': count,
'scene_id': t_obj._scene_id,
})
# Filter the Tcorr image collection based on the pixel counts
tcorr_coll = ee.ImageCollection(landsat_coll.map(tcorr_img_func)) \
.filterMetadata('tcorr_pixel_count', 'not_less_than', min_pixel_count)
# Use a common reducer for the image and property stats
reducer = ee.Reducer.median() \
.combine(ee.Reducer.count(), sharedInputs=True)
# Compute stats from the collection images
# This might be used when Tcorr is spatial
# tcorr_img = tcorr_coll.reduce(reducer).rename(['tcorr', 'count'])
# Compute stats from the image properties
tcorr_stats = ee.List(tcorr_coll.aggregate_array('tcorr_value')) \
.reduce(reducer)
tcorr_stats = ee.Dictionary(tcorr_stats) \
.combine({'median': 0, 'count': 0}, overwrite=False)
tcorr = ee.Number(tcorr_stats.get('median'))
count = ee.Number(tcorr_stats.get('count'))
index = ee.Algorithms.If(count.gte(min_scene_count), 1, 9)
# Write an empty image if the pixel count is too low
# CGM: Check/test if this can be combined into a single If()
tcorr_img = ee.Algorithms.If(count.gte(min_scene_count),
tmax_mask.add(tcorr),
tmax_mask.updateMask(0))
count_img = ee.Algorithms.If(count.gte(min_scene_count),
tmax_mask.add(count),
tmax_mask.updateMask(0))
# Clip to the Landsat image footprint
output_img = ee.Image([tcorr_img, count_img]) \
.rename(['tcorr', 'count']) \
.clip(ee.Geometry(wrs2_ftr['geometry']))
# Clear the transparency mask
output_img = output_img.updateMask(output_img.unmask(0))
output_img = output_img.set({
'date_ingested':
datetime.datetime.today().strftime('%Y-%m-%d'),
'model_name':
model_name,
'model_version':
ssebop.__version__,
'month':
int(month),
# 'system:time_start': utils.millis(start_dt),
'tcorr_value':
tcorr,
'tcorr_index':
index,
'tcorr_scene_count':
count,
'tmax_source':
tmax_source.upper(),
'tmax_version':
tmax_version.upper(),
'wrs2_path':
wrs2_path,
'wrs2_row':
wrs2_row,
'wrs2_tile':
wrs2_tile,
'years':
','.join(map(str, year_list)),
# 'year_start': year_list[0],
# 'year_end': year_list[-1],
})
# pprint.pprint(output_img.getInfo())
# input('ENTER')
logging.debug(' Building export task')
task = ee.batch.Export.image.toAsset(
image=output_img,
description=export_id,
assetId=asset_id,
crs=export_crs,
crsTransform='[' + ','.join(list(map(str, export_geo))) + ']',
dimensions='{0}x{1}'.format(*export_shape),
)
logging.info(' Starting export task')
utils.ee_task_start(task)
# Pause before starting the next export task
utils.delay_task(delay_time, max_ready)
logging.debug('')
# Load a Landsat collection, map the NDVI and cloud masking functions over it.
collection = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA') \
.filterBounds(ee.Geometry.Point([-122.262, 37.8719])) \
.filterDate('2014-03-01', '2014-05-31') \
.map(addNDVI) \
.map(cloudMask)
# Reduce the collection to the mean of each pixel and display.
meanImage = collection.reduce(ee.Reducer.mean())
vizParams = {'bands': ['B5_mean', 'B4_mean', 'B3_mean'], 'min': 0, 'max': 0.5}
Map.setCenter(-122.262, 37.8719, 10)
Map.addLayer(meanImage, vizParams, 'mean')
# Load a region in which to compute the mean and display it.
counties = ee.FeatureCollection('TIGER/2016/Counties')
santaClara = ee.Feature(
counties.filter(ee.Filter.eq('NAME', 'Santa Clara')).first())
Map.addLayer(ee.Image().paint(santaClara, 0, 2), {'palette': 'yellow'},
'Santa Clara')
# Get the mean of NDVI in the region.
mean = meanImage.select(['nd_mean'
]).reduceRegion(**{
'reducer': ee.Reducer.mean(),
'geometry': santaClara.geometry(),
'scale': 30
})
# Print mean NDVI for the region.
print('Santa Clara spring mean NDVI:', mean.get('nd_mean').getInfo())
Map
# %%
"""
## Add Earth Engine Python script
"""
# %%
# Add Earth Engine dataset
# Example of FeatureCollection.reduceToImage()
# Define a feature collection with a value we want to average.
fc = ee.FeatureCollection([
ee.Feature(ee.Geometry.Rectangle(-122.4550, 37.8035, -122.4781, 37.7935),
{'value': 0}),
ee.Feature(
ee.Geometry.Polygon([[-122.4427, 37.8027], [-122.4587, 37.7987],
[-122.4440, 37.7934]]), {'value': 1})
])
# Reduce the collection to an image, where each pixel
# is the mean of the 'value' property in all features
# intersecting that pixel.
image_reduced = fc.reduceToImage(['value'], 'mean')
Map.setCenter(-122.4561, 37.7983, 14)
Map.addLayer(image_reduced, {
'min': 0,
'max': 1,
'palette': ['008800', '00FF00']
}, "Image")
def gee_to_drive(s1, s2):
today = ee.Date(datetime.now())
woreda = ee.FeatureCollection("users/ramcharankankanala/Final")
gpm = ee.ImageCollection("NASA/GPM_L3/IMERG_V06")
lstTerra8 = ee.ImageCollection("MODIS/006/MOD11A2").filterDate(
'2001-06-26', today)
brdfReflect = ee.ImageCollection("MODIS/006/MCD43A4")
brdfQA = ee.ImageCollection("MODIS/006/MCD43A2")
# string1 = str(input('Start date:'))
# string2 = str(input('End date:'))
string1 = s1
string2 = s2
reqStartDate = ee.Date(string1)
reqEndDate = ee.Date(string2)
# print(reqStartDate)
lstEarliestDate = lstTerra8.first().date()
# print(lstEarliestDate)
# Filter collection to dates from beginning to requested
priorLstImgcol = lstTerra8.filterDate(lstEarliestDate, reqStartDate)
# Get the latest (max) date of this collection of earlier images
lstPrevMax = priorLstImgcol.reduceColumns(ee.Reducer.max(),
["system:time_start"])
lstStartDate = ee.Date(lstPrevMax.get('max'))
# print('lstStartDate', lstStartDate);
gpmAllMax = gpm.reduceColumns(ee.Reducer.max(), ["system:time_start"])
gpmAllEndDateTime = ee.Date(gpmAllMax.get('max'))
gpmAllEndDate = ee.Date.fromYMD(
**{
'year': gpmAllEndDateTime.get('year'),
'month': gpmAllEndDateTime.get('month'),
'day': gpmAllEndDateTime.get('day')
})
precipStartDate = ee.Date(
ee.Algorithms.If(
gpmAllEndDate.millis().lt(reqStartDate.millis()),
# if data ends before requested start, take last data date
gpmAllEndDate,
# otherwise use requested date as normal
reqStartDate))
# print('precipStartDate', precipStartDate);
brdfAllMax = brdfReflect.reduceColumns(ee.Reducer.max(),
["system:time_start"])
brdfAllEndDate = ee.Date(brdfAllMax.get('max'))
brdfStartDate = ee.Date(
ee.Algorithms.If(
brdfAllEndDate.millis().lt(reqStartDate.millis()),
# if data ends before requested start, take last data date
brdfAllEndDate,
# otherwise use requested date as normal
reqStartDate))
# print('brdfStartDate', brdfStartDate);
# Step 2: Precipitation
# Step 2a: Precipitation filtering and dates
# Filter gpm by date, using modified start if necessary
gpmFiltered = gpm.filterDate(precipStartDate, reqEndDate.advance(
1, 'day')).select('precipitationCal')
# Calculate date of most recent measurement for gpm (in modified requested window)
gpmMax = gpmFiltered.reduceColumns(ee.Reducer.max(), ["system:time_start"])
gpmEndDate = ee.Date(gpmMax.get('max'))
precipEndDate = gpmEndDate
# print('precipEndDate ', precipEndDate);
precipDays = precipEndDate.difference(precipStartDate, 'day')
precipDatesPrep = ee.List.sequence(0, precipDays, 1)
precipDatesPrep.getInfo()
def makePrecipDates(n):
return precipStartDate.advance(n, 'day')
precipDates = precipDatesPrep.map(makePrecipDates)
# precipDates.getInfo()
def calcDailyPrecip(curdate):
curyear = ee.Date(curdate).get('year')
curdoy = ee.Date(curdate).getRelative('day', 'year').add(1)
totprec = gpmFiltered.select('precipitationCal').filterDate(
ee.Date(curdate),
ee.Date(curdate).advance(
1, 'day')).sum().multiply(0.5).rename('totprec')
return totprec.set('doy',
curdoy).set('year',
curyear).set('system:time_start',
curdate)
dailyPrecipExtended = ee.ImageCollection.fromImages(
precipDates.map(calcDailyPrecip))
dailyPrecip = dailyPrecipExtended.filterDate(
reqStartDate, precipEndDate.advance(1, 'day'))
precipSummary = dailyPrecip.filterDate(reqStartDate,
reqEndDate.advance(1, 'day'))
def sumZonalPrecip(image):
# To get the doy and year, we convert the metadata to grids and then summarize
image2 = image.addBands(
[image.metadata('doy').int(),
image.metadata('year').int()])
# Reduce by regions to get zonal means for each county
output = image2.select(['year', 'doy', 'totprec'],
['year', 'doy', 'totprec']).reduceRegions(
**{
'collection': woreda,
'reducer': ee.Reducer.mean(),
'scale': 1000
})
return output
# Map the zonal statistics function over the filtered precip data
precipWoreda = precipSummary.map(sumZonalPrecip)
# Flatten the results for export
precipFlat = precipWoreda.flatten()
# Step 3a: Calculate LST variables
# Filter Terra LST by altered LST start date
lstFiltered = lstTerra8.filterDate(
lstStartDate,
reqEndDate.advance(8, 'day')).filterBounds(woreda).select(
'LST_Day_1km', 'QC_Day', 'LST_Night_1km', 'QC_Night')
def filterLstQA(image):
qaday = image.select(['QC_Day'])
qanight = image.select(['QC_Night'])
dayshift = qaday.rightShift(6)
nightshift = qanight.rightShift(6)
daymask = dayshift.lte(2)
nightmask = nightshift.lte(2)
outimage = ee.Image(image.select(['LST_Day_1km', 'LST_Night_1km']))
outmask = ee.Image([daymask, nightmask])
return outimage.updateMask(outmask)
lstFilteredQA = lstFiltered.map(filterLstQA)
def rescaleLst(image):
lst_day = image.select('LST_Day_1km').multiply(0.02).subtract(
273.15).rename('lst_day')
lst_night = image.select('LST_Night_1km').multiply(0.02).subtract(
273.15).rename('lst_night')
lst_mean = image.expression(
'(day + night) / 2', {
'day': lst_day.select('lst_day'),
'night': lst_night.select('lst_night')
}).rename('lst_mean')
return image.addBands(lst_day).addBands(lst_night).addBands(lst_mean)
lstVars = lstFilteredQA.map(rescaleLst)
lstRange = lstVars.reduceColumns(ee.Reducer.max(), ["system:time_start"])
lstEndDate = ee.Date(lstRange.get('max')).advance(7, 'day')
lstDays = lstEndDate.difference(lstStartDate, 'day')
lstDatesPrep = ee.List.sequence(0, lstDays, 1)
def makeLstDates(n):
return lstStartDate.advance(n, 'day')
lstDates = lstDatesPrep.map(makeLstDates)
def calcDailyLst(curdate):
curyear = ee.Date(curdate).get('year')
curdoy = ee.Date(curdate).getRelative('day', 'year').add(1)
moddoy = curdoy.divide(8).ceil().subtract(1).multiply(8).add(1)
basedate = ee.Date.fromYMD(curyear, 1, 1)
moddate = basedate.advance(moddoy.subtract(1), 'day')
lst_day = lstVars.select('lst_day').filterDate(
moddate, moddate.advance(1, 'day')).first().rename('lst_day')
lst_night = lstVars.select('lst_night').filterDate(
moddate, moddate.advance(1, 'day')).first().rename('lst_night')
lst_mean = lstVars.select('lst_mean').filterDate(
moddate, moddate.advance(1, 'day')).first().rename('lst_mean')
return lst_day.addBands(lst_night).addBands(lst_mean).set(
'doy', curdoy).set('year', curyear).set('system:time_start',
curdate)
dailyLstExtended = ee.ImageCollection.fromImages(
lstDates.map(calcDailyLst))
dailyLst = dailyLstExtended.filterDate(reqStartDate,
lstEndDate.advance(1, 'day'))
lstSummary = dailyLst.filterDate(reqStartDate,
reqEndDate.advance(1, 'day'))
def sumZonalLst(image):
# To get the doy and year, we convert the metadata to grids and then summarize
image2 = image.addBands(
[image.metadata('doy').int(),
image.metadata('year').int()])
# Reduce by regions to get zonal means for each county
output = image2.select(
['doy', 'year', 'lst_day', 'lst_night', "lst_mean"],
['doy', 'year', 'lst_day', 'lst_night', 'lst_mean']).reduceRegions(
**{
'collection': woreda,
'reducer': ee.Reducer.mean(),
'scale': 1000
})
return output
# Map the zonal statistics function over the filtered lst data
lstWoreda = lstSummary.map(sumZonalLst)
# Flatten the results for export
lstFlat = lstWoreda.flatten()
# Step 4: BRDF / Spectral Indices
# Step 4a: Calculate spectral indices
# Filter BRDF-Adjusted Reflectance by Date
brdfReflectVars = brdfReflect.filterDate(
brdfStartDate,
reqEndDate.advance(1, 'day')).filterBounds(woreda).select([
'Nadir_Reflectance_Band1', 'Nadir_Reflectance_Band2',
'Nadir_Reflectance_Band3', 'Nadir_Reflectance_Band4',
'Nadir_Reflectance_Band5', 'Nadir_Reflectance_Band6',
'Nadir_Reflectance_Band7'
], ['red', 'nir', 'blue', 'green', 'swir1', 'swir2', 'swir3'])
# Filter BRDF QA by Date
brdfReflectQA = brdfQA.filterDate(
brdfStartDate,
reqEndDate.advance(1, 'day')).filterBounds(woreda).select([
'BRDF_Albedo_Band_Quality_Band1', 'BRDF_Albedo_Band_Quality_Band2',
'BRDF_Albedo_Band_Quality_Band3', 'BRDF_Albedo_Band_Quality_Band4',
'BRDF_Albedo_Band_Quality_Band5', 'BRDF_Albedo_Band_Quality_Band6',
'BRDF_Albedo_Band_Quality_Band7', 'BRDF_Albedo_LandWaterType'
], ['qa1', 'qa2', 'qa3', 'qa4', 'qa5', 'qa6', 'qa7', 'water'])
idJoin = ee.Filter.equals(leftField='system:time_end',
rightField='system:time_end')
# Define the join
innerJoin = ee.Join.inner('NBAR', 'QA')
# Apply the join
brdfJoined = innerJoin.apply(brdfReflectVars, brdfReflectQA, idJoin)
def addQABands(image):
nbar = ee.Image(image.get('NBAR'))
qa = ee.Image(image.get('QA')).select(['qa2'])
water = ee.Image(image.get('QA')).select(['water'])
return nbar.addBands([qa, water])
brdfMerged = ee.ImageCollection(brdfJoined.map(addQABands))
def filterBrdf(image):
qaband = image.select(['qa2'])
# Right now, only using QA info for the NIR band
wband = image.select(['water'])
qamask = qaband.lte(2) and wband.eq(1)
nir_r = image.select('nir').multiply(0.0001).rename('nir_r')
red_r = image.select('red').multiply(0.0001).rename('red_r')
swir1_r = image.select('swir1').multiply(0.0001).rename('swir1_r')
swir2_r = image.select('swir2').multiply(0.0001).rename('swir2_r')
blue_r = image.select('blue').multiply(0.0001).rename('blue_r')
return image.addBands(nir_r).addBands(red_r).addBands(
swir1_r).addBands(swir2_r).addBands(blue_r).updateMask(qamask)
brdfFilteredVars = brdfMerged.map(filterBrdf)
def calcBrdfIndices(image):
curyear = ee.Date(image.get("system:time_start")).get('year')
curdoy = ee.Date(image.get("system:time_start")).getRelative(
'day', 'year').add(1)
ndvi = image.normalizedDifference(['nir_r', 'red_r']).rename('ndvi')
savi = image.expression('1.5 * (nir - red) / (nir + red + 0.5)', {
'nir': image.select('nir_r'),
'red': image.select('red_r')
}).rename('savi')
evi = image.expression(
'2.5 * (nir - red) / (nir + 6 * red - 7.5 * blue + 1)', {
'nir': image.select('nir_r'),
'red': image.select('red_r'),
'blue': image.select('blue_r')
}).rename('evi')
ndwi5 = image.normalizedDifference(['nir_r',
'swir1_r']).rename('ndwi5')
ndwi6 = image.normalizedDifference(['nir_r',
'swir2_r']).rename('ndwi6')
return image.addBands(ndvi).addBands(savi).addBands(evi).addBands(
ndwi5).addBands(ndwi6).set('doy', curdoy).set('year', curyear)
brdfFilteredVars = brdfFilteredVars.map(calcBrdfIndices)
brdfRange = brdfFilteredVars.reduceColumns(ee.Reducer.max(),
["system:time_start"])
brdfEndDate = ee.Date(brdfRange.get('max'))
brdfDays = brdfEndDate.difference(brdfStartDate, 'day')
brdfDatesPrep = ee.List.sequence(0, brdfDays, 1)
def makeBrdfDates(n):
return brdfStartDate.advance(n, 'day')
brdfDates = brdfDatesPrep.map(makeBrdfDates)
def calcDailyBrdf(curdate):
curyear = ee.Date(curdate).get('year')
curdoy = ee.Date(curdate).getRelative('day', 'year').add(1)
brdfTemp = brdfFilteredVars.filterDate(
ee.Date(curdate),
ee.Date(curdate).advance(1, 'day'))
brdfSize = brdfTemp.size()
outimg = ee.Image(
ee.Algorithms.If(
brdfSize.eq(0),
ee.Image.constant(0).selfMask().addBands(
ee.Image.constant(0).selfMask()).addBands(
ee.Image.constant(0).selfMask()).addBands(
ee.Image.constant(0).selfMask()).addBands(
ee.Image.constant(0).selfMask()).rename([
'ndvi', 'evi', 'savi', 'ndwi5', 'ndwi6'
]).set('doy', curdoy).set('year', curyear).set(
'system:time_start', curdate),
brdfTemp.first()))
return outimg
dailyBrdfExtended = ee.ImageCollection.fromImages(
brdfDates.map(calcDailyBrdf))
dailyBrdf = dailyBrdfExtended.filterDate(reqStartDate,
brdfEndDate.advance(1, 'day'))
brdfSummary = dailyBrdf.filterDate(reqStartDate,
reqEndDate.advance(1, 'day'))
# Function to calculate zonal statistics for spectral indices by county
def sumZonalBrdf(image):
# To get the doy and year, we convert the metadata to grids and then summarize
image2 = image.addBands(
[image.metadata('doy').int(),
image.metadata('year').int()])
# educe by regions to get zonal means for each county
output = image2.select(
['doy', 'year', 'ndvi', 'savi', 'evi', 'ndwi5', 'ndwi6'],
['doy', 'year', 'ndvi', 'savi', 'evi', 'ndwi5', 'ndwi6'
]).reduceRegions(**{
'collection': woreda,
'reducer': ee.Reducer.mean(),
'scale': 1000
})
return output
# ap the zonal statistics function over the filtered spectral index data
brdfWoreda = brdfSummary.map(sumZonalBrdf)
# latten the results for export
brdfFlat = brdfWoreda.flatten()
def downloadsummary():
link = exportSummaries()
url1 = link[0]
url2 = link[1]
url3 = link[2]
print('precipURL:', url1)
print('lstURL:', url2)
print('brdfURL:', url3)
wget.download(link[0], string1 + 'to' + string2 + 'precipFlat.csv')
wget.download(link[1], string1 + 'to' + string2 + 'lstFlat.csv')
wget.download(link[2], string1 + 'to' + string2 + 'brdfFlat.csv')
print("Data downloaded to local drive")
def datatolocaldrive():
link = exportSummaries()
url1 = link[0]
url2 = link[1]
url3 = link[2]
print('precipURL:', url1)
print('lstURL:', url2)
print('brdfURL:', url3)
r = requests.get(url1, allow_redirects=True)
with open(string1 + 'to' + string2 + 'precipFlat.csv', 'wb') as f:
f.write(r.content)
r1 = requests.get(url2, allow_redirects=True)
with open(string1 + 'to' + string2 + 'lstFlat.csv', 'wb') as f1:
f1.write(r1.content)
r2 = requests.get(url3, allow_redirects=True)
with open(string1 + 'to' + string2 + 'brdfFlat.csv', 'wb') as f2:
f2.write(r2.content)
def datatolocal():
link = exportSummaries()
url1 = link[0]
url2 = link[1]
url3 = link[2]
print('precipURL:', url1)
print('lstURL:', url2)
print('brdfURL:', url3)
request.urlretrieve(url1, string1 + 'to' + string2 + 'precipFlat.csv')
request.urlretrieve(url2, string1 + 'to' + string2 + 'lstFlat.csv')
request.urlretrieve(url3, string1 + 'to' + string2 + 'brdfFlat.csv')
def ExportToDrive():
props1 = {
'driveFolder':
'Ethiopiadata',
'driveFileNamePrefix':
'precip' + string1 + 'to' + string2,
'selectors': [
'NewPCODE', 'R_NAME', 'W_NAME', 'Z_NAME', 'doy', 'year',
'totprec'
],
'fileFormat':
'CSV'
}
task1 = ee.batch.Export.table(
precipFlat, 'Export_precip' + string1 + 'to' + string2, props1)
props2 = {
'driveFolder':
'Ethiopiadata',
'driveFileNamePrefix':
'lst' + string1 + 'to' + string2,
'selectors': [
'NewPCODE', 'R_NAME', 'W_NAME', 'Z_NAME', 'doy', 'year',
'lst_day', 'lst_night', "lst_mean"
],
'fileFormat':
'CSV'
}
task2 = ee.batch.Export.table(lstFlat,
'Export_lst' + string1 + 'to' + string2,
props2)
props3 = {
'driveFolder':
'Ethiopiadata',
'driveFileNamePrefix':
'brdf' + string1 + 'to' + string2,
'selectors': [
'NewPCODE', 'R_NAME', 'W_NAME', 'Z_NAME', 'doy', 'year',
'ndvi', 'savi', 'evi', 'ndwi5', 'ndwi6'
],
'fileFormat':
'CSV'
}
task3 = ee.batch.Export.table(brdfFlat,
'Export_brdf' + string1 + 'to' + string2,
props3)
task1.start()
task2.start()
task3.start()
print(
"Data will Export to google drive in to Ethiopiadata folder which will take a while depending on date range:--------"
)
#downloadsummary()
ExportToDrive()
# Load input NAIP imagery and build a mosaic.
naipCollection = ee.ImageCollection('USDA/NAIP/DOQQ') \
.filterBounds(redwoods) \
.filterDate('2012-01-01', '2012-12-31')
naip = naipCollection.mosaic()
# Compute NDVI from the NAIP imagery.
naipNDVI = naip.normalizedDifference(['N', 'R'])
# Compute standard deviation (SD) as texture of the NDVI.
texture = naipNDVI.reduceNeighborhood(**{
'reducer': ee.Reducer.stdDev(),
'kernel': ee.Kernel.circle(7),
})
# Display the results.
Map.centerObject(ee.FeatureCollection(redwoods), 12)
Map.addLayer(naip, {}, 'NAIP input imagery')
Map.addLayer(naipNDVI, {'min': -1, 'max': 1, 'palette': ['FF0000', '00FF00']}, 'NDVI')
Map.addLayer(texture, {'min': 0, 'max': 0.3}, 'SD of NDVI')
# %%
"""
## Display Earth Engine data layers
"""
# %%
Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.
Map
y2012 = []
y2015 = []
for row in out.iterrows():
geom = ee.Geometry.Point(row[1]['longitude'], row[1]['latitude']).buffer(7500)
feat = ee.Feature(geom, {'hh_refno':row[1]['hh_refno'], 'year':str(row[1]['year'])})
if row[1]['year'] == 2011:
y2011.append(feat)
elif row[1]['year'] == 2012:
y2012.append(feat)
elif row[1]['year'] == 2015:
y2015.append(feat)
else:
raise ValueError('Invalid year. Must be 2011, 2012, or 2015')
y2011 = ee.FeatureCollection(y2011)
y2012a = ee.FeatureCollection(y2012[:3000])
y2012b = ee.FeatureCollection(y2012[3000:6000])
y2012c = ee.FeatureCollection(y2012[6000:9000])
y2012d = ee.FeatureCollection(y2012[9000:12000])
y2012e = ee.FeatureCollection(y2012[12000:])
y2015a = ee.FeatureCollection(y2015[:3000])
y2015b = ee.FeatureCollection(y2015[3000:6000])
y2015c = ee.FeatureCollection(y2015[6000:9000])
y2015d = ee.FeatureCollection(y2015[9000:12000])
y2015e = ee.FeatureCollection(y2015[12000:])
y2011 = [y2011]
y2012 = [y2012a, y2012b, y2012c, y2012e, y2012e]
y2015 = [y2015a, y2015b, y2015c, y2015d, y2015e]
'''
Created on Aug 11, 2019
@author: nbiswas
'''
import ee, os
ee.Initialize()
allreservoirs = ee.FeatureCollection("users/nbiswas/grand1p3_reservoirs_saafseasia");
L8 = ee.ImageCollection("LANDSAT/LT05/C01/T1");
# geometry = table2
Date_Start = ee.Date('1984-01-01');
Date_End = ee.Date('2013-12-31');
cloud_thresh = 20;
def clipimage(img):
return img.clip(bgeometry);
def getQABits(image, start, end, newName):
#Compute the bits we need to extract.
pattern = 0;
for i in range(start, end+1):
pattern += 2**i
#Return a single band image of the extracted QA bits, giving the band a new name.
return image.select([0], [newName]).bitwiseAnd(pattern).rightShift(start);
def wrs2_tile_export_generator(study_area_path,
wrs2_coll,
cell_size=30,
output_crs=None,
output_osr=None,
wrs2_tile_list=[],
wrs2_tile_field='WRS2_TILE',
snap_x=15,
snap_y=15,
wrs2_buffer=0,
n_max=1000,
simplify_buffer=1000):
"""Generate WRS2 tile image metadata for the study area geometry
Args:
study_area_path (str): File path of the study area shapefile
wrs2_coll (str): WRS2 Landsat footprint asset ID.
(should default to "projects/eeflux/wrs2_descending_custom")
cell_size (float): Cell size [m]. Defaults to 30.
output_crs (str): Output CRS (for setting 'crs' parameter in EE calls).
Defaults to None.
output_osr (osr.SpatialReference): Output coordinate system.
Defaults to None.
wrs2_tile_field (str): WRS2 tile field name in the fusion table
Defaults to 'WRS2_TILE'
wrs2_tile_list (list): User defined WRS2 tile subset
snap_x (float): X snap coordinate [m]. Defaults to 15.
snap_y (float): Y snap coordinate [m]. Defaults to 15.
wrs2_buffer (float): WRS2 footprint buffer distance [m].
Defaults to 10000.
n_max (int): Maximum number of WRS2 tiles to join to feature.
Defaults to 1000.
simplify_buffer (float): Study area buffer/simplify distance [m].
Defaults to 1000.
Yields:
dict: export information
"""
logging.info('\nReading study area shapefile')
logging.info(' {}'.format(study_area_path))
study_area_ds = ogr.Open(study_area_path, 0)
study_area_lyr = study_area_ds.GetLayer()
study_area_osr = study_area_lyr.GetSpatialRef()
study_area_proj = study_area_osr.ExportToWkt()
# study_area_osr = study_area_osr.ExportToProj4()
# logging.debug(' Projection: {}'.format(study_area_proj))
# Convert WKT to EE WKT
# study_area_crs = re.sub(
# '\s+', '', ee.Projection(study_area_proj).wkt().getInfo())
study_area_crs = str(study_area_proj)
logging.debug(' Study area projection: {}'.format(study_area_crs))
# Get the dissolved/unioned geometry of the study area
output_geom = ogr.Geometry(ogr.wkbMultiPolygon)
# shape_list = []
for study_area_ftr in study_area_lyr:
# Union each feature
output_geom = output_geom.Union(study_area_ftr.GetGeometryRef())
study_area_ds = None
# Project the study area geometry to the EPSG:3857
# so units will be meters for buffering and simplifying
temp_crs = 'EPSG:3857'
temp_osr = osr.SpatialReference()
temp_osr.ImportFromEPSG(3857)
output_tx = osr.CoordinateTransformation(study_area_osr, temp_osr)
output_geom.Transform(output_tx)
# Buffer/simplify values are assuming the geometry units are in meters
output_simplify = output_geom.Buffer(simplify_buffer) \
.SimplifyPreserveTopology(simplify_buffer)
# Generate an EE feature
output_ee_geom = ee.Geometry(json.loads(output_simplify.ExportToJson()),
temp_crs, False)
# Pre-filter the WRS2 descending collection
# with the buffered study area geometry
# Then buffer the WRS2 descending collection
if wrs2_buffer:
wrs2_coll = ee.FeatureCollection(wrs2_coll) \
.filterBounds(output_ee_geom.buffer(wrs2_buffer, 1)) \
.map(lambda ftr: ftr.buffer(wrs2_buffer, 1))
else:
wrs2_coll = ee.FeatureCollection(wrs2_coll) \
.filterBounds(output_ee_geom)
# Join intersecting geometries
join_coll = ee.Join.saveAll(matchesKey='scenes').apply(
ee.FeatureCollection([ee.Feature(output_ee_geom)]), wrs2_coll,
ee.Filter.intersects(leftField='.geo', rightField='.geo', maxError=10))
# It is not necessary to map over the join collection
# since there is only one study area feature
output_wrs2_tiles = ee.List(ee.Feature(join_coll.first()).get('scenes'))
def wrs2_bounds(ftr):
crs = ee.String('EPSG:').cat(
ee.Number(ee.Feature(ftr).get('EPSG')).format('%d'))
extent = ee.Feature(ftr).geometry() \
.bounds(1, ee.Projection(crs)).coordinates().get(0)
# extent = ee.Array(extent).transpose().toList()
# extent = ee.List([
# ee.List(extent.get(0)).reduce(ee.Reducer.min()),
# ee.List(extent.get(1)).reduce(ee.Reducer.min()),
# ee.List(extent.get(0)).reduce(ee.Reducer.max()),
# ee.List(extent.get(1)).reduce(ee.Reducer.max())
# ])
return ee.Feature(
None, {
'crs': crs,
'extent': extent,
'wrs2_tile': ee.Feature(ftr).get(wrs2_tile_field)
})
output_list = output_wrs2_tiles.map(wrs2_bounds).getInfo()
for output_info in output_list:
wrs2_tile = output_info['properties']['wrs2_tile']
if wrs2_tile_list and wrs2_tile not in wrs2_tile_list:
logging.debug(
' WRS2 tile {} not in INI WRS2 tiles, skipping'.format(
wrs2_tile))
continue
# Use output CRS if it was set, otherwise use WRS2 tile CRS
if output_crs is None:
wrs2_tile_crs = output_info['properties']['crs']
else:
wrs2_tile_crs = output_crs
output_extent = output_info['properties']['extent']
output_extent = [
min([x[0] for x in output_extent]),
min([x[1] for x in output_extent]),
max([x[0] for x in output_extent]),
max([x[1] for x in output_extent])
]
# Adjust extent to the cell size
adjust_size = 2 * cell_size
output_extent[0] = math.floor(
(output_extent[0] - snap_x) / adjust_size) * adjust_size + snap_x
output_extent[1] = math.floor(
(output_extent[1] - snap_y) / adjust_size) * adjust_size + snap_y
output_extent[2] = math.ceil(
(output_extent[2] - snap_x) / adjust_size) * adjust_size + snap_x
output_extent[3] = math.ceil(
(output_extent[3] - snap_y) / adjust_size) * adjust_size + snap_y
output_geo = [
cell_size, 0, output_extent[0], 0, -cell_size, output_extent[3]
]
# output_geom = extent_geom(output_extent)
output_shape = '{0}x{1}'.format(
int(abs(output_extent[2] - output_extent[0]) / cell_size),
int(abs(output_extent[3] - output_extent[1]) / cell_size))
max_pixels = 2 * reduce(mul, map(int, output_shape.split('x')))
yield {
'crs': wrs2_tile_crs,
'extent': output_extent,
'geo': output_geo,
# 'geojson': json.loads(output_geom.ExportToJson()),
'index': wrs2_tile,
'maxpixels': max_pixels,
'wrs2_tiles': [wrs2_tile],
'shape': output_shape
}
import ee
import time
import pandas as pd
from pull_MODIS import export_oneimage, appendBand_0
ee.Initialize()
locations = pd.read_csv('locations_major.csv')
county_region = ee.FeatureCollection(
'ft:18Ayj5e7JxxtTPm1BdMnnzWbZMrxMB49eqGDTsaSp')
imgcoll = ee.ImageCollection('MODIS/051/MCD12Q1') \
.filterBounds(ee.Geometry.Rectangle(-106.5, 50,-64, 23))\
.filterDate('2001-12-31','2015-12-31')
img = imgcoll.iterate(appendBand_0)
img = ee.Image(img)
# img_0=ee.Image(ee.Number(0))
# img_5000=ee.Image(ee.Number(5000))
#
# img=img.min(img_5000)
# img=img.max(img_0)
# img=ee.Image(ee.Number(100))
# img=ee.ImageCollection('LC8_L1T').mosaic()
for loc1, loc2, lat, lon in locations.values:
file_name = '{}_{}'.format(int(loc1), int(loc2))
offset = 0.11
scale = 500
The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function.
"""
# %%
Map = geemap.Map(center=[40, -100], zoom=4)
Map
# %%
"""
## Add Earth Engine Python script
"""
# %%
# Add Earth Engine dataset
collection = ee.ImageCollection('USDA/NAIP/DOQQ')
fromFT = ee.FeatureCollection('ft:1CLldB-ULPyULBT2mxoRNv7enckVF0gCQoD2oH7XP')
polys = fromFT.geometry()
# polys = ee.Geometry.Polygon(
# [[[-99.29615020751953, 46.725459351792374],
# [-99.2116928100586, 46.72404725733022],
# [-99.21443939208984, 46.772037733479884],
# [-99.30267333984375, 46.77321343419932]]])
centroid = polys.centroid()
lng, lat = centroid.getInfo()['coordinates']
print("lng = {}, lat = {}".format(lng, lat))
lng_lat = ee.Geometry.Point(lng, lat)
naip = collection.filterBounds(polys)
naip_2015 = naip.filterDate('2015-01-01', '2015-12-31')
ppr = naip_2015.mosaic()
## Create an interactive map
The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function.
"""
# %%
Map = geemap.Map(center=[40, -100], zoom=4)
Map
# %%
"""
## Add Earth Engine Python script
"""
# %%
# Add Earth Engine dataset
fc = ee.FeatureCollection('TIGER/2018/States')
print(fc.first().getInfo())
new_fc = fc.select(['STUSPS', 'NAME', 'ALAND'], ['abbr', 'name', 'area'])
print(new_fc.first().getInfo())
propertyNames = new_fc.first().propertyNames()
print(propertyNames.getInfo())
# %%
"""
## Display Earth Engine data layers
"""
# %%
# Import Modules
import ee, datetime
# Check User Credentials
ee.Initialize()
# Load in AOI Fusion Table
ft2 = ee.FeatureCollection("ft:137bhlHOilFr5iTTuSMWJo7jRLCnSCHQaICzLzmJQ")
# Load in Flood Scene
flood = ee.Image(
'COPERNICUS/S1_GRD/S1A_IW_GRDH_1SDV_20160103T062204_20160103T062229_009326_00D7AC_C9F2'
)
# Determine Orbit/Track Metadata
im_meta1 = flood.getInfo()
im_meta2 = im_meta1['properties']
direction = im_meta2['orbitProperties_pass']
orbit = im_meta2['relativeOrbitNumber_start']
print 'Orbit Direction: ', direction
print 'Track Number: ', orbit
# Load in Sentinel-1 Collection
collection = ee.ImageCollection("COPERNICUS/S1_GRD")
# Filter S1 Collection
filter = collection.filterDate(datetime.datetime(2015,7,3), datetime.datetime(2015,11,5)).filterBounds(ft2)\
.filter(ee.Filter().eq('transmitterReceiverPolarisation', 'VH')).filter(ee.Filter().eq('instrumentMode', 'IW'))\
.filter(ee.Filter().eq('orbitProperties_pass', direction)).filter(ee.Filter().eq('relativeOrbitNumber_start', orbit))
### Be useful to get a number of images in the collection and use in the filter.toList() command
### Theoretically there could be hundreds of images going into this calculation in the future
list = filter.toList(100)
metalist = list.getInfo()
for image in metalist:
props = image['properties']
print props['system:index']
# Create Median reference image
The default basemap is `Google Satellite`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/geemap.py#L13) can be added using the `Map.add_basemap()` function.
"""
# %%
Map = emap.Map(center=[40, -100], zoom=4)
Map.add_basemap('ROADMAP') # Add Google Map
Map
# %%
"""
## Add Earth Engine Python script
"""
# %%
# Add Earth Engine dataset
HUC10 = ee.FeatureCollection("USGS/WBD/2017/HUC10")
HUC08 = ee.FeatureCollection('USGS/WBD/2017/HUC08')
roi = HUC08.filter(ee.Filter.eq('name', 'Pipestem'))
Map.centerObject(roi, 10)
Map.addLayer(ee.Image().paint(roi, 0, 1), {}, 'HUC8')
bound = ee.Geometry(roi.geometry()).bounds()
Map.addLayer(ee.Image().paint(bound, 0, 1), {'palette': 'red'},
"Minimum bounding geometry")
# %%
"""
## Display Earth Engine data layers
"""
import ee
from datetime import datetime
import pandas as pd
ee.Initialize()
#Importação do Shapefile com massas d'água
table = ee.FeatureCollection(
"users/mvcastro1975/massa_dagua/massa_dagua_maior_100ha")
#Importa dataset com recorrência mensal de água
monthly_hist = ee.ImageCollection('JRC/GSW1_0/MonthlyHistory').select('water')
def retorna_lista(img, featCol):
data_count = img.reduceRegions(featCol, ee.Reducer.count(), 500)
data_count = data_count.map(
lambda feat: feat.set('data_analise',
image_fst.date().format()))
data_mean = img.reduceRegions(featCol, ee.Reducer.mean(), 500)
data_mean = data_mean.map(lambda feat: feat.set('data_analise',
image_fst.date().format()))
data_median = img.reduceRegions(featCol, ee.Reducer.median(), 500)
data_median = data_median.map(
lambda feat: feat.set('data_analise',
image_fst.date().format()))
return [data_count.getInfo(), data_mean.getInfo(), data_median.getInfo()]
"""Buffer Example.
Display the area within 2 kilometers of any San Francisco BART station.
"""
import ee
import ee.mapclient
ee.Initialize()
ee.mapclient.centerMap(-122.4, 37.7, 11)
bart_stations = ee.FeatureCollection(
'ft:1xCCZkVn8DIkB7i7RVkvsYWxAxsdsQZ6SbD9PCXw')
buffered = bart_stations.map(lambda f: f.buffer(2000))
unioned = buffered.union()
ee.mapclient.addToMap(unioned, {'color': '800080'})
# %%
Map = folium.Map(location=[40, -100], zoom_start=4)
Map.setOptions('HYBRID')
# %%
'''
## Add Earth Engine Python script
'''
# %%
def addArea(feature):
return feature.set({'areaHa': feature.geometry().area().divide(100 * 100)})
# Load watersheds from a data table.
sheds = ee.FeatureCollection('USGS/WBD/2017/HUC06')
# This function computes the feature's geometry area and adds it as a property.
# addArea = function(feature) {
# return feature.set({areaHa: feature.geometry().area().divide(100 * 100)})
# }
# Map the area getting function over the FeatureCollection.
areaAdded = sheds.map(addArea)
# Print the first feature from the collection with the added property.
print('First feature:', areaAdded.first().getInfo())
# %%
def __init__(self):
"""Initialize the environment."""
# Initialize the Earth Engine object, using the authentication credentials.
ee.Initialize()
self.timeString = time.strftime("%Y%m%d_%H%M%S")
# SEASONS:
# '0': Dry Cool: Nov - Feb (305 - 59)
# '1': Dry Hot: Mar - Apr (60 - 181)
# '2': Rainy: May - Oct (182 - 304)
startjulian = {'drycool':305,'dryhot':60,'rainy':182}
endjulian = {'drycool':59,'dryhot':181,'rainy':304}
# set dates
self.startYear = int(args.year);
self.endYear = int(args.year);
self.startJulian = startjulian[args.season]
self.endJulian = endjulian[args.season]
if args.season == 'drycool':
self.startYear = int(args.year)-1
self.NgheAn = [[103.876,18.552],[105.806,18.552],[105.806,19.999],[103.876,19.999],[103.876,18.552]]
#self.NgheAn = [[100.876,18.552],[105.806,18.552],[105.806,22.999],[100.876,22.999],[100.876,18.552]]
collectionName = "projects/servir-mekong/usgs_sr_composites/" + args.season
self.mekongRegion = ee.FeatureCollection('ft:1LEGeqwlBCAlN61ie5ol24NdUDqB1MgpFR_sJNWQJ')
self.collection = ee.ImageCollection(collectionName)
# variables for the tdom filter
self.applyTDOM = True
self.TDOMyears = 25
self.shadowSumBands = ['nir','swir1'];
self.zScoreThresh = -0.8
self.shadowSumThresh = 0.35;
self.dilatePixels = 2
#users/servirmekong/usgs_sr_composites/drycool
self.outputName = args.season + str(self.startYear) + "_" + str(self.endYear)
# variable to filter cloud threshold
self.metadataCloudCoverMax = 40
# threshold for landsatCloudScore
self.cloudThreshold = 10
self.hazeThresh = 200
# apply a filter to filter for high values
self.filterPercentile = True
self.filterPercentileYears = 25
# percentiles to filter for bad data
self.lowPercentile = 2
self.highPercentile = 80
# whether to use imagecolletions
self.useL4=True
self.useL5=True
self.useL7=True
self.useL7scanline = False
self.useL8=True
# On May 31, 2003 the Scan Line Corrector (SLC) in the ETM+ instrument failed
self.l7Failed = ee.Date.fromYMD(2003,5,31)
# apply cloud masks
self.maskSR = True
# get indicices
self.calcIndices = True
# bands for tasselcap !maybe move
self.tcInputBands = ee.List(['blue','green','red','nir','swir1','swir2'])
# bands to select
self.bandNamesLandsat = ee.List(['blue','green','red','nir','swir1','thermal','swir2','sr_atmos_opacity','pixel_qa','radsat_qa'])
# bands for export
self.exportBands = ee.List(['blue','green','red','nir','swir1','thermal','swir2'])
# bands for dividing
self.divideBands = ee.List(['blue','green','red','nir','swir1','swir2'])
#bands for stdev
self.stdDevBands = ee.List(['blue','green','red','nir','swir1','thermal','swir2']) #,'ND_nir_red','ND_nir_swir2','ND_green_swir1']);
self.stdDevExportsBands = ee.List(['blue_stdev','green_stdev','red_stdev','nir_stdev','swir1_stdev','thermal_stdev','swir2_stdev']) #,'ND_nir_red','ND_nir_swir2','ND_green_swir1']);
# calculate stdev for indices
self.stdIndiceDevBands = ee.List(["ND_nir_swir2","ND_green_swir1","ND_nir_red"])
self.stdIndiceDevBandsExport = ee.List(["ND_nir_swir2_stdDev","ND_green_swir1_stdDev","ND_nir_red_stdDev"])
# apply defringe
self.defringe = True
# pixel size
self.pixSize = 30
# user ID
#self.userID = "users/servirmekong/assemblage/"
#self.userID = "projects/servir-mekong/temp/nghean_medoid_"
#self.userID = "projects/servir-mekong/usgs_sr_composites/" + args.season + "/"
self.userID = "projects/servir-mekong/usgs_sr_composites/" + args.season + "/SC_"
self.landsat4count = 0
self.landsat5count = 0
self.landsat7count = 0
self.landsat8count = 0
# define the landsat bands
self.sensorBandDictLandsatSR = ee.Dictionary({'L8' : ee.List([1,2,3,4,5,7,6,9,10,11]),
'L7' : ee.List([0,1,2,3,4,5,6,7,9,10]),
'L5' : ee.List([0,1,2,3,4,5,6,7,9,10]),
'L4' : ee.List([0,1,2,3,4,5,6,7,9,10])})
# just placeholders for now
self.calcMedoid = False
self.calcMedian = True
self.calcMean = False
self.fillGaps = True
self.fillGapYears = 25
# threshold for defringing landsat5 and 7
self.fringeCountThreshold = 279
self.k = ee.Kernel.fixed(41, 41,
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]);
def aggregate_daily(image_coll, start_date, end_date, agg_type='mean'):
"""Aggregate images by day
The primary purpose of this function is to join separate Landsat images
from the same path into a single daily image.
Parameters
----------
image_coll : ee.ImageCollection
Input image collection.
start_date : date, number, string
Start date. Needs to be an EE readable date (i.e. ISO Date string
or milliseconds).
end_date : date, number, string
End date. Needs to be an EE readable date (i.e. ISO Date string or
milliseconds).
agg_type : {'mean'}, optional
Aggregation type (the default is 'mean').
Returns
-------
ee.ImageCollection()
Notes
-----
This function should be used to mosaic Landsat images from same path
but different rows
Aggregation is currently hardcoded to 'mean'
system:time_start of returned images will be 0 UTC (not the image time)
"""
# Build a collection of date "features" to join to
date_list = ee.List.sequence(
ee.Date(start_date).millis(),
ee.Date(end_date).millis(),
# ee.Date(end_date).advance(1, 'day').millis(),
24 * 3600 * 1000)
def set_date(time):
return ee.Feature(
None, {
'system:index': ee.Date(time).format('yyyy-MM-dd'),
'system:time_start': ee.Number(time).int64(),
'DATE': ee.Date(time).format('yyyy-MM-dd')
})
# Add a date property to the image collection
def set_image_date(img):
return ee.Image(
img.set('DATE',
ee.Date(
img.get('system:time_start')).format('yyyy-MM-dd')))
join_coll = ee.FeatureCollection(
ee.Join.saveAll('join').apply(
ee.FeatureCollection(date_list.map(set_date)),
ee.ImageCollection(image_coll.map(set_image_date)),
ee.Filter.equals(leftField='DATE', rightField='DATE')))
def aggregate_func(ftr):
# The composite image time will be 0 UTC (not Landsat time)
# if agg_type.lower() == 'mean':
return ee.Image(
ee.ImageCollection.fromImages(
ftr.get('join')).mean().copyProperties(
ftr, system_properties + ['DATE']))
return ee.ImageCollection(join_coll.map(aggregate_func))