def create_median_mosaic(dataset_in,
clean_mask=None,
no_data=-9999,
dtype=None,
**kwargs):
"""
Method for calculating the median pixel value for a given dataset.
Parameters
----------
dataset_in: xarray.Dataset
A dataset retrieved from the Data Cube; should contain:
coordinates: time, latitude, longitude
variables: variables to be mosaicked (e.g. red, green, and blue bands)
clean_mask: np.ndarray
An ndarray of the same shape as `dataset_in` - specifying which values to mask out.
If no clean mask is specified, then all values are kept during compositing.
no_data: int or float
The no data value.
dtype: str or numpy.dtype
A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g.
np.int16, np.float32) to convert the data to.
Returns
-------
dataset_out: xarray.Dataset
Compositited data with the format:
coordinates: latitude, longitude
variables: same as dataset_in
"""
# Default to masking nothing.
if clean_mask is None:
clean_mask = create_default_clean_mask(dataset_in)
dataset_in_dtypes = None
if dtype is None:
# Save dtypes because masking with Dataset.where() converts to float64.
band_list = list(dataset_in.data_vars)
dataset_in_dtypes = {}
for band in band_list:
dataset_in_dtypes[band] = dataset_in[band].dtype
# Mask out clouds and Landsat 7 scan lines.
dataset_in = dataset_in.where((dataset_in != no_data) & (clean_mask))
dataset_out = dataset_in.median(dim='time', skipna=True, keep_attrs=False)
# Handle datatype conversions.
dataset_out = restore_or_convert_dtypes(dtype, band_list,
dataset_in_dtypes, dataset_out,
no_data)
return dataset_out
def nazeer_chlorophyll(dataset_in, clean_mask=None, no_data=0):
# Default to masking nothing.
if clean_mask is None:
clean_mask = create_default_clean_mask(dataset_in)
chl_a = (0.57 * (dataset_in.red.astype('float64') * 0.0001) /
(dataset_in.blue.astype('float64') * 0.0001)**2) - 2.61
chl_a.values[np.invert(clean_mask)] = no_data # Contains data for clear pixels
# Create xarray of data
time = dataset_in.time
latitude = dataset_in.latitude
longitude = dataset_in.longitude
dataset_out = xr.Dataset(
{
'nazeer_chlorophyll': chl_a
}, coords={'time': time,
'latitude': latitude,
'longitude': longitude})
return dataset_out
def watanabe_chlorophyll(dataset_in, clean_mask=None, no_data=0):
assert 'red' in dataset_in and 'nir' in dataset_in, "Red and NIR bands are required for the Watanabe Chlorophyll analysis."
# Default to masking nothing.
if clean_mask is None:
clean_mask = create_default_clean_mask(dataset_in)
chl_a = 925.001 * (dataset_in.nir.astype('float64') / dataset_in.red.astype('float64')) - 77.16
chl_a.values[np.invert(clean_mask)] = no_data # Contains data for clear pixels
# Create xarray of data
time = dataset_in.time
latitude = dataset_in.latitude
longitude = dataset_in.longitude
dataset_out = xr.Dataset(
{
'watanabe_chlorophyll': chl_a
}, coords={'time': time,
'latitude': latitude,
'longitude': longitude})
return dataset_out
def tsm(dataset_in, clean_mask=None, no_data=0):
"""
Inputs:
dataset_in (xarray.Dataset) - dataset retrieved from the Data Cube.
Optional Inputs:
clean_mask (numpy.ndarray with dtype boolean) - true for values user considers clean;
if user does not provide a clean mask, all values will be considered clean
no_data (int/float) - no data pixel value; default: -9999
Throws:
ValueError - if dataset_in is an empty xarray.Dataset.
"""
assert 'red' in dataset_in and 'green' in dataset_in, "Red and Green bands are required for the TSM analysis."
# Default to masking nothing.
if clean_mask is None:
clean_mask = create_default_clean_mask(dataset_in)
tsm = 3983 * _tsmi(dataset_in)**1.6246
tsm.values[np.invert(clean_mask)] = no_data # Contains data for clear pixels
# Create xarray of data
_coords = { key:dataset_in[key] for key in dataset_in.dims.keys()}
dataset_out = xr.Dataset({'tsm': tsm}, coords=_coords)
return dataset_out
def create_hdmedians_multiple_band_mosaic(dataset_in,
clean_mask=None,
no_data=-9999,
dtype=None,
intermediate_product=None,
operation="median",
**kwargs):
"""
Calculates the geomedian or geomedoid using a multi-band processing method.
Parameters
----------
dataset_in: xarray.Dataset
A dataset retrieved from the Data Cube; should contain:
coordinates: time, latitude, longitude (in that order)
variables: variables to be mosaicked (e.g. red, green, and blue bands)
clean_mask: np.ndarray
An ndarray of the same shape as `dataset_in` - specifying which values to mask out.
If no clean mask is specified, then all values are kept during compositing.
no_data: int or float
The no data value.
dtype: str or numpy.dtype
A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g.
np.int16, np.float32) to convert the data to.
operation: str in ['median', 'medoid']
Returns
-------
dataset_out: xarray.Dataset
Compositited data with the format:
coordinates: latitude, longitude
variables: same as dataset_in
"""
# Default to masking nothing.
if clean_mask is None:
clean_mask = create_default_clean_mask(dataset_in)
assert operation in ['median', 'medoid'
], "Only median and medoid operations are supported."
band_list = list(dataset_in.data_vars)
dataset_in_dtypes = None
if dtype is None:
# Save dtypes because masking with Dataset.where() converts to float64.
dataset_in_dtypes = {}
for band in band_list:
dataset_in_dtypes[band] = dataset_in[band].dtype
# Mask out clouds and scan lines.
dataset_in = dataset_in.where((dataset_in != no_data) & clean_mask)
arrays = [dataset_in[band] for band in band_list]
stacked_data = np.stack(arrays)
bands_shape, time_slices_shape, lat_shape, lon_shape = stacked_data.shape[0], \
stacked_data.shape[1], stacked_data.shape[2], \
stacked_data.shape[3]
# Reshape to remove lat/lon
reshaped_stack = stacked_data.reshape(bands_shape, time_slices_shape,
lat_shape * lon_shape)
# Build zeroes array across time slices.
hdmedians_result = np.zeros((bands_shape, lat_shape * lon_shape))
# For each pixel (lat/lon combination), find the geomedian or geomedoid across time.
for x in range(reshaped_stack.shape[2]):
try:
hdmedians_result[:, x] = hd.nangeomedian(
reshaped_stack[:, :, x],
axis=1) if operation == "median" else hd.nanmedoid(
reshaped_stack[:, :, x], axis=1)
except ValueError as e:
# If all bands have nan values across time, the geomedians are nans.
hdmedians_result[:, x] = np.full((bands_shape), np.nan)
output_dict = {
value:
(('y', 'x'), hdmedians_result[index, :].reshape(lat_shape, lon_shape))
for index, value in enumerate(band_list)
}
dataset_out = xr.Dataset(output_dict,
coords={
'y': dataset_in['y'],
'x': dataset_in['x']
},
attrs=dataset_in.attrs)
dataset_out = restore_or_convert_dtypes(dtype, band_list,
dataset_in_dtypes, dataset_out,
no_data)
return dataset_out
def create_mosaic(dataset_in,
clean_mask=None,
no_data=-9999,
dtype=None,
intermediate_product=None,
**kwargs):
"""
Creates a most-recent-to-oldest mosaic of the input dataset.
Parameters
----------
dataset_in: xarray.Dataset
A dataset retrieved from the Data Cube; should contain:
coordinates: time, latitude, longitude
variables: variables to be mosaicked (e.g. red, green, and blue bands)
clean_mask: np.ndarray
An ndarray of the same shape as `dataset_in` - specifying which values to mask out.
If no clean mask is specified, then all values are kept during compositing.
no_data: int or float
The no data value.
dtype: str or numpy.dtype
A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g.
np.int16, np.float32) to convert the data to.
Returns
-------
dataset_out: xarray.Dataset
Compositited data with the format:
coordinates: latitude, longitude
variables: same as dataset_in
"""
dataset_in = dataset_in.copy(deep=True)
# Default to masking nothing.
if clean_mask is None:
clean_mask = create_default_clean_mask(dataset_in)
# Mask data with clean_mask. All values where clean_mask==False are set to no_data.
for key in list(dataset_in.data_vars):
dataset_in[key].values[np.invert(clean_mask)] = no_data
dataset_in_dtypes = None
if dtype is None:
# Save dtypes because masking with Dataset.where() converts to float64.
band_list = list(dataset_in.data_vars)
dataset_in_dtypes = {}
for band in band_list:
dataset_in_dtypes[band] = dataset_in[band].dtype
if intermediate_product is not None:
dataset_out = intermediate_product.copy(deep=True)
else:
dataset_out = None
time_slices = reversed(range(len(
dataset_in.time))) if 'reverse_time' in kwargs else range(
len(dataset_in.time))
for index in time_slices:
dataset_slice = dataset_in.isel(time=index).drop('time')
if dataset_out is None:
dataset_out = dataset_slice.copy(deep=True)
utilities.clear_attrs(dataset_out)
else:
for key in list(dataset_in.data_vars):
dataset_out[key].values[dataset_out[key].values ==
-9999] = dataset_slice[key].values[
dataset_out[key].values == -9999]
dataset_out[key].attrs = OrderedDict()
# Handle datatype conversions.
dataset_out = restore_or_convert_dtypes(dtype, band_list,
dataset_in_dtypes, dataset_out,
no_data)
return dataset_out
def create_min_ndvi_mosaic(dataset_in,
clean_mask=None,
no_data=-9999,
dtype=None,
intermediate_product=None,
**kwargs):
"""
Method for calculating the pixel value for the min ndvi value.
Parameters
----------
dataset_in: xarray.Dataset
A dataset retrieved from the Data Cube; should contain:
coordinates: time, latitude, longitude
variables: variables to be mosaicked (e.g. red, green, and blue bands)
clean_mask: np.ndarray
An ndarray of the same shape as `dataset_in` - specifying which values to mask out.
If no clean mask is specified, then all values are kept during compositing.
no_data: int or float
The no data value.
dtype: str or numpy.dtype
A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g.
np.int16, np.float32) to convert the data to.
Returns
-------
dataset_out: xarray.Dataset
Compositited data with the format:
coordinates: latitude, longitude
variables: same as dataset_in
"""
dataset_in = dataset_in.copy(deep=True)
# Default to masking nothing.
if clean_mask is None:
clean_mask = create_default_clean_mask(dataset_in)
dataset_in_dtypes = None
if dtype is None:
# Save dtypes because masking with Dataset.where() converts to float64.
band_list = list(dataset_in.data_vars)
dataset_in_dtypes = {}
for band in band_list:
dataset_in_dtypes[band] = dataset_in[band].dtype
# Mask out clouds and scan lines.
dataset_in = dataset_in.where((dataset_in != -9999) & clean_mask)
if intermediate_product is not None:
dataset_out = intermediate_product.copy(deep=True)
else:
dataset_out = None
time_slices = range(len(dataset_in.time))
for timeslice in time_slices:
dataset_slice = dataset_in.isel(time=timeslice).drop('time')
ndvi = (dataset_slice.nir - dataset_slice.red) / (dataset_slice.nir +
dataset_slice.red)
ndvi.values[np.invert(clean_mask)[timeslice, ::]] = 1000000000
dataset_slice['ndvi'] = ndvi
if dataset_out is None:
dataset_out = dataset_slice.copy(deep=True)
utilities.clear_attrs(dataset_out)
else:
for key in list(dataset_slice.data_vars):
dataset_out[key].values[
dataset_slice.ndvi.values <
dataset_out.ndvi.values] = dataset_slice[key].values[
dataset_slice.ndvi.values < dataset_out.ndvi.values]
# Handle datatype conversions.
dataset_out = restore_or_convert_dtypes(dtype, None, dataset_in_dtypes,
dataset_out, no_data)
return dataset_out
def wofs_classify(dataset_in,
clean_mask=None,
x_coord='longitude',
y_coord='latitude',
time_coord='time',
no_data=-9999,
mosaic=False,
enforce_float64=False):
"""
Description:
Performs WOfS algorithm on given dataset.
Assumption:
- The WOfS algorithm is defined for Landsat 5/Landsat 7
References:
- Mueller, et al. (2015) "Water observations from space: Mapping surface water from
25 years of Landsat imagery across Australia." Remote Sensing of Environment.
- https://github.com/GeoscienceAustralia/eo-tools/blob/stable/eotools/water_classifier.py
-----
Inputs:
dataset_in (xarray.Dataset) - dataset retrieved from the Data Cube; should contain
coordinates: time, latitude, longitude
variables: blue, green, red, nir, swir1, swir2
x_coord, y_coord, time_coord: (str) - Names of DataArrays in `dataset_in` to use as x, y,
and time coordinates.
Optional Inputs:
clean_mask (nd numpy array with dtype boolean) - true for values user considers clean;
if user does not provide a clean mask, all values will be considered clean
no_data (int/float) - no data pixel value; default: -9999
mosaic (boolean) - flag to indicate if dataset_in is a mosaic. If mosaic = False, dataset_in
should have a time coordinate and wofs will run over each time slice; otherwise, dataset_in
should not have a time coordinate and wofs will run over the single mosaicked image
enforce_float64 (boolean) - flag to indicate whether or not to enforce float64 calculations;
will use float32 if false
Output:
dataset_out (xarray.DataArray) - wofs water classification results: 0 - not water; 1 - water
Throws:
ValueError - if dataset_in is an empty xarray.Dataset.
"""
def _band_ratio(a, b):
"""
Calculates a normalized ratio index
"""
return (a - b) / (a + b)
def _run_regression(band1, band2, band3, band4, band5, band7):
"""
Regression analysis based on Australia's training data
TODO: Return type
"""
# Compute normalized ratio indices
ndi_52 = _band_ratio(band5, band2)
ndi_43 = _band_ratio(band4, band3)
ndi_72 = _band_ratio(band7, band2)
#classified = np.ones(shape, dtype='uint8')
classified = np.full(shape, no_data, dtype='uint8')
# Start with the tree's left branch, finishing nodes as needed
# Left branch
r1 = ndi_52 <= -0.01
r2 = band1 <= 2083.5
classified[r1 & ~r2] = 0 #Node 3
r3 = band7 <= 323.5
_tmp = r1 & r2
_tmp2 = _tmp & r3
_tmp &= ~r3
r4 = ndi_43 <= 0.61
classified[_tmp2 & r4] = 1 #Node 6
classified[_tmp2 & ~r4] = 0 #Node 7
r5 = band1 <= 1400.5
_tmp2 = _tmp & ~r5
r6 = ndi_43 <= -0.01
classified[_tmp2 & r6] = 1 #Node 10
classified[_tmp2 & ~r6] = 0 #Node 11
_tmp &= r5
r7 = ndi_72 <= -0.23
_tmp2 = _tmp & ~r7
r8 = band1 <= 379
classified[_tmp2 & r8] = 1 #Node 14
classified[_tmp2 & ~r8] = 0 #Node 15
_tmp &= r7
r9 = ndi_43 <= 0.22
classified[_tmp & r9] = 1 #Node 17
_tmp &= ~r9
r10 = band1 <= 473
classified[_tmp & r10] = 1 #Node 19
classified[_tmp & ~r10] = 0 #Node 20
# Left branch complete; cleanup
del r2, r3, r4, r5, r6, r7, r8, r9, r10
gc.collect()
# Right branch of regression tree
r1 = ~r1
r11 = ndi_52 <= 0.23
_tmp = r1 & r11
r12 = band1 <= 334.5
_tmp2 = _tmp & ~r12
classified[_tmp2] = 0 #Node 23
_tmp &= r12
r13 = ndi_43 <= 0.54
_tmp2 = _tmp & ~r13
classified[_tmp2] = 0 #Node 25
_tmp &= r13
r14 = ndi_52 <= 0.12
_tmp2 = _tmp & r14
classified[_tmp2] = 1 #Node 27
_tmp &= ~r14
r15 = band3 <= 364.5
_tmp2 = _tmp & r15
r16 = band1 <= 129.5
classified[_tmp2 & r16] = 1 #Node 31
classified[_tmp2 & ~r16] = 0 #Node 32
_tmp &= ~r15
r17 = band1 <= 300.5
_tmp2 = _tmp & ~r17
_tmp &= r17
classified[_tmp] = 1 #Node 33
classified[_tmp2] = 0 #Node 34
_tmp = r1 & ~r11
r18 = ndi_52 <= 0.34
classified[_tmp & ~r18] = 0 #Node 36
_tmp &= r18
r19 = band1 <= 249.5
classified[_tmp & ~r19] = 0 #Node 38
_tmp &= r19
r20 = ndi_43 <= 0.45
classified[_tmp & ~r20] = 0 #Node 40
_tmp &= r20
r21 = band3 <= 364.5
classified[_tmp & ~r21] = 0 #Node 42
_tmp &= r21
r22 = band1 <= 129.5
classified[_tmp & r22] = 1 #Node 44
classified[_tmp & ~r22] = 0 #Node 45
# Completed regression tree
return classified
# Default to masking nothing.
if clean_mask is None:
clean_mask = create_default_clean_mask(dataset_in)
# Extract dataset bands needed for calculations
blue = dataset_in.blue
green = dataset_in.green
red = dataset_in.red
nir = dataset_in.nir
swir1 = dataset_in.swir1
swir2 = dataset_in.swir2
# Enforce float calculations - float64 if user specified, otherwise float32 will do
dtype = blue.values.dtype # This assumes all dataset bands will have
# the same dtype (should be a reasonable
# assumption)
# Save dtypes because the `astype()` calls below modify `dataset_in`.
band_list = ['red', 'green', 'blue', 'nir', 'swir1', 'swir2']
dataset_in_dtypes = {}
for band in band_list:
dataset_in_dtypes[band] = dataset_in[band].dtype
if enforce_float64:
if dtype != 'float64':
blue.values = blue.values.astype('float64')
green.values = green.values.astype('float64')
red.values = red.values.astype('float64')
nir.values = nir.values.astype('float64')
swir1.values = swir1.values.astype('float64')
swir2.values = swir2.values.astype('float64')
else:
if dtype == 'float64':
pass
elif dtype != 'float32':
blue.values = blue.values.astype('float32')
green.values = green.values.astype('float32')
red.values = red.values.astype('float32')
nir.values = nir.values.astype('float32')
swir1.values = swir1.values.astype('float32')
swir2.values = swir2.values.astype('float32')
shape = blue.values.shape
classified = _run_regression(blue.values, green.values, red.values,
nir.values, swir1.values, swir2.values)
classified_clean = np.full(classified.shape, no_data, dtype='float64')
classified_clean[clean_mask] = classified[
clean_mask] # Contains data for clear pixels
# Create xarray of data
x_coords = dataset_in[x_coord]
y_coords = dataset_in[y_coord]
time = None
coords = None
dims = None
if mosaic:
coords = [y_coords, x_coords]
dims = [y_coord, x_coord]
else:
time_coords = dataset_in[time_coord]
coords = [time_coords, y_coords, x_coords]
dims = [time_coord, y_coord, x_coord]
data_array = xr.DataArray(classified_clean, coords=coords, dims=dims)
if mosaic:
dataset_out = xr.Dataset({'wofs': data_array},
coords={
y_coord: y_coords,
x_coord: x_coords
})
else:
dataset_out = xr.Dataset({'wofs': data_array},
coords={
time_coord: time_coords,
y_coord: y_coords,
x_coord: x_coords
})
# Handle datatype conversions.
restore_or_convert_dtypes(None, band_list, dataset_in_dtypes, dataset_in,
no_data)
return dataset_out
def frac_coverage_classify(dataset_in, clean_mask=None, no_data=-9999):
"""
Description:
Performs fractional coverage algorithm on given dataset. If no clean mask is given, the 'cf_mask'
variable must be included in the input dataset, as it will be used to create a
clean mask
Assumption:
- The implemented algqorithm is defined for Landsat 5/Landsat 7; in order for it to
be used for Landsat 8, the bands will need to be adjusted
References:
- Guerschman, Juan P., et al. "Assessing the effects of site heterogeneity and soil
properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation
and bare soil fractions from Landsat and MODIS data." Remote Sensing of Environment
161 (2015): 12-26.
-----
Inputs:
dataset_in (xarray.Dataset) - dataset retrieved from the Data Cube (can be a derived
product, such as a cloudfree mosaic; should contain
coordinates: latitude, longitude
variables: blue, green, red, nir, swir1, swir2
If user does not provide a clean_mask, dataset_in must also include the cf_mask
variable
Optional Inputs:
clean_mask (nd numpy array with dtype boolean) - true for values user considers clean;
If none is provided, one will be created which considers all values to be clean.
Output:
dataset_out (xarray.Dataset) - fractional coverage results with no data = -9999; containing
coordinates: latitude, longitude
variables: bs, pv, npv
where bs -> bare soil, pv -> photosynthetic vegetation, npv -> non-photosynthetic vegetation
"""
# Default to masking nothing.
if clean_mask is None:
clean_mask = create_default_clean_mask(dataset_in)
band_stack = []
mosaic_clean_mask = clean_mask.flatten()
for band in [
dataset_in.blue.values, dataset_in.green.values,
dataset_in.red.values, dataset_in.nir.values,
dataset_in.swir1.values, dataset_in.swir2.values
]:
band = band.astype(np.float32)
band = band * 0.0001
band = band.flatten()
band_clean = np.full(band.shape, np.nan)
band_clean[mosaic_clean_mask] = band[mosaic_clean_mask]
band_stack.append(band_clean)
band_stack = np.array(band_stack).transpose()
for b in range(6):
band_stack = np.hstack(
(band_stack, np.expand_dims(np.log(band_stack[:, b]), axis=1)))
for b in range(6):
band_stack = np.hstack(
(band_stack,
np.expand_dims(np.multiply(band_stack[:, b], band_stack[:,
b + 6]),
axis=1)))
for b in range(6):
for b2 in range(b + 1, 6):
band_stack = np.hstack(
(band_stack,
np.expand_dims(np.multiply(band_stack[:, b], band_stack[:,
b2]),
axis=1)))
for b in range(6):
for b2 in range(b + 1, 6):
band_stack = np.hstack(
(band_stack,
np.expand_dims(np.multiply(band_stack[:, b + 6],
band_stack[:, b2 + 6]),
axis=1)))
for b in range(6):
for b2 in range(b + 1, 6):
band_stack = np.hstack((band_stack,
np.expand_dims(np.divide(
band_stack[:, b2] - band_stack[:, b],
band_stack[:, b2] + band_stack[:, b]),
axis=1)))
band_stack = np.nan_to_num(
band_stack) # Now a n x 63 matrix (assuming one acquisition)
ones = np.ones(band_stack.shape[0])
ones = ones.reshape(ones.shape[0], 1)
band_stack = np.concatenate(
(band_stack, ones),
axis=1) # Now a n x 64 matrix (assuming one acquisition)
end_members = np.loadtxt(csv_file_path,
delimiter=',') # Creates a 64 x 3 matrix
SumToOneWeight = 0.02
ones = np.ones(end_members.shape[1]) * SumToOneWeight
ones = ones.reshape(1, end_members.shape[1])
end_members = np.concatenate((end_members, ones),
axis=0).astype(np.float32)
result = np.zeros((band_stack.shape[0], end_members.shape[1]),
dtype=np.float32) # Creates an n x 3 matrix
for i in range(band_stack.shape[0]):
if mosaic_clean_mask[i]:
result[i, :] = (
opt.nnls(end_members, band_stack[i, :])[0].clip(0, 2.54) *
100).astype(np.int16)
else:
result[i, :] = np.ones((end_members.shape[1]), dtype=np.int16) * (
-9999) # Set as no data
latitude = dataset_in.latitude
longitude = dataset_in.longitude
result = result.reshape(latitude.size, longitude.size, 3)
pv_band = result[:, :, 0]
npv_band = result[:, :, 1]
bs_band = result[:, :, 2]
pv_clean = np.full(pv_band.shape, -9999)
npv_clean = np.full(npv_band.shape, -9999)
bs_clean = np.full(bs_band.shape, -9999)
pv_clean[clean_mask] = pv_band[clean_mask]
npv_clean[clean_mask] = npv_band[clean_mask]
bs_clean[clean_mask] = bs_band[clean_mask]
rapp_bands = collections.OrderedDict([('bs', (['latitude',
'longitude'], bs_band)),
('pv', (['latitude',
'longitude'], pv_band)),
('npv', (['latitude',
'longitude'], npv_band))])
rapp_dataset = xr.Dataset(rapp_bands,
coords={
'latitude': latitude,
'longitude': longitude
})
return rapp_dataset