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Updated landsat_init.py to 0.6b
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@OSINT-TECHNOLOGIES
OSINT-TECHNOLOGIES authored Feb 15, 2024
1 parent bf51a13 commit 298ff86
Showing 1 changed file with 32 additions and 87 deletions.
119 changes: 32 additions & 87 deletions gee/modules/landsat_init.py
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import ee
import mercury as mr

class LandsatInitialization:
class SentinelInitialization:
def __init__(self, m, date_one, date_two, max_cloud_covering):
self.m = m
self.date_one = date_one
self.date_two = date_two
self.max_cloud_covering = max_cloud_covering

self.ls7bands = mr.MultiSelect(label="Select LandSat 7 SR bands",
value=['SR_B3', 'SR_B2', 'SR_B1'],
choices=['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7'])
def sen2msisr_init(self):
self.sen2msibands = mr.MultiSelect(label="Select Sentinel 2 MSI (TOA/SR) bands",
value=['B2', 'B3', 'B4'],
choices=['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B11', 'B12'])

self.ls8bands = mr.MultiSelect(label="Select LandSat 8 TOA/RAW bands",
value=['B4', 'B3', 'B2'],
choices=['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9', 'B10', 'B11'])
sen2msi_sr = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED').filterDate(str(self.date_one.value),str(self.date_two.value)).filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', self.max_cloud_covering.value))
composite_sr = sen2msi_sr.median()

self.ls8srbands = mr.MultiSelect(label="Select LandSat 8 SR bands",
value=['SR_B4', 'SR_B3', 'SR_B2'],
choices=['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7'])

self.ls9bands = mr.MultiSelect(label="Select LandSat 9 SR bands",
value=['SR_B4', 'SR_B3', 'SR_B2'],
choices=['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'SR_B7'])

self.ls9rawbands = mr.MultiSelect(label="Select LandSat 9 RAW/TOA bands",
value=['B4', 'B3', 'B2'],
choices=['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9', 'B10', 'B11'])

def ls7sr_init(self):
ls7 = ee.ImageCollection("LANDSAT/LE07/C02/T1_L2").filterDate(str(self.date_one.value), str(self.date_two.value)).filter(
ee.Filter.lte('CLOUD_COVER', self.max_cloud_covering.value))
vis_ls7 = {'bands': self.ls7bands.value}
self.m.addLayer(ls7, vis_ls7, "LandSat 7 SR", True, 0.7)

def ls8toa_init(self):
ls8 = ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA").filterDate(str(self.date_one.value), str(self.date_two.value)).filter(
ee.Filter.lte('CLOUD_COVER', self.max_cloud_covering.value))
vis_ls8 = {
'bands': self.ls8bands.value,
'min': 0.0,
'max': 0.4,
vis_sen2sr = {
'bands': self.sen2msibands.value,
'min': 0,
'max': 3000,
'gamma': 1.4,
}
self.m.addLayer(ls8, vis_ls8, 'LandSat 8 TOA')

def ls8rawtc_init(self):
ls8raw = ee.ImageCollection('LANDSAT/LC09/C02/T1').filterDate(str(self.date_one.value), str(self.date_two.value)).filter(
ee.Filter.lte('CLOUD_COVER', self.max_cloud_covering.value))
ls8raw_tc = ls8raw.select(self.ls8bands.value)
vis_ls8raw_tc = {
min: 0.0,
max: 30000.0,
}
self.m.addLayer(ls8raw_tc, vis_ls8raw_tc, "LandSat 8 RAW")
self.m.addLayer(composite_sr, vis_sen2sr, 'Sentinel 2 MSI SR')

def ls8sr_init(self):
ls8sr = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').filterDate(str(self.date_one.value),
str(self.date_two.value)).filter(
ee.Filter.lte('CLOUD_COVER', self.max_cloud_covering.value))
vis_ls8sr = {
'bands': self.ls8srbands.value,
min: 0.0,
max: 0.3,
}
self.m.addLayer(ls8sr, vis_ls8sr, 'LandSat 8 SR')

def ls9_init(self):
def apply_scale_factors(image):
opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2)
thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0)
return image.addBands(opticalBands, None, True).addBands(thermalBands, None, True)
def sen2msitoa_init(self):
self.sen2msibands = mr.MultiSelect(label="Select Sentinel 2 MSI (TOA/SR) bands",
value=['B2', 'B3', 'B4'],
choices=['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B11', 'B12'])

collection = ee.ImageCollection('LANDSAT/LC09/C02/T1_L2').filterDate(str(self.date_one.value),
str(self.date_two.value)).filter(
ee.Filter.lte('CLOUD_COVER', self.max_cloud_covering.value))
sen2msi_toa = ee.ImageCollection('COPERNICUS/S2_HARMONIZED').filterDate(str(self.date_one.value), str(self.date_two.value)).filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE', self.max_cloud_covering.value))
composite_toa = sen2msi_toa.median()

median = collection.median()
ls9 = apply_scale_factors(median)

vis_ls9 = {
'bands': self.ls9bands.value,
'min': 0.0,
'max': 0.3,
vis_sen2toa = {
'bands': self.sen2msibands.value,
'min': 0,
'max': 3000,
'gamma': 1.4,
}
self.m.addLayer(ls9, vis_ls9, 'LandSat 9 SR')

self.m.addLayer(composite_toa, vis_sen2toa, 'Sentinel 2 MSI TOA')

def ls9rawtc_init(self):
ls9raw = ee.ImageCollection("LANDSAT/LC09/C02/T2").filterDate(str(self.date_one.value), str(self.date_two.value)).filter(
ee.Filter.lte('CLOUD_COVER', self.max_cloud_covering.value))
ls9raw_tc = ls9raw.select(self.ls9rawbands.value)
vis_ls9raw_tc = {
'min': 0.0,
'max': 30000.0,
def sen5pc_init(self):
sen5pc = ee.ImageCollection("COPERNICUS/S5P/OFFL/L3_CLOUD").select('cloud_fraction').filterDate(str(self.date_one.value),str(self.date_two.value))
vis_sen5pc = {
min: 0,
max: 0.95,
'palette': ['black', 'blue', 'purple', 'cyan', 'green', 'yellow', 'red']
}
self.m.addLayer(ls9raw_tc, vis_ls9raw_tc, 'LandSat 9 RAW')

def ls9toatc_init(self):
ls9toa = ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA').filterDate(str(self.date_one.value),
str(self.date_two.value)).filter(
ee.Filter.lte('CLOUD_COVER', self.max_cloud_covering.value))
ls9toa_tc = ls9toa.select(self.ls9rawbands.value)
vis_ls9toa_tc = {
min: 0.0,
max: 0.4,
}
self.m.addLayer(ls9toa_tc, vis_ls9toa_tc, 'LandSat 9 TOA')
self.m.addLayer(sen5pc, vis_sen5pc, 'Sentinel 5P Cloud')

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