Annexes
algiers_map = folium.Map(location=[36.8, 3.05], zoom_start=10)
Importation des ROI pour le découpage des images
coast = ee.Collection.loadTable('users/izehichem/coastline_poly')
coast_geo = coast.geometry()
algiers_roi = ee.Geometry.Rectangle([2.2,36.5, 3.8, 36.9])
Importation de la collection d'image Landsat 8
landsat = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR").filterBounds(algier
s_roi).filterMetadata('CLOUD_COVER', 'LESS_THAN', 0.5).map(maskQuality);
Definition d'une fonction englobant les algorithmes
d'éxtraction
def oceanL8(imageCollection, roi, folder):
for m in [9]:
#Filtration des images par mois
landsat_season = imageCollection.filter(ee.Filter.calendarRange(20
14, 2019, 'year')).filter(ee.Filter.calendarRange(m, m, 'month')).mean()
#--------- Chlorophyll algorithme ------------blue1 = landsat_season.select('B1')
blue2 = landsat_season.select('B2')
green = landsat_season.select('B3')
R1 = blue1.divide(green)
R2 = blue2.divide(green)
array = [R1, R2]
R_max = R1.max(R2)
R = R_max.log10()
chl = landsat_season.expression('10**(0.2412 - 2.0546*R + 1.1776*R
**2 - 0.5538*R**3 -0.4570*R**4) ', {
'R' : R
}).clip(roi);
#Exportation du résultat vers google drive
task1 = ee.batch.Export.image.toDrive(image=chl,
folder=folder,
region=roi,
scale=30,
fileNamePrefix='chl_%d' % (m))
task1.start()
#-------- Total particulate matter algorithme -----b4 = landsat_season.select('B5')
A = 2971.93
C= 0.2115