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.idea/pysmm_v02.iml
View file @ d137178d
......@@ -2,7 +2,7 @@
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
<orderEntry type="jdk" jdkName="Python 3.7 (pysmm_v02)" jdkType="Python SDK" />
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......
.idea/workspace.xml
View file @ d137178d
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......@@ -17,6 +12,11 @@
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......@@ -24,21 +24,25 @@
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......@@ -63,6 +67,12 @@
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......@@ -120,11 +130,36 @@
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......@@ -135,91 +170,9 @@
<MESSAGE value="corrects readthedocs settings and creates new build" />
<MESSAGE value="updates the read the docs theme" />
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......@@ -244,6 +197,21 @@
<line>216</line>
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<url>file://$PROJECT_DIR$/pysmm/derive_SM.py</url>
<line>61</line>
<option name="timeStamp" value="163" />
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<line>2513</line>
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<url>file://$PROJECT_DIR$/pysmm/derive_SM.py</url>
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......
pysmm/GEE_wrappers.py
View file @ d137178d
......@@ -1672,7 +1672,7 @@ class GEE_extent(object):
mask_snow_frozen_GLDAS=False)
self.get_sand_content()
# self.get_clay_content()
self.get_clay_content()
self.get_bulk_density()
self.OVERWRITE = overwrite
......@@ -1691,9 +1691,10 @@ class GEE_extent(object):
tmp = ee.Image(image)
# Covert to linear
out = ee.Image(10).pow(tmp.select('VV_gamma0vol').divide(10))
out = ee.Image(10).pow(tmp.select(['VV_gamma0vol', 'VH_gamma0vol', 'VV_gamma0surf', 'VH_gamma0surf']).divide(10))
# rename
out = out.select(['constant'], ['VV_gamma0vol'])
#out = out.select(['constant_0', 'constant_2', 'constant_3'],
# ['VV_gamma0vol', 'VV_gamma0surf', 'VH_gamma0surf'])
return out.set('system:time_start', tmp.get('system:time_start'))
......@@ -1743,6 +1744,7 @@ class GEE_extent(object):
doi = self.S1_DATE
s1_selected = ee.Image(gee_s1_filtered.map(getddist).sort('dateDist').first())
s1_g0vol = s1_selected.select(['VV_gamma0vol', 'VH_gamma0vol'])
s1_g0surf = s1_selected.select(['VV_gamma0surf', 'VH_gamma0surf'])
if tempfilter == True:
# despeckle
......@@ -1760,35 +1762,45 @@ class GEE_extent(object):
# extract information
s1_g0vol_vv = s1_g0vol.select('VV_gamma0vol')
s1_g0vol_vh = s1_g0vol.select('VH_gamma0vol')
s1_g0surf_vv = s1_g0surf.select('VV_gamma0surf')
s1_g0surf_vh = s1_g0surf.select('VH_gamma0surf')
# calculate statistical moments
gee_s1_filtered = gee_s1_filtered.filterDate(str(doi.year) + '-01-01', str(doi.year) + '-12-31').select('VV_gamma0vol')
gee_s1_filtered = gee_s1_filtered.filterDate(str(doi.year) + '-01-01', str(doi.year) + '-12-31')#.select('VV_gamma0vol')
gee_s1_lin = gee_s1_filtered.map(tolin)
# check if median was alread computed
tmpcoords = self.roi.getInfo()['coordinates']
mean_asset_path = 's1med_' + str(abs(tmpcoords[0][0][0])) + \
'_' + str(abs(tmpcoords[0][0][1])) + '_' + \
str(abs(tmpcoords[0][2][0])) + \
'_' + str(abs(tmpcoords[0][2][1])) + \
'_' + str(self.sampling) + '_' + str(self.TRACK_NR) + '_' + str(doi.year)
mean_asset_path = mean_asset_path.replace('.', '')
mean_gvv_v = ee.Image('users/felixgreifeneder/' + mean_asset_path)
try:
mean_gvv_v.getInfo()
print('S1 median exists')
except:
# compute median
mean_gvv_v = ee.Image(gee_s1_lin.select('VV_gamma0vol').reduce(ee.Reducer.median(), parallelScale=16))
# export asset
self.GEE_2_asset(raster=mean_gvv_v, name=mean_asset_path, timeout=False, outdir='')
mean_gvv_v = ee.Image('users/felixgreifeneder/' + mean_asset_path)
# tmpcoords = self.roi.getInfo()['coordinates']
# mean_asset_path = 's1med_' + str(abs(tmpcoords[0][0][0])) + \
# '_' + str(abs(tmpcoords[0][0][1])) + '_' + \
# str(abs(tmpcoords[0][2][0])) + \
# '_' + str(abs(tmpcoords[0][2][1])) + \
# '_' + str(self.sampling) + '_' + str(self.TRACK_NR) + '_' + str(doi.year)
# mean_asset_path = mean_asset_path.replace('.', '')
# mean_gvv_v = ee.Image('users/felixgreifeneder/' + mean_asset_path)
# try:
# mean_gvv_v.getInfo()
# print('S1 median exists')
# except:
# compute median
mean_gvv_v = ee.Image(gee_s1_lin.select('VV_gamma0vol').reduce(ee.Reducer.median(), parallelScale=16))
mean_gvh_v = ee.Image(gee_s1_lin.select('VH_gamma0vol').reduce(ee.Reducer.median(), parallelScale=16))
mean_gvv_s = ee.Image(gee_s1_lin.select('VV_gamma0surf').reduce(ee.Reducer.median(), parallelScale=16))
mean_gvh_s = ee.Image(gee_s1_lin.select('VH_gamma0surf').reduce(ee.Reducer.median(), parallelScale=16))
# export asset
# self.GEE_2_asset(raster=mean_gvv_v, name=mean_asset_path, timeout=False, outdir='')
# mean_gvv_v = ee.Image('users/felixgreifeneder/' + mean_asset_path)
# std_gvv_v = ee.Image(gee_s1_lin.select('VV_gamma0vol').reduce(ee.Reducer.stdDev(), parallelScale=24))
# g0 - surf
# k1gvv_s = ee.Image(gee_s1_ln.select('VV_gamma0surf').reduce(ee.Reducer.mean(), parallelScale=24))
# k2gvv_s = ee.Image(gee_s1_ln.select('VV_gamma0surf').reduce(ee.Reducer.stdDev(), parallelScale=24))
k1gvv_v = ee.Image(gee_s1_filtered.select('VV_gamma0vol').reduce(ee.Reducer.mean(), parallelScale=24))
k1gvh_v = ee.Image(gee_s1_filtered.select('VH_gamma0vol').reduce(ee.Reducer.mean(), parallelScale=24))
k2gvv_v = ee.Image(gee_s1_filtered.select('VV_gamma0vol').reduce(ee.Reducer.stdDev(), parallelScale=24))
k1gvh_s = ee.Image(gee_s1_filtered.select('VH_gamma0surf').reduce(ee.Reducer.mean(), parallelScale=24))
k2gvv_s = ee.Image(gee_s1_filtered.select('VV_gamma0surf').reduce(ee.Reducer.stdDev(), parallelScale=24))
k2gvh_s = ee.Image(gee_s1_filtered.select('VH_gamma0surf').reduce(ee.Reducer.stdDev(), parallelScale=24))
# k3gvv_s = ee.Image(gee_s1_ln.select('VV_gamma0surf').reduce(ee.Reducer.skew(), parallelScale=24))
# k4gvv_s = ee.Image(gee_s1_ln.select('VV_gamma0surf').reduce(ee.Reducer.kurtosis(), parallelScale=24))
# mean_gvv_s = ee.Image(gee_s1_lin.select('VV_gamma0surf').reduce(ee.Reducer.mean(), parallelScale=24))
......@@ -1821,45 +1833,87 @@ class GEE_extent(object):
# self.S1_SIG0_VV_db = s1_sig0_vv
self.S1_G0VOL_VV_db = s1_g0vol_vv
self.S1G0VOLMEAN_VV = ee.Image(10).multiply(mean_gvv_v.log10()).copyProperties(mean_gvv_v)
self.S1_G0VOL_VH_db = s1_g0vol_vh
self.S1G0VOLMEAN_VH = ee.Image(10).multiply(mean_gvh_v.log10()).copyProperties(mean_gvh_v)
self.S1_G0SURF_VV_db = s1_g0surf_vv
self.S1_G0SURF_VH_db = s1_g0surf_vh
self.S1G0SURFMEAN_VV = ee.Image(10).multiply(mean_gvv_s.log10()).copyProperties(mean_gvv_s)
self.S1G0SURFMEAN_VH = ee.Image(10).multiply(mean_gvh_s.log10()).copyProperties(mean_gvh_s)
self.K1G0VV_V = k1gvv_v
self.K1G0VH_V = k1gvh_v
self.K2G0VV_V = k2gvv_v
self.K1G0VH_S = k1gvh_s
self.K2G0VV_S = k2gvv_s
self.K2G0VH_S = k2gvh_s
def estimate_SM_GBR_1step(self):
# load GBR models
from pysmm.no_GLDAS_decisiontree_GEE__1step import tree as GBR_tree
from pysmm.no_GLDAS_decisiontree_GEE__1step_w_grapex_data import tree as GBR_tree
import sys
g0_v_vv = self.S1_G0VOL_VV_db
g0_v_vh = self.S1_G0VOL_VH_db
g0_s_vv = self.S1_G0SURF_VV_db
g0_s_vh = self.S1_G0SURF_VH_db
dg0_v_vv = g0_v_vv.subtract(self.S1G0VOLMEAN_VV)
dg0_v_vh = g0_v_vh.subtract(self.S1G0VOLMEAN_VH)
dg0_s_vv = g0_s_vv.subtract(self.S1G0SURFMEAN_VV)
dg0_s_vh = g0_s_vh.subtract(self.S1G0SURFMEAN_VH)
g0_v_vv_k1 = self.K1G0VV_V
g0_v_vh_k1 = self.K1G0VH_V
g0_v_vv_k2 = self.K2G0VV_V
g0_s_vh_k1 = self.K1G0VH_S
g0_s_vv_k2 = self.K2G0VV_S
g0_s_vh_k2 = self.K2G0VH_S
crops = self.CROPS_COVER
grass = self.GRASS_COVER
moss = self.MOSS_COVER
l8b1 = self.L8_IMG.select('B1')
l8b2 = self.L8_IMG.select('B2')
l8b2med = self.L8_MEAN.select('B2_median')
l8b3 = self.L8_IMG.select('B3')
l8b3med = self.L8_MEAN.select('B3_median')
l8b4 = self.L8_IMG.select('B4')
l8b4med = self.L8_MEAN.select('B4_median')
l8b5 = self.L8_IMG.select('B5')
l8b5med = self.L8_MEAN.select('B5_median')
l8b6 = self.L8_IMG.select('B6')
l8b6med = self.L8_MEAN.select('B6_median')
l8b7 = self.L8_IMG.select('B7')
l8b10 = self.L8_IMG.select('B10')
l8b10med = self.L8_MEAN.select('B10_median')
l8b11 = self.L8_IMG.select('B11')
l8b11med = self.L8_MEAN.select('B11_median')
l8dt = self.L8_DDATE
ndvi = self.EVI_IMG
ndvi_med = self.EVI_MEAN
bulk = self.BULK
clay = self.CLAY
sand = self.SAND
input_image1 = ee.Image([dg0_v_vv.toFloat(),
dg0_v_vh.toFloat(),
g0_s_vv.toFloat(),
dg0_s_vv.toFloat(),
g0_v_vv_k1.toFloat(),
g0_s_vh_k2.toFloat(),
crops.toFloat(),
grass.toFloat(),
moss.toFloat(),
l8b1.toFloat(),
l8b4.toFloat(),
l8b4med.toFloat(),
l8b5.toFloat(),
l8b5med.toFloat(),
l8b11.toFloat(),
l8b11med.toFloat(),
l8b6.toFloat(),
l8b6med.toFloat(),
l8dt.toFloat(),
ndvi.toFloat(),
ndvi_med.toFloat(),
bulk.toFloat(),
clay.toFloat(),
sand.toFloat()])
input_image1 = input_image1.rename(['dg0_v_vv', 'crops', 'grass', 'moss', 'l8b4', 'l8b4_med', 'l8b5',
'l8b5_med', 'l8b11', 'l8b11_med', 'ndvi', 'ndvi_med', 'bulk', 'sand'])
input_image1 = input_image1.rename(['dg0_v_vv', 'dg0_v_vh', 'g0_s_vv', 'dg0_s_vv', 'k1_v_vv',
'k2_s_vh', 'crops', 'grass', 'moss', 'l8b1', 'l8b4', 'l8b5',
'l8b6', 'l8b6_med', 'l8dt','ndvi', 'ndvi_med', 'bulk', 'clay', 'sand'])
ipt_img_mask1 = input_image1.mask().reduce(ee.Reducer.allNonZero())
......@@ -2008,7 +2062,12 @@ class GEE_extent(object):
ee.Image(dem),
model)
gee_s1_filtered = gee_s1_fltd_vol
model = 'surface'
gee_s1_fltd_surf = self._slope_correction(gee_s1_filtered,
ee.Image(dem),
model)
gee_s1_filtered = gee_s1_fltd_vol.combine(gee_s1_fltd_surf, overwrite=True)
# apply no data mask
def mask_no_data(image):
......@@ -2143,7 +2202,7 @@ class GEE_extent(object):
gee_l8_collection_all = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
# apply landsat mask
gee_l8_collection_all = gee_l8_collection_all.map(mask).select(['B4', 'B5', 'B11'])
gee_l8_collection_all = gee_l8_collection_all.map(mask)#.select(['B4', 'B5', 'B11'])
def date_mosaic(image):
......@@ -2276,7 +2335,7 @@ class GEE_extent(object):
gee_l8_collection_all = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
# apply landsat mask
gee_l8_collection = gee_l8_collection_all.map(mask).select(['B4', 'B5', 'B11'])
gee_l8_collection = gee_l8_collection_all.map(mask)#.select(['B4', 'B5', 'B11'])
if date is None:
doi = self.S1_DATE
......@@ -2291,18 +2350,19 @@ class GEE_extent(object):
'_' + str(abs(tmpcoords[0][2][1])) + \
'_' + str(self.sampling) + '_' + str(doi.year)
mean_asset_path = mean_asset_path.replace('.', '')
gee_l8_mean = ee.Image('users/felixgreifeneder/' + mean_asset_path)
gee_l8_mean = ee.Image('projects/ee-felixgreifeneder/assets/' + mean_asset_path)
try:
gee_l8_mean.getInfo()
print('L8 median exists')
except:
# compute median
gee_l8_mean = gee_l8_collection.filterDate(str(doi.year) + '-01-01', str(doi.year) + '-12-31') \
# TODO l8 median is now based on the current year - 1. This has to be applied also for training
gee_l8_mean = gee_l8_collection.filterDate(str(doi.year-1) + '-01-01', str(doi.year-1) + '-12-31') \
.reduce(ee.Reducer.median(), parallelScale=16)
#export asset
self.GEE_2_asset(raster=gee_l8_mean, name=mean_asset_path, timeout=False, outdir='')
gee_l8_mean = ee.Image('users/felixgreifeneder/' + mean_asset_path)
gee_l8_mean = ee.Image('projects/ee-felixgreifeneder/assets/' + mean_asset_path)
def addDate(image2):
date_img = ee.Image(image2.date().difference(doi.strftime('%Y-%m-%dT%H:%M:%S'), 'second')).abs().float().rename(
......@@ -2310,7 +2370,7 @@ class GEE_extent(object):
return image2.addBands(date_img)
# create mosaic for the doi
gee_l8_date = gee_l8_collection_all.filterDate((doi - dt.timedelta(days=40)).strftime('%Y-%m-%d'),
gee_l8_date = gee_l8_collection_all.filterDate((doi - dt.timedelta(days=90)).strftime('%Y-%m-%d'),
(doi + dt.timedelta(days=40)).strftime('%Y-%m-%d'))
gee_l8_date = gee_l8_date.map(addDate)
gee_l8_date = gee_l8_date.qualityMosaic('Ddate').float()
......@@ -2374,18 +2434,19 @@ class GEE_extent(object):
'_' + str(abs(tmpcoords[0][2][1])) + \
'_' + str(self.sampling) + '_' + str(doi.year)
mean_asset_path = mean_asset_path.replace('.', '')
evi_mean = ee.Image('users/felixgreifeneder/' + mean_asset_path)
evi_mean = ee.Image('projects/ee-felixgreifeneder/assets/' + mean_asset_path)
try:
evi_mean.getInfo()
print('EVI median exists')
except:
# compute avg
evi_mean = evi_collection.filterDate(str(doi.year) + '-01-01', str(doi.year) + '-12-31') \
# TODO median evi is now based on current year-1 - this has to be implemented also for training
evi_mean = evi_collection.filterDate(str(doi.year-1) + '-01-01', str(doi.year-1) + '-12-31') \
.reduce(ee.Reducer.median(), parallelScale=16)
# export asset
self.GEE_2_asset(raster=evi_mean, name=mean_asset_path, timeout=False, outdir='')
evi_mean = ee.Image('users/felixgreifeneder/' + mean_asset_path)
evi_mean = ee.Image('projects/ee-felixgreifeneder/assets/' + mean_asset_path)
# fiter
# filter
......@@ -2447,9 +2508,9 @@ class GEE_extent(object):
outdir = self.workdir
if outdir != '':
impath = 'users/felixgreifeneder/' + outdir + '/' + name
impath = 'projects/ee-felixgreifeneder/assets/' + outdir + '/' + name
else:
impath = 'users/felixgreifeneder/' + name
impath = 'projects/ee-felixgreifeneder/assets/' + name
try:
file_avail = ee.Image(impath)
......@@ -2478,3 +2539,33 @@ class GEE_extent(object):
break
else:
print('Export completed')
def GEE_2_drive(self, outdir=None, raster='ESTIMATED_SM', name='SM', timeout=True):
# Export GEE rasters as asset - specify raster as string
if isinstance(raster, str):
geds = self.__getattribute__(raster)
else:
geds = raster
if outdir is None:
outdir = self.workdir
file_exp = ee.batch.Export.image.toDrive(image=geds, description='fileexp' + name,
folder=outdir,
fileNamePrefix=name,
region=self.roi.getInfo()['coordinates'],
scale=self.sampling,
maxPixels=1000000000000)
file_exp.start()
start = time.time()
while file_exp.active():
time.sleep(2)
if timeout and (time.time() - start) > 4800:
success = 0
break
else:
print('Export completed')
\ No newline at end of file
pysmm/derive_SM.py
View file @ d137178d
......@@ -80,7 +80,7 @@ def get_map(minlon, minlat, maxlon, maxlat,
# initialise retrieval
GEE_interface.init_SM_retrieval(dateI.year, dateI.month, dateI.day, hour=dateI.hour, minute=dateI.minute,
track=orbitI)
track=orbitI, overwrite=overwrite)
if GEE_interface.ORBIT == 'ASCENDING':
orbit_prefix = 'A'
......@@ -95,15 +95,16 @@ def get_map(minlon, minlat, maxlon, maxlat,
'{:02d}'.format(GEE_interface.S1_DATE.second) + '_' + \
'{:03d}'.format(math.trunc(GEE_interface.TRACK_NR)) + '_' + orbit_prefix
if overwrite == False and os.path.exists(outpath + outname + '.tif'):
print(outname + ' already done')
continue
# if overwrite == False and os.path.exists(outpath + outname + '.tif'):
# print(outname + ' already done')
# continue
# Estimate soil moisture
GEE_interface.estimate_SM_GBR_1step()
if GEE_interface.ESTIMATED_SM is not None:
GEE_interface.GEE_2_asset(name=outname, timeout=False)
#GEE_interface.GEE_2_asset(name=outname, timeout=False)
GEE_interface.GEE_2_drive(name=outname, timeout=False)
GEE_interface = None
......
pysmm/no_GLDAS_decisiontree_GEE__1step_w_grapex_data.py 0 → 100644
View file @ d137178d
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