Compute emissivity

Import libraries¶

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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")

#
'Author': Sofia Ermida (sofia.ermida@ipma.pt; @ermida_sofia)

This code is free and open.
By using this code and any data derived with it,
you agree to cite the following reference
'in any publications derived from them':
Ermida, S.L., Soares, P., Mantas, V., Göttsche, F.-M., Trigo, I.F., 2020.
    Google Earth Engine open-source code for Land Surface Temperature estimation from the Landsat series.
    'Remote Sensing, 12 (9), 1471; https':#doi.Org/10.3390/rs12091471

this function computes surface emissivity for Landsat
'requires values of FVC': compute_FVC.js

'ref': Malakar, N.K., Hulley, G.C., Hook, S.J., Laraby, K., Cook, M., Schott, J.R., 2018.
    'An Operational Land Surface Temperature Product for Landsat Thermal Data': Methodology
    and Validation. IEEE Trans. Geosci. Remote Sens. 56, 5717–5735.
    'https':#doi.Org/10.1109/TGRS.2018.2824828

'to call this function use':

EMfun = require('users/sofiaermida/landsat_smw_lst:modules/compute_emissivity.js')
ImagewithEM = EMfun.addBand(landsat)(image)
or
collectionwithEM = ImageCollection.map(EMfun.addBand(landsat))

'USES':
    - ASTER_bare_emiss.js

'INPUTS':
        '- landsat': <string>
                  the Landsat satellite id
                  'valid inputs': 'L4', 'L5', 'L7' and 'L8'
        '- use_ndvi': <boolean>
                if True, NDVI values are used to obtain a
                dynamic emissivity; if False, emissivity is
                obtained directly from ASTER
        '- image': <ee.Image>
                image for which to calculate the emissivity
'OUTPUTS':
        - <ee.Image>
          'the input image with 1 new band':
          'EM': surface emissivity of TIR band

  '11-07-2022': update to prescribe emissivity of snow and water surfaces
#

ASTERGED = require('users/sofiaermida/landsat_smw_lst:modules/ASTER_bare_emiss.js')

# this function computes the emissivity of the
# Landsat TIR band using ASTER and FVC
def exports.addBand(landsat, use_ndvi):
  def wrap(image):

    c13 = ee.Number(ee.Algorithms.If(landsat==='L4',0.3222,
                            ee.Algorithms.If(landsat==='L5',-0.0723,
                            ee.Algorithms.If(landsat==='L7',0.2147,
                            0.6820))))
    c14 = ee.Number(ee.Algorithms.If(landsat==='L4',0.6498,
                            ee.Algorithms.If(landsat==='L5',1.0521,
                            ee.Algorithms.If(landsat==='L7',0.7789,
                            0.2578))))
    c = ee.Number(ee.Algorithms.If(landsat==='L4',0.0272,
                            ee.Algorithms.If(landsat==='L5',0.0195,
                            ee.Algorithms.If(landsat==='L7',0.0059,
                            0.0584))))

    # get ASTER emissivity
    # convolve to Landsat band
    emiss_bare = image.expression('c13*EM13 + c14*EM14 + c',{
      'EM13':ASTERGED.emiss_bare_band13(image),
      'EM14':ASTERGED.emiss_bare_band14(image),
      'c13':ee.Image(c13),
      'c14':ee.Image(c14),
      'c':ee.Image(c)
      })

    # compute the dynamic emissivity for Landsat
    EMd = image.expression('fvc*0.99+(1-fvc)*em_bare',
      {'fvc':image.select('FVC'),'em_bare':emiss_bare})

    # compute emissivity directly from ASTER
    # without vegetation correction
    # get ASTER emissivity
    aster = ee.Image("NASA/ASTER_GED/AG100_003") \
      .clip(image.geometry())
    EM0 = image.expression('c13*EM13 + c14*EM14 + c',{
      'EM13':aster.select('emissivity_band13').multiply(0.001),
      'EM14':aster.select('emissivity_band14').multiply(0.001),
      'c13':ee.Image(c13),
      'c14':ee.Image(c14),
      'c':ee.Image(c)
      })

    # select which emissivity to output based on user selection
    EM = ee.Image(ee.Algorithms.If(use_ndvi,EMd,EM0))

    # prescribe emissivity of water bodies
    qa = image.select('QA_PIXEL')
    EM = EM.where(qa.bitwiseAnd(1 << 7),0.99)
    # prescribe emissivity of snow/ice bodies
    EM = EM.where(qa.bitwiseAnd(1 << 5),0.989)

    return image.addBands(EM.rename('EM'))

  return wrap

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # 'Author': Sofia Ermida (sofia.ermida@ipma.pt; @ermida_sofia) This code is free and open. By using this code and any data derived with it, you agree to cite the following reference 'in any publications derived from them': Ermida, S.L., Soares, P., Mantas, V., Göttsche, F.-M., Trigo, I.F., 2020. Google Earth Engine open-source code for Land Surface Temperature estimation from the Landsat series. 'Remote Sensing, 12 (9), 1471; https':#doi.Org/10.3390/rs12091471 this function computes surface emissivity for Landsat 'requires values of FVC': compute_FVC.js 'ref': Malakar, N.K., Hulley, G.C., Hook, S.J., Laraby, K., Cook, M., Schott, J.R., 2018. 'An Operational Land Surface Temperature Product for Landsat Thermal Data': Methodology and Validation. IEEE Trans. Geosci. Remote Sens. 56, 5717–5735. 'https':#doi.Org/10.1109/TGRS.2018.2824828 'to call this function use': EMfun = require('users/sofiaermida/landsat_smw_lst:modules/compute_emissivity.js') ImagewithEM = EMfun.addBand(landsat)(image) or collectionwithEM = ImageCollection.map(EMfun.addBand(landsat)) 'USES': - ASTER_bare_emiss.js 'INPUTS': '- landsat': the Landsat satellite id 'valid inputs': 'L4', 'L5', 'L7' and 'L8' '- use_ndvi': if True, NDVI values are used to obtain a dynamic emissivity; if False, emissivity is obtained directly from ASTER '- image': image for which to calculate the emissivity 'OUTPUTS': - 'the input image with 1 new band': 'EM': surface emissivity of TIR band '11-07-2022': update to prescribe emissivity of snow and water surfaces # ASTERGED = require('users/sofiaermida/landsat_smw_lst:modules/ASTER_bare_emiss.js') # this function computes the emissivity of the # Landsat TIR band using ASTER and FVC def exports.addBand(landsat, use_ndvi): def wrap(image): c13 = ee.Number(ee.Algorithms.If(landsat==='L4',0.3222, ee.Algorithms.If(landsat==='L5',-0.0723, ee.Algorithms.If(landsat==='L7',0.2147, 0.6820)))) c14 = ee.Number(ee.Algorithms.If(landsat==='L4',0.6498, ee.Algorithms.If(landsat==='L5',1.0521, ee.Algorithms.If(landsat==='L7',0.7789, 0.2578)))) c = ee.Number(ee.Algorithms.If(landsat==='L4',0.0272, ee.Algorithms.If(landsat==='L5',0.0195, ee.Algorithms.If(landsat==='L7',0.0059, 0.0584)))) # get ASTER emissivity # convolve to Landsat band emiss_bare = image.expression('c13*EM13 + c14*EM14 + c',{ 'EM13':ASTERGED.emiss_bare_band13(image), 'EM14':ASTERGED.emiss_bare_band14(image), 'c13':ee.Image(c13), 'c14':ee.Image(c14), 'c':ee.Image(c) }) # compute the dynamic emissivity for Landsat EMd = image.expression('fvc*0.99+(1-fvc)*em_bare', {'fvc':image.select('FVC'),'em_bare':emiss_bare}) # compute emissivity directly from ASTER # without vegetation correction # get ASTER emissivity aster = ee.Image("NASA/ASTER_GED/AG100_003") \ .clip(image.geometry()) EM0 = image.expression('c13*EM13 + c14*EM14 + c',{ 'EM13':aster.select('emissivity_band13').multiply(0.001), 'EM14':aster.select('emissivity_band14').multiply(0.001), 'c13':ee.Image(c13), 'c14':ee.Image(c14), 'c':ee.Image(c) }) # select which emissivity to output based on user selection EM = ee.Image(ee.Algorithms.If(use_ndvi,EMd,EM0)) # prescribe emissivity of water bodies qa = image.select('QA_PIXEL') EM = EM.where(qa.bitwiseAnd(1 << 7),0.99) # prescribe emissivity of snow/ice bodies EM = EM.where(qa.bitwiseAnd(1 << 5),0.989) return image.addBands(EM.rename('EM')) return wrap

Display the interactive map¶

In [ ]:

Map

Map