def Open_landsat(src_FileName, proyDEM_fileName):
"""
This function opens a landsat image and returns the data array of a specific landsat band.
"""
import SEBAL.pySEBAL.pySEBAL_code as SEBAL
# crop band to the DEM extent
ls, ulx, uly, lrx, lry, epsg_to = SEBAL.reproject_dataset_example(src_FileName, proyDEM_fileName)
# Open the cropped Landsat image for the band number
ls_data = ls.GetRasterBand(1).ReadAsArray()
return(ls_data)
def Open_landsat(src_FileName, proyDEM_fileName):
"""
This function opens a landsat image and returns the data array of a specific landsat band.
"""
import SEBAL.pySEBAL.pySEBAL_code as SEBAL
# crop band to the DEM extent
ls, ulx, uly, lrx, lry, epsg_to = SEBAL.reproject_dataset_example(src_FileName, proyDEM_fileName)
# Open the cropped Landsat image for the band number
ls_data = ls.GetRasterBand(1).ReadAsArray()
return(ls_data)
def Open_reprojected_hdf(input_name, Band, epsg_to, scale_factor, Example_fileName):
import SEBAL.pySEBAL.pySEBAL_code as SEBAL
g=gdal.Open(input_name, gdal.GA_ReadOnly)
folder_out = os.path.dirname(input_name)
output_name_temp = os.path.join(folder_out, "temporary.tif")
# Open and reproject
name_in = g.GetSubDatasets()[Band][0]
reproject_MODIS(name_in, output_name_temp, epsg_to)
dest, ulx_dem, lry_dem, lrx_dem, uly_dem, epsg_to = SEBAL.reproject_dataset_example(output_name_temp, Example_fileName)
Array = dest.GetRasterBand(1).ReadAsArray() * scale_factor
os.remove(output_name_temp)
return(Array)
def Get_LS_Para_Veg(workbook, number, Example_fileName, year, month, day,
path_radiance, Apparent_atmosf_transm, cos_zn, dr):
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 Landsat VIS -----------------------')
# Open the Landsat_Input sheet
ws = workbook['Landsat_Input']
# Extract Landsat name, number and amount of thermal bands from excel file
Name_Landsat_Image = str(ws['B%d' % number].value)
Landsat_nr = int(
ws['C%d' % number].value
) # Type of Landsat (LS) image used (LS5, LS7, or LS8)
# temperature: 1 = Band 6 for LS_5 & 7, Band 10 for LS_8 (optional)
# Define bands used for each Landsat number
if Landsat_nr == 5 or Landsat_nr == 7:
Bands = np.array([1, 2, 3, 4, 5, 7, 6])
elif Landsat_nr == 8:
Bands = np.array([2, 3, 4, 5, 6, 7, 10, 11])
else:
print('Landsat image not supported, use Landsat 7 or 8')
# Open MTL landsat and get the correction parameters
Landsat_meta_fileName = os.path.join(input_folder,
'%s_MTL.txt' % Name_Landsat_Image)
Lmin, Lmax, k1_c, k2_c = info_band_metadata(Landsat_meta_fileName,
Bands)
print('Lmin= ', Lmin)
print('Lmax= ', Lmax)
print('k1= ', k1_c)
print('k2= ', k2_c)
sensor1 = 'LS%d' % Landsat_nr
res1 = '30m'
res2 = '30m'
res3 = '30m'
# Mean solar exo-atmospheric irradiance for each band (W/m2/microm)
# for the different Landsat images (L5, L7, or L8)
ESUN_L5 = np.array([1983, 1796, 1536, 1031, 220, 83.44])
ESUN_L7 = np.array([1997, 1812, 1533, 1039, 230.8, 84.9])
ESUN_L8 = np.array([1973.28, 1842.68, 1565.17, 963.69, 245, 82.106])
# Open one band - To get the metadata of the landsat images only once (to get the extend)
src_FileName = os.path.join(input_folder, '%s_B2.TIF' %
Name_Landsat_Image) # before 10!
ls, band_data, ulx, uly, lrx, lry, x_size_ls, y_size_ls = Get_Extend_Landsat(
src_FileName)
print('Original LANDSAT Image - ')
print(' Size :', x_size_ls, y_size_ls)
print(' Upper Left corner x, y: ', ulx, ', ', uly)
print(' Lower right corner x, y: ', lrx, ', ', lry)
lsc, ulx, uly, lrx, lry, epsg_to = SEBAL.reproject_dataset_example(
src_FileName, Example_fileName)
# Get the extend of the remaining landsat file after clipping based on the DEM file
y_size_lsc = lsc.RasterYSize
x_size_lsc = lsc.RasterXSize
shape_lsc = [x_size_lsc, y_size_lsc]
print('--- ')
print('Cropped LANDSAT Image - ')
print(' Size :', x_size_lsc, y_size_lsc)
print(' Upper Left corner x, y: ', ulx, ', ', uly)
print(' Lower right corner x, y: ', lrx, ', ', lry)
# if landsat 5 or 7 is used then first create a mask for removing the no data stripes
if Landsat_nr == 5 or Landsat_nr == 7:
src_FileName = os.path.join(
input_folder,
'%s_B6.TIF' % (Name_Landsat_Image)) #open smallest band
if not os.path.exists(src_FileName):
src_FileName = os.path.join(
input_folder, '%s_B6_VCID_2.TIF' % (Name_Landsat_Image))
src_FileName_2 = os.path.join(
input_folder,
'%s_B1.TIF' % (Name_Landsat_Image)) #open smallest band
src_FileName_3 = os.path.join(
input_folder,
'%s_B3.TIF' % (Name_Landsat_Image)) #open smallest band
src_FileName_4 = os.path.join(
input_folder,
'%s_B4.TIF' % (Name_Landsat_Image)) #open smallest band
src_FileName_5 = os.path.join(
input_folder,
'%s_B7.TIF' % (Name_Landsat_Image)) #open smallest band
src_FileName_6 = os.path.join(
input_folder,
'%s_B2.TIF' % (Name_Landsat_Image)) #open smallest band
src_FileName_7 = os.path.join(
input_folder,
'%s_B5.TIF' % (Name_Landsat_Image)) #open smallest band
ls_data = Open_landsat(src_FileName, Example_fileName)
ls_data_2 = Open_landsat(src_FileName_2, Example_fileName)
ls_data_3 = Open_landsat(src_FileName_3, Example_fileName)
ls_data_4 = Open_landsat(src_FileName_4, Example_fileName)
ls_data_5 = Open_landsat(src_FileName_5, Example_fileName)
ls_data_6 = Open_landsat(src_FileName_6, Example_fileName)
ls_data_7 = Open_landsat(src_FileName_7, Example_fileName)
# create and save the landsat mask for all images based on band 11 (smallest map)
QC_Map = np.zeros((shape_lsc[1], shape_lsc[0]))
QC_Map = np.where(
np.logical_or.reduce(
(ls_data == 0, ls_data_2 == 0, ls_data_3 == 0,
ls_data_4 == 0, ls_data_5 == 0, ls_data_6 == 0,
ls_data_7 == 0)), 1, 0)
# If landsat 8 then use landsat band 10 and 11
elif Landsat_nr == 8:
src_FileName_11 = os.path.join(
input_folder,
'%s_B11.TIF' % (Name_Landsat_Image)) #open smallest band
ls_data_11 = Open_landsat(src_FileName_11, Example_fileName)
src_FileName_10 = os.path.join(
input_folder,
'%s_B10.TIF' % (Name_Landsat_Image)) #open smallest band
ls_data_10 = Open_landsat(src_FileName_10, Example_fileName)
# create and save the landsat mask for all images based on band 10 and 11
QC_Map = np.zeros((shape_lsc[1], shape_lsc[0]))
QC_Map = np.where(np.logical_or(ls_data_11 == 0, ls_data_10 == 0),
1, 0)
else:
print('Landsat image not supported, use Landsat 7 or 8')
# Open data of the landsat mask
ls_data = Open_landsat(src_FileName, Example_fileName)
# Create 3D array to store Spectral radiance and Reflectivity for each band
Reflect, Spec_Rad = Landsat_Reflect(Bands, input_folder,
Name_Landsat_Image, output_folder,
shape_lsc, QC_Map, Lmax, Lmin,
ESUN_L5, ESUN_L7, ESUN_L8, cos_zn,
dr, Landsat_nr, Example_fileName)
# save spectral data
for i in range(0, 6):
spec_ref_fileName = os.path.join(
output_folder, 'Output_radiation_balance',
'%s_spectral_reflectance_B%s_%s_%s%02d%02d.tif' %
(sensor1, Bands[i], res3, year, month, day))
SEBAL.save_GeoTiff_proy(lsc,
Reflect[:, :, i],
spec_ref_fileName,
shape_lsc,
nband=1)
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%02d%02d.tif' % (res3, year, month, day))
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:
# use the Landsat reflectance to calculate the surface albede, NDVI
NDVI = SEBAL.Calc_NDVI(Reflect)
# Calculate temporal water mask
water_mask_temp = SEBAL.Water_Mask(shape_lsc, Reflect)
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%02d%02d.tif' %
(res2, year, month, day))
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%02d%02d.tif' %
(res2, year, month, day))
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:
# use the Landsat reflectance to calculate the surface albede, NDVI
Surf_albedo = SEBAL.Calc_albedo(Reflect, path_radiance,
Apparent_atmosf_transm, Landsat_nr)
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)
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))
if not "QC_Map" in locals():
QC_Map = np.zeros((shape_lsc[1], shape_lsc[0]))
return (Surf_albedo, NDVI, LAI, vegt_cover, FPAR, Nitrogen, tir_emis,
b10_emissivity, water_mask_temp, QC_Map)
def Get_USER_Para_Veg(workbook, number, Example_fileName, year, month, day):
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
output_folder = r"%s" %str(ws['C%d' %number].value)
ws = workbook['Additional_Input']
# Define the bands that will be used
res2 = '10m'
res3 = '10m'
# 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 #####################################
# Extract the name of the NDVI USER image from the excel file
ws = workbook['USER_Input']
src_FileName_NDVI = r"%s" %str(ws['B%d' %number].value) #NDVI
# Open the Additional input excel sheet
ws = workbook['Additional_Input']
# Check NDVI and Calculate NDVI
try:
if (ws['B%d' % number].value) is None:
# Open User Albedo
dest_NDVI = SEBAL.reproject_dataset_example(src_FileName_NDVI, Example_fileName)[0]
NDVI = dest_NDVI.GetRasterBand(1).ReadAsArray()
# Create Water mask based on PROBA-V
water_mask_temp = np.where(NDVI < 0.0, 1.0, 0.0)
else:
# Output folder NDVI
if not os.path.exists(os.path.join(output_folder, 'Output_vegetation')):
os.makedirs(os.path.join(output_folder, 'Output_vegetation'))
ndvi_fileName_user = os.path.join(output_folder, 'Output_vegetation', 'User_NDVI_%s_%s%02d%02d.tif' %(res3, year, month, day))
NDVI = SEBAL.Reshape_Reproject_Input_data(r'%s' %str(ws['B%d' % number].value),ndvi_fileName_user,Example_fileName)
water_mask_temp = np.where(NDVI < 0.0, 1.0, 0.0)
except:
print("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:
if not os.path.exists(os.path.join(output_folder, 'Output_soil_moisture')):
os.makedirs(os.path.join(output_folder, 'Output_soil_moisture'))
# 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%02d%02d.tif' %(res2, year, month, day))
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:
print("Please check the Water Mask input path")
# Extract the name of the surface albedo USER image from the excel file
ws = workbook['USER_Input']
src_FileName_Surf_albedo = r"%s" %str(ws['C%d' %number].value) #surface albedo
ws = workbook['Additional_Input']
# Check Surface albedo
try:
if (ws['C%d' % number].value) is not None:
# Output folder surface albedo
if not os.path.exists(os.path.join(output_folder, 'Output_vegetation')):
os.makedirs(os.path.join(output_folder, 'Output_vegetation'))
surface_albedo_fileName = os.path.join(output_folder, 'Output_vegetation','User_surface_albedo_%s_%s%02d%02d.tif' %(res2, year, month, day))
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:
# Open User Albedo
dest_Surf_albedo = SEBAL.reproject_dataset_example(src_FileName_Surf_albedo, Example_fileName)[0]
Surf_albedo = dest_Surf_albedo.GetRasterBand(1).ReadAsArray()
# Set limit surface albedo
Surf_albedo = np.minimum(Surf_albedo, 0.6)
except:
print("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)
# 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, water_mask_temp, QC_Map)
def Get_USER_Para_Thermal(workbook, number, Example_fileName, year, month, day, water_mask_temp, surf_temp_offset, Thermal_Sharpening_not_needed):
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
output_folder = r"%s" %str(ws['C%d' %number].value)
# Open General information example file
lsc = gdal.Open(Example_fileName)
nrow = lsc.RasterYSize
ncol = lsc.RasterXSize
shape_lsc = [ncol, nrow]
# General sensor resolution and names'
res2 = '250m'
# Extract the name of the thermal and quality MODIS image from the excel file
ws = workbook['USER_Input']
src_FileName_LST = r"%s" %str(ws['D%d' %number].value) #land surface temperature
ws = workbook['Additional_Input']
try:
# If all additional fields are filled in than do not open the datasets
if ws['D%d' % number].value is None:
print('...................... Open USER Thermal ........................')
# Open User LST
dest_Surface_temp = SEBAL.reproject_dataset_example(src_FileName_LST, Example_fileName)[0]
Surface_temp = dest_Surface_temp.GetRasterBand(1).ReadAsArray()
Thermal_Sharpening_not_needed = 1
else:
# Output folder surface temperature
surf_temp_fileName = os.path.join(output_folder, 'Output_vegetation','User_surface_temp_%s_%s%02d%02d.tif' %(res2, year, month, day))
Surface_temp = SEBAL.Reshape_Reproject_Input_data(r'%s' %str(ws['D%d' % number].value), surf_temp_fileName, Example_fileName)
Thermal_Sharpening_not_needed = 1
except:
assert "Please check the surface temperature input path"
# Cloud mask:
temp_water = np.zeros((shape_lsc[1], shape_lsc[0]))
temp_water = np.copy(Surface_temp)
temp_water[water_mask_temp == 0.0] = np.nan
temp_water_sd = np.nanstd(temp_water) # Standard deviation
temp_water_mean = np.nanmean(temp_water) # Mean
print('Mean water temperature = ', '%0.3f (Kelvin)' % temp_water_mean)
print('SD water temperature = ', '%0.3f (Kelvin)' % temp_water_sd)
cloud_mask_temp = np.zeros((shape_lsc[1], shape_lsc[0]))
cloud_mask_temp[Surface_temp < np.minimum((temp_water_mean - 1.0 * temp_water_sd -
surf_temp_offset),290)] = 1.0
cloud_mask_temp[np.isnan(Surface_temp)] = 1
# remove wrong values VIIRS defined by user
Surface_temp[cloud_mask_temp == 1] = np.nan
print('Mean Surface Temperature = %s Kelvin' %np.nanmean(Surface_temp))
return(Surface_temp, cloud_mask_temp, Thermal_Sharpening_not_needed)
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_VIIRS_Para_Thermal(workbook, number, Example_fileName, year, month,
day, water_mask_temp, b10_emissivity, Temp_inst, Rp,
tau_sky, surf_temp_offset,
Thermal_Sharpening_not_needed):
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)
# Open General information example file
lsc = gdal.Open(Example_fileName)
nrow = lsc.RasterYSize
ncol = lsc.RasterXSize
shape_lsc = [ncol, nrow]
# Open the VIIRS_PROBAV_Input sheet
ws = workbook['VIIRS_PROBAV_Input']
# Extract VIIRS name from input excel
Name_VIIRS_Image = str(ws['B%d' % number].value)
Name_VIIRS_Image_QC = str(ws['C%d' % number].value)
k1 = 606.399172
k2 = 1258.78
sensor1 = 'PROBAV'
sensor2 = 'VIIRS'
res1 = '375m'
res2 = '100m'
res3 = '30m'
# Open the Landsat_Input sheet
ws = workbook['Additional_Input']
try:
# If all additional fields are filled in than do not open the datasets
if ws['D%d' % number].value is None:
print(
'...................... Open VIIRS Thermal ........................'
)
# Define the VIIRS thermal data name
VIIRS_data_name = os.path.join(input_folder,
'%s' % (Name_VIIRS_Image))
# Reproject VIIRS thermal data
VIIRS, ulx_dem, lry_dem, lrx_dem, uly_dem, epsg_to = SEBAL.reproject_dataset_example(
VIIRS_data_name, Example_fileName)
# Open VIIRS thermal data
data_VIIRS = VIIRS.GetRasterBand(1).ReadAsArray()
# Set the conditions for the brightness temperature
brightness_temp = np.where(data_VIIRS >= 250, data_VIIRS, np.nan)
# Constants
L_lambda_b10 = ((2 * 6.63e-34 * (3.0e8)**2) /
((11.45e-6)**5 * (np.exp(
(6.63e-34 * 3e8) /
(1.38e-23 *
(11.45e-6) * brightness_temp)) - 1))) * 1e-6
# Get Surface Temperature
Surface_temp = SEBAL.Get_Thermal(L_lambda_b10, Rp, Temp_inst,
tau_sky, b10_emissivity, k1, k2)
Surface_temp = Surface_temp.clip(230.0, 360.0)
else:
# Output folder surface temperature
surf_temp_fileName = os.path.join(
output_folder, 'Output_vegetation',
'User_surface_temp_%s_%s%02d%02d.tif' %
(res2, year, month, day))
Surface_temp = SEBAL.Reshape_Reproject_Input_data(
r'%s' % str(ws['D%d' % number].value), surf_temp_fileName,
Example_fileName)
Thermal_Sharpening_not_needed = 1
except:
assert "Please check the surface temperature input path"
try:
# Check Quality
if Name_VIIRS_Image_QC != 'None':
# Define the VIIRS Quality data name
VIIRS_data_name = os.path.join(input_folder,
'%s' % (Name_VIIRS_Image_QC))
# Reproject VIIRS Quality data
dest_VIIRS_QC, ulx_dem, lry_dem, lrx_dem, uly_dem, epsg_to = SEBAL.reproject_dataset_example(
VIIRS_data_name, Example_fileName)
# Open VIIRS Quality data
cloud_mask_temp = dest_VIIRS_QC.GetRasterBand(1).ReadAsArray()
else:
# Cloud mask:
temp_water = np.zeros((shape_lsc[1], shape_lsc[0]))
temp_water = np.copy(Surface_temp)
temp_water[water_mask_temp == 0.0] = np.nan
temp_water_sd = np.nanstd(temp_water) # Standard deviation
temp_water_mean = np.nanmean(temp_water) # Mean
print('Mean water temperature = ',
'%0.3f (Kelvin)' % temp_water_mean)
print('SD water temperature = ', '%0.3f (Kelvin)' % temp_water_sd)
cloud_mask_temp = np.zeros((shape_lsc[1], shape_lsc[0]))
cloud_mask_temp[Surface_temp < np.minimum(
(temp_water_mean - 1.0 * temp_water_sd -
surf_temp_offset), 290)] = 1.0
except:
assert "Please check the QC temperature input path"
# remove wrong values VIIRS defined by user
Surface_temp[cloud_mask_temp == 1] = np.nan
print('Mean Surface Temperature = %s Kelvin' % np.nanmean(Surface_temp))
return (Surface_temp, cloud_mask_temp, Thermal_Sharpening_not_needed)
def Get_VIIRS_Para_Thermal(workbook, number, Example_fileName, year, DOY, water_mask_temp, b10_emissivity, Temp_inst, Rp, tau_sky, surf_temp_offset, Thermal_Sharpening_not_needed):
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)
# Open General information example file
lsc = gdal.Open(Example_fileName)
nrow = lsc.RasterYSize
ncol = lsc.RasterXSize
shape_lsc = [ncol, nrow]
# Open the VIIRS_PROBAV_Input sheet
ws = workbook['VIIRS_PROBAV_Input']
# Extract VIIRS name from input excel
Name_VIIRS_Image = str(ws['B%d' %number].value)
Name_VIIRS_Image_QC = str(ws['C%d' %number].value)
k1=606.399172
k2=1258.78
sensor1 = 'PROBAV'
sensor2 = 'VIIRS'
res1 = '375m'
res2 = '100m'
res3 = '30m'
# Open the Landsat_Input sheet
ws = workbook['Additional_Input']
try:
# If all additional fields are filled in than do not open the datasets
if ws['D%d' % number].value is None:
print('...................... Open VIIRS Thermal ........................')
# Define the VIIRS thermal data name
VIIRS_data_name=os.path.join(input_folder, '%s' % (Name_VIIRS_Image))
# Reproject VIIRS thermal data
VIIRS, ulx_dem, lry_dem, lrx_dem, uly_dem, epsg_to = SEBAL.reproject_dataset_example(
VIIRS_data_name, Example_fileName)
# Open VIIRS thermal data
data_VIIRS = VIIRS.GetRasterBand(1).ReadAsArray()
# Set the conditions for the brightness temperature
brightness_temp=np.where(data_VIIRS>=250, data_VIIRS, np.nan)
# Constants
L_lambda_b10 = ((2*6.63e-34*(3.0e8)**2)/((11.45e-6)**5*(np.exp((6.63e-34*3e8)/(1.38e-23*(11.45e-6)*brightness_temp))-1)))*1e-6
# Get Surface Temperature
Surface_temp = SEBAL.Get_Thermal(L_lambda_b10, Rp, Temp_inst, tau_sky, b10_emissivity, k1, k2)
Surface_temp = Surface_temp.clip(230.0, 360.0)
else:
surf_temp_fileName = os.path.join(output_folder, 'Output_vegetation','User_surface_temp_%s_%s_%s.tif' %(res2, year, DOY))
Surface_temp = SEBAL.Reshape_Reproject_Input_data(r'%s' %str(ws['D%d' % number].value),surf_temp_fileName, Example_fileName)
Thermal_Sharpening_not_needed = 1
except:
assert "Please check the surface temperature input path"
try:
# Check Quality
if Name_VIIRS_Image_QC != 'None':
# Define the VIIRS Quality data name
VIIRS_data_name=os.path.join(input_folder, '%s' % (Name_VIIRS_Image_QC))
# Reproject VIIRS Quality data
dest_VIIRS_QC, ulx_dem, lry_dem, lrx_dem, uly_dem, epsg_to = SEBAL.reproject_dataset_example(
VIIRS_data_name, Example_fileName)
# Open VIIRS Quality data
cloud_mask_temp = dest_VIIRS_QC.GetRasterBand(1).ReadAsArray()
else:
# Cloud mask:
temp_water = np.zeros((shape_lsc[1], shape_lsc[0]))
temp_water = np.copy(Surface_temp)
temp_water[water_mask_temp == 0.0] = np.nan
temp_water_sd = np.nanstd(temp_water) # Standard deviation
temp_water_mean = np.nanmean(temp_water) # Mean
print('Mean water temperature = ', '%0.3f (Kelvin)' % temp_water_mean)
print('SD water temperature = ', '%0.3f (Kelvin)' % temp_water_sd)
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)
def Get_LS_Para_Veg(workbook, number, Example_fileName, year, DOY, path_radiance, Apparent_atmosf_transm, cos_zn, dr):
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 Landsat VIS -----------------------')
# Open the Landsat_Input sheet
ws = workbook['Landsat_Input']
# Extract Landsat name, number and amount of thermal bands from excel file
Name_Landsat_Image = str(ws['B%d' %number].value)
Landsat_nr = int(ws['C%d' %number].value) # Type of Landsat (LS) image used (LS5, LS7, or LS8)
# temperature: 1 = Band 6 for LS_5 & 7, Band 10 for LS_8 (optional)
# Define bands used for each Landsat number
if Landsat_nr == 5 or Landsat_nr == 7:
Bands = np.array([1, 2, 3, 4, 5, 7, 6])
elif Landsat_nr == 8:
Bands = np.array([2, 3, 4, 5, 6, 7, 10, 11])
else:
print('Landsat image not supported, use Landsat 7 or 8')
# Open MTL landsat and get the correction parameters
Landsat_meta_fileName = os.path.join(input_folder, '%s_MTL.txt' %Name_Landsat_Image)
Lmin, Lmax, k1_c, k2_c = info_band_metadata(Landsat_meta_fileName, Bands)
print('Lmin= ', Lmin)
print('Lmax= ', Lmax)
print('k1= ', k1_c)
print('k2= ', k2_c)
sensor1 = 'LS%d' %Landsat_nr
res1 = '30m'
res2 = '30m'
res3 = '30m'
# Mean solar exo-atmospheric irradiance for each band (W/m2/microm)
# for the different Landsat images (L5, L7, or L8)
ESUN_L5 = np.array([1983, 1796, 1536, 1031, 220, 83.44])
ESUN_L7 = np.array([1997, 1812, 1533, 1039, 230.8, 84.9])
ESUN_L8 = np.array([1973.28, 1842.68, 1565.17, 963.69, 245, 82.106])
# Open one band - To get the metadata of the landsat images only once (to get the extend)
src_FileName = os.path.join(input_folder, '%s_B2.TIF' %Name_Landsat_Image) # before 10!
ls, band_data, ulx, uly, lrx, lry, x_size_ls, y_size_ls = Get_Extend_Landsat(src_FileName)
print('Original LANDSAT Image - ')
print(' Size :', x_size_ls, y_size_ls)
print(' Upper Left corner x, y: ', ulx, ', ', uly)
print(' Lower right corner x, y: ', lrx, ', ', lry)
lsc, ulx, uly, lrx, lry, epsg_to = SEBAL.reproject_dataset_example(src_FileName, Example_fileName)
# Get the extend of the remaining landsat file after clipping based on the DEM file
y_size_lsc = lsc.RasterYSize
x_size_lsc = lsc.RasterXSize
shape_lsc = [x_size_lsc, y_size_lsc]
print('--- ')
print('Cropped LANDSAT Image - ')
print(' Size :', x_size_lsc, y_size_lsc)
print(' Upper Left corner x, y: ', ulx, ', ', uly)
print(' Lower right corner x, y: ', lrx, ', ', lry)
# if landsat 5 or 7 is used then first create a mask for removing the no data stripes
if Landsat_nr == 5 or Landsat_nr == 7:
src_FileName = os.path.join(input_folder, '%s_B6.TIF' % (Name_Landsat_Image)) #open smallest band
if not os.path.exists(src_FileName):
src_FileName = os.path.join(input_folder, '%s_B6_VCID_2.TIF' % (Name_Landsat_Image))
src_FileName_2 = os.path.join(input_folder, '%s_B1.TIF' % (Name_Landsat_Image)) #open smallest band
src_FileName_3 = os.path.join(input_folder, '%s_B3.TIF' % (Name_Landsat_Image)) #open smallest band
src_FileName_4 = os.path.join(input_folder, '%s_B4.TIF' % (Name_Landsat_Image)) #open smallest band
src_FileName_5 = os.path.join(input_folder, '%s_B7.TIF' % (Name_Landsat_Image)) #open smallest band
src_FileName_6 = os.path.join(input_folder, '%s_B2.TIF' % (Name_Landsat_Image)) #open smallest band
src_FileName_7 = os.path.join(input_folder, '%s_B5.TIF' % (Name_Landsat_Image)) #open smallest band
ls_data=Open_landsat(src_FileName,Example_fileName)
ls_data_2=Open_landsat(src_FileName_2,Example_fileName)
ls_data_3=Open_landsat(src_FileName_3,Example_fileName)
ls_data_4=Open_landsat(src_FileName_4,Example_fileName)
ls_data_5=Open_landsat(src_FileName_5,Example_fileName)
ls_data_6=Open_landsat(src_FileName_6,Example_fileName)
ls_data_7=Open_landsat(src_FileName_7,Example_fileName)
# create and save the landsat mask for all images based on band 11 (smallest map)
QC_Map=np.zeros((shape_lsc[1], shape_lsc[0]))
QC_Map=np.where(np.logical_or.reduce((ls_data==0,ls_data_2==0,ls_data_3==0,ls_data_4==0,ls_data_5==0,ls_data_6==0,ls_data_7==0)),1,0)
# If landsat 8 then use landsat band 10 and 11
elif Landsat_nr == 8:
src_FileName_11 = os.path.join(input_folder, '%s_B11.TIF' % (Name_Landsat_Image)) #open smallest band
ls_data_11=Open_landsat(src_FileName_11,Example_fileName)
src_FileName_10 = os.path.join(input_folder, '%s_B10.TIF' % (Name_Landsat_Image)) #open smallest band
ls_data_10=Open_landsat(src_FileName_10, Example_fileName)
# create and save the landsat mask for all images based on band 10 and 11
QC_Map=np.zeros((shape_lsc[1], shape_lsc[0]))
QC_Map=np.where(np.logical_or(ls_data_11==0, ls_data_10==0),1,0)
else:
print('Landsat image not supported, use Landsat 7 or 8')
# Open data of the landsat mask
ls_data=Open_landsat(src_FileName, Example_fileName)
# Create 3D array to store Spectral radiance and Reflectivity for each band
Reflect, Spec_Rad = Landsat_Reflect(Bands, input_folder, Name_Landsat_Image, output_folder, shape_lsc, QC_Map, Lmax, Lmin, ESUN_L5, ESUN_L7, ESUN_L8, cos_zn, dr, Landsat_nr, Example_fileName)
# save spectral data
for i in range(0,6):
spec_ref_fileName = os.path.join(output_folder, 'Output_radiation_balance','%s_spectral_reflectance_B%s_%s_%s_%s.tif' %(sensor1, Bands[i], res3, year, DOY))
SEBAL.save_GeoTiff_proy(lsc, Reflect[:, :, i], spec_ref_fileName, shape_lsc, nband=1)
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:
# use the Landsat reflectance to calculate the surface albede, NDVI
NDVI = SEBAL.Calc_NDVI(Reflect)
# Calculate temporal water mask
water_mask_temp=SEBAL.Water_Mask(shape_lsc,Reflect)
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:
# use the Landsat reflectance to calculate the surface albede, NDVI
Surf_albedo = SEBAL.Calc_albedo(Reflect, path_radiance, Apparent_atmosf_transm)
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)
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)
