Basic example#
Let’s use EOReader for the first time.
Imports#
import os
import tempfile
# EOReader
from eoreader.reader import Reader
from eoreader.bands import GREEN, NDVI, CLOUDS
Data#
First of all, we need some satellite data.
Let’s open a Sentinel-2 product.
path = os.path.join("/home", "prods", "S2", "PB 02.07+", "S2B_MSIL2A_20200114T065229_N0213_R020_T40REQ_20200114T094749.SAFE")
Create the Reader#
First, create the Reader object.
This object will automatically detect the type of constellation of your product.
It relies on the internal composition of the product (usually the presence of the metadata file), so please do not remove them.
The Reader is a singleton that should be called only once.
It can be used several times to open all your satellite products.
No need to extract the product here: archived Sentinel-2 are handled by EOReader.
reader = Reader()
Open the product#
The reader is used to open your product, just call the open function.
prod = reader.open(path)
prod
eoreader.S2Product 'S2B_MSIL2A_20200114T065229_N0213_R020_T40REQ_20200114T094749'
Attributes:
condensed_name: 20200114T065229_S2_T40REQ_L2A_094749
path: /home/prods/S2/PB 02.07+/S2B_MSIL2A_20200114T065229_N0213_R020_T40REQ_20200114T094749.SAFE
constellation: Sentinel-2
sensor type: Optical
product type: MSIL2A
default pixel size: 10.0
default resolution: 10.0
acquisition datetime: 2020-01-14T06:52:29
band mapping:
COASTAL_AEROSOL: 01
BLUE: 02
GREEN: 03
RED: 04
VEGETATION_RED_EDGE_1: 05
VEGETATION_RED_EDGE_2: 06
VEGETATION_RED_EDGE_3: 07
NIR: 08
NARROW_NIR: 8A
WATER_VAPOUR: 09
SWIR_1: 11
SWIR_2: 12
needs extraction: False
cloud cover: 63.86422
tile name: T40REQ
Output#
You can specify an output folder after creation, or on creation of the Product object.
If not specified, the output will be stored in a temporary folder and will be removed when the product object is deleted.
By default, your temporary files such as cleaned spectral bands are written on disk in this output folder.
You can prevent them from being written by specifying the remove_tmp argument.
prod = reader.open(path, output_path="/my/output", remove_tmp=True)
Condensed name#
If you chose to create the output folder after creation of the Product object, you can leverage the condensed_name to name it.
The condensed_name of your product is a powerful feature: it is the more compact name possible to identify your product in a unique way, with a pattern common to every EOReader product.
It is very convenient to name in the same way all your results, to be able to sort them by date, sensors, etc.
It is constructed this way: {date}_{constellation}_{other_identifiers} (tiles, ID, instrument, etc. depending on the sensor type)
# Write the output in a temporary folder which will b edeleted at the end of this notebook.
# This is for shocasing purposes only as this is done automatically under the hood
tmp_folder = tempfile.TemporaryDirectory()
prod.output = os.path.join(tmp_folder.name, prod.condensed_name)
prod.output
PosixPath('/tmp/tmpw5zsz6lk/20200114T065229_S2_T40REQ_L2A_094749')
Load#
Just load easily some bands and index. The load function outputs a dictionary of xarray.DataArray.
The bands can be called by their ID, name or mapped name.
For example, for Sentinel-3 OLCI you can use 7, Oa07 or YELLOW. For Landsat-8, you can use BLUE or 2.
# Load those bands as a xarray.Dataset
band_ds = prod.load([GREEN, NDVI, CLOUDS])
band_ds
/opt/conda/lib/python3.11/site-packages/rasterio/warp.py:387: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.
dest = _reproject(
<xarray.Dataset> Size: 1GB
Dimensions: (band: 1, y: 10980, x: 10980)
Coordinates:
spatial_ref int64 8B 0
* band (band) int64 8B 1
* y (y) float64 88kB 3e+06 3e+06 ... 2.89e+06 2.89e+06
* x (x) float64 88kB 5e+05 5e+05 ... 6.098e+05
Data variables:
SpectralBandNames.GREEN (band, y, x) float32 482MB dask.array<chunksize=(1, 1024, 1024), meta=np.ndarray>
NDVI (band, y, x) float32 482MB dask.array<chunksize=(1, 1024, 1024), meta=np.ndarray>
CloudsBandNames.CLOUDS (band, y, x) float32 482MB dask.array<chunksize=(1, 1024, 1024), meta=np.ndarray>
Attributes:
long_name: GREEN NDVI CLOUDS
constellation: Sentinel-2
constellation_id: S2
product_path: /home/prods/S2/PB 02.07+/S2B_MSIL2A_20200114T065229_N0...
product_name: S2B_MSIL2A_20200114T065229_N0213_R020_T40REQ_20200114T...
product_filename: S2B_MSIL2A_20200114T065229_N0213_R020_T40REQ_20200114T...
instrument: MSI
product_type: MSIL2A
acquisition_date: 20200114T065229
condensed_name: 20200114T065229_S2_T40REQ_L2A_094749
orbit_direction: DESCENDING
cloud_cover: 63.86422# Load individual bands (as xarray.DataArray)
green = band_ds[GREEN]
ndvi = band_ds[NDVI]
clouds = band_ds[CLOUDS]
green
<xarray.DataArray <SpectralBandNames.GREEN: 'GREEN'> (band: 1, y: 10980,
x: 10980)> Size: 482MB
dask.array<clip, shape=(1, 10980, 10980), dtype=float32, chunksize=(1, 1024, 1024), chunktype=numpy.ndarray>
Coordinates:
spatial_ref int64 8B 0
* band (band) int64 8B 1
* y (y) float64 88kB 3e+06 3e+06 3e+06 ... 2.89e+06 2.89e+06
* x (x) float64 88kB 5e+05 5e+05 5e+05 ... 6.098e+05 6.098e+05
Attributes: (12/14)
path: /tmp/tmpw5zsz6lk/20200114T065229_S2_T40REQ_L2A_094749/...
long_name: GREEN
constellation: Sentinel-2
constellation_id: S2
product_path: /home/prods/S2/PB 02.07+/S2B_MSIL2A_20200114T065229_N0...
product_name: S2B_MSIL2A_20200114T065229_N0213_R020_T40REQ_20200114T...
... ...
product_type: MSIL2A
acquisition_date: 20200114T065229
condensed_name: 20200114T065229_S2_T40REQ_L2A_094749
orbit_direction: DESCENDING
radiometry: reflectance
cloud_cover: 63.86422# Let's see what's inside your output folder
[str(f.relative_to(prod.output)) for f in prod.output.glob("**/*.tif")]
['tmp_20200114T065229_S2_T40REQ_L2A_094749/20200114T065229_S2_T40REQ_L2A_094749_CLDPRB_10m.tif',
'tmp_20200114T065229_S2_T40REQ_L2A_094749/20200114T065229_S2_T40REQ_L2A_094749_CLOUDS_10m.tif',
'tmp_20200114T065229_S2_T40REQ_L2A_094749/20200114T065229_S2_T40REQ_L2A_094749_NDVI_10m.tif',
'tmp_20200114T065229_S2_T40REQ_L2A_094749/20200114T065229_S2_T40REQ_L2A_094749_GREEN_10m_nodata.tif',
'tmp_20200114T065229_S2_T40REQ_L2A_094749/20200114T065229_S2_T40REQ_L2A_094749_RED_10m_nodata.tif',
'tmp_20200114T065229_S2_T40REQ_L2A_094749/20200114T065229_S2_T40REQ_L2A_094749_NIR_10m_nodata.tif']
# Plot a subsampled version of the GREEN band
green[:, ::10, ::10].plot(robust=True, cmap="mako");
# Plot a subsampled version of the NDVI spectral index
ndvi[:, ::10, ::10].plot(robust=True, cmap="mako");
# Plot a subsampled version of the CLOUD mask
clouds[:, ::10, ::10].plot(robust=True, cmap="mako");
# Remove the output (it would have been best to call the temporary directory in a context manager)
tmp_folder.cleanup()