S2E84Product

S2E84Product#

Bases: OpticalProduct

Class for Sentinel-2 stored on AWS and processed by Element 84 (COGs) products

https://element84.com/geospatial/introducing-earth-search-v1-new-datasets-now-available/

__init__(product_path: str | CloudPath | Path, archive_path: str | CloudPath | Path | None = None, output_path: str | CloudPath | Path | None = None, remove_tmp: bool = False, **kwargs) → None[source]#

clean_tmp()#: Clean the temporary directory of the current product

clear()#: Clear this product’s cache

get_band_paths(band_list: list, pixel_size: float | None = None, **kwargs) → dict[source]#

Return the paths of required bands.

>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_band_paths([GREEN, RED])
{
    <SpectralBandNames.GREEN: 'GREEN'>:
        'LC08_L1GT_023030_20200518_20200527_01_T2/LC08_L1GT_023030_20200518_20200527_01_T2_B3.TIF',
    <SpectralBandNames.RED: 'RED'>:
        'LC08_L1GT_023030_20200518_20200527_01_T2/LC08_L1GT_023030_20200518_20200527_01_T2_B4.TIF'
}

Parameters:

band_list (list) – List of the wanted bands
pixel_size (float) – Useless here
kwargs – Other arguments used to load bands

Returns:

Dictionary containing the path of each queried band

Return type:

dict

get_date(as_date: bool = False) → str | date#

Get the product’s acquisition date.

>>> from eoreader.reader import Reader
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_date(as_date=True)
datetime.datetime(2020, 8, 24, 0, 0)
>>> prod.get_date(as_date=False)
'20200824'

Parameters:: as_date (bool) – Return the date as a datetime.date. If false, returns a string.
Returns:: Its acquisition date
Return type:: str

get_datetime(as_datetime: bool = False) → str | datetime[source]#

Get the product’s acquisition datetime, with format YYYYMMDDTHHMMSS <-> %Y%m%dT%H%M%S

Warning

Sentinel-2 datetime is the datatake sensing time, not the granule sensing time ! (the one displayed in the product’s name)

>>> from eoreader.reader import Reader
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_datetime(as_datetime=True)
datetime.datetime(2020, 8, 24, 11, 6, 31)
>>> prod.get_datetime(as_datetime=False)
'20200824T110631'

Parameters:: as_datetime (bool) – Return the date as a datetime.datetime. If false, returns a string.
Returns:: Its acquisition datetime
Return type:: Union[str, datetime]

get_default_band() → BandNames#

Get default band: GREEN for optical data as every optical satellite has a GREEN band.

>>> from eoreader.reader import Reader
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_default_band()
<SpectralBandNames.GREEN: 'GREEN'>

Returns:: Default band
Return type:: str

get_default_band_path(**kwargs) → CloudPath | Path#

Get default band (GREEN for optical data) path.

>>> from eoreader.reader import Reader
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_default_band_path()
'zip+file://S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip!/S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE/GRANULE/L1C_T30TTK_A027018_20200824T111345/IMG_DATA/T30TTK_20200824T110631_B03.jp2'

Parameters:: kwargs – Additional arguments
Returns:: Default band path
Return type:: AnyPathType

get_existing_band_paths() → dict#

Return the existing band paths (orthorectified if needed).

>>> from eoreader.reader import Reader
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_existing_band_paths()
{
    <SpectralBandNames.CA: 'COASTAL_AEROSOL'>: 'zip+file://S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip!/S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE/GRANULE/L1C_T30TTK_A027018_20200824T111345/IMG_DATA/T30TTK_20200824T110631_B01.jp2',
    ...,
    <SpectralBandNames.SWIR_2: 'SWIR_2'>: 'zip+file://S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip!/S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE/GRANULE/L1C_T30TTK_A027018_20200824T111345/IMG_DATA/T30TTK_20200824T110631_B12.jp2'
}

Returns:: Dictionary containing the path of each queried band
Return type:: dict

get_existing_bands() → list#

Return the existing band.

>>> from eoreader.reader import Reader
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_existing_bands()
[<SpectralBandNames.CA: 'COASTAL_AEROSOL'>,
<SpectralBandNames.BLUE: 'BLUE'>,
<SpectralBandNames.GREEN: 'GREEN'>,
<SpectralBandNames.RED: 'RED'>,
<SpectralBandNames.VRE_1: 'VEGETATION_RED_EDGE_1'>,
<SpectralBandNames.VRE_2: 'VEGETATION_RED_EDGE_2'>,
<SpectralBandNames.VRE_3: 'VEGETATION_RED_EDGE_3'>,
<SpectralBandNames.NIR: 'NIR'>,
<SpectralBandNames.NNIR: 'NARROW_NIR'>,
<SpectralBandNames.WV: 'WATER_VAPOUR'>,
<SpectralBandNames.CIRRUS: 'CIRRUS'>,
<SpectralBandNames.SWIR_1: 'SWIR_1'>,
<SpectralBandNames.SWIR_2: 'SWIR_2'>]

Returns:: List of existing bands in the products
Return type:: list

get_quicklook_path() → str[source]#

Get quicklook path if existing.

Returns:: Quicklook path
Return type:: str

get_raw_band_paths(**kwargs) → dict#

Return the raw band paths.

Parameters:: kwargs – Additional arguments
Returns:: Dictionary containing the path of each queried band
Return type:: dict

has_band(band: BandNames | str) → bool#

Does this product has the specified band ?

By band, we mean:

satellite band
index
DEM band
cloud band

>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.has_band(GREEN)
True
>>> prod.has_band(TIR_2)
False
>>> prod.has_band(NDVI)
True
>>> prod.has_band(SHADOWS)
False
>>> prod.has_band(HILLSHADE)
True

Parameters:: band (Union[BandNames, str]) – EOReader band (optical, SAR, clouds, DEM)
Returns:: True if the products has the specified band
Return type:: bool

has_bands(bands: list | BandNames | str) → bool#

Does this product has the specified bands ?

By band, we mean:

satellite band
index
DEM band
cloud band

See has_band for a code example.

Parameters:: bands (Union[list, BandNames, str]) – EOReader bands (optical, SAR, clouds, DEM)
Returns:: True if the products has the specified band
Return type:: bool

Open the bands and compute the wanted index.

For Optical data:
The bands will be purged of nodata and invalid pixels (if specified with the CLEAN_OPTICAL keyword), the nodata will be set to -9999 and the bands will be DataArrays in float32.
For SAR data:
The bands will be purged of nodata (not over the sea), the nodata will be set to 0 to respect SNAP’s behavior and the bands will be DataArray in float32.

Bands that come out this function at the same time are collocated and therefore have the same shapes. This can be broken if you load data separately. Its is best to always load DEM data with some real bands.

>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> bands = prod.load([GREEN, NDVI], pixel_size=20)

Parameters:

bands (Union[list, BandNames, str]) – Band list
pixel_size (float) – Pixel size of the band, in meters
size (Union[tuple, list]) – Size of the array (width, height). Not used if pixel_size is provided.
kwargs – Other arguments used to load bands

Returns:

Dataset with a variable per band

Return type:

xr.Dataset

open_mask(pixel_size: float | None = None, size: list | tuple | None = None, **kwargs) → DataArray | None[source]#

Open a Scene classification map (SCL) mask. See https://sentinels.copernicus.eu/ca/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm-overview Paragraph: Classification Mask Generation

Values: - 0 : NO_DATA - 1 : SATURATED_OR_DEFECTIVE - 2 : CAST_SHADOWS - 3 : CLOUD_SHADOWS - 4 : VEGETATION - 5 : NOT_VEGETATED - 6 : WATER - 7 : UNCLASSIFIED - 8 : CLOUD_MEDIUM_PROBABILITY - 9 : CLOUD_HIGH_PROBABILITY - 10 : THIN_CIRRUS - 11 : SNOW or ICE

Parameters:

pixel_size (float) – Band pixel size in meters
size (Union[tuple, list]) – Size of the array (width, height). Not used if pixel_size is provided.

Returns:

Mask array

Return type:

Union[xarray.DataArray, None]

plot() → None#: Plot the quicklook if existing

read_mtd()#

Read metadata and outputs the metadata XML root and its namespaces as a dict.

>>> from eoreader.reader import Reader
>>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip"
>>> prod = Reader().open(path)
>>> prod.read_mtd()
(<Element product at 0x1832895d788>, '')

Returns:: Metadata XML root and its namespace
Return type:: (etree._Element, dict)

Stack bands and index of a products.

>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> stack = prod.stack([NDVI, MNDWI, GREEN], pixel_size=20)  # In meters

Parameters:

bands (list) – Bands and index combination
pixel_size (float) – Stack pixel size. . If not specified, use the product pixel size.
size (Union[tuple, list]) – Size of the array (width, height). Not used if pixel_size is provided.
stack_path (AnyPathStrType) – Stack path
save_as_int (bool) – Convert stack to uint16 to save disk space (and therefore multiply the values by 10.000)
**kwargs – Other arguments passed to load or rioxarray.to_raster() (such as compress)

Returns:

Stack as a DataArray

Return type:

xr.DataArray

to_band(raw_bands: list | BandNames | str | int) → list#

Convert any raw band identifier to a usable band.

Bands can be called with their name, ID 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.

Parameters:: raw_bands (Union[list, BandNames, str, int]) – Raw bands
Returns:: Mapped bands
Return type:: list

archive_path#: Archive path, same as the product path if not specified. Useful when you want to know where both the extracted and archived version of your product are stored.

bands#: Band mapping between band wrapping names such as GREEN and band real number such as 03 for Sentinel-2.

condensed_name#: Condensed name, the filename with only useful data to keep the name unique (ie. 20191215T110441_S2_30TXP_L2A_122756). Used to shorten names and paths.

constellation#: Product constellation, such as Sentinel-2

constellation_id#: Constellation ID, i.e. S2 for Sentinel-2

corresponding_ref#: The corresponding reference products to the current one (if the product is not a reference but has a reference data corresponding to it). A list because of multiple ref in case of non-stackable products (S3, S1…)

crs = <methodtools._LruCacheWire object>#

date#: Acquisition date.

datetime#: Acquisition datetime.

default_transform = <methodtools._LruCacheWire object>#

extent = <methodtools._LruCacheWire object>#

filename#: Product filename

footprint = <methodtools._LruCacheWire object>[source]#

get_cloud_cover = <methodtools._LruCacheWire object>[source]#

get_mean_sun_angles = <methodtools._LruCacheWire object>[source]#

get_mean_viewing_angles = <methodtools._LruCacheWire object>#

get_orbit_direction = <methodtools._LruCacheWire object>#

instrument#: Product instrument, such as MSI for Sentinel-2 data.

is_archived#: Is the archived product is processed (a products is considered as archived if its products path is a directory).

is_ortho#: True if the images are orthorectified and the footprint is retrieved easily.

is_reference#: If the product is a reference, used for algorithms that need pre and post data, such as fire detection.

is_stacked#: True if the bands are stacked (like for VHR data).

name#: Product true name (as specified in the metadata)

needs_extraction#: Does this product needs to be extracted to be processed ? (True by default).

nodata#: Product nodata, set to -9999 by default

property output: CloudPath | Path#: Output directory of the product, to write orthorectified data for example.

path#: Usable path to the product, either extracted or archived path, according to the satellite.

pixel_size#: For SAR data, it is important to distinguish (square) pixel spacing from actual resolution. (see this <https://natural-resources.canada.ca/maps-tools-and-publications/satellite-imagery-and-air-photos/tutorial-fundamentals-remote-sensing/satellites-and-sensors/spatial-resolution-pixel-size-and-scale/9407> for more information). For optical data, those two terms have usually the same meaning (for a fully zoomed raster).

product_type#: Product type, satellite-related field, such as L1C or L2A for Sentinel-2 data.

resolution#: Default resolution in meters of the current product. For SAR product, we use Ground Range resolution as we will automatically orthorectify the tiles.

sensor_type#: Sensor type, SAR or optical.

split_name#: Split name, to retrieve every information from its true name (dates, tile, product type…).

property stac: StacItem#

tile_name#: Tile if possible (for data that can be piled, for example S2 and Landsats).

S2E84Product

Contents

S2E84Product#