# Generalized Spectral Index Calculation¶

Elaborating on the work done in previous sections, this section contains a complete implementation of the calculation of various spectral indicators.

It does not contain code to download products from the Open Access Hub1. It is rather a re-usable notebook that can be re-used for the calculation of indices only.

The calculation in this notebook depends on three parameters:

• product_path is the path to a previously downloaded product

• index_to_calculate is one of the supported indices; these are

• BSI: Bare Soil Index

• NBR: Normalized Burn Ratio

• NDVI: Normalized Difference Vegetation Index

• NDWI: normalized Difference Water Index

The formulas for the indices below are ported from versions provided by Sentinel Hub, which implements indices listed in the Index DataBase.

When running this notebook interactively, Kernel → Restart and Run All Cells can be used to re-evaluate all cells in this notebook after configuring the pipeline. The path of the output file containing the processed values will be printed below the last cell.

from pathlib import Path

# pick one product to calculate the index for and define it as product_path
products = sorted(Path('resources/a_folder_containing_downloaded_products').glob('*.zip'))
product_path = products[0]
# pick one of the indices above
index_to_calculate = 'ndwi'


## Product Preview¶

The following lines can be uncommented to plot a true color image of the downloaded data:

#from sentinel_helpers import scihub_band_paths
#import rasterio.plot as rplot
#
#tci = scihub_band_paths(product_path, 'TCI', '60m')[0]
#with r.open(tci) as src:
#    rplot.show(src)


## Define Formulas¶

Formulas are defined as data so that the bands can be substituted with actual values later on. By declaratively expressing the formula calculations computations can be executed lazily and only when needed. This is done so the formulas can be defined independent from actual data, which is needed only much later.

### Operators¶

Many spectral indicators can be expressed using band arithmetic with formulas similar to the NDVI:

$\text{NDVI} = \frac{\text{B08} - \text{B04}}{\text{B08} + \text{B04}}$

To define the index calculation formulas in this way, first the basic arithmetic operations +, -, * and / are wrapped in functions taking variadic arguments:

from functools import reduce

def add(nums):
return reduce(lambda a, b: a + b, nums)

def sub(nums):
return reduce(lambda a, b: a - b, nums)

def div(nums):
return reduce(lambda a, b: a / b, nums)

def mul(nums):
return reduce(lambda a, b: a * b, nums)


### Indices¶

These function are used to define formulas for the selection of indices mentioned in the introduction. These indices are not exhaustive - there are many spectral indices which are not implemented in this notebook. The general shape of theses formulas however allows for enough flexibility to implement other indices as well.

The formulas are defined in a lisp-like prefix notation: (add, 1, 2, 3) translates to 1 + 2 + 3. Each element in a formula can be either a function, a string or a tuple. Tuples are delimited using (). The first element of these tuples is one of the operations defined above. It is followed by at least one other element, which can be any of the following:

• Tuples, allowing the recursive expression of formulas.

• Strings, which encode band numbers.

• Constants, i.e. integers or floats.

indices = {
# normalized burn ratio
'nbr': (div, (sub, 'B08', 'B12'), (add, 'B08', 'B12')),
# normalized difference vegetation index
'ndvi': (div, (sub, 'B08', 'B04'), (add, 'B08', 'B04')),
# normalized difference water index
'ndwi': (div, (sub, 'B03', 'B08'), (add, 'B03', 'B08')),
# bare soil index
'bsi': (div, (sub, (add, 'B11', 'B04'), (add, 'B08', 'B02')),
(add, (add, 'B11', 'B04'), (add, 'B08', 'B02')))
}


An error is thrown if index_to_calculate does not mach any of the implemented indices above:

supported_indices = ', '.join(indices.keys())
assert index_to_calculate in supported_indices, f'Only the following indices are supported: {supported_indices}'


### Resolving the Formulas¶

get_bands is a function that returns all of the bands referenced in a recursive formula. This is necessary to resolve these references to actual data sets.

def get_bands(formula):
bands = set()
for element in formula:
if type(element) == tuple:
# recur for sub-formula
for band in get_bands(element):
bands.add(band)
elif type(element) == str:
bands.add(element)
return bands


The resolving process needs a band_map in the form of band_numnumpy.array. By defining the arithmetic operations like above, it can be treated like any other python function - read from the formula and called using op(args):

def evaluate_formula(band_map, formula):
op = formula[0]
args = []
for element in formula[1:]:
if type(element) == tuple:
# recur on sub-formula
args.append(evaluate_formula(band_map, element))
elif type(element) == str:
# substitute band number
args.append(band_map[element])
else:
# just append the number
args.append(element)
return op(args)


Because the prefix-notation is not commonly used to define mathematical formula, a function is defined that converts prefix a formula from above to infix notation. This should help to avoid errors when transcribing the index formulas, which are usually given in common infix notation:

def prefix_to_infix_str(formula):
'''
Returns a human-readable string of the above data-based notation. Useful
for debugging.
'''
result = ['(']

# operation
op = formula[0]
if op == add:
sym = ' + '
elif op == sub:
sym = ' - '
elif op == div:
sym = ' / '
elif op == mul:
sym = ' * '

operands = formula[1:]
for idx, operand in enumerate(operands, start=1):
if type(operand) == tuple:
result.append(prefix_to_infix_str(operand))
else:
result.append(str(operand))
if idx < len(operands):
result.append(sym)

result.append(')')
return ''.join(result)


#### Test Cases¶

formula = (add, *range(5), (mul, *range(1, 4))) # == (add, 0, 1, 2, 3, 4, (mul, 1, 2, 3))

prefix_to_infix_str(formula)

'(0 + 1 + 2 + 3 + 4 + (1 * 2 * 3))'

evaluate_formula({}, formula)

16

prefix_to_infix_str(indices['nbr'])

'((B08 - B12) / (B08 + B12))'


## Extraction of Relevant Band File Paths¶

Subsections from here on below contain the actual calculations. They start with the list of bands are referenced by the index formula given by index_to_calculate:

bands = get_bands(indices[index_to_calculate])
bands = list(bands)
bands

['B03', 'B08']


## Resampling¶

Some bands are not available in all resolutions - the band B08 for example is available only at a resolution of 10m and the band B12 only up to 20m.

The lower-resolution bands are upsampled to the highest resolution band:

# sorting the band path alphabetically sorts the bands first by band number,
# then by resolution
from sentinel_helpers import scihub_band_paths

band_paths = sorted(scihub_band_paths(product_path, bands))
band_paths[:3]

[PosixPath('zip+file:/home/jovyan/sources/resources/forest_fires/S2B_MSIL2A_20190701T102029_N0212_R065_T32UPE_20190701T134657.zip!/S2B_MSIL2A_20190701T102029_N0212_R065_T32UPE_20190701T134657.SAFE/GRANULE/L2A_T32UPE_A012103_20190701T102032/IMG_DATA/R10m/T32UPE_20190701T102029_B03_10m.jp2'),
PosixPath('zip+file:/home/jovyan/sources/resources/forest_fires/S2B_MSIL2A_20190701T102029_N0212_R065_T32UPE_20190701T134657.zip!/S2B_MSIL2A_20190701T102029_N0212_R065_T32UPE_20190701T134657.SAFE/GRANULE/L2A_T32UPE_A012103_20190701T102032/IMG_DATA/R10m/T32UPE_20190701T102029_B08_10m.jp2'),
PosixPath('zip+file:/home/jovyan/sources/resources/forest_fires/S2B_MSIL2A_20190701T102029_N0212_R065_T32UPE_20190701T134657.zip!/S2B_MSIL2A_20190701T102029_N0212_R065_T32UPE_20190701T134657.SAFE/GRANULE/L2A_T32UPE_A012103_20190701T102032/IMG_DATA/R20m/T32UPE_20190701T102029_B03_20m.jp2')]

highest_resolution_band_paths = []

# covered_bands ensures that only the highest resolution band is kept
covered_bands = set()
for band in band_paths:
band_num = band.name.split('_')[-2]
if band_num in covered_bands:
continue
else:
highest_resolution_band_paths.append(band)
covered_bands.add(band_num)

def resolution(band_path):
'''
Return the resolution encoded in a raster file's path.
'''
if isinstance(band_path, Path):
band_path = band_path.name

return int(band_path.split('_')[-1].split('.')[0].replace('m', ''))

# using the function above, we parse the highest resolution out of the first band path
target_resolution = sorted(resolution(band) for band in highest_resolution_band_paths)[0]


## Raster Cloud Mask Generation¶

Spectral indices can get distorted by highly reflective clouds. To avoid this the products include cloud masks, which contain information about the cloud positions in a product in order to discard them.

To construct a cloud mask, it is essential to know the transformation from the coordinates given by the raster’s coordinate reference system to the pixel coordinates of the highest resolution raster.

These are encoded as metadata in the raster file:

from sentinel_helpers import scihub_cloud_mask
import matplotlib.pyplot as plt

# pixels with clouds are True, pixels without are False
raster_cloud_mask, _ = scihub_cloud_mask(product_path)
plt.imshow(raster_cloud_mask[0])

<matplotlib.image.AxesImage at 0x7f240ea481c0>


## Index Calculation¶

import numpy as np
import numpy.ma as ma

import rasterio as r
from rasterio.enums import Resampling
import rasterio.plot as rplt

from sentinel_helpers import RasterReaderList, scihub_cloud_mask
from tqdm import tqdm

from tempfile import NamedTemporaryFile

out_dir = Path('resources/spectral_indices/')
out_dir.mkdir(exist_ok=True, parents=True)

%%time

with RasterReaderList(highest_resolution_band_paths) as readers:
# build the band_map as described above and scale up where needed
band_map = {}
for reader in tqdm(readers, desc='Resampling datasets'):
band_num = reader.name.split('_')[-2]
data_resolution = resolution(reader.name)
scale_factor = int(data_resolution / target_resolution)
out_shape = (
int(reader.height * scale_factor),
int(reader.width * scale_factor)
)

band_map[band_num] = (np.clip(
# we read only the first band to obtain a two-dimensional ndarray
reader.read(1, out_shape=out_shape, resampling=Resampling.bilinear, masked=True),
0, 10_000) / 10_000).astype('float32')

if scale_factor == 1:
# this is the target resolution which defines the shape of the
# raster file the data is written to
out_name = Path(reader.name).name.replace(band_num, index_to_calculate.upper())
out_name = out_name.replace('.jp2', '.tif')
out_path = out_dir / out_name
out_meta = reader.meta.copy()

# ignore numpy division errors (i.e. divide by 0) in this context;
# divide by zero results in nan. clouds are masked using the
# raster_cloud_mask created above
with np.errstate(divide='ignore', invalid='ignore'):
index = evaluate_formula(band_map, indices[index_to_calculate])
# we need to invert the cloud mask (~) because we want to hide those pixels that are cloudy
index.mask = index.mask | raster_cloud_mask

out_meta.update({
'count': 1,
'driver': 'COG',
'dtype': 'float32'
})

with r.open(out_path, 'w+', **out_meta) as dst:
dst.write(index.data, 1)
# note that the mask has to be inverted because "False" means masked
# when written, but unmasked for a numpy.ma.MaskedArray
dst.write_mask(~index.mask)
print(f'Wrote result to {out_path}')

Resampling datasets: 100%|██████████| 2/2 [00:32<00:00, 16.22s/it]

Wrote result to resources/spectral_indices/T32UPE_20190701T102029_NDWI_10m.tif
CPU times: user 1min 32s, sys: 19.8 s, total: 1min 52s
Wall time: 1min 4s


### Plot of Spectral Index¶

plt.figure(figsize=(6,6))
plt.imshow(index.data)

<matplotlib.image.AxesImage at 0x7f240d1265b0>


### Plot of Mask¶

White values are visible, black values are masked out.

plt.figure(figsize=(6,6))
plt.imshow(index.mask, cmap='Greys')

<matplotlib.image.AxesImage at 0x7f240c8837c0>


1

See Download Process for details