def copy_bands_to_file(src_file_path, dst_file_path, bands=None):
# Get info from source product
src_prod = ProductIO.readProduct(src_file_path)
prod_name = src_prod.getName()
prod_type = src_prod.getProductType()
width = src_prod.getSceneRasterWidth()
height = src_prod.getSceneRasterHeight()
if bands is None:
bands = src_prod.getBandNames()
# Copy geocoding and selected bands from source to destination product
dst_prod = Product(prod_name, prod_type, width, height)
ProductUtils.copyGeoCoding(src_prod.getBandAt(0), dst_prod)
for band in bands:
r = ProductUtils.copyBand(band, src_prod, dst_prod, True)
if r is None:
src_prod.closeIO()
raise RuntimeError(src_file_path + " does not contain band " +
band)
# Write destination product to disk
ext = os.path.splitext(dst_file_path)[1]
if ext == '.dim':
file_type = 'BEAM_DIMAP'
elif ext == '.nc':
file_type = 'NetCDF-CF'
elif ext == '.tif':
file_type = 'GeoTIFF-BigTIFF'
else:
file_type = 'GeoTIFF-BigTIFF'
ProductIO.writeProduct(dst_prod, dst_file_path, file_type)
src_prod.closeIO()
dst_prod.closeIO()
def do_vegetation_indices (source):
print ('\tVegetation Indices ...')
## Input product and dimensions
input_product = ProductIO.readProduct(source)
width = input_product.getSceneRasterWidth()
height = input_product.getSceneRasterHeight()
product_name = input_product.getName()
product_description = input_product.getDescription()
product_band_names = input_product.getBandNames()
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
## input product red and nir bands
b4 = input_product.getBand('B4')
b8 = input_product.getBand('B8')
## output product (ndvi) new band
output_product = Product('NDVI', 'NDVI', width, height)
ProductUtils.copyGeoCoding(input_product, output_product)
output_band = output_product.addBand('ndvi', ProductData.TYPE_FLOAT32)
## output writer
output_product_writer = ProductIO.getProductWriter('BEAM-DIMAP')
output_product.setProductWriter(output_product_writer)
output_product.writeHeader(product_name + '_ndvi.dim')
## compute & save ndvi line by line
red_row = numpy.zeros(width, dtype=numpy.float32)
nir_row = numpy.zeros(width, dtype=numpy.float32)
for y in xrange (height):
red_row = b4.readPixels(0, y, width, 1, red_row)
nir_row = b8.readPixels(0, y, width, 1, nir_row)
ndvi = (nir_row - red_row)/(nir_row + red_row)
output = output_band.writePixels(0, y, width, 1, ndvi)
output_product.CloseIO()
return output
#SVR training
pipeline = make_pipeline(
StandardScaler(),
SVR(kernel='rbf', epsilon=0.105, C=250, gamma=2.8),
)
SVRmodel = pipeline.fit(X, Y)
# Predictfor validation data
valX = X
y_out = pipeline.predict(valX)
##---------------------------------------------------------------------------------
bandc11 = product.getBand('C11')
bandc22 = product.getBand('C22')
laiProduct = Product('LAI', 'LAI', width, height)
laiBand = laiProduct.addBand('lai', ProductData.TYPE_FLOAT32)
laiFlagsBand = laiProduct.addBand('lai_flags', ProductData.TYPE_UINT8)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
ProductUtils.copyGeoCoding(product, laiProduct)
ProductUtils.copyMetadata(product, laiProduct)
ProductUtils.copyTiePointGrids(product, laiProduct)
laiFlagCoding = FlagCoding('lai_flags')
laiFlagCoding.addFlag("LAI_LOW", 1, "LAI below 0")
laiFlagCoding.addFlag("LAI_HIGH", 2, "LAI above 5")
group = laiProduct.getFlagCodingGroup()
#print(dir(group))
group.add(laiFlagCoding)
import snappy
from snappy import ProductIO
from snappy import Product
from snappy import ProductData
from snappy import ProductUtils
jpy = snappy.jpy
if os.path.exists(file1):
# Read sourceProduct and get information needed to create target product:
sourceProduct = snappy.ProductIO.readProduct(file1)
width = sourceProduct.getSceneRasterWidth()
height = sourceProduct.getSceneRasterHeight()
#Create target product:
targetProduct = Product('FLH_Product', 'FLH_Type', width, height)
targetBand = targetProduct.addBand('FLH', ProductData.TYPE_FLOAT32)
ProductUtils.copyGeoCoding(sourceProduct, targetProduct)
targetProduct.setProductWriter(ProductIO.getProductWriter('GeoTIFF'))
# Use calibration operator - I've taken "org.esa.s1tbx.calibration.gpf.CalibrationOp" from the help window
CalibrationOp = jpy.get_type("org.esa.s1tbx.calibration.gpf.CalibrationOp")
CalOp = CalibrationOp()
CalOp = CalibrationOp()
CalOp.setSourceProduct(sourceProduct)
CalOp.setParameter('doSomethng', True)
targetProduct = CalOp.getTargetProduct()
snappy.ProductIO.writeProduct(targetProduct, 'toFile.dim', 'BEAM-DIMAP')
#### HERE I DO NOT KNOW HOW TO EXECUTE this operator
def create_product(self):
from snappy import Product, ProductUtils, ProductIO, ProductData, String
product = self.product
ac_product = Product('L2h', 'L2h', self.width, self.height)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
ac_product.setProductWriter(writer)
ProductUtils.copyGeoCoding(product, ac_product)
ProductUtils.copyMetadata(product, ac_product)
ac_product.setStartTime(product.getStartTime())
ac_product.setEndTime(product.getEndTime())
# add metadata: ancillary data used for processing
meta = jpy.get_type('org.esa.snap.core.datamodel.MetadataElement')
att = jpy.get_type('org.esa.snap.core.datamodel.MetadataAttribute')
# att(name=string,type=int), type: 41L->ascii; 12L->int32;
att0 = att('AERONET file', ProductData.TYPE_ASCII)
att0.setDataElems(self.aeronetfile)
att1 = att('AOT', ProductData.TYPE_ASCII)
att1.setDataElems(str(self.aot))
meta = meta('L2')
meta.setName('Ancillary Data')
meta.addAttribute(att0)
meta.addAttribute(att1)
ac_product.getMetadataRoot().addElement(meta)
# add data
# Water-leaving radiance + sunglint
for iband in range(self.N):
bname = "Lnw_g_" + self.band_names[iband]
acband = ac_product.addBand(bname, ProductData.TYPE_FLOAT32)
acband.setSpectralWavelength(self.wl[iband])
acband.setSpectralBandwidth(self.B[iband].getSpectralBandwidth())
acband.setModified(True)
acband.setNoDataValue(np.nan)
acband.setNoDataValueUsed(True)
acband.setValidPixelExpression(bname + ' >= -1')
ac_product.getBand(bname).setDescription(
"Water-leaving plus sunglint normalized radiance (Lnw + Lg) in mW cm-2 sr-1 μm-1 at "
+ self.band_names[iband])
# Water-leaving radiance
for iband in range(self.N):
bname = "Lnw_" + self.band_names[iband]
acband = ac_product.addBand(bname, ProductData.TYPE_FLOAT32)
acband.setSpectralWavelength(self.wl[iband])
acband.setSpectralBandwidth(self.B[iband].getSpectralBandwidth())
acband.setModified(True)
acband.setNoDataValue(np.nan)
acband.setNoDataValueUsed(True)
acband.setValidPixelExpression(bname + ' >= -1')
ac_product.getBand(bname).setDescription(
"Normalized water-leaving radiance in mW cm-2 sr-1 μm-1 at " +
self.band_names[iband])
# Sunglint reflection factor
# for iband in range(self.N):
bname = "BRDFg" # + self.band_names[iband]
acband = ac_product.addBand(bname, ProductData.TYPE_FLOAT32)
# acband.setSpectralWavelength(self.wl[iband])
# acband.setSpectralBandwidth(self.b[iband].getSpectralBandwidth())
acband.setModified(True)
acband.setNoDataValue(np.nan)
acband.setNoDataValueUsed(True)
acband.setValidPixelExpression(bname + ' >= 0')
ac_product.getBand(bname).setDescription(
"Glint reflection factor (BRDF) ") # + self.band_names[iband])
# Viewing geometry
acband = ac_product.addBand("SZA", ProductData.TYPE_FLOAT32)
acband.setModified(True)
acband.setNoDataValue(np.nan)
acband.setNoDataValueUsed(True)
ac_product.getBand("SZA").setDescription("Solar zenith angle in deg.")
acband = ac_product.addBand("VZA", ProductData.TYPE_FLOAT32)
acband.setModified(True)
acband.setNoDataValue(np.nan)
acband.setNoDataValueUsed(True)
ac_product.getBand("VZA").setDescription(
"Mean viewing zenith angle in deg.")
acband = ac_product.addBand("AZI", ProductData.TYPE_FLOAT32)
acband.setModified(True)
acband.setNoDataValue(np.nan)
acband.setNoDataValueUsed(True)
ac_product.getBand("AZI").setDescription(
"Mean relative azimuth angle in deg.")
ac_product.setAutoGrouping("Lnw:Lnw_g_")
ac_product.writeHeader(String(self.outfile + ".dim"))
self.l2_product = ac_product
def write_BalticP_AC_Product(product,
baltic__product_path,
sensor,
data_dict,
singleBand_dict=None):
File = jpy.get_type('java.io.File')
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
bandShape = (height, width)
balticPACProduct = Product('balticPAC', 'balticPAC', width, height)
balticPACProduct.setFileLocation(File(baltic__product_path))
ProductUtils.copyGeoCoding(product, balticPACProduct)
ProductUtils.copyTiePointGrids(product, balticPACProduct)
if (sensor == 'OLCI'):
nbands = 21
band_name = ["Oa01_radiance"]
for i in range(1, nbands):
if (i < 9):
band_name += ["Oa0" + str(i + 1) + "_radiance"]
else:
band_name += ["Oa" + str(i + 1) + "_radiance"]
# Create empty bands for rhow, rhown, uncertainties for rhow
for i in range(nbands):
bsource = product.getBand(band_name[i]) # TOA radiance
for key in data_dict.keys():
brtoa_name = key + "_" + str(i + 1)
rtoaBand = balticPACProduct.addBand(brtoa_name,
ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rtoaBand)
rtoaBand.setNoDataValue(np.nan)
rtoaBand.setNoDataValueUsed(True)
dataNames = [*data_dict.keys()]
autoGroupingString = dataNames[0]
for key in dataNames[1:]:
autoGroupingString += ':' + key
balticPACProduct.setAutoGrouping(autoGroupingString)
if not singleBand_dict is None:
for key in singleBand_dict.keys():
singleBand = balticPACProduct.addBand(key,
ProductData.TYPE_FLOAT32)
singleBand.setNoDataValue(np.nan)
singleBand.setNoDataValueUsed(True)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
balticPACProduct.setProductWriter(writer)
balticPACProduct.writeHeader(baltic__product_path)
writer.writeProductNodes(balticPACProduct, baltic__product_path)
# set datarhow, rhown, uncertainties for rhow
for key in data_dict.keys():
x = data_dict[key].get('data')
if not x is None:
for i in range(nbands):
brtoa_name = key + "_" + str(i + 1)
rtoaBand = balticPACProduct.getBand(brtoa_name)
out = np.array(x[:, i]).reshape(bandShape)
rtoaBand.writeRasterData(
0, 0, width, height,
snp.ProductData.createInstance(np.float32(out)),
ProgressMonitor.NULL)
if not singleBand_dict is None:
for key in singleBand_dict.keys():
x = singleBand_dict[key].get('data')
if not x is None:
singleBand = balticPACProduct.getBand(key)
out = np.array(x).reshape(bandShape)
singleBand.writeRasterData(
0, 0, width, height,
snp.ProductData.createInstance(np.float32(out)),
ProgressMonitor.NULL)
# # Create flag coding
# raycorFlagsBand = balticPACProduct.addBand('raycor_flags', ProductData.TYPE_UINT8)
# raycorFlagCoding = FlagCoding('raycor_flags')
# raycorFlagCoding.addFlag("testflag_1", 1, "Flag 1 for Rayleigh Correction")
# raycorFlagCoding.addFlag("testflag_2", 2, "Flag 2 for Rayleigh Correction")
# group = balticPACProduct.getFlagCodingGroup()
# group.add(raycorFlagCoding)
# raycorFlagsBand.setSampleCoding(raycorFlagCoding)
balticPACProduct.closeIO()
sys.exit(1)
print("Reading...")
product = ProductIO.readProduct(sys.argv[1])
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
name = product.getName()
desc = product.getDescription()
band_names = product.getBandNames()
print("Product: %s, %d x %d pixels, %s" % (name, width, height, desc))
print("Bands: %s" % (band_names))
b7 = product.getBand('radiance_7')
b10 = product.getBand('radiance_10')
ndviProduct = Product('NDVI', 'NDVI', width, height)
ndviBand = ndviProduct.addBand('ndvi', ProductData.TYPE_FLOAT32)
ndviBand.setNoDataValue(numpy.nan)
ndviBand.setNoDataValueUsed(True)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
ProductUtils.copyGeoCoding(product, ndviProduct)
ndviProduct.setProductWriter(writer)
ndviProduct.writeHeader(String('snappy_ndvi_with_masks_output.dim'))
r7 = numpy.zeros(width, dtype=numpy.float32)
r10 = numpy.zeros(width, dtype=numpy.float32)
v7 = numpy.zeros(width, dtype=numpy.uint8)
product = ProductIO.readProduct(File)
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
name = product.getName()
description = product.getDescription()
band_names = product.getBandNames()
print("Product: %s, %s" % (name, description))
print("Raster size: %d x %d pixels" % (width, height))
print("Start time: " + str(product.getStartTime()))
print("Description: %s" % description)
print("End time: " + str(product.getEndTime()))
print("Bands: %s" % (list(band_names)))
KNNProduct = Product('KNN', 'KNN', width, height)
KNNBand = KNNProduct.addBand('KNN', ProductData.TYPE_FLOAT32)
KNNFlagsBand = KNNProduct.addBand('KNN_flags', ProductData.TYPE_UINT8)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
ProductUtils.copyGeoCoding(product, KNNProduct)
KNNFlagCoding = FlagCoding('KNN_flags')
KNNFlagCoding.addFlag("1", 1, "KNN above 0")
KNNFlagCoding.addFlag("2", 2, "KNN above 1")
KNNFlagCoding.addFlag("3", 3, "KNN above 2")
KNNFlagCoding.addFlag("4", 4, "KNN above 3")
KNNFlagCoding.addFlag("5", 5, "KNN above 4")
KNNFlagCoding.addFlag("6", 6, "KNN above 5")
group = KNNProduct.getFlagCodingGroup()
group.add(KNNFlagCoding)
for S2_SAFE in os.listdir('products'):
#NDVI:
NDVI_im = S2_SAFE.split(".")[0] + "_NDVI"
NIR_im = S2_SAFE.split(".")[0] + "_NIR"
os.system('mkdir checked_products/' + NDVI_im)
os.system('mkdir checked_products/' + NIR_im)
S2_product = ProductIO.readProduct('products/' + S2_SAFE +
'/GRANULE/output.dim')
band_names = S2_product.getBandNames()
width = S2_product.getSceneRasterWidth()
height = S2_product.getSceneRasterHeight()
b4 = S2_product.getBand('B4')
b8 = S2_product.getBand('B8')
newProduct = Product('NDVI', 'NDVI', width, height)
newBand = newProduct.addBand('ndvi', ProductData.TYPE_FLOAT32)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
ProductUtils.copyGeoCoding(S2_product, newProduct)
newProduct.setProductWriter(writer)
newProduct.writeHeader('NDVI.dim')
rb4 = np.zeros(width, dtype=np.float32)
rb8 = np.zeros(width, dtype=np.float32)
for y in range(height):
rb4 = b4.readPixels(0, y, width, 1, rb4)
rb8 = b8.readPixels(0, y, width, 1, rb8)
NDVI = (rb8 - rb4) / (rb8 + rb4)
newBand.writePixels(0, y, width, 1, NDVI)
newProduct.closeIO()
def write_snappy_product(file_path, bands, product_name, geo_coding):
try:
(height, width) = bands[0]['band_data'].shape
except AttributeError:
raise RuntimeError(bands[0]['band_name'] + "contains no data.")
product = Product(product_name, product_name, width, height)
product.setSceneGeoCoding(geo_coding)
# Ensure that output is saved in BEAM-DIMAP format,
# otherwise writeHeader does not work.
file_path = os.path.splitext(file_path)[0] + '.dim'
# Bands have to be created before header is written
# but header has to be written before band data is written.
for b in bands:
band = product.addBand(b['band_name'], ProductData.TYPE_FLOAT32)
if 'description' in b.keys():
band.setDescription(b['description'])
if 'unit' in b.keys():
band.setUnit(b['unit'])
product.setProductWriter(ProductIO.getProductWriter('BEAM-DIMAP'))
product.writeHeader(String(file_path))
for b in bands:
band = product.getBand(b['band_name'])
band.writePixels(0, 0, width, height,
b['band_data'].astype(np.float32))
product.closeIO()
def main(args=sys.argv[1:]):
if len(args) != 1:
print("usage: raycorr-processor <SENSOR>")
sys.exit(1)
SENSOR = args[0]
# SENSOR = 'OLCI'
# SENSOR = 'MERIS'
# PRODPATH = "C:\\Users\\carsten\\Dropbox\\Carsten\\SWProjects\\Rayleigh-Correction\\testdata\\"
# AUXPATH = "C:\\Users\\carsten\\Dropbox\\Carsten\\Tagesordner\\20160104\\Rayleigh-Correction-Processor\\"
# O3PATH="C:\\Users\\carsten\\Dropbox\\Carsten\\SWProjects\\Rayleigh-Correction\\raycorr\\"
PRODPATH = "D:\\Dropbox\\Carsten\\SWProjects\\Rayleigh-Correction\\testdata\\"
# AUXPATH = "D:\\Dropbox\\Carsten\\Tagesordner\\20160104\\Rayleigh-Correction-Processor\\"
O3PATH="D:\\Dropbox\\Carsten\\SWProjects\\Rayleigh-Correction\\raycorr\\"
DEMFactory = jpy.get_type('org.esa.snap.dem.dataio.DEMFactory')
Resampling = jpy.get_type('org.esa.snap.core.dataop.resamp.Resampling')
GeoPos = jpy.get_type('org.esa.snap.core.datamodel.GeoPos')
if (SENSOR=='MERIS'):
IN_FILE = PRODPATH+"subset_1_of_MER_RR__1PTACR20050713_094325_000002592039_00022_17611_0000.dim"
OUT_FILE = PRODPATH+'Testprodukt1_MER_RR_20050713.dim'
else:
if (SENSOR=='OLCI'):
IN_FILE = PRODPATH+'subset_3_of_S3A_OL_1_EFR____20160509T103945_20160509T104245_20160509T124907_0180_004_051_1979_SVL_O_NR_001.dim'
OUT_FILE = PRODPATH+'Testproduct3_OL_1_EFR____20160509T103945.dim'
else:
print("Sensor ",SENSOR," not supported - exit")
return
file = IN_FILE
# AUX_FILE = AUXPATH+'ADF\\MER_ATP_AXVACR20091026_144725_20021224_121445_20200101_000000'
# adf = ADF(AUX_FILE)
# ray_coeff_matrix = adf.ray_coeff_matrix
# rayADF = readRayADF(AUX_FILE)
# new_aux = OrderedDict()
# new_aux['tau_ray'] = rayADF['tR']
# new_aux['theta'] = rayADF['theta']
# new_aux['ray_albedo_lut'] = rayADF['rayAlbLUT']
# new_aux['ray_coeff_matrix'] = ray_coeff_matrix
# with open('raycorr_auxdata.json', 'w') as fp:
# json.dumps(new_aux, fp, cls=JSONNumpyEncoder, indent=2)
# fp.close()
with open('../test/raycorr_auxdata.json', 'r') as fp:
obj = json.load(fp, object_hook=json_as_numpy)
# json_str = json.dumps(new_aux, cls=JSONNumpyEncoder, indent=2)
# print(json_str)
# obj = json.loads(json_str, object_hook=json_as_numpy)
# rayADF = new_aux
rayADF = obj
ray_coeff_matrix=rayADF['ray_coeff_matrix']
print("Reading...")
product = ProductIO.readProduct(file)
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
name = product.getName()
description = product.getDescription()
band_names = product.getBandNames()
print("Sensor: %s" % SENSOR)
print("Product: %s, %s" % (name, description))
print("Raster size: %d x %d pixels" % (width, height))
print("Start time: " + str(product.getStartTime()))
print("End time: " + str(product.getEndTime()))
print("Bands: %s" % (list(band_names)))
raycorProduct = Product('RayCorr', 'RayCorr', width, height)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
raycorProduct.setProductWriter(writer)
if (SENSOR == 'MERIS'):
nbands = product.getNumBands() - 2 # the last 2 bands are l1flags and detector index; we don't need them
band_name = ["radiance_1"]
for i in range(1,nbands):
band_name += ["radiance_" + str(i+1)]
if (SENSOR == 'OLCI'):
nbands = 21
band_name = ["Oa01_radiance"]
sf_name = ["solar_flux_band_1"]
for i in range(1,nbands):
if (i < 9):
band_name += ["Oa0" + str(i + 1) + "_radiance"]
sf_name += ["solar_flux_band_" + str(i + 1)]
else:
band_name += ["Oa" + str(i + 1) + "_radiance"]
sf_name += ["solar_flux_band_" + str(i + 1)]
# Create TOA reflectance and Rayleig optical thickness bands
for i in range(nbands):
# bsource = product.getBandAt(i)
bsource = product.getBand(band_name[i])
btoa_name = "rtoa_" + str(i + 1)
toareflBand = raycorProduct.addBand(btoa_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, toareflBand)
btaur_name = "taur_" + str(i + 1)
taurBand = raycorProduct.addBand(btaur_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, taurBand)
brhor_name = "rRay_" + str(i + 1)
rhorBand = raycorProduct.addBand(brhor_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rhorBand)
# Fourier Terms, during debugging only
brhorF1_name = "rRayF1_" + str(i + 1)
rhorF1Band = raycorProduct.addBand(brhorF1_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rhorF1Band)
brhorF2_name = "rRayF2_" + str(i + 1)
rhorF2Band = raycorProduct.addBand(brhorF2_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rhorF2Band)
brhorF3_name = "rRayF3_" + str(i + 1)
rhorF3Band = raycorProduct.addBand(brhorF3_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rhorF3Band)
rayTransS_name = "transSRay_" + str(i + 1)
rayTransSBand = raycorProduct.addBand(rayTransS_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rayTransSBand)
rayTransV_name = "transVRay_" + str(i + 1)
rayTransVBand = raycorProduct.addBand(rayTransV_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rayTransVBand)
sARay_name = "sARay_" + str(i + 1)
sARayBand = raycorProduct.addBand(sARay_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, sARayBand)
rtoaR_name = "rtoaRay_" + str(i + 1)
rtoaRBand = raycorProduct.addBand(rtoaR_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rtoaRBand)
rBRR_name = "rBRR_" + str(i + 1)
rBRRBand = raycorProduct.addBand(rBRR_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rBRRBand)
spf_name = "sphericalAlbedoFactor_" + str(i + 1)
spfBand = raycorProduct.addBand(spf_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, spfBand)
# simple Rayleigh reflectance (Roland's formular)
rRaySimple_name = "RayleighSimple_" + str(i + 1)
rRaySimpleBand = raycorProduct.addBand(rRaySimple_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rRaySimpleBand)
# gaseous absorption corrected TOA reflectances
rho_ng_name = "rtoa_ng_" + str(i + 1)
rho_ngBand = raycorProduct.addBand(rho_ng_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, rho_ngBand)
# simple Rayleigh optical thickness, for debugging
taurS_name = "taurS_" + str(i + 1)
taurSBand = raycorProduct.addBand(taurS_name, ProductData.TYPE_FLOAT32)
ProductUtils.copySpectralBandProperties(bsource, taurSBand)
raycorProduct.setAutoGrouping(
'rtoa:taur:rRay:rRayF1:rRayF2:rRayF3:transSRay:transVRay:sARay:rtoaRay:rBRR:sphericalAlbedoFactor:RayleighSimple:rtoa_ng:taurS')
airmassBand = raycorProduct.addBand('airmass', ProductData.TYPE_FLOAT32)
azidiffBand = raycorProduct.addBand('azidiff', ProductData.TYPE_FLOAT32)
altBand = raycorProduct.addBand('altitude', ProductData.TYPE_FLOAT32)
# Create flag coding
raycorFlagsBand = raycorProduct.addBand('raycor_flags', ProductData.TYPE_UINT8)
raycorFlagCoding = FlagCoding('raycor_flags')
raycorFlagCoding.addFlag("testflag_1", 1, "Flag 1 for Rayleigh Correction")
raycorFlagCoding.addFlag("testflag_2", 2, "Flag 2 for Rayleigh Correction")
group = raycorProduct.getFlagCodingGroup()
group.add(raycorFlagCoding)
raycorFlagsBand.setSampleCoding(raycorFlagCoding)
# add geocoding and create the product on disk (meta data, empty bands)
ProductUtils.copyGeoCoding(product, raycorProduct) #geocoding is copied when tie point grids are copied,
ProductUtils.copyTiePointGrids(product, raycorProduct)
raycorProduct.writeHeader(OUT_FILE)
# Calculate and write toa reflectances and Rayleigh optical thickness
# ===================================================================
# some stuff needed to get the altitude from an external DEM; can be omitted if altitude is used from the product
# resamplingMethod = 'NEAREST_NEIGHBOUR' # Resampling.NEAREST_NEIGHBOUR.getName()
resamplingMethod = Resampling.NEAREST_NEIGHBOUR.getName()
demName = 'GETASSE30' # alternative 'SRTM 3Sec'
dem = DEMFactory.createElevationModel(demName, resamplingMethod)
# constants
AVO = 6.0221367E+23 # Avogadro's number
m_a_zero = 28.9595 # Mean molecular weight of dry ait (zero CO2)
g0_45 = 980.616 # Acceleration of gravity (sea level and 458 latitude)
Ns = 2.5469E19 # Molecular density of gas in molecules / cm3
# constants describing the state of the atmosphere and which we don't know; better values may be used if known
CO2 = 3.E-4 # CO2 concentration at pixel; typical values are 300 to 360 ppm
C_CO2 = CO2 * 100 # CO2 concentration in ppm
m_a = 15.0556 * CO2 + m_a_zero # mean molecular weight of dry air as function of actual CO2
# other constants
PA = 0.9587256 # Rayleigh Phase function, molecular asymetry factor 1
PB = 1. - PA # Rayleigh Phase function, molecular asymetry factor 2
tpoly = rayADF['tau_ray'] # Polynomial coefficients for Rayleigh transmittance
h2o_cor_poly = np.array(
[0.3832989, 1.6527957, -1.5635101, 0.5311913]) # Polynomial coefficients for WV transmission @ 709nm
# absorb_ozon = np.array([0.0, 0.0002174, 0.0034448, 0.0205669, 0.0400134, 0.105446, 0.1081787, 0.0501634, 0.0410249, \
# 0.0349671, 0.0187495, 0.0086322, 0.0, 0.0, 0.0, 0.0084989, 0.0018944, 0.0012369, 0.0, 0.0, 0.0000488]) # OLCI
# absorb_ozon = np.array([0.0002174, 0.0034448, 0.0205669, 0.0400134, 0.105446, 0.1081787, 0.0501634, \
# 0.0349671, 0.0187495, 0.0086322, 0.0, 0.0084989, 0.0018944, 0.0012369, 0.0]) # MERIS
O3_FILE = O3PATH+'ozone-highres.txt'
ozoneO = O3(O3_FILE)
absorb_ozon = ozoneO.convolveInstrument(SENSOR)
# arrays which are needed to store some stuff
E0 = np.zeros(width, dtype=np.float32)
radiance = np.zeros(width, dtype=np.float32)
reflectance = np.zeros((nbands, width), dtype=np.float32)
taur = np.zeros((nbands, width), dtype=np.float32)
sigma = np.zeros(nbands, dtype=np.float32)
airmass = np.zeros(width, dtype=np.float32)
azidiff = np.zeros(width, dtype=np.float32)
PR = np.zeros(3, dtype=np.float32) # Fourier coefficients of the Rayleigh Phase function
rho_Rf = np.zeros(3, dtype=np.float32) # Fourier terms of the Rayleigh primary scattering reflectance
rho_Rm = np.zeros((3, nbands, width),
dtype=np.float32) # Fourier terms of the Rayleigh scattering reflectance, corrected for multiple scattering
rho_R = np.zeros((nbands, width), dtype=np.float32) # first approximation of Rayleigh reflectance
rho_toaR = np.zeros((nbands, width), dtype=np.float32) # toa reflectance corrected for Rayleigh scattering
rho_BRR = np.zeros((nbands, width),
dtype=np.float32) # top of aerosol reflectance, which is equal to bottom of Rayleigh reflectance
sphericalFactor = np.zeros((nbands, width),
dtype=np.float32) # spherical Albedo Correction Factor (for testing only, can be integrated into the equation later)
rRaySimple = np.zeros((nbands, width),
dtype=np.float32) # simple Rayleigh reflectance formular, after Roland (for testing only)
rho_ng = np.zeros((nbands, width),
dtype=np.float32) # toa reflectance corrected for gaseous absorption (rho_ng = "rho no gas")
X2 = np.zeros(width, dtype=np.float32) # temporary variable used for WV correction algorithm for gaseous absorption
trans709 = np.zeros(width,
dtype=np.float32) # WV transmission at 709nm, used for WV correction algorithm for gaseous absorption
taurS = np.zeros((nbands, width), dtype=np.float32) # simple Rayleigh optical thickness, for debugging only
if (SENSOR == 'MERIS'):
dem_alt = 'dem_alt'
atm_press = 'atm_press'
ozone = 'ozone'
latitude = 'latitude'
longitude = 'longitude'
sun_zenith = 'sun_zenith'
view_zenith = 'view_zenith'
sun_azimuth = 'sun_azimuth'
view_azimuth = 'view_azimuth'
# water vapour correction:
# MERIS band 9 @ 709nm to be corrected; WV absorption 900nm = band 15, WV reference 885nm= band 14
b709 = 8 # the band to be corrected
bWVRef = 13 # the reference reflectance outside WV absorption band
bWV = 14 # the reflectance within the WV absorption band
if (SENSOR == 'OLCI'):
dem_alt = 'N/A'
atm_press = 'sea_level_pressure'
ozone = 'total_ozone'
latitude = 'TP_latitude'
longitude = 'TP_longitude'
sun_zenith = 'SZA'
view_zenith = 'OZA'
sun_azimuth = 'SAA'
view_azimuth = 'OAA'
# water vapour correction:
# OLCI band 11 @ 709nm, WV absorption 900nm = band 19, WV reference 885nm = band 18
b709 = 11 # the band to be corrected
bWVRef=17 # the reference reflectance outside WV absorption band
bWV=18 # the reference reflectance outside WV absorption band
if (SENSOR == 'MERIS'): # check if this is required at all!
tp_alt = product.getTiePointGrid(dem_alt)
alt = np.zeros(width, dtype=np.float32)
tp_press = product.getTiePointGrid(atm_press)
press0 = np.zeros(width, dtype=np.float32)
tp_ozone = product.getTiePointGrid(ozone)
ozone = np.zeros(width, dtype=np.float32)
tp_latitude = product.getTiePointGrid(latitude)
lat = np.zeros(width, dtype=np.float32)
tp_longitude = product.getTiePointGrid(longitude)
lon = np.zeros(width, dtype=np.float32)
tp_theta_s = product.getTiePointGrid(sun_zenith)
theta_s = np.zeros(width, dtype=np.float32)
tp_theta_v = product.getTiePointGrid(view_zenith)
theta_v = np.zeros(width, dtype=np.float32)
tp_azi_s = product.getTiePointGrid(sun_azimuth)
azi_s = np.zeros(width, dtype=np.float32)
tp_azi_v = product.getTiePointGrid(view_azimuth)
azi_v = np.zeros(width, dtype=np.float32)
# Rayleigh multiple scattering
# - Coefficients LUT
dimTheta = 12
dimThetaS = dimThetaV = dimTheta
gridThetaS = rayADF['theta']
gridThetaV = rayADF['theta']
gridGeometry = [gridThetaS, gridThetaV]
RayScattCoeffA = ray_coeff_matrix[:, :, :, 0]
RayScattCoeffB = ray_coeff_matrix[:, :, :, 1]
RayScattCoeffC = ray_coeff_matrix[:, :, :, 2]
RayScattCoeffD = ray_coeff_matrix[:, :, :, 3]
# - Fourier terms
a = np.zeros(3, dtype=np.float32)
b = np.zeros(3, dtype=np.float32)
c = np.zeros(3, dtype=np.float32)
d = np.zeros(3, dtype=np.float32)
rayMultiCorr = np.zeros(3, dtype=np.float32)
# Rayleigh transmittances and spherical albedo
tR_thetaS = np.zeros((nbands, width), dtype=np.float32) # Rayleigh Transmittance sun - surface
tR_thetaV = np.zeros((nbands, width), dtype=np.float32) # Rayleigh Transmittance surface - sun
dimTaur = 17
taurTab = np.linspace(0.0, 1.0, num=dimTaur)
rayAlb_f = interp1d(taurTab, rayADF['ray_albedo_lut'])
sARay = np.zeros((nbands, width), dtype=np.float32) # Rayleigh spherical albedo
print("Processing ...")
# Calculate the Rayleigh cross section, which depends only on wavelength but not on air pressure
for i in range(nbands):
print("processing Rayleigh cross section of band", i)
# b_source = product.getBandAt(i)
b_source = product.getBand(band_name[i])
lam = b_source.getSpectralWavelength() # wavelength of band i in nm
lam = lam / 1000.0 # wavelength in micrometer
lam2 = lam / 10000.0 # wavelength in cm
F_N2 = 1.034 + 0.000317 / (lam ** 2) # King factor of N2
F_O2 = 1.096 + 0.001385 / (lam ** 2) + 0.0001448 / (lam ** 4) # King factor of O2
F_air = (78.084 * F_N2 + 20.946 * F_O2 + 0.934 * 1 + C_CO2 * 1.15) / (
78.084 + 20.946 + 0.934 + C_CO2) # depolarization ratio or King Factor, (6+3rho)/(6-7rho)
n_ratio = 1 + 0.54 * (CO2 - 0.0003)
n_1_300 = (8060.51 + (2480990. / (132.274 - lam ** (-2))) + (17455.7 / (39.32957 - lam ** (-2)))) / 100000000.0
nCO2 = n_ratio * (1 + n_1_300) # reflective index at CO2
sigma[i] = (24 * math.pi ** 3 * (nCO2 ** 2 - 1) ** 2) / (lam2 ** 4 * Ns ** 2 * (nCO2 ** 2 + 2) ** 2) * F_air
for y in range(height):
print("processing line ", y, " of ", height)
# start radiance to reflectance conversion
theta_s = tp_theta_s.readPixels(0, y, width, 1, theta_s) # sun zenith angle in degree
for i in range(nbands):
b_source = product.getBand(band_name[i])
radiance = b_source.readPixels(0, y, width, 1, radiance)
if (SENSOR == 'MERIS'):
E0.fill(b_source.getSolarFlux())
if (SENSOR == 'OLCI'):
b_source = product.getBand(sf_name[i])
E0 = b_source.readPixels(0, y, width, 1, E0)
reflectance[i] = radiance * math.pi / (E0 * np.cos(np.radians(theta_s)))
b_out = raycorProduct.getBand("rtoa_" + str(i + 1))
b_out.writePixels(0, y, width, 1, reflectance[i])
# radiance to reflectance conversion completed
# this is dummy code to create a flag
flag1 = np.zeros(width, dtype=np.bool_)
flag2 = np.zeros(width, dtype=np.bool_)
raycorFlags = flag1 + 2 * flag2
raycorFlagsBand.writePixels(0, y, width, 1, raycorFlags)
# end flags dummy code
# raycorProduct.closeIO()
# if (0==1):
lat = tp_latitude.readPixels(0, y, width, 1, lat)
lon = tp_longitude.readPixels(0, y, width, 1, lon)
# start Rayleigh optical thickness calculation
# alt = tp_alt.readPixels(0, y, width, 1, alt) # using the tie-point DEM in a MERIS product
# get the altitude from an external DEM
for x in range(width): alt[x] = dem.getElevation(GeoPos(lat[x], lon[x]))
press0 = tp_press.readPixels(0, y, width, 1, press0)
ozone = tp_ozone.readPixels(0, y, width, 1, ozone)
theta_s = tp_theta_s.readPixels(0, y, width, 1, theta_s) # sun zenith angle in degree
theta_v = tp_theta_v.readPixels(0, y, width, 1, theta_v) # view zenith angle in degree
azi_s = tp_azi_s.readPixels(0, y, width, 1, azi_s) # sun azimuth angle in degree
azi_v = tp_azi_v.readPixels(0, y, width, 1, azi_v) # view azimuth angle in degree
# gaseous absorption correction
rho_ng = reflectance # to start: gaseous corrected reflectances equals toa reflectances
# water vapour correction:
# MERIS band 9 @ 709nm to be corrected; WV absorption 900nm = band 15, WV reference 885nm= band 14
# b709 = 8 # the band to be corrected
# bWVRef = 13 # the reference reflectance outside WV absorption band
# bWV = 14 # the reflectance within the WV absorption band
# OLCI band 11 @ 709nm, WV absorption 900nm = band 19, WV reference 885nm = band 18
# b709 = 11 # the band to be corrected
# bWVRef=17 # the reference reflectance outside WV absorption band
# bWV=18 # the reference reflectance outside WV absorption band
for i in range(width):
if (reflectance[(bWV, i)] > 0):
X2[i] = reflectance[(bWV, i)] / reflectance[(bWVRef, i)]
else:
X2[i] = 1
trans709 = h2o_cor_poly[0] + (h2o_cor_poly[1] + (h2o_cor_poly[2] + h2o_cor_poly[3] * X2) * X2) * X2
rho_ng[b709] /= trans709
# ozone correction
model_ozone = 0
for x in range(width):
ts = math.radians(theta_s[x]) # sun zenith angle in radian
cts = math.cos(ts) # cosine of sun zenith angle
sts = math.sin(ts) # sinus of sun zenith angle
tv = math.radians(theta_v[x]) # view zenith angle in radian
ctv = math.cos(tv) # cosine of view zenith angle
stv = math.sin(tv) # sinus of view zenith angle
for i in range(nbands):
trans_ozoned12 = math.exp(-(absorb_ozon[i] * ozone[x] / 1000.0 - model_ozone) / cts)
trans_ozoneu12 = math.exp(-(absorb_ozon[i] * ozone[x] / 1000.0 - model_ozone) / ctv)
trans_ozone12 = trans_ozoned12 * trans_ozoneu12
rho_ng[(i, x)] /= trans_ozone12
# here we can decide if we continue with gaseous corrected reflectances or not
reflectance = rho_ng
# Now calculate the pixel dependent terms (like pressure) and finally the Rayleigh optical thickness
for x in range(width):
# Calculation to get the pressure
z = alt[x] # altitude at pixel in meters, taken from MERIS tie-point grid
z = max(z, 0) # clip to sea level
Psurf0 = press0[x] # pressure at sea level in hPa, taken from MERIS tie-point grid
Psurf = Psurf0 * (
1. - 0.0065 * z / 288.15) ** 5.255 # air pressure at the pixel (i.e. at altitude) in hPa, using the international pressure equation
P = Psurf * 1000. # air pressure at pixel location in dyn / cm2, which is hPa * 1000
# calculation to get the constant of gravity at the pixel altitude, taking the air mass above into account
dphi = math.radians(lat[x]) # latitude in radians
cos2phi = math.cos(2 * dphi)
g0 = g0_45 * (1 - 0.0026373 * cos2phi + 0.0000059 * cos2phi ** 2)
zs = 0.73737 * z + 5517.56 # effective mass-weighted altitude
g = g0 - (0.0003085462 + 0.000000227 * cos2phi) * zs + (0.00000000007254 + 0.0000000000001 * cos2phi) * \
zs ** 2 - (1.517E-17 + 6E-20 * cos2phi) * zs ** 3
# calculations to get the Rayeigh optical thickness
factor = (P * AVO) / (m_a * g)
for i in range(nbands):
taur[(i, x)] = sigma[i] * factor
# Calculate Rayleigh Phase function
ts = math.radians(theta_s[x]) # sun zenith angle in radian
cts = math.cos(ts) # cosine of sun zenith angle
sts = math.sin(ts) # sinus of sun zenith angle
tv = math.radians(theta_v[x]) # view zenith angle in radian
ctv = math.cos(tv) # cosine of view zenith angle
stv = math.sin(tv) # sinus of view zenith angle
airmass[x] = 1 / cts + 1 / ctv # air mass
# Rayleigh Phase function, 3 Fourier terms
PR[0] = 3. * PA / 4. * (1. + cts ** 2 * ctv ** 2 + (sts ** 2 * stv ** 2) / 2.) + PB
PR[1] = -3. * PA / 4. * cts * ctv * sts * stv
PR[2] = 3. * PA / 16. * sts ** 2 * stv ** 2
# Calculate azimuth difference
azs = math.radians(azi_s[x])
azv = math.radians(azi_v[x])
cosdeltaphi = math.cos(azv - azs)
azidiff[x] = math.acos(cosdeltaphi) # azimuth difference in radian
# Fourier components of multiple scattering
for j in [0, 1, 2]:
a[j] = interpn(gridGeometry, RayScattCoeffA[j, :, :], [theta_s[x], theta_v[x]], method='linear',
bounds_error=False, fill_value=None)
b[j] = interpn(gridGeometry, RayScattCoeffB[j, :, :], [theta_s[x], theta_v[x]], method='linear',
bounds_error=False, fill_value=None)
c[j] = interpn(gridGeometry, RayScattCoeffC[j, :, :], [theta_s[x], theta_v[x]], method='linear',
bounds_error=False, fill_value=None)
d[j] = interpn(gridGeometry, RayScattCoeffD[j, :, :], [theta_s[x], theta_v[x]], method='linear',
bounds_error=False, fill_value=None)
for i in range(nbands):
# Fourier series, loop
for j in [0, 1, 2]:
# Rayleigh primary scattering
rho_Rf[j] = (PR[j] / (4.0 * (cts + ctv))) * (1. - math.exp(-airmass[x] * taur[(i, x)]))
# correction for multiple scattering
rayMultiCorr[j] = a[j] + b[j] * taur[(i, x)] + c[j] * taur[(i, x)] ** 2 + d[j] * taur[(i, x)] ** 3
rho_Rm[(j, i, x)] = rho_Rf[j] * rayMultiCorr[j]
# rho_Rm[(0, i, x)] = rho_Rf[0]
# rho_Rm[(1, i, x)] = 0.
# rho_Rm[(2, i, x)] = 0.
# Fourier sum to get the Rayleigh Reflectance
rho_R[(i, x)] = rho_Rm[(0, i, x)] + 2.0 * rho_Rm[(1, i, x)] * math.cos(azidiff[x]) + 2. * rho_Rm[
(2, i, x)] * math.cos(2. * azidiff[x])
# complete the Rayleigh correction: see MERIS DPM PDF-p251 or DPM 9-16
# polynomial coefficients tpoly0, tpoly1 and tpoly2 from MERIS LUT
tRs = ((2. / 3. + cts) + (2. / 3. - cts) * math.exp(-taur[(i, x)] / cts)) / (4. / 3. + taur[(i, x)])
tR_thetaS[(i, x)] = tpoly[0] + tpoly[1] * tRs + tpoly[
2] * tRs ** 2 # Rayleigh Transmittance sun - surface
tRv = ((2. / 3. + ctv) + (2. / 3. - ctv) * math.exp(-taur[(i, x)] / ctv)) / (4. / 3. + taur[(i, x)])
tR_thetaV[(i, x)] = tpoly[0] + tpoly[1] * tRv + tpoly[
2] * tRv ** 2 # Rayleigh Transmittance surface - sensor
sARay[(i, x)] = rayAlb_f(taur[(i, x)]) # Rayleigh spherical albedo
rho_toaR[(i, x)] = (reflectance[(i, x)] - rho_R[(i, x)]) / (
tR_thetaS[(i, x)] * tR_thetaV[(i, x)]) # toa reflectance corrected for Rayleigh scattering
sphericalFactor[(i, x)] = 1.0 / (1.0 + sARay[(i, x)] * rho_toaR[
(i, x)]) # factor used in the next equation to account for the spherical albedo
rho_BRR[(i, x)] = rho_toaR[(i, x)] * sphericalFactor[
(i, x)] # top of aerosol reflectance, which is equal to bottom of Rayleigh reflectance
# simple Rayleigh correction
azi_diff_deg = math.fabs(azi_v[x] - azi_s[x])
if (azi_diff_deg > 180.0):
azi_diff_deg = 360.0 - azi_diff_deg
azi_diff_rad = math.radians(azi_diff_deg)
cos_scat_ang = (-ctv * cts) - (stv * sts * math.cos(azi_diff_rad))
phase_rayl_min = 0.75 * (1.0 + cos_scat_ang * cos_scat_ang)
for i in range(nbands):
# b_source = product.getBandAt(i)
b_source = product.getBand(band_name[i])
lam = b_source.getSpectralWavelength()
taurS[(i, x)] = math.exp(-4.637) * math.pow((lam / 1000.0), -4.0679)
pressureAtms = press0[x] * math.exp(-alt[x] / 8000.0)
pressureFactor = taurS[(i, x)] / 1013.0
taurS[(i, x)] = pressureAtms * pressureFactor
rRaySimple[(i, x)] = cts * taurS[(i, x)] * phase_rayl_min / (4 * 3.1415926) * (1 / ctv) * 3.1415926
# Write bands to product
airmassBand.writePixels(0, y, width, 1, airmass)
azidiffBand.writePixels(0, y, width, 1, azidiff)
altBand.writePixels(0, y, width, 1, alt)
for i in range(nbands):
taurBand = raycorProduct.getBand("taur_" + str(i + 1))
taurBand.writePixels(0, y, width, 1, taur[i])
rhorBand = raycorProduct.getBand("rRay_" + str(i + 1))
rhorBand.writePixels(0, y, width, 1, rho_R[i])
rhorF1Band = raycorProduct.getBand("rRayF1_" + str(i + 1))
rhorF1Band.writePixels(0, y, width, 1, rho_Rm[0, i])
rhorF2Band = raycorProduct.getBand("rRayF2_" + str(i + 1))
rhorF2Band.writePixels(0, y, width, 1, rho_Rm[1, i])
rhorF3Band = raycorProduct.getBand("rRayF3_" + str(i + 1))
rhorF3Band.writePixels(0, y, width, 1, rho_Rm[2, i])
rayTransSBand = raycorProduct.getBand("transSRay_" + str(i + 1))
rayTransSBand.writePixels(0, y, width, 1, tR_thetaS[i])
rayTransVBand = raycorProduct.getBand("transVRay_" + str(i + 1))
rayTransVBand.writePixels(0, y, width, 1, tR_thetaV[i])
sARayBand = raycorProduct.getBand("sARay_" + str(i + 1))
sARayBand.writePixels(0, y, width, 1, sARay[i])
rtoaRBand = raycorProduct.getBand("rtoaRay_" + str(i + 1))
rtoaRBand.writePixels(0, y, width, 1, rho_toaR[i])
rBRRBand = raycorProduct.getBand("rBRR_" + str(i + 1))
rBRRBand.writePixels(0, y, width, 1, rho_BRR[i])
spfBand = raycorProduct.getBand("sphericalAlbedoFactor_" + str(i + 1))
spfBand.writePixels(0, y, width, 1, sphericalFactor[i])
rRaySimpleBand = raycorProduct.getBand("RayleighSimple_" + str(i + 1))
rRaySimpleBand.writePixels(0, y, width, 1, rRaySimple[i])
rho_ngBand = raycorProduct.getBand("rtoa_ng_" + str(i + 1))
rho_ngBand.writePixels(0, y, width, 1, rho_ng[i])
taurSBand = raycorProduct.getBand("taurS_" + str(i + 1))
taurSBand.writePixels(0, y, width, 1, taurS[i])
# Rayleigh calculation completed
raycorProduct.closeIO()
print("Done.")
if len(sys.argv) < 2:
print 'Product file requires'
sys.exit(1)
# input product & dimensions
input_product = ProductIO.readProduct(sys.argv[1])
width = input_product.getSceneRasterWidth()
height = input_product.getSceneRasterHeight()
product_name = input_product.getName()
# input product red & nir bands
red_band = input_product.getBand('B4')
nir_band = input_product.getBand('B8')
# output product (ndvi) & new band
output_product = Product('NDVI', 'NDVI', width, height)
ProductUtils.copyGeoCoding(input_product, output_product)
output_band = output_product.addBand('ndvi', ProductData.TYPE_FLOAT32)
# output writer
output_product_writer = ProductIO.getProductWriter('BEAM-DIMAP')
output_product.setProductWriter(output_product_writer)
output_product.writeHeader(product_name + '.ndvi.dim')
# compute & save ndvi line by line
red_row = numpy.zeros(width, dtype=numpy.float32)
nir_row = numpy.zeros(width, dtype=numpy.float32)
for y in xrange(height):
red_row = red_band.readPixels(0, y, width, 1, red_row)
nir_row = nir_band.readPixels(0, y, width, 1, nir_row)
def process_product(file, sensor):
in_product = ProductIO.readProduct(file)
width = in_product.getSceneRasterWidth()
height = in_product.getSceneRasterHeight()
in_name = in_product.getName()
in_description = in_product.getDescription()
in_band_names = in_product.getBandNames()
c2x_log.info("Product: %s, %s" % (in_name, in_description))
c2x_log.debug("Raster size: %d x %d pixels" % (width, height))
c2x_log.debug("Start time: " + str(in_product.getStartTime()))
c2x_log.debug("End time: " + str(in_product.getEndTime()))
c2x_log.debug("Bands: %s" % (list(in_band_names)))
# Output product Definition
# 1. define the target product and its file format
c2x_product = Product('%s_%s' % (in_name, PRODUCT_TYPE), '%s' % PRODUCT_TYPE, width, height)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
c2x_product.setProductWriter(writer)
fpath = in_product.getFileLocation().getAbsolutePath()
fpath = os.path.split(fpath)[0] + "/out/" + os.path.split(fpath)[1]
fpath = fpath.split(".")[0]
fpath = "{0}_{1}.dim".format(fpath, PRODUCT_TYPE.lower())
c2x_product.setFileLocation(File(fpath))
sensor_outputs = sensor["outputs"]
sensor_wavelengths = sensor["wavelengths"]
# 2. define the bands for the results of the different algorithms
outbands = dict()
for cnt in range(len(sensor_outputs)):
cnt = sensor_outputs[cnt]
outbands[cnt[0]] = c2x_product.addBand(cnt[0], cnt[1])
# 3. copy tie point grids from input product to target product
ProductUtils.copyTiePointGrids(in_product, c2x_product)
ProductUtils.copyMetadata(in_product, c2x_product)
ProductUtils.copyGeoCoding(in_product, c2x_product)
ProductUtils.copyFlagBands(in_product, c2x_product, False)
# 4. write the header to disk
location = c2x_product.getFileLocation()
c2x_product.writeHeader(location)
# assigning aux arrays
rhow_arrays = dict()
for wls in sensor_wavelengths:
rhow_arrays[str(wls)] = np.zeros(width, dtype=np.float32)
# get all specified bands from input product
c2x_log.info("Processing and writing to %s" % file)
algo_names = dict()
for cnt in range(len(sensor_outputs)):
algo_names[cnt] = sensor_outputs[cnt][0]
c2x_log.debug("Processing with following algos: %s " % list(algo_names.values()))
bsource = dict()
for i in range(len(sensor_wavelengths)):
band_name = create_source_band_name(sensor_wavelengths[i])
bsource[band_name] = in_product.getBand(sensor[band_name])
flag_bands = []
for b in in_product.getBands():
if b.isFlagBand():
flag_bands.append(b)
flags_data = np.zeros (width, dtype=np.int32)
# loop through the product line by line and application of algorithms
for y in range(height):
rhow = dict()
for wl in sensor_wavelengths:
source_band = bsource[create_source_band_name(wl)]
# dealing with no-data; setting no-data to to NaN
invalidMask = read_invalid_mask(source_band, width, y)
source_band.readPixels(0, y, width, 1, rhow_arrays[str(wl)])
rhow["band" + str(wl)] = np.ma.array(rhow_arrays[str(wl)], mask=invalidMask, fill_value=np.nan)
for algo in range(len(sensor_outputs)):
res = sensor_outputs[algo][2](rhow, sensor_outputs[algo][4], sensor_outputs[algo][5])
name = sensor_outputs[algo][0]
outbands[name].writePixels(0, y, width, 1, res)
for fband in flag_bands:
fband.readPixels(0, y, width, 1, flags_data)
c2x_product.getBand(fband.getName()).writePixels(0, y, width, 1, flags_data)
# all computations and writing is completed; close all data streams and finish the program
c2x_product.closeIO()
print("Done.")
return 0
sys.exit(1)
print("Reading...")
sourceProduct = ProductIO.readProduct(sys.argv[1])
b1 = sourceProduct.getBand('reflec_5')
b2 = sourceProduct.getBand('reflec_7')
b3 = sourceProduct.getBand('reflec_9')
w1 = b1.getSpectralWavelength()
w2 = b2.getSpectralWavelength()
w3 = b3.getSpectralWavelength()
a = (w2 - w1) / (w3 - w1)
k = 1.03
width = sourceProduct.getSceneRasterWidth()
height = sourceProduct.getSceneRasterHeight()
targetProduct = Product('FLH_Product', 'FLH_Type', width, height)
targetBand = targetProduct.addBand('FLH', ProductData.TYPE_FLOAT32)
ProductUtils.copyGeoCoding(sourceProduct, targetProduct)
targetProduct.setProductWriter(ProductIO.getProductWriter('GeoTIFF'))
targetProduct.writeHeader(String('snappy_flh_output.tif'))
r1 = numpy.zeros(width, dtype=numpy.float32)
r2 = numpy.zeros(width, dtype=numpy.float32)
r3 = numpy.zeros(width, dtype=numpy.float32)
print("Writing...")
for y in range(height):
b1.readPixels(0, y, width, 1, r1)
b2.readPixels(0, y, width, 1, r2)
print 'Product file and band index required'
sys.exit(1)
# check if band index given is correct
if not sys.argv[2] in ['2', '3', '4', '8']:
print 'Incorrect band index'
# get cli arguments
product_file = sys.argv[1]
band_index = sys.argv[2]
band_name = 'B' + band_index
product_name = {
'B2': 'blue',
'B3': 'green',
'B4': 'red',
'B8': 'nir',
}[band_name]
# input product: open and get dimensions & name
input_product = ProductIO.readProduct(product_file)
product_width = input_product.getSceneRasterWidth()
product_height = input_product.getSceneRasterHeight()
product_name = input_product.getName()
# output product: copy selected band & save product
output_product = Product(product_name, product_name, product_width, product_height)
ProductUtils.copyGeoCoding(input_product, output_product)
ProductUtils.copyBand(band_name, input_product, output_product, True)
ProductIO.writeProduct(output_product, product_name + '.band.dim', 'BEAM-DIMAP')
output_product.closeIO()
product = ProductIO.readProduct(file)
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
name = product.getName()
description = product.getDescription()
band_names = product.getBandNames()
print("Product: %s, %s" % (name, description))
print("Raster size: %d x %d pixels" % (width, height))
print("Start time: " + str(product.getStartTime()))
print("End time: " + str(product.getEndTime()))
print("Bands: %s" % (list(band_names)))
b7 = product.getBand('radiance_7')
b10 = product.getBand('radiance_10')
ndviProduct = Product('NDVI', 'NDVI', width, height)
ndviBand = ndviProduct.addBand('ndvi', ProductData.TYPE_FLOAT32)
ndviFlagsBand = ndviProduct.addBand('ndvi_flags', ProductData.TYPE_UINT8)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
ProductUtils.copyGeoCoding(product, ndviProduct)
ndviFlagCoding = FlagCoding('ndvi_flags')
ndviFlagCoding.addFlag("NDVI_LOW", 1, "NDVI below 0")
ndviFlagCoding.addFlag("NDVI_HIGH", 2, "NDVI above 1")
group = ndviProduct.getFlagCodingGroup()
#print(dir(group))
group.add(ndviFlagCoding)
ndviFlagsBand.setSampleCoding(ndviFlagCoding)
sys.exit(1)
print("Reading...")
sourceProduct = ProductIO.readProduct(sys.argv[1])
b1 = sourceProduct.getBand('reflec_5')
b2 = sourceProduct.getBand('reflec_7')
b3 = sourceProduct.getBand('reflec_9')
w1 = b1.getSpectralWavelength()
w2 = b2.getSpectralWavelength()
w3 = b3.getSpectralWavelength()
a = (w2 - w1) / (w3 - w1)
k = 1.03
width = sourceProduct.getSceneRasterWidth()
height = sourceProduct.getSceneRasterHeight()
targetProduct = Product('FLH_Product', 'FLH_Type', width, height)
targetBand = targetProduct.addBand('FLH', ProductData.TYPE_FLOAT32)
ProductUtils.copyGeoCoding(sourceProduct, targetProduct)
targetProduct.setProductWriter(ProductIO.getProductWriter('GeoTIFF'))
targetProduct.writeHeader(String('snappy_flh_output.tif'))
r1 = numpy.zeros(width, dtype=numpy.float32)
r2 = numpy.zeros(width, dtype=numpy.float32)
r3 = numpy.zeros(width, dtype=numpy.float32)
print("Writing...")
for y in range(height):
b1.readPixels(0, y, width, 1, r1)
b2.readPixels(0, y, width, 1, r2)
# check if band index given is correct
if not sys.argv[2] in ['2', '3', '4', '8']:
print 'Incorrect band index'
# get cli arguments
product_file = sys.argv[1]
band_index = sys.argv[2]
band_name = 'B' + band_index
product_name = {
'B2': 'blue',
'B3': 'green',
'B4': 'red',
'B8': 'nir',
}[band_name]
# input product: open and get dimensions & name
input_product = ProductIO.readProduct(product_file)
product_width = input_product.getSceneRasterWidth()
product_height = input_product.getSceneRasterHeight()
product_name = input_product.getName()
# output product: copy selected band & save product
output_product = Product(product_name, product_name, product_width,
product_height)
ProductUtils.copyGeoCoding(input_product, output_product)
ProductUtils.copyBand(band_name, input_product, output_product, True)
ProductIO.writeProduct(output_product, product_name + '.band.dim',
'BEAM-DIMAP')
output_product.closeIO()
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
name = product.getName()
description = product.getDescription()
band_names = product.getBandNames()
print("Product: %s, %s" % (name, description))
print("Raster size: %d x %d pixels" % (width, height))
print("Start time: " + str(product.getStartTime()))
print("End time: " + str(product.getEndTime()))
print("Bands: %s" % (list(band_names)))
b7 = product.getBand('radiance_7')
b10 = product.getBand('radiance_10')
ndviProduct = Product('NDVI', 'NDVI', width, height)
ndviBand = ndviProduct.addBand('ndvi', ProductData.TYPE_FLOAT32)
ndviFlagsBand = ndviProduct.addBand('ndvi_flags', ProductData.TYPE_UINT8)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
ProductUtils.copyGeoCoding(product, ndviProduct)
ndviFlagCoding = FlagCoding('ndvi_flags')
ndviFlagCoding.addFlag("NDVI_LOW", 1, "NDVI below 0")
ndviFlagCoding.addFlag("NDVI_HIGH", 2, "NDVI above 1")
group = ndviProduct.getFlagCodingGroup()
#print(dir(group))
group.add(ndviFlagCoding)
ndviFlagsBand.setSampleCoding(ndviFlagCoding)
sys.exit(1)
print("Reading...")
product = ProductIO.readProduct(sys.argv[1])
width = product.getSceneRasterWidth()
height = product.getSceneRasterHeight()
name = product.getName()
desc = product.getDescription()
band_names = product.getBandNames()
print("Product: %s, %d x %d pixels, %s" % (name, width, height, desc))
print("Bands: %s" % (band_names))
b7 = product.getBand('radiance_7')
b10 = product.getBand('radiance_10')
ndviProduct = Product('NDVI', 'NDVI', width, height)
ndviBand = ndviProduct.addBand('ndvi', ProductData.TYPE_FLOAT32)
ndviBand.setNoDataValue(numpy.nan)
ndviBand.setNoDataValueUsed(True)
writer = ProductIO.getProductWriter('BEAM-DIMAP')
ProductUtils.copyGeoCoding(product, ndviProduct)
ndviProduct.setProductWriter(writer)
ndviProduct.writeHeader(String('snappy_ndvi_with_masks_output.dim'))
r7 = numpy.zeros(width, dtype=numpy.float32)
r10 = numpy.zeros(width, dtype=numpy.float32)
v7 = numpy.zeros(width, dtype=numpy.uint8)
