L8Product¶
- class L8Product(product_path: Union[str, cloudpathlib.cloudpath.CloudPath, pathlib.Path], archive_path: Optional[Union[str, cloudpathlib.cloudpath.CloudPath, pathlib.Path]] = None, output_path: Optional[Union[str, cloudpathlib.cloudpath.CloudPath, pathlib.Path]] = None, remove_tmp: bool = False)[source]¶
Bases:
eoreader.products.optical.landsat_product.LandsatProduct
Class of Landsat-8 Products
Methods
__init__
(product_path[, archive_path, ...])Clean the temporary directory of the current product
clear
()Clear this product's cache
get_band_paths
(band_list[, resolution])Return the paths of required bands.
get_date
([as_date])Get the product's acquisition date.
get_datetime
([as_datetime])Get the product's acquisition datetime, with format YYYYMMDDTHHMMSS <-> %Y%m%dT%H%M%S
Get default band: GREEN for optical data as every optical satellite has a GREEN band.
get_default_band_path
(**kwargs)Get default band (GREEN for optical data) path.
Return the existing band paths.
Return the existing band paths.
has_band
(band)Does this products has the specified band ?
has_bands
(bands)Does this products has the specified bands ?
load
(bands[, resolution, size])Open the bands and compute the wanted index.
read_mtd
()Read metadata and outputs the metadata XML root and its namespaces as a dict most of the time, except from L8-collection 1 data which outputs a pandas.DataFrame
stack
(bands[, resolution, size, stack_path, ...])Stack bands and index of a products.
Attributes
Get UTM projection of the tile
Get UTM extent of the tile
Get real footprint in UTM of the products (without nodata, in french == emprise utile)
Output directory of the product, to write orthorectified data for example.
- clean_tmp()¶
Clean the temporary directory of the current product
- clear()¶
Clear this product’s cache
- get_band_paths(band_list: list, resolution: Optional[float] = None, **kwargs) dict ¶
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]) { <OpticalBandNames.GREEN: 'GREEN'>: 'LC08_L1GT_023030_20200518_20200527_01_T2/LC08_L1GT_023030_20200518_20200527_01_T2_B3.TIF', <OpticalBandNames.RED: 'RED'>: 'LC08_L1GT_023030_20200518_20200527_01_T2/LC08_L1GT_023030_20200518_20200527_01_T2_B4.TIF' }
- get_date(as_date: bool = False) Union[str, datetime.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'
- get_datetime(as_datetime: bool = False) Union[str, datetime.datetime] ¶
Get the product’s acquisition datetime, with format YYYYMMDDTHHMMSS <-> %Y%m%dT%H%M%S
>>> from eoreader.reader import Reader >>> path = r"LC08_L1GT_023030_20200518_20200527_01_T2" >>> prod = Reader().open(path) >>> prod.get_datetime(as_datetime=True) datetime.datetime(2020, 5, 18, 16, 34, 7) >>> prod.get_datetime(as_datetime=False) '20200518T163407'
- 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.datetime]
- get_default_band() eoreader.bands.bands.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() <OpticalBandNames.GREEN: 'GREEN'>
- Returns
Default band
- Return type
- get_default_band_path(**kwargs) Union[cloudpathlib.cloudpath.CloudPath, pathlib.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
Union[CloudPath, Path]
- get_existing_band_paths() dict ¶
Return the existing band paths.
>>> 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() { <OpticalBandNames.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', ..., <OpticalBandNames.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
- get_existing_bands() list ¶
Return the existing band paths.
>>> from eoreader.reader import Reader >>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip" >>> prod = Reader().open(path) >>> prod.get_existing_bands() [<OpticalBandNames.CA: 'COASTAL_AEROSOL'>, <OpticalBandNames.BLUE: 'BLUE'>, <OpticalBandNames.GREEN: 'GREEN'>, <OpticalBandNames.RED: 'RED'>, <OpticalBandNames.VRE_1: 'VEGETATION_RED_EDGE_1'>, <OpticalBandNames.VRE_2: 'VEGETATION_RED_EDGE_2'>, <OpticalBandNames.VRE_3: 'VEGETATION_RED_EDGE_3'>, <OpticalBandNames.NIR: 'NIR'>, <OpticalBandNames.NNIR: 'NARROW_NIR'>, <OpticalBandNames.WV: 'WATER_VAPOUR'>, <OpticalBandNames.CIRRUS: 'CIRRUS'>, <OpticalBandNames.SWIR_1: 'SWIR_1'>, <OpticalBandNames.SWIR_2: 'SWIR_2'>]
- Returns
List of existing bands in the products
- Return type
- has_band(band: Union[eoreader.bands.bands.BandNames, Callable]) bool ¶
Does this products 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
- has_bands(bands: Union[list, eoreader.bands.bands.BandNames, Callable]) bool ¶
Does this products has the specified bands ?
By band, we mean:
satellite band
index
DEM band
cloud band
See has_bands for a code example.
- load(bands: Union[list, eoreader.bands.bands.BandNames, Callable], resolution: Optional[float] = None, size: Optional[Union[list, tuple]] = None, **kwargs) dict ¶
Open the bands and compute the wanted index.
The bands will be purged of nodata and invalid pixels, the nodata will be set to 0 and the bands will be masked arrays in float.
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], resolution=20) >>> bands { <function NDVI at 0x000001EFFFF5DD08>: <xarray.DataArray 'NDVI' (band: 1, y: 5490, x: 5490)> array([[[0.949506 , 0.92181516, 0.9279379 , ..., 1.8002278 , 1.5424857 , 1.6747767 ], [0.95369846, 0.91685396, 0.8957871 , ..., 1.5847116 , 1.5248713 , 1.5011379 ], [2.9928885 , 1.3031474 , 1.0076253 , ..., 1.5969834 , 1.5590671 , 1.5018653 ], ..., [1.4245619 , 1.6115025 , 1.6201663 , ..., 1.2387121 , 1.4025431 , 1.800678 ], [1.5627214 , 1.822388 , 1.7245892 , ..., 1.1694248 , 1.2573677 , 1.5767351 ], [1.653781 , 1.6424649 , 1.5923225 , ..., 1.3072611 , 1.2181134 , 1.2478763 ]]], dtype=float32) 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, <OpticalBandNames.GREEN: 'GREEN'>: <xarray.DataArray (band: 1, y: 5490, x: 5490)> array([[[0.0615 , 0.061625, 0.061 , ..., 0.12085 , 0.120225, 0.113575], [0.061075, 0.06045 , 0.06025 , ..., 0.114625, 0.119625, 0.117625], [0.06475 , 0.06145 , 0.060925, ..., 0.111475, 0.114925, 0.115175], ..., [0.1516 , 0.14195 , 0.1391 , ..., 0.159975, 0.14145 , 0.127075], [0.140325, 0.125975, 0.131875, ..., 0.18245 , 0.1565 , 0.13015 ], [0.133475, 0.1341 , 0.13345 , ..., 0.15565 , 0.170675, 0.16405 ]]], dtype=float32) 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 }
- Parameters
- Returns
{band_name, band xarray}
- Return type
- read_mtd() Any ¶
Read metadata and outputs the metadata XML root and its namespaces as a dict most of the time, except from L8-collection 1 data which outputs a pandas.DataFrame
>>> 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 or pd.DataFrame
- Return type
Any
- stack(bands: list, resolution: Optional[float] = None, size: Optional[Union[list, tuple]] = None, stack_path: Optional[Union[str, cloudpathlib.cloudpath.CloudPath, pathlib.Path]] = None, save_as_int: bool = False, **kwargs) xarray.core.dataarray.DataArray ¶
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], resolution=20) # In meters >>> stack <xarray.DataArray 'NDVI_MNDWI_GREEN' (z: 3, y: 5490, x: 5490)> array([[[ 0.949506 , 0.92181516, 0.9279379 , ..., 1.8002278 , 1.5424857 , 1.6747767 ], [ 0.95369846, 0.91685396, 0.8957871 , ..., 1.5847116 , 1.5248713 , 1.5011379 ], [ 2.9928885 , 1.3031474 , 1.0076253 , ..., 1.5969834 , 1.5590671 , 1.5018653 ], ..., [ 1.4245619 , 1.6115025 , 1.6201663 , ..., 1.2387121 , 1.4025431 , 1.800678 ], [ 1.5627214 , 1.822388 , 1.7245892 , ..., 1.1694248 , 1.2573677 , 1.5767351 ], [ 1.653781 , 1.6424649 , 1.5923225 , ..., 1.3072611 , 1.2181134 , 1.2478763 ]], [[ 0.27066118, 0.23466069, 0.18792598, ..., -0.4611526 , -0.49751845, -0.4865216 ], [ 0.22425456, 0.28004232, 0.27851456, ..., -0.5032771 , -0.501796 , -0.502669 ], [-0.07466951, 0.06360884, 0.1207174 , ..., -0.50617427, -0.50219285, -0.5034222 ], [-0.47076276, -0.4705828 , -0.4747971 , ..., -0.32138503, -0.36619243, -0.37428448], [-0.4826967 , -0.5032287 , -0.48544118, ..., -0.278925 , -0.31404778, -0.36052078], [-0.488381 , -0.48253912, -0.4697526 , ..., -0.38105175, -0.30813277, -0.27739233]], [[ 0.0615 , 0.061625 , 0.061 , ..., 0.12085 , 0.120225 , 0.113575 ], [ 0.061075 , 0.06045 , 0.06025 , ..., 0.114625 , 0.119625 , 0.117625 ], [ 0.06475 , 0.06145 , 0.060925 , ..., 0.111475 , 0.114925 , 0.115175 ], ..., [ 0.1516 , 0.14195 , 0.1391 , ..., 0.159975 , 0.14145 , 0.127075 ], [ 0.140325 , 0.125975 , 0.131875 , ..., 0.18245 , 0.1565 , 0.13015 ], [ 0.133475 , 0.1341 , 0.13345 , ..., 0.15565 , 0.170675 , 0.16405 ]]], dtype=float32) Coordinates: * 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 * z (z) MultiIndex - variable (z) object 'NDVI' 'MNDWI' 'GREEN' - band (z) int64 1 1 1 -Attributes: long_name: ['NDVI', 'MNDWI', 'GREEN']
- Parameters
bands (list) – Bands and index combination
resolution (float) – Stack resolution. . If not specified, use the product resolution.
size (Union[tuple, list]) – Size of the array (width, height). Not used if resolution is provided.
stack_path (Union[str, CloudPath, Path]) – 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
- 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.
- band_names¶
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.
- 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…)
- property crs: rasterio.crs.CRS¶
Get UTM projection of the tile
>>> from eoreader.reader import Reader >>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip" >>> prod = Reader().open(path) >>> prod.crs CRS.from_epsg(32630)
- Returns
CRS object
- Return type
rasterio.crs.CRS
- date¶
Acquisition date.
- datetime¶
Acquisition datetime.
- default_transform = <methodtools._LruCacheWire object>¶
- property extent: geopandas.geodataframe.GeoDataFrame¶
Get UTM extent of the tile
>>> from eoreader.reader import Reader >>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip" >>> prod = Reader().open(path) >>> prod.extent geometry 0 POLYGON ((309780.000 4390200.000, 309780.000 4...
- Returns
Footprint in UTM
- Return type
gpd.GeoDataFrame
- filename¶
Product filename
- property footprint: geopandas.geodataframe.GeoDataFrame¶
Get real footprint in UTM of the products (without nodata, in french == emprise utile)
>>> from eoreader.reader import Reader >>> path = r"LC08_L1GT_023030_20200518_20200527_01_T2" >>> prod = Reader().open(path) >>> prod.footprint index geometry 0 0 POLYGON ((366165.000 4899735.000, 366165.000 4...
Overload of the generic function because landsat nodata seems to be different in QA than in regular bands. Indeed, nodata pixels vary according to the band sensor footprint, whereas QA nodata is where at least one band has nodata.
We chose to keep QA nodata values for the footprint in order to show where all bands are valid.
TL;DR: We use the QA nodata value to determine the product’s footprint.
- Returns
Footprint as a GeoDataFrame
- Return type
gpd.GeoDataFrame
- get_mean_sun_angles = <methodtools._LruCacheWire object>¶
- is_archived¶
Is the archived product is processed (a products is considered as archived if its products path is a directory).
- is_reference¶
If the product is a reference, used for algorithms that need pre and post data, such as fire detection.
- name¶
Product true name (as specified in the metadata)
- needs_extraction¶
Does this products needs to be extracted to be processed ? (True by default).
- nodata¶
Product nodata, set to -999 by default
- property output: Union[cloudpathlib.cloudpath.CloudPath, pathlib.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.
- platform¶
Product platform, such as Sentinel-2
- 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.
- sat_id¶
Satellite ID, i.e. S2 for Sentinel-2
- sensor_type¶
Sensor type, SAR or optical.
- split_name¶
Split name, to retrieve every information from its true name (dates, tile, product type…).
- tile_name¶
Tile if possible (for data that can be piled, for example S2 and Landsats).