A16g Checkpoint

Import libraries¶

In [ ]:

import ee
import geemap

import ee import geemap

Create an interactive map¶

In [ ]:

Map = geemap.Map(center=[40, -100], zoom=4)

Map = geemap.Map(center=[40, -100], zoom=4)

Add Earth Engine Python script¶

In [ ]:

# Add Earth Engine dataset
image = ee.Image("USGS/SRTMGL1_003")

#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      A1.6 Health Applications
#  Checkpoint:   A16g
#  Author:       Dawn Nekorchuk
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Section 1: Data Import
woredas = ee.FeatureCollection(
    'projects/gee-book/assets/A1-6/amhara_woreda_20170207')
# Create region outer boundary to filter products on.
amhara = woredas.geometry().bounds()
gpm = ee.ImageCollection('NASA/GPM_L3/IMERG_V06')
LSTTerra8 = ee.ImageCollection('MODIS/061/MOD11A2') \
    .filterDate('2001-06-26', Date.now())
brdfReflect = ee.ImageCollection('MODIS/006/MCD43A4')
brdfQa = ee.ImageCollection('MODIS/006/MCD43A2')

# Visualize woredas with black borders and no fill.
# Create an empty image into which to paint the features, cast to byte.
empty = ee.Image().byte()
# Paint all the polygon edges with the same number and width.
outline = empty.paint({
    'featureCollection': woredas,
    'color': 1,
    'width': 1
})
# Add woreda boundaries to the map.
Map.setCenter(38, 11.5, 7)
Map.addLayer(outline, {
    'palette': '000000'
}, 'Woredas')

#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Section 2: Handling of dates

# 2.1 Requested start and end dates.
reqStartDate = ee.Date('2021-10-01')
reqEndDate = ee.Date('2021-11-30')

# 2.2 LST Dates
# LST MODIS is every 8 days, and a user-requested date will likely not match.
# We want to get the latest previous image date,
#  i.e. the date the closest, but prior to, the requested date.
# We will filter later.
# Get date of first image.
LSTEarliestDate = LSTTerra8.first().date()
# Filter collection to dates from beginning to requested start date.
priorLstImgCol = LSTTerra8.filterDate(LSTEarliestDate,
    reqStartDate)
# Get the latest (max) date of this collection of earlier images.
LSTPrevMax = priorLstImgCol.reduceColumns({
    'reducer': ee.Reducer.max(),
    'selectors': ['system:time_start']
})
LSTStartDate = ee.Date(LSTPrevMax.get('max'))
print('LSTStartDate', LSTStartDate)

# 2.3 Last available data dates
# Different variables have different data lags.
# Data may not be available in user range.
# To prevent errors from stopping script,
#  grab last available (if relevant) & filter at end.

# 2.3.1 Precipitation
# Calculate date of most recent measurement for gpm (of all time).
gpmAllMax = gpm.reduceColumns(ee.Reducer.max(), [
    'system:time_start'
])
gpmAllEndDateTime = ee.Date(gpmAllMax.get('max'))
# GPM every 30 minutes, so get just date part.
gpmAllEndDate = ee.Date.fromYMD({
    'year': gpmAllEndDateTime.get('year'),
    'month': gpmAllEndDateTime.get('month'),
    'day': gpmAllEndDateTime.get('day')
})

# If data ends before requested start, take last data date,
# otherwise use requested date.
precipStartDate = ee.Date(gpmAllEndDate.millis() \
    .min(reqStartDate.millis()))
print('precipStartDate', precipStartDate)

# 2.3.2 BRDF
# Calculate date of most recent measurement for brdf (of all time).
brdfAllMax = brdfReflect.reduceColumns({
    'reducer': ee.Reducer.max(),
    'selectors': ['system:time_start']
})
brdfAllEndDate = ee.Date(brdfAllMax.get('max'))
# If data ends before requested start, take last data date,
# otherwise use the requested date.
brdfStartDate = ee.Date(brdfAllEndDate.millis() \
    .min(reqStartDate.millis()))
print('brdfStartDate', brdfStartDate)
print('brdfEndDate', brdfAllEndDate)

#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Section 3: Precipitation

# Section 3.1: Precipitation filtering and dates

# Filter gpm by date, using modified start if necessary.
gpmFiltered = gpm \
    .filterDate(precipStartDate, reqEndDate.advance(1, 'day')) \
    .filterBounds(amhara) \
    .select('precipitationCal')

# Calculate date of most recent measurement for gpm
# (in the modified requested window).
gpmMax = gpmFiltered.reduceColumns({
    'reducer': ee.Reducer.max(),
    'selectors': ['system:time_start']
})
gpmEndDate = ee.Date(gpmMax.get('max'))
precipEndDate = gpmEndDate
print('precipEndDate ', precipEndDate)

# Create a list of dates for the precipitation time series.
precipDays = precipEndDate.difference(precipStartDate, 'day')
precipDatesPrep = ee.List.sequence(0, precipDays, 1)

def makePrecipDates(n):
    return precipStartDate.advance(n, 'day')

precipDates = precipDatesPrep.map(makePrecipDates)

# Section 3.2: Calculate daily precipitation

# Function to calculate daily precipitation:
def calcDailyPrecip(curdate):
    curdate = ee.Date(curdate)
    curyear = curdate.get('year')
    curdoy = curdate.getRelative('day', 'year').add(1)
    totprec = gpmFiltered \
        .filterDate(curdate, curdate.advance(1, 'day')) \
        .select('precipitationCal') \
        .sum() \
        .multiply(0.5) \
        .rename('totprec')

    return totprec \
        .set('doy', curdoy) \
        .set('year', curyear) \
        .set('system:time_start', curdate)

# Map function over list of dates.
dailyPrecipExtended =
    ee.ImageCollection.fromImages(precipDates.map(calcDailyPrecip))

# Filter back to the original user requested start date.
dailyPrecip = dailyPrecipExtended \
    .filterDate(reqStartDate, precipEndDate.advance(1, 'day'))

# Section 3.3: Summarize daily precipitation by woreda

# Filter precip data for zonal summaries.
precipSummary = dailyPrecip \
    .filterDate(reqStartDate, reqEndDate.advance(1, 'day'))

# Function to calculate zonal statistics for precipitation by woreda.
def sumZonalPrecip(image):
    # To get the doy and year,
    # 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']) \
        .reduceRegions({
            'collection': woredas,
            '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()

#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Section 4: Land surface temperature

# Section 4.1: Calculate LST variables

# Filter Terra LST by altered LST start date.
# Rarely, but at the end of the year if the last image is late in the year
#  with only a few days in its period, it will sometimes not grab
#  the next image. Add extra padding to reqEndDate and
#  it will be trimmed at the end.
LSTFiltered = LSTTerra8 \
    .filterDate(LSTStartDate, reqEndDate.advance(8, 'day')) \
    .filterBounds(amhara) \
    .select('LST_Day_1km', 'QC_Day', 'LST_Night_1km', 'QC_Night')

# Filter Terra LST by QA information.
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)

# Rescale temperature data and convert to degrees Celsius (C).
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)

# Section 4.2: Calculate daily LST

# Create list of dates for time series.
LSTRange = LSTVars.reduceColumns({
    'reducer': ee.Reducer.max(),
    'selectors': ['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)

# Function to calculate daily LST by assigning the 8-day composite summary
# to each day in the composite period:
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)

# Map the function over the image collection
dailyLstExtended =
    ee.ImageCollection.fromImages(LSTDates.map(calcDailyLst))

# Filter back to original user requested start date
dailyLst = dailyLstExtended \
    .filterDate(reqStartDate, LSTEndDate.advance(1, 'day'))

# Section 4.3: Summarize daily LST by woreda

# Filter LST data for zonal summaries.
LSTSummary = dailyLst \
    .filterDate(reqStartDate, reqEndDate.advance(1, 'day'))
# Function to calculate zonal statistics for LST by woreda:
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']) \
        .reduceRegions({
            'collection': woredas,
            '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()

#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Section 5: Spectral index NDWI

# Section 5.1: Calculate NDWI

# Filter BRDF-Adjusted Reflectance by date.
brdfReflectVars = brdfReflect \
    .filterDate(brdfStartDate, reqEndDate.advance(1, 'day')) \
    .filterBounds(amhara) \
    .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(amhara) \
    .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'])

# Join the 2 collections.
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)

# Add QA bands to the NBAR collection.
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))

# Function to mask out pixels based on QA and water/land flags.
def filterBrdf(image):
    # Using QA info for the NIR band.
    qaband = image.select(['qa2'])
    wband = image.select(['water'])
    qamask = qaband.lte(2).And(wband.eq(1))
    nir_r = image.select('nir').multiply(0.0001).rename('nir_r')
    swir2_r = image.select('swir2').multiply(0.0001).rename(
        'swir2_r')
    return image.addBands(nir_r) \
        .addBands(swir2_r) \
        .updateMask(qamask)

brdfFilteredVars = brdfMerged.map(filterBrdf)

# Function to calculate spectral indices:
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)
    ndwi6 = image.normalizedDifference(['nir_r', 'swir2_r']) \
        .rename('ndwi6')
    return image.addBands(ndwi6) \
        .set('doy', curdoy) \
        .set('year', curyear)

# Map function over image collection.
brdfFilteredVars = brdfFilteredVars.map(calcBrdfIndices)

# Section 5.2: Calculate daily NDWI

# Create list of dates for full time series.
brdfRange = brdfFilteredVars.reduceColumns({
    'reducer': ee.Reducer.max(),
    'selectors': ['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)

# List of dates that exist in BRDF data.
brdfDatesExist = brdfFilteredVars \
    .aggregate_array('system:time_start')

# Get daily brdf values.
def calcDailyBrdfExists(curdate):
    curdate = ee.Date(curdate)
    curyear = curdate.get('year')
    curdoy = curdate.getRelative('day', 'year').add(1)
    brdfTemp = brdfFilteredVars \
        .filterDate(curdate, curdate.advance(1, 'day'))
    outImg = brdfTemp.first()
    return outImg

dailyBrdfExtExists =
    ee.ImageCollection.fromImages(brdfDatesExist.map(
        calcDailyBrdfExists))

# Create empty results, to fill in dates when BRDF data does not exist.
def calcDailyBrdfFiller(curdate):
    curdate = ee.Date(curdate)
    curyear = curdate.get('year')
    curdoy = curdate.getRelative('day', 'year').add(1)
    brdfTemp = brdfFilteredVars \
        .filterDate(curdate, curdate.advance(1, 'day'))
    brdfSize = brdfTemp.size()
    outImg = 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) \
        .set('brdfSize', brdfSize)
    return outImg

# Create filler for all dates.
dailyBrdfExtendedFiller =
    ee.ImageCollection.fromImages(brdfDates.map(calcDailyBrdfFiller))
# But only used if and when size was 0.
dailyBrdfExtFillFilt = dailyBrdfExtendedFiller \
    .filter(ee.Filter.eq('brdfSize', 0))
# Merge the two collections.
dailyBrdfExtended = dailyBrdfExtExists \
    .merge(dailyBrdfExtFillFilt)

# Filter back to original user requested start date.
dailyBrdf = dailyBrdfExtended \
    .filterDate(reqStartDate, brdfEndDate.advance(1, 'day'))

# Section 5.3: Summarize daily spectral indices by woreda

# Filter spectral indices for zonal summaries.
brdfSummary = dailyBrdf \
    .filterDate(reqStartDate, reqEndDate.advance(1, 'day'))

# Function to calculate zonal statistics for spectral indices by woreda:
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()
    ])
    # Reduce by regions to get zonal means for each woreda.
    output = image2.select(['doy', 'year', 'ndwi6']) \
        .reduceRegions({
            'collection': woredas,
            'reducer': ee.Reducer.mean(),
            'scale': 1000
        })
    return output


# Map the zonal statistics function over the filtered spectral index data.
brdfWoreda = brdfSummary.map(sumZonalBrdf)
# Flatten the results for export.
brdfFlat = brdfWoreda.flatten()

#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Section 6: Map display of calculated environmental variables
displayDate = ee.Date('2021-10-01')

precipDisp = dailyPrecip \
    .filterDate(displayDate, displayDate.advance(1, 'day'))
brdfDisp = dailyBrdf \
    .filterDate(displayDate, displayDate.advance(1, 'day'))
LSTDisp = dailyLst \
    .filterDate(displayDate, displayDate.advance(1, 'day'))

# Select the image (should be only one) from each collection.
precipImage = precipDisp.first().select('totprec')
LSTmImage = LSTDisp.first().select('LST_mean')
ndwi6Image = brdfDisp.first().select('ndwi6')

# Palettes for environmental variable maps:
palettePrecip = ['f7fbff', '08306b']
paletteLst = ['fff5f0', '67000d']
paletteSpectral = ['ffffe5', '004529']

# Add layers to the map.
# Show precipitation by default,
#  others hidden until users picks them from layers drop down.
Map.addLayer({
    'eeObject': precipImage,
    'visParams': {
        'min': 0,
        'max': 20,
        'palette': palettePrecip
    },
    'name': 'Precipitation',
    'shown': True,
    'opacity': 0.75
})
Map.addLayer({
    'eeObject': LSTmImage,
    'visParams': {
        'min': 0,
        'max': 40,
        'palette': paletteLst
    },
    'name': 'LST Mean',
    'shown': False,
    'opacity': 0.75
})
Map.addLayer({
    'eeObject': ndwi6Image,
    'visParams': {
        'min': 0,
        'max': 1,
        'palette': paletteSpectral
    },
    'name': 'NDWI6',
    'shown': False,
    'opacity': 0.75
})

#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Section 7: Exporting

# 7.1 Export naming
reqStartDateText = reqStartDate.format('yyyy-MM-dd').getInfo()

# Precipitation
precipPrefix = 'Export_Precip_Data'
precipLastDate = ee.Date(reqEndDate.millis() \
    .min(precipEndDate.millis()))
precipSummaryEndDate = precipLastDate \
    .format('yyyy-MM-dd').getInfo()
precipFilename = precipPrefix \
    .concat('_', reqStartDateText,
        '_', precipSummaryEndDate)
# LST
LSTPrefix = 'Export_LST_Data'
LSTLastDate = ee.Date(reqEndDate.millis() \
    .min(LSTEndDate.millis()))
LSTSummaryEndDate = LSTLastDate \
    .format('yyyy-MM-dd').getInfo()
LSTFilename = LSTPrefix \
    .concat('_', reqStartDateText,
        '_', LSTSummaryEndDate)
# BRDF
brdfPrefix = 'Export_Spectral_Data'
brdfLastDate = ee.Date(reqEndDate.millis() \
    .min(brdfEndDate.millis()))
brdfSummaryEndDate = brdfLastDate \
    .format('yyyy-MM-dd').getInfo()
brdfFilename = brdfPrefix \
    .concat('_', reqStartDateText,
        '_', brdfSummaryEndDate)

# 7.2 Export flattened tables to Google Drive
# Need to click 'RUN in the Tasks tab to configure and start each export.
Export.table.toDrive({
    'collection': precipFlat,
    'description': precipFilename,
    'selectors': ['wid', 'woreda', 'doy', 'year', 'totprec']
})
Export.table.toDrive({
    'collection': LSTFlat,
    'description': LSTFilename,
    'selectors': ['wid', 'woreda', 'doy', 'year',
        'LST_day', 'LST_night', 'LST_mean'
    ]
})
Export.table.toDrive({
    'collection': brdfFlat,
    'description': brdfFilename,
    'selectors': ['wid', 'woreda', 'doy', 'year', 'ndwi6']
})

#  -----------------------------------------------------------------------
#  CHECKPOINT
#  -----------------------------------------------------------------------

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: A1.6 Health Applications # Checkpoint: A16g # Author: Dawn Nekorchuk # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Section 1: Data Import woredas = ee.FeatureCollection( 'projects/gee-book/assets/A1-6/amhara_woreda_20170207') # Create region outer boundary to filter products on. amhara = woredas.geometry().bounds() gpm = ee.ImageCollection('NASA/GPM_L3/IMERG_V06') LSTTerra8 = ee.ImageCollection('MODIS/061/MOD11A2') \ .filterDate('2001-06-26', Date.now()) brdfReflect = ee.ImageCollection('MODIS/006/MCD43A4') brdfQa = ee.ImageCollection('MODIS/006/MCD43A2') # Visualize woredas with black borders and no fill. # Create an empty image into which to paint the features, cast to byte. empty = ee.Image().byte() # Paint all the polygon edges with the same number and width. outline = empty.paint({ 'featureCollection': woredas, 'color': 1, 'width': 1 }) # Add woreda boundaries to the map. Map.setCenter(38, 11.5, 7) Map.addLayer(outline, { 'palette': '000000' }, 'Woredas') # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # Section 2: Handling of dates # 2.1 Requested start and end dates. reqStartDate = ee.Date('2021-10-01') reqEndDate = ee.Date('2021-11-30') # 2.2 LST Dates # LST MODIS is every 8 days, and a user-requested date will likely not match. # We want to get the latest previous image date, # i.e. the date the closest, but prior to, the requested date. # We will filter later. # Get date of first image. LSTEarliestDate = LSTTerra8.first().date() # Filter collection to dates from beginning to requested start date. priorLstImgCol = LSTTerra8.filterDate(LSTEarliestDate, reqStartDate) # Get the latest (max) date of this collection of earlier images. LSTPrevMax = priorLstImgCol.reduceColumns({ 'reducer': ee.Reducer.max(), 'selectors': ['system:time_start'] }) LSTStartDate = ee.Date(LSTPrevMax.get('max')) print('LSTStartDate', LSTStartDate) # 2.3 Last available data dates # Different variables have different data lags. # Data may not be available in user range. # To prevent errors from stopping script, # grab last available (if relevant) & filter at end. # 2.3.1 Precipitation # Calculate date of most recent measurement for gpm (of all time). gpmAllMax = gpm.reduceColumns(ee.Reducer.max(), [ 'system:time_start' ]) gpmAllEndDateTime = ee.Date(gpmAllMax.get('max')) # GPM every 30 minutes, so get just date part. gpmAllEndDate = ee.Date.fromYMD({ 'year': gpmAllEndDateTime.get('year'), 'month': gpmAllEndDateTime.get('month'), 'day': gpmAllEndDateTime.get('day') }) # If data ends before requested start, take last data date, # otherwise use requested date. precipStartDate = ee.Date(gpmAllEndDate.millis() \ .min(reqStartDate.millis())) print('precipStartDate', precipStartDate) # 2.3.2 BRDF # Calculate date of most recent measurement for brdf (of all time). brdfAllMax = brdfReflect.reduceColumns({ 'reducer': ee.Reducer.max(), 'selectors': ['system:time_start'] }) brdfAllEndDate = ee.Date(brdfAllMax.get('max')) # If data ends before requested start, take last data date, # otherwise use the requested date. brdfStartDate = ee.Date(brdfAllEndDate.millis() \ .min(reqStartDate.millis())) print('brdfStartDate', brdfStartDate) print('brdfEndDate', brdfAllEndDate) # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # Section 3: Precipitation # Section 3.1: Precipitation filtering and dates # Filter gpm by date, using modified start if necessary. gpmFiltered = gpm \ .filterDate(precipStartDate, reqEndDate.advance(1, 'day')) \ .filterBounds(amhara) \ .select('precipitationCal') # Calculate date of most recent measurement for gpm # (in the modified requested window). gpmMax = gpmFiltered.reduceColumns({ 'reducer': ee.Reducer.max(), 'selectors': ['system:time_start'] }) gpmEndDate = ee.Date(gpmMax.get('max')) precipEndDate = gpmEndDate print('precipEndDate ', precipEndDate) # Create a list of dates for the precipitation time series. precipDays = precipEndDate.difference(precipStartDate, 'day') precipDatesPrep = ee.List.sequence(0, precipDays, 1) def makePrecipDates(n): return precipStartDate.advance(n, 'day') precipDates = precipDatesPrep.map(makePrecipDates) # Section 3.2: Calculate daily precipitation # Function to calculate daily precipitation: def calcDailyPrecip(curdate): curdate = ee.Date(curdate) curyear = curdate.get('year') curdoy = curdate.getRelative('day', 'year').add(1) totprec = gpmFiltered \ .filterDate(curdate, curdate.advance(1, 'day')) \ .select('precipitationCal') \ .sum() \ .multiply(0.5) \ .rename('totprec') return totprec \ .set('doy', curdoy) \ .set('year', curyear) \ .set('system:time_start', curdate) # Map function over list of dates. dailyPrecipExtended = ee.ImageCollection.fromImages(precipDates.map(calcDailyPrecip)) # Filter back to the original user requested start date. dailyPrecip = dailyPrecipExtended \ .filterDate(reqStartDate, precipEndDate.advance(1, 'day')) # Section 3.3: Summarize daily precipitation by woreda # Filter precip data for zonal summaries. precipSummary = dailyPrecip \ .filterDate(reqStartDate, reqEndDate.advance(1, 'day')) # Function to calculate zonal statistics for precipitation by woreda. def sumZonalPrecip(image): # To get the doy and year, # 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']) \ .reduceRegions({ 'collection': woredas, '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() # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # Section 4: Land surface temperature # Section 4.1: Calculate LST variables # Filter Terra LST by altered LST start date. # Rarely, but at the end of the year if the last image is late in the year # with only a few days in its period, it will sometimes not grab # the next image. Add extra padding to reqEndDate and # it will be trimmed at the end. LSTFiltered = LSTTerra8 \ .filterDate(LSTStartDate, reqEndDate.advance(8, 'day')) \ .filterBounds(amhara) \ .select('LST_Day_1km', 'QC_Day', 'LST_Night_1km', 'QC_Night') # Filter Terra LST by QA information. 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) # Rescale temperature data and convert to degrees Celsius (C). 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) # Section 4.2: Calculate daily LST # Create list of dates for time series. LSTRange = LSTVars.reduceColumns({ 'reducer': ee.Reducer.max(), 'selectors': ['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) # Function to calculate daily LST by assigning the 8-day composite summary # to each day in the composite period: 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) # Map the function over the image collection dailyLstExtended = ee.ImageCollection.fromImages(LSTDates.map(calcDailyLst)) # Filter back to original user requested start date dailyLst = dailyLstExtended \ .filterDate(reqStartDate, LSTEndDate.advance(1, 'day')) # Section 4.3: Summarize daily LST by woreda # Filter LST data for zonal summaries. LSTSummary = dailyLst \ .filterDate(reqStartDate, reqEndDate.advance(1, 'day')) # Function to calculate zonal statistics for LST by woreda: 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']) \ .reduceRegions({ 'collection': woredas, '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() # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # Section 5: Spectral index NDWI # Section 5.1: Calculate NDWI # Filter BRDF-Adjusted Reflectance by date. brdfReflectVars = brdfReflect \ .filterDate(brdfStartDate, reqEndDate.advance(1, 'day')) \ .filterBounds(amhara) \ .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(amhara) \ .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']) # Join the 2 collections. 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) # Add QA bands to the NBAR collection. 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)) # Function to mask out pixels based on QA and water/land flags. def filterBrdf(image): # Using QA info for the NIR band. qaband = image.select(['qa2']) wband = image.select(['water']) qamask = qaband.lte(2).And(wband.eq(1)) nir_r = image.select('nir').multiply(0.0001).rename('nir_r') swir2_r = image.select('swir2').multiply(0.0001).rename( 'swir2_r') return image.addBands(nir_r) \ .addBands(swir2_r) \ .updateMask(qamask) brdfFilteredVars = brdfMerged.map(filterBrdf) # Function to calculate spectral indices: 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) ndwi6 = image.normalizedDifference(['nir_r', 'swir2_r']) \ .rename('ndwi6') return image.addBands(ndwi6) \ .set('doy', curdoy) \ .set('year', curyear) # Map function over image collection. brdfFilteredVars = brdfFilteredVars.map(calcBrdfIndices) # Section 5.2: Calculate daily NDWI # Create list of dates for full time series. brdfRange = brdfFilteredVars.reduceColumns({ 'reducer': ee.Reducer.max(), 'selectors': ['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) # List of dates that exist in BRDF data. brdfDatesExist = brdfFilteredVars \ .aggregate_array('system:time_start') # Get daily brdf values. def calcDailyBrdfExists(curdate): curdate = ee.Date(curdate) curyear = curdate.get('year') curdoy = curdate.getRelative('day', 'year').add(1) brdfTemp = brdfFilteredVars \ .filterDate(curdate, curdate.advance(1, 'day')) outImg = brdfTemp.first() return outImg dailyBrdfExtExists = ee.ImageCollection.fromImages(brdfDatesExist.map( calcDailyBrdfExists)) # Create empty results, to fill in dates when BRDF data does not exist. def calcDailyBrdfFiller(curdate): curdate = ee.Date(curdate) curyear = curdate.get('year') curdoy = curdate.getRelative('day', 'year').add(1) brdfTemp = brdfFilteredVars \ .filterDate(curdate, curdate.advance(1, 'day')) brdfSize = brdfTemp.size() outImg = 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) \ .set('brdfSize', brdfSize) return outImg # Create filler for all dates. dailyBrdfExtendedFiller = ee.ImageCollection.fromImages(brdfDates.map(calcDailyBrdfFiller)) # But only used if and when size was 0. dailyBrdfExtFillFilt = dailyBrdfExtendedFiller \ .filter(ee.Filter.eq('brdfSize', 0)) # Merge the two collections. dailyBrdfExtended = dailyBrdfExtExists \ .merge(dailyBrdfExtFillFilt) # Filter back to original user requested start date. dailyBrdf = dailyBrdfExtended \ .filterDate(reqStartDate, brdfEndDate.advance(1, 'day')) # Section 5.3: Summarize daily spectral indices by woreda # Filter spectral indices for zonal summaries. brdfSummary = dailyBrdf \ .filterDate(reqStartDate, reqEndDate.advance(1, 'day')) # Function to calculate zonal statistics for spectral indices by woreda: 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() ]) # Reduce by regions to get zonal means for each woreda. output = image2.select(['doy', 'year', 'ndwi6']) \ .reduceRegions({ 'collection': woredas, 'reducer': ee.Reducer.mean(), 'scale': 1000 }) return output # Map the zonal statistics function over the filtered spectral index data. brdfWoreda = brdfSummary.map(sumZonalBrdf) # Flatten the results for export. brdfFlat = brdfWoreda.flatten() # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # Section 6: Map display of calculated environmental variables displayDate = ee.Date('2021-10-01') precipDisp = dailyPrecip \ .filterDate(displayDate, displayDate.advance(1, 'day')) brdfDisp = dailyBrdf \ .filterDate(displayDate, displayDate.advance(1, 'day')) LSTDisp = dailyLst \ .filterDate(displayDate, displayDate.advance(1, 'day')) # Select the image (should be only one) from each collection. precipImage = precipDisp.first().select('totprec') LSTmImage = LSTDisp.first().select('LST_mean') ndwi6Image = brdfDisp.first().select('ndwi6') # Palettes for environmental variable maps: palettePrecip = ['f7fbff', '08306b'] paletteLst = ['fff5f0', '67000d'] paletteSpectral = ['ffffe5', '004529'] # Add layers to the map. # Show precipitation by default, # others hidden until users picks them from layers drop down. Map.addLayer({ 'eeObject': precipImage, 'visParams': { 'min': 0, 'max': 20, 'palette': palettePrecip }, 'name': 'Precipitation', 'shown': True, 'opacity': 0.75 }) Map.addLayer({ 'eeObject': LSTmImage, 'visParams': { 'min': 0, 'max': 40, 'palette': paletteLst }, 'name': 'LST Mean', 'shown': False, 'opacity': 0.75 }) Map.addLayer({ 'eeObject': ndwi6Image, 'visParams': { 'min': 0, 'max': 1, 'palette': paletteSpectral }, 'name': 'NDWI6', 'shown': False, 'opacity': 0.75 }) # ----------------------------------------------------------------------- # CHECKPOINT # ----------------------------------------------------------------------- # Section 7: Exporting # 7.1 Export naming reqStartDateText = reqStartDate.format('yyyy-MM-dd').getInfo() # Precipitation precipPrefix = 'Export_Precip_Data' precipLastDate = ee.Date(reqEndDate.millis() \ .min(precipEndDate.millis())) precipSummaryEndDate = precipLastDate \ .format('yyyy-MM-dd').getInfo() precipFilename = precipPrefix \ .concat('_', reqStartDateText, '_', precipSummaryEndDate) # LST LSTPrefix = 'Export_LST_Data' LSTLastDate = ee.Date(reqEndDate.millis() \ .min(LSTEndDate.millis())) LSTSummaryEndDate = LSTLastDate \ .format('yyyy-MM-dd').getInfo() LSTFilename = LSTPrefix \ .concat('_', reqStartDateText, '_', LSTSummaryEndDate) # BRDF brdfPrefix = 'Export_Spectral_Data' brdfLastDate = ee.Date(reqEndDate.millis() \ .min(brdfEndDate.millis())) brdfSummaryEndDate = brdfLastDate \ .format('yyyy-MM-dd').getInfo() brdfFilename = brdfPrefix \ .concat('_', reqStartDateText, '_', brdfSummaryEndDate) # 7.2 Export flattened tables to Google Drive # Need to click 'RUN in the Tasks tab to configure and start each export. Export.table.toDrive({ 'collection': precipFlat, 'description': precipFilename, 'selectors': ['wid', 'woreda', 'doy', 'year', 'totprec'] }) Export.table.toDrive({ 'collection': LSTFlat, 'description': LSTFilename, 'selectors': ['wid', 'woreda', 'doy', 'year', 'LST_day', 'LST_night', 'LST_mean' ] }) Export.table.toDrive({ 'collection': brdfFlat, 'description': brdfFilename, 'selectors': ['wid', 'woreda', 'doy', 'year', 'ndwi6'] }) # ----------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------

Display the interactive map¶

In [ ]:

Map

Map