Main features

These features can be seen in the basic tutorial.

Open

The reader singleton is your unique entry. It will create for you the product object corresponding to your satellite data.

You can load products from the cloud, see this tutorial. S3 and S3 Compatible Storage are working and maybe Google and Azure if rasterio supports it, but they have not been tested.

>>> import os
>>> from reader import Reader

>>> # Path to your satellite data, ie. Sentinel-2
>>> path = r'S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.zip'  # You can work with the archive for S2 data

>>> # Path to your output directory (if not set, it will work in a temp directory)
>>> output = os.path.abspath('.')

>>> # Create the reader singleton
>>> eoreader = Reader()
>>> prod = eoreader.open(path, output_path=output, remove_tmp=True)
>>> # remove_tmp allows you to automatically delete processing files 
>>> # such as cleaned or orthorectified bands when the product is deleted
>>> # False by default to speed up the computation if you want to use the same product in several part of your code

>>> # NOTE: you can set the output directory after the creation, that allows you to use the product condensed name
>>> prod.output = os.path.join(output, prod.condensed_name)  # It will automatically create it if needed

Optical

The recognized paths for products directories are:

Sensor group

Folder to link

Sentinel 2 and 3

Main directory, .SAFE, .SEN3 or .zip, i.e. S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE

Sentinel 2 Theia

Main directory containing the .tif images, i.e. SENTINEL2A_20190625-105728-756_L2A_T31UEQ_C_V2-2

Landsats

Main directory extracted or archived if Collection 2 (.tar), i.e. LC08_L1TP_200030_20201220_20210310_02_T1.tar

Planet

Directory containing the manifest.json file, i.e. 20210406_015904_37_2407

DIMAP (Pleiades, SPOTs, ...)

Directory containing the .JP2 files, i.e. IMG_PHR1B_PMS_001

Maxar (WorldView, GeoEye...)

Directory containing the .TIL file, i.e. 013187549010_01_P001_PSH

SAR

The recognized paths for products directories are:

Sensor group

Folder to link

Sentinel-1 and RADARSAT-Constellation Mission

SAFE directory containing the manifest.safe file, i.e. S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.SAFE

COSMO-Skymed 1 and 2nd Generation

Directory containing the .h5 image, i.e. 1011117-766193

RADARSAT-2

Main directory containing the .tif image, i.e. RS2_OK73950_PK661843_DK590667_U25W2_20160228_112418_HH_SGF.zip

TerraSAR-X, TanDEM-X or PAZ SAR

Directory containing the IMAGEDATA directory, i.e. TDX1_SAR__MGD_SE___SM_S_SRA_20201016T231611_20201016T231616

ICEYE

Directory containing the .tif file, i.e. SC_124020

Load

load is the function for accessing product-related bands. It can load satellite bands, index, DEM bands and cloud bands according to this workflow: load_workflow

>>> import os
>>> from eoreader.reader import Reader
>>> from eoreader.bands import *

>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.zip"
>>> output = os.path.abspath("./output")
>>>  # WARNING: you can leave the output_path empty, but EOReader will create a temporary output directory
>>>  # and you won't be able to retrieve what's has been written on disk
>>> prod = Reader().open(path, output_path=output)

>>>  # Specify a DEM to load DEM bands
>>> import os
>>> from eoreader.env_vars import DEM_PATH
>>> os.environ[DEM_PATH] = r"my_dem.tif"

>>> # Get the wanted bands and check if the product can produce them
>>> band_list = [GREEN, NDVI, TIR_1, SHADOWS, HILLSHADE]
>>> ok_bands = [band for band in band_list if prod.has_band(band)]
[GREEN, NDVI, HILLSHADE]
>>> # Sentinel-2 cannot produce satellite band TIR_1 and cloud band SHADOWS

>>> # Load bands
>>> bands = prod.load(ok_bands)  # resolution not specified -> load at default resolution (20.0 m for S2 data)
>>> # NOTE: every array that comes out `load` are collocated, which isn't the case if you load arrays separately
>>> # (important for DEM data as they may have different grids)

>>> bands
{<function NDVI at 0x000001C47FF05E18>: <xarray.DataArray 'NDVI' (band: 1, y: 5490, x: 5490)>
array([[[0.94786006, 0.92717856, 0.92240528, ..., 1.73572724,
         1.55314477, 1.63242706],
        [1.04147187, 0.93668633, 0.91499688, ..., 1.59941784,
         1.52895995, 1.51386761],
        [2.86996677, 1.69360304, 1.2413562 , ..., 1.61172353,
         1.55742907, 1.50568275],
        ...,
        [1.45807257, 1.61071344, 1.64620751, ..., 1.25498441,
         1.42998927, 1.70447076],
        [1.57802352, 1.77086658, 1.69901482, ..., 1.19999853,
         1.27813254, 1.52287237],
        [1.63569594, 1.66751277, 1.63474646, ..., 1.27617084,
         1.22456033, 1.27022877]]])
Coordinates:
  * x            (x) float64 2e+05 2e+05 2e+05 ... 3.097e+05 3.098e+05 3.098e+05
  * y            (y) float64 4.5e+06 4.5e+06 4.5e+06 ... 4.39e+06 4.39e+06
  * band         (band) int32 1
    spatial_ref  int32 0,
<OpticalBandNames.GREEN: 'GREEN'>: <xarray.DataArray 'T30TTK_20200824T110631_B03' (band: 1, y: 5490, x: 5490)>
array([[[0.06146327, 0.06141786, 0.06100179, ..., 0.11880179,
         0.12087143, 0.11468571],
        [0.06123214, 0.06071094, 0.06029063, ..., 0.11465781,
         0.11858906, 0.11703929],
        [0.06494643, 0.06226562, 0.06169219, ..., 0.11174062,
         0.11434844, 0.11491964],
        ...,
        [0.1478125 , 0.13953906, 0.13751719, ..., 0.15949688,
         0.14200781, 0.12982321],
        [0.14091429, 0.12959531, 0.13144844, ..., 0.17246719,
         0.156175  , 0.13453036],
        [0.13521429, 0.13274286, 0.13084821, ..., 0.16064821,
         0.16847143, 0.16009592]]])
Coordinates:
  * x            (x) float64 2e+05 2e+05 2e+05 ... 3.097e+05 3.098e+05 3.098e+05
  * y            (y) float64 4.5e+06 4.5e+06 4.5e+06 ... 4.39e+06 4.39e+06
  * band         (band) int32 1
    spatial_ref  int32 0,
<DemBandNames.HILLSHADE: 'HILLSHADE'>: <xarray.DataArray '20200824T110631_S2_T30TTK_L1C_150432_HILLSHADE' (band: 1, y: 5490, x: 5490)>
array([[[220., 221., 221., ..., 210., 210., 210.],
        [222., 222., 221., ..., 210., 210., 210.],
        [221., 221., 220., ..., 210., 210., 210.],
        ...,
        [215., 214., 212., ..., 207., 207., 207.],
        [214., 212., 211., ..., 206., 205., 205.],
        [213., 211., 209., ..., 205., 204., 205.]]])
Coordinates:
  * band         (band) int32 1
  * y            (y) float64 4.5e+06 4.5e+06 4.5e+06 ... 4.39e+06 4.39e+06
  * x            (x) float64 2e+05 2e+05 2e+05 ... 3.097e+05 3.098e+05 3.098e+05
    spatial_ref  int32 0
Attributes:
    grid_mapping:    spatial_ref
    original_dtype:  uint8}

Note

Index and bands are opened as xarrays with rioxarray, in float with the nodata set to np.nan. The nodata written back on disk is by convention:

  • -9999 for optical bands (saved in float32)

  • 65535 for optical bands (saved in uint16)

  • 0 for SAR bands (saved in float32), to be compliant with SNAP default nodata

  • 255 for masks (saved in uint8)

For optical bands, only the pixels outside of the detector are set to nodata by default but this can be changed according to the user’s needs (see below).

Some additional arguments can be passed to this function, please see keywords for the list.

  • Methods to clean optical bands are best described here,

  • Sentinel-3 additional keywords use is highlighted in the corresponding notebook.

Stack

stack is the function stacking all possible bands. It is based on the load function and then just stacks the bands and write it on disk if needed.

The bands are ordered as asked in the stack. However, they cannot be duplicated (the stack cannot contain 2 RED bands for instance)! If the same band is asked several time, its order will be the one of the last demand.

>>> # Create a stack with the previous OK bands
>>> stack = prod.stack(ok_bands, resolution=300., stack_path=os.path.join(prod.output, "stack.tif")
<xarray.DataArray 'GREEN_NDVI_HILLSHADE' (z: 3, y: 5490, x: 5490)>
array([[[9.47860062e-01, 9.27178562e-01, 9.22405303e-01, ...,
         1.73572719e+00, 1.55314481e+00, 1.63242710e+00],
        [1.04147184e+00, 9.36686337e-01, 9.14996862e-01, ...,
         1.59941781e+00, 1.52895999e+00, 1.51386762e+00],
        [2.86996675e+00, 1.69360304e+00, 1.24135625e+00, ...,
         1.61172354e+00, 1.55742908e+00, 1.50568271e+00],
        ...,
        [1.45807254e+00, 1.61071348e+00, 1.64620745e+00, ...,
         1.25498438e+00, 1.42998922e+00, 1.70447075e+00],
        [1.57802355e+00, 1.77086663e+00, 1.69901478e+00, ...,
         1.19999850e+00, 1.27813256e+00, 1.52287233e+00],
        [1.63569593e+00, 1.66751277e+00, 1.63474643e+00, ...,
         1.27617085e+00, 1.22456038e+00, 1.27022874e+00]],
       [[6.14632666e-02, 6.14178553e-02, 6.10017851e-02, ...,
         1.18801787e-01, 1.20871432e-01, 1.14685714e-01],
        [6.12321422e-02, 6.07109368e-02, 6.02906235e-02, ...,
         1.14657812e-01, 1.18589066e-01, 1.17039286e-01],
        [6.49464279e-02, 6.22656234e-02, 6.16921857e-02, ...,
         1.11740626e-01, 1.14348434e-01, 1.14919640e-01],
        [1.47812501e-01, 1.39539063e-01, 1.37517184e-01, ...,
         1.59496874e-01, 1.42007813e-01, 1.29823208e-01],
        [1.40914291e-01, 1.29595309e-01, 1.31448433e-01, ...,
         1.72467187e-01, 1.56175002e-01, 1.34530351e-01],
        [1.35214284e-01, 1.32742852e-01, 1.30848214e-01, ...,
         1.60648212e-01, 1.68471426e-01, 1.60095915e-01]],
       [[2.20000000e+02, 2.21000000e+02, 2.21000000e+02, ...,
         2.10000000e+02, 2.10000000e+02, 2.10000000e+02],
        [2.22000000e+02, 2.22000000e+02, 2.21000000e+02, ...,
         2.10000000e+02, 2.10000000e+02, 2.10000000e+02],
        [2.21000000e+02, 2.21000000e+02, 2.20000000e+02, ...,
         2.10000000e+02, 2.10000000e+02, 2.10000000e+02],
        ...,
        [2.15000000e+02, 2.14000000e+02, 2.12000000e+02, ...,
         2.07000000e+02, 2.07000000e+02, 2.07000000e+02],
        [2.14000000e+02, 2.12000000e+02, 2.11000000e+02, ...,
         2.06000000e+02, 2.05000000e+02, 2.05000000e+02],
        [2.13000000e+02, 2.11000000e+02, 2.09000000e+02, ...,
         2.05000000e+02, 2.04000000e+02, 2.05000000e+02]]], dtype=float32)
Coordinates:
  * x            (x) float64 2e+05 2e+05 2e+05 ... 3.097e+05 3.098e+05 3.098e+05
  * y            (y) float64 4.5e+06 4.5e+06 4.5e+06 ... 4.39e+06 4.39e+06
    spatial_ref  int32 0
  * z            (z) MultiIndex
  - variable     (z) object 'GREEN' 'NDVI' 'HILLSHADE'
  - band         (z) int64 1 1 1
Attributes:
    long_name:  ['GREEN', 'NDVI', 'HILLSHADE']

Some additional arguments can be passed to this function, please see keywords for the list.

  • Methods to clean optical bands are best described here,

  • Sentinel-3 additional keywords use is highlighted in the corresponding notebook.

Read Metadata

EOReader gives you the access to the metadata of your product as a lxml.etree._Element followed by the namespace you may need to read them


>>> # Access the raw metadata as an lxml.etree._Element and its namespaces as a dict:
>>> mtd, nmsp = prod.read_mtd()
(
    <Element {https://psd-14.sentinel2.eo.esa.int/PSD/S2_PDI_Level-1C_Tile_Metadata.xsd}Level-1C_Tile_ID at 0x1e396036ec8>, 
    {'n1': '{https://psd-14.sentinel2.eo.esa.int/PSD/S2_PDI_Level-1C_Tile_Metadata.xsd}'}
)

>>> # You can access a field like that: 
>>> datastrip_id = mtd.findtext(".//DATASTRIP_ID")

>>> # Pay attention, for some products you will need a namespace, i.e. for planet data:
>>> # name = mtd.findtext(f".//{nsmap['eop']}identifier")

Note

Landsat Collection 1 have no metadata with XML format, so the XML is simulated from the text file.

Note

Sentinel-3 sensors have no metadata file but have global attributes repeated in every NetCDF files. This is what you will have when calling this function:

  • absolute_orbit_number

  • comment

  • contact

  • creation_time

  • history

  • institution

  • netCDF_version

  • product_name

  • references

  • resolution

  • source

  • start_offset

  • start_time

  • stop_time

  • title

  • ac_subsampling_factor (OLCI only)

  • al_subsampling_factor (OLCI only)

  • track_offset (SLSTR only)

Other features

CRS

Get the product CRS, always in UTM

>>> # Product CRS (always in UTM)
>>> prod.crs()
CRS.from_epsg(32630)

Extent and footprint

Get the product extent and footprint, always in UTM as a gpd.GeoDataFrame

>>> # Full extent of the bands as a geopandas GeoDataFrame
>>> prod.extent()
                                            geometry
0   POLYGON((309780.000 4390200.000, 309780.000 4...

>>> # Footprint: extent of the useful pixels (minus nodata) as a geopandas GeoDataFrame
>>> prod.footprint()
                                            geometry
0 POLYGON Z((199980.000 4390200.000 0.000, 1999...

Please note the difference between footprint and extent:

Without nodata

With nodata

without_nodata

with_nodata

Optical data

Get product azimuth (between [0, 360] degrees) and zenith solar angles, useful for computing the Hillshade for example.

>>> # Get azimuth and zenith solar angles
>>> prod.get_mean_sun_angles()
(151.750970396115, 35.4971906983449)