def reproject_MODIS(input_name, output_name, epsg_to):
'''
Reproject the merged data file
Keywords arguments:
output_folder -- 'C:/file/to/path/'
'''
import SEBAL.pySEBAL.pySEBAL_code as SEBAL
# Get environmental variable
SEBAL_env_paths = os.environ["SEBAL"].split(';')
GDAL_env_path = SEBAL_env_paths[0]
GDALWARP_PATH = os.path.join(GDAL_env_path, 'gdalwarp.exe')
split_input = input_name.split('hdf":')
inputname = '%shdf":"%s"' % (split_input[0], split_input[1])
# find path to the executable
fullCmd = ' '.join([
"%s" % (GDALWARP_PATH),
'-overwrite -s_srs "+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs"',
'-t_srs EPSG:%s -of GTiff' % (epsg_to), inputname, output_name
])
SEBAL.Run_command_window(fullCmd)
return ()
os.chdir(input_folder)
for file_name in [e for e in glob.glob('HDF5_*') if not e.endswith('.tif')]:
print('Processing: ', file_name)
extension = os.path.splitext(file_name)[1]
file_name_only = os.path.splitext(file_name)[0]
file_name_tiff = file_name_only + '.tif'
out_name = os.path.join(input_folder, file_name_tiff)
if not os.path.exists(out_name):
# unzip bunzipfile if needed
if str(extension) == '.bz2':
fullCmd = '7z e %s -o%s' % (file_name, temp_folder)
SEBAL.Run_command_window(fullCmd)
input_folder_landsaf = temp_folder
else:
input_folder_landsaf = input_folder
# Get environmental variable
SEBAL_env_paths = os.environ["SEBAL"].split(';')
GDAL_env_path = SEBAL_env_paths[0]
GDAL_TRANSLATE = os.path.join(GDAL_env_path, 'gdal_translate.exe')
GDALWARP = os.path.join(GDAL_env_path, 'gdalwarp.exe')
# Set projection of GOES/LANDSAF data
output_GOES_projected = os.path.join(temp_folder,
'GOES_projected_LANDSAF.tif')
fullCmd = '"%s" -a_srs "+proj=geos +a=6378169 +b=6356583.8 +lon_0=0 +h=35785831" -a_ullr -5570248.832537 5570248.832537 5570248.832537 -5570248.832537 HDF5:"%s"://DSSF "%s"' % (
GDAL_TRANSLATE, os.path.join(
def Open_PROBAV_Reflectance(Name_PROBAV_Image, input_folder, output_folder,
Example_fileName):
import SEBAL.pySEBAL.pySEBAL_code as SEBAL
# Define bands probav
bands = ['SM', 'B1', 'B2', 'B3', 'B4'] #'SM', 'BLUE', 'RED', 'NIR', 'SWIR'
# Get extension of PROBAV dataset
Name_PROBAV, Name_PROBAV_exe = os.path.splitext(Name_PROBAV_Image)
# Set the index number at 0
index = 0
# constants
n188_float = 248
if (Name_PROBAV_exe == '.hdf5' or Name_PROBAV_exe == '.HDF5'):
# write the data one by one to the spectral_reflectance_PROBAV
for bandnmr in bands:
# Translate the PROBA-V names to the Landsat band names
Band_number = {'SM': 7, 'B1': 8, 'B2': 10, 'B3': 9, 'B4': 11}
# Open the hdf file
Band_PROBAVhdf_fileName = os.path.join(input_folder,
Name_PROBAV_Image)
g = gdal.Open(Band_PROBAVhdf_fileName, gdal.GA_ReadOnly)
# Define temporary file out and band name in
name_out = os.path.join(input_folder,
'%s_test.tif' % (Name_PROBAV_Image))
name_in = g.GetSubDatasets()[Band_number[bandnmr]][0]
# Get environmental variable
SEBAL_env_paths = os.environ["SEBAL"].split(';')
GDAL_env_path = SEBAL_env_paths[0]
GDAL_TRANSLATE = os.path.join(GDAL_env_path, 'gdal_translate.exe')
# run gdal translate command
FullCmd = '%s -of GTiff %s %s' % (GDAL_TRANSLATE, name_in,
name_out)
SEBAL.Run_command_window(FullCmd)
# Get the data array
dest_PV = gdal.Open(name_out)
Data = dest_PV.GetRasterBand(1).ReadAsArray()
dest_PV = None
# Define the x and y spacing
Meta_data = g.GetMetadata()
Lat_Top = float(Meta_data['LEVEL3_GEOMETRY_TOP_RIGHT_LATITUDE'])
Lon_Left = float(
Meta_data['LEVEL3_GEOMETRY_BOTTOM_LEFT_LONGITUDE'])
Pixel_size = float(
(Meta_data['LEVEL3_GEOMETRY_VNIR_VAA_MAPPING']).split(' ')[-3])
x_size_pv = int(Data.shape[1])
y_size_pv = int(Data.shape[0])
ulx = Lon_Left - 0.5 * Pixel_size
uly = Lat_Top + 0.5 * Pixel_size
lrx = ulx + x_size_pv * Pixel_size
lry = uly - y_size_pv * Pixel_size
# Define the georeference of the PROBA-V data
geo_PROBAV = [
Lon_Left - 0.5 * Pixel_size, Pixel_size, 0,
Lat_Top + 0.5 * Pixel_size, 0, -Pixel_size
] #0.000992063492063
################################# Create a MEMORY file ##############################
# create memory output with the PROBA-V band
fmt = 'MEM'
driver = gdal.GetDriverByName(fmt)
dst_dataset = driver.Create('', int(Data.shape[1]),
int(Data.shape[0]), 1,
gdal.GDT_Float32)
dst_dataset.SetGeoTransform(geo_PROBAV)
# set the reference info
srs = osr.SpatialReference()
srs.SetWellKnownGeogCS("WGS84")
dst_dataset.SetProjection(srs.ExportToWkt())
# write the array in the geotiff band
dst_dataset.GetRasterBand(1).WriteArray(Data)
################################# reproject PROBAV MEMORY file ##############################
# Reproject the PROBA-V band to match DEM's resolution
PROBAV, ulx_dem, lry_dem, lrx_dem, uly_dem, epsg_to = SEBAL.reproject_dataset_example(
dst_dataset, Example_fileName)
dst_dataset = None
#################################### Get example information ################################
if not "shape_lsc" in locals():
nrow = PROBAV.RasterYSize
ncol = PROBAV.RasterXSize
shape_lsc = [ncol, nrow]
# Open the reprojected PROBA-V band data
data_PROBAV_DN = PROBAV.GetRasterBand(1).ReadAsArray(
0, 0, ncol, nrow)
# Define the filename to store the cropped Landsat image
dst_FileName = os.path.join(output_folder,
'Output_radiation_balance',
'proy_PROBAV_%s.tif' % bandnmr)
# close the PROBA-V
g = None
if not "spectral_reflectance_PROBAV" in locals():
spectral_reflectance_PROBAV = np.zeros(
[shape_lsc[1], shape_lsc[0], 5])
# If the band data is the SM band than write the data into the spectral_reflectance_PROBAV and create cloud mask
if bandnmr is 'SM':
cloud_mask_temp = np.zeros(data_PROBAV_DN.shape)
cloud_mask_temp[data_PROBAV_DN[:, :] != n188_float] = 1
spectral_reflectance_PROBAV[:, :, index] = cloud_mask_temp
# If the band data is not SM change the DN values into PROBA-V values and write into the spectral_reflectance_PROBAV
else:
data_PROBAV = data_PROBAV_DN / 2000
spectral_reflectance_PROBAV[:, :, index] = data_PROBAV[:, :]
# Change the spectral reflectance to meet certain limits
spectral_reflectance_PROBAV[:, :, index] = np.where(
spectral_reflectance_PROBAV[:, :, index] <= 0, np.nan,
spectral_reflectance_PROBAV[:, :, index])
spectral_reflectance_PROBAV[:, :, index] = np.where(
spectral_reflectance_PROBAV[:, :, index] >= 150, np.nan,
spectral_reflectance_PROBAV[:, :, index])
# Save the PROBA-V as a tif file
SEBAL.save_GeoTiff_proy(PROBAV,
spectral_reflectance_PROBAV[:, :, index],
dst_FileName,
shape_lsc,
nband=1)
# Go to the next index
index = index + 1
# Remove temporary file
os.remove(name_out)
else:
for bandnmr in bands:
# Open information
Band_PROBAVhdf_fileName = os.path.join(
input_folder, "%s_%s.tif" % (Name_PROBAV_Image, bandnmr))
g = gdal.Open(Band_PROBAVhdf_fileName, gdal.GA_ReadOnly)
# Open original raster band
x_size_pv = g.RasterXSize
y_size_pv = g.RasterYSize
geo_out = g.GetGeoTransform()
ulx = geo_out[0]
uly = geo_out[3]
xDist = geo_out[1]
yDist = geo_out[5]
lrx = ulx + x_size_pv * xDist
lry = uly - y_size_pv * yDist
# Reproject the PROBA-V band to match DEM's resolution
PROBAV, ulx_dem, lry_dem, lrx_dem, uly_dem, epsg_to = SEBAL.reproject_dataset_example(
g, Example_fileName)
data_PROBAV = PROBAV.GetRasterBand(1).ReadAsArray()
# Define example array properties
if not "shape_lsc" in locals():
nrow = PROBAV.RasterYSize
ncol = PROBAV.RasterXSize
shape_lsc = [ncol, nrow]
# Define spectal reflactance empty array
if not "spectral_reflectance_PROBAV" in locals():
spectral_reflectance_PROBAV = np.zeros(
[shape_lsc[1], shape_lsc[0], 5])
# If the band data is the SM band than write the data into the spectral_reflectance_PROBAV and create cloud mask
if bandnmr is 'SM':
cloud_mask_temp = np.zeros(data_PROBAV.shape)
cloud_mask_temp[data_PROBAV[:, :] != n188_float] = 1
spectral_reflectance_PROBAV[:, :, index] = cloud_mask_temp
# If the band data is not SM change the DN values into PROBA-V values and write into the spectral_reflectance_PROBAV
else:
spectral_reflectance_PROBAV[:, :,
index] = data_PROBAV[:, :] / 2000
# Define the filename to store the cropped Landsat image
dst_FileName = os.path.join(output_folder,
'Output_radiation_balance',
'proy_PROBAV_%s.tif' % bandnmr)
# Define shape
if not "shape_lsc" in locals():
nrow = PROBAV.RasterYSize
ncol = PROBAV.RasterXSize
shape_lsc = [ncol, nrow]
SEBAL.save_GeoTiff_proy(PROBAV,
spectral_reflectance_PROBAV[:, :, index],
dst_FileName,
shape_lsc,
nband=1)
# Go to the next index
index = index + 1
# Original size PROBAV dataset
print('Original PROBA-V Image - ')
print(' Size :', x_size_pv, y_size_pv)
print(' Upper Left corner x, y: ', ulx, ', ', uly)
print(' Lower right corner x, y: ', lrx, ', ', lry)
print('Reprojected PROBA-V Image - ')
print(' Size :', shape_lsc[1], shape_lsc[0])
print(' Upper Left corner x, y: ', ulx_dem, ', ', uly_dem)
print(' Lower right corner x, y: ', lrx_dem, ', ', lry_dem)
return (spectral_reflectance_PROBAV, cloud_mask_temp)
def Get_PROBAV_Para_Veg(workbook, number, Example_fileName, year, DOY,
path_radiance, Apparent_atmosf_transm, cos_zn, dr,
DEM_resh):
import SEBAL.pySEBAL.pySEBAL_code as SEBAL
# Open the General input sheet
ws = workbook['General_Input']
# Extract the input and output folder, and Image type from the excel file
input_folder = r"%s" % str(ws['B%d' % number].value)
output_folder = r"%s" % str(ws['C%d' % number].value)
ws = workbook['Additional_Input']
# If all additional fields are filled in than do not open the datasets
if ws['B%d' % number].value is None or ws['C%d' % number].value is None:
print(
'--------------------- Open PROBA-V VIS ------------------------')
# Open the Landsat_Input sheet
ws = workbook['VIIRS_PROBAV_Input']
Name_PROBAV_Image = '%s' % str(
ws['D%d' % number].value) # Must be a tiff file
# Define the bands that will be used
bands = ['SM', 'B1', 'B2', 'B3',
'B4'] #'SM', 'BLUE', 'RED', 'NIR', 'SWIR'
sensor1 = 'PROBAV'
sensor2 = 'VIIRS'
res1 = '375m'
res2 = '100m'
res3 = '30m'
# Set the index number at 0
index = 0
# constants
n188_float = 248 # Now it is 248, but we do not exactly know what this really means and if this is for constant for all images.
# write the data one by one to the spectral_reflectance_PROBAV
for bandnmr in bands:
# Translate the PROBA-V names to the Landsat band names
Band_number = {'SM': 7, 'B1': 8, 'B2': 10, 'B3': 9, 'B4': 11}
# Open the hdf file
Band_PROBAVhdf_fileName = os.path.join(
input_folder, '%s.HDF5' % (Name_PROBAV_Image))
g = gdal.Open(Band_PROBAVhdf_fileName, gdal.GA_ReadOnly)
# Define temporary file out and band name in
name_out = os.path.join(input_folder,
'%s_test.tif' % (Name_PROBAV_Image))
name_in = g.GetSubDatasets()[Band_number[bandnmr]][0]
# Get environmental variable
SEBAL_env_paths = os.environ["SEBAL"].split(';')
GDAL_env_path = SEBAL_env_paths[0]
GDAL_TRANSLATE = os.path.join(GDAL_env_path, 'gdal_translate.exe')
# run gdal translate command
FullCmd = '%s -of GTiff %s %s' % (GDAL_TRANSLATE, name_in,
name_out)
SEBAL.Run_command_window(FullCmd)
# Get the data array
dest_PV = gdal.Open(name_out)
Data = dest_PV.GetRasterBand(1).ReadAsArray()
dest_PV = None
# Remove temporary file
os.remove(name_out)
# Define the x and y spacing
Meta_data = g.GetMetadata()
Lat_Top = float(Meta_data['LEVEL3_GEOMETRY_TOP_RIGHT_LATITUDE'])
Lon_Left = float(
Meta_data['LEVEL3_GEOMETRY_BOTTOM_LEFT_LONGITUDE'])
Pixel_size = float(
(Meta_data['LEVEL3_GEOMETRY_VNIR_VAA_MAPPING']).split(' ')[-3])
# Define the georeference of the PROBA-V data
geo_PROBAV = [
Lon_Left - 0.5 * Pixel_size, Pixel_size, 0,
Lat_Top + 0.5 * Pixel_size, 0, -Pixel_size
] #0.000992063492063
################################# Create a MEMORY file ##############################
# create memory output with the PROBA-V band
fmt = 'MEM'
driver = gdal.GetDriverByName(fmt)
dst_dataset = driver.Create('', int(Data.shape[1]),
int(Data.shape[0]), 1,
gdal.GDT_Float32)
dst_dataset.SetGeoTransform(geo_PROBAV)
# set the reference info
srs = osr.SpatialReference()
srs.SetWellKnownGeogCS("WGS84")
dst_dataset.SetProjection(srs.ExportToWkt())
# write the array in the geotiff band
dst_dataset.GetRasterBand(1).WriteArray(Data)
################################# reproject PROBAV MEMORY file ##############################
# Reproject the PROBA-V band to match DEM's resolution
PROBAV, ulx_dem, lry_dem, lrx_dem, uly_dem, epsg_to = SEBAL.reproject_dataset_example(
dst_dataset, Example_fileName)
dst_dataset = None
#################################### Get example information ################################
if not "shape_lsc" in locals():
nrow = PROBAV.RasterYSize
ncol = PROBAV.RasterXSize
shape_lsc = [ncol, nrow]
# Open the reprojected PROBA-V band data
data_PROBAV_DN = PROBAV.GetRasterBand(1).ReadAsArray(
0, 0, ncol, nrow)
# Define the filename to store the cropped Landsat image
dst_FileName = os.path.join(output_folder,
'Output_radiation_balance',
'proy_PROBAV_%s.tif' % bandnmr)
# close the PROBA-V
g = None
if not "spectral_reflectance_PROBAV" in locals():
spectral_reflectance_PROBAV = np.zeros(
[shape_lsc[1], shape_lsc[0], 5])
# If the band data is not SM change the DN values into PROBA-V values and write into the spectral_reflectance_PROBAV
if bandnmr is not 'SM':
data_PROBAV = data_PROBAV_DN / 2000
spectral_reflectance_PROBAV[:, :, index] = data_PROBAV[:, :]
# If the band data is the SM band than write the data into the spectral_reflectance_PROBAV and create cloud mask
else:
cloud_mask_temp = np.zeros(data_PROBAV_DN.shape)
cloud_mask_temp[data_PROBAV_DN[:, :] != n188_float] = 1
spectral_reflectance_PROBAV[:, :, index] = cloud_mask_temp
# Change the spectral reflectance to meet certain limits
spectral_reflectance_PROBAV[:, :, index] = np.where(
spectral_reflectance_PROBAV[:, :, index] <= 0, np.nan,
spectral_reflectance_PROBAV[:, :, index])
spectral_reflectance_PROBAV[:, :, index] = np.where(
spectral_reflectance_PROBAV[:, :, index] >= 150, np.nan,
spectral_reflectance_PROBAV[:, :, index])
# Save the PROBA-V as a tif file
SEBAL.save_GeoTiff_proy(PROBAV,
spectral_reflectance_PROBAV[:, :, index],
dst_FileName,
shape_lsc,
nband=1)
# Go to the next index
index = index + 1
# Original size PROBAV dataset
x_size_pv = int(Data.shape[1])
y_size_pv = int(Data.shape[0])
ulx = Lon_Left - 0.5 * Pixel_size
uly = Lat_Top + 0.5 * Pixel_size
lrx = ulx + x_size_pv * Pixel_size
lry = uly - y_size_pv * Pixel_size
print('Original PROBA-V Image - ')
print(' Size :', x_size_pv, y_size_pv)
print(' Upper Left corner x, y: ', ulx, ', ', uly)
print(' Lower right corner x, y: ', lrx, ', ', lry)
print('Reprojected PROBA-V Image - ')
print(' Size :', shape_lsc[1], shape_lsc[0])
print(' Upper Left corner x, y: ', ulx_dem, ', ', uly_dem)
print(' Lower right corner x, y: ', lrx_dem, ', ', lry_dem)
else:
# Get General information example file
lsc = gdal.Open(Example_fileName)
nrow = lsc.RasterYSize
ncol = lsc.RasterXSize
shape_lsc = [ncol, nrow]
######################### Calculate Vegetation Parameters Based on VIS data #####################################
# Open the Additional input excel sheet
ws = workbook['Additional_Input']
# Check NDVI and Calculate NDVI
try:
if (ws['B%d' % number].value) is not None:
# Output folder NDVI
ndvi_fileName_user = os.path.join(
output_folder, 'Output_vegetation',
'User_NDVI_%s_%s_%s.tif' % (res3, year, DOY))
NDVI = SEBAL.Reshape_Reproject_Input_data(
r'%s' % str(ws['B%d' % number].value), ndvi_fileName_user,
Example_fileName)
water_mask_temp = np.zeros((shape_lsc[1], shape_lsc[0]))
water_mask_temp[NDVI < 0.0] = 1.0
SEBAL.save_GeoTiff_proy(lsc,
NDVI,
ndvi_fileName_user,
shape_lsc,
nband=1)
else:
n218_memory = spectral_reflectance_PROBAV[:, :,
2] + spectral_reflectance_PROBAV[:, :,
3]
NDVI = np.zeros((shape_lsc[1], shape_lsc[0]))
NDVI[n218_memory != 0] = (
spectral_reflectance_PROBAV[:, :, 3][n218_memory != 0] -
spectral_reflectance_PROBAV[:, :, 2][n218_memory != 0]) / (
spectral_reflectance_PROBAV[:, :, 2][n218_memory != 0] +
spectral_reflectance_PROBAV[:, :, 3][n218_memory != 0])
# Create Water mask based on PROBA-V
water_mask_temp = np.zeros((shape_lsc[1], shape_lsc[0]))
water_mask_temp[np.logical_and(
spectral_reflectance_PROBAV[:, :, 2] >=
spectral_reflectance_PROBAV[:, :, 3], DEM_resh > 0)] = 1
except:
assert "Please check the NDVI input path"
# Check Water Mask and replace if it is filled in the additianal data sheet
try:
if (ws['E%d' % number].value) is not None:
# Overwrite the Water mask and change the output name
water_mask_temp_fileName = os.path.join(
output_folder, 'Output_soil_moisture',
'User_Water_mask_temporary_%s_%s_%s.tif' % (res2, year, DOY))
water_mask_temp = SEBAL.Reshape_Reproject_Input_data(
r'%s' % str(ws['E%d' % number].value),
water_mask_temp_fileName, Example_fileName)
SEBAL.save_GeoTiff_proy(lsc,
water_mask_temp,
water_mask_temp_fileName,
shape_lsc,
nband=1)
except:
assert "Please check the Water Mask input path"
# Check Surface albedo
try:
if (ws['C%d' % number].value) is not None:
# Output folder surface albedo
surface_albedo_fileName = os.path.join(
output_folder, 'Output_vegetation',
'User_surface_albedo_%s_%s_%s.tif' % (res2, year, DOY))
Surf_albedo = SEBAL.Reshape_Reproject_Input_data(
r'%s' % str(ws['C%d' % number].value), surface_albedo_fileName,
Example_fileName)
SEBAL.save_GeoTiff_proy(lsc,
Surf_albedo,
surface_albedo_fileName,
shape_lsc,
nband=1)
else:
# Calculate surface albedo based on PROBA-V
Surf_albedo = 0.219 * spectral_reflectance_PROBAV[:, :,
1] + 0.361 * spectral_reflectance_PROBAV[:, :,
2] + 0.379 * spectral_reflectance_PROBAV[:, :,
3] + 0.041 * spectral_reflectance_PROBAV[:, :,
4]
# Set limit surface albedo
Surf_albedo = np.minimum(Surf_albedo, 0.6)
except:
assert "Please check the Albedo input path"
# calculate vegetation properties
FPAR, tir_emis, Nitrogen, vegt_cover, LAI, b10_emissivity = SEBAL.Calc_vegt_para(
NDVI, water_mask_temp, shape_lsc)
# create quality map
QC_Map = np.zeros(NDVI.shape)
QC_Map[np.isnan(NDVI)] = 1
print('Average NDVI = %s' % np.nanmean(NDVI))
print('Average Surface Albedo = %s' % np.nanmean(Surf_albedo))
print('Average LAI = %s' % np.nanmean(LAI))
print('Average Vegetation Cover = %s' % np.nanmean(vegt_cover))
print('Average FPAR = %s' % np.nanmean(FPAR))
return (Surf_albedo, NDVI, LAI, vegt_cover, FPAR, Nitrogen, tir_emis,
b10_emissivity, water_mask_temp, QC_Map)