SMWalgorithm

Import libraries¶

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import ee
import geemap

import ee import geemap

Create an interactive map¶

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

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

Add Earth Engine Python script¶

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# 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 applies the Stastical Mono-Window algorithm to compute the LST

'to call this function use':

LSTfun = require('users/sofiaermida/landsat_smw_lst:modules/SMWalgorithm.js')
ImagewithLST = LSTfun.addBand(landsat)(image)
or
collectionwithLST = ImageCollection.map(LSTfun.addBand(landsat))

'USES':
    - SMW_coefficients.js

'INPUTS':
        '- landsat': <string>
                  the Landsat satellite id
                  'valid inputs': 'L4', 'L5', 'L7' and 'L8'
        '- image': <ee.Image>
                image for which to calculate the LSTy
'OUTPUTS':
        - <ee.Image>
          'the input image with 1 new band':
          'LST': land surface temperature
#


# coefficients for the Statistical Mono-Window Algorithm
SMWcoef = require('users/sofiaermida/landsat_smw_lst:modules/SMW_coefficients.js')

# Function to create a lookup between two columns in a
# feature collection
def get_lookup_table(fc, prop_1, prop_2):
  reducer = ee.Reducer.toList().repeat(2)
  lookup = fc.reduceColumns(reducer, [prop_1, prop_2])
  return ee.List(lookup.get('list'))



def exports.addBand(landsat):

  def wrap(image):

    # Select algorithm coefficients
    coeff_SMW = ee.FeatureCollection(ee.Algorithms.If(landsat==='L4',SMWcoef.coeff_SMW_L4,
                                        ee.Algorithms.If(landsat==='L5',SMWcoef.coeff_SMW_L5,
                                        ee.Algorithms.If(landsat==='L7',SMWcoef.coeff_SMW_L7,
                                        SMWcoef.coeff_SMW_L8))))

    # Create lookups for the algorithm coefficients
    A_lookup = get_lookup_table(coeff_SMW, 'TPWpos', 'A')
    B_lookup = get_lookup_table(coeff_SMW, 'TPWpos', 'B')
    C_lookup = get_lookup_table(coeff_SMW, 'TPWpos', 'C')

    # Map coefficients to the image using the TPW bin position
    A_img = image.remap(A_lookup.get(0), A_lookup.get(1),0.0,'TPWpos').resample('bilinear')
    B_img = image.remap(B_lookup.get(0), B_lookup.get(1),0.0,'TPWpos').resample('bilinear')
    C_img = image.remap(C_lookup.get(0), C_lookup.get(1),0.0,'TPWpos').resample('bilinear')

    # select TIR band
    tir = ee.String(ee.Algorithms.If(landsat==='L8','B10',
                        ee.Algorithms.If(landsat==='L7','B6_VCID_1',
                        'B6')))
    # compute the LST
    lst = image.expression(
      'A*Tb1/em1 + B/em1 + C',
         {'A': A_img,
          'B': B_img,
          'C': C_img,
          'em1': image.select('EM'),
          'Tb1': image.select(tir)
         }).updateMask(image.select('TPW').lt(0).Not())


    return image.addBands(lst.rename('LST'))
  
  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 applies the Stastical Mono-Window algorithm to compute the LST 'to call this function use': LSTfun = require('users/sofiaermida/landsat_smw_lst:modules/SMWalgorithm.js') ImagewithLST = LSTfun.addBand(landsat)(image) or collectionwithLST = ImageCollection.map(LSTfun.addBand(landsat)) 'USES': - SMW_coefficients.js 'INPUTS': '- landsat': the Landsat satellite id 'valid inputs': 'L4', 'L5', 'L7' and 'L8' '- image': image for which to calculate the LSTy 'OUTPUTS': - 'the input image with 1 new band': 'LST': land surface temperature # # coefficients for the Statistical Mono-Window Algorithm SMWcoef = require('users/sofiaermida/landsat_smw_lst:modules/SMW_coefficients.js') # Function to create a lookup between two columns in a # feature collection def get_lookup_table(fc, prop_1, prop_2): reducer = ee.Reducer.toList().repeat(2) lookup = fc.reduceColumns(reducer, [prop_1, prop_2]) return ee.List(lookup.get('list')) def exports.addBand(landsat): def wrap(image): # Select algorithm coefficients coeff_SMW = ee.FeatureCollection(ee.Algorithms.If(landsat==='L4',SMWcoef.coeff_SMW_L4, ee.Algorithms.If(landsat==='L5',SMWcoef.coeff_SMW_L5, ee.Algorithms.If(landsat==='L7',SMWcoef.coeff_SMW_L7, SMWcoef.coeff_SMW_L8)))) # Create lookups for the algorithm coefficients A_lookup = get_lookup_table(coeff_SMW, 'TPWpos', 'A') B_lookup = get_lookup_table(coeff_SMW, 'TPWpos', 'B') C_lookup = get_lookup_table(coeff_SMW, 'TPWpos', 'C') # Map coefficients to the image using the TPW bin position A_img = image.remap(A_lookup.get(0), A_lookup.get(1),0.0,'TPWpos').resample('bilinear') B_img = image.remap(B_lookup.get(0), B_lookup.get(1),0.0,'TPWpos').resample('bilinear') C_img = image.remap(C_lookup.get(0), C_lookup.get(1),0.0,'TPWpos').resample('bilinear') # select TIR band tir = ee.String(ee.Algorithms.If(landsat==='L8','B10', ee.Algorithms.If(landsat==='L7','B6_VCID_1', 'B6'))) # compute the LST lst = image.expression( 'A*Tb1/em1 + B/em1 + C', {'A': A_img, 'B': B_img, 'C': C_img, 'em1': image.select('EM'), 'Tb1': image.select(tir) }).updateMask(image.select('TPW').lt(0).Not()) return image.addBands(lst.rename('LST')) return wrap

Display the interactive map¶

In [ ]:

Map

Map