sen2nbar v2023.8.1 :artificial_satellite:

• Fixed the occurrence of infinite values.

sen2nbar v2023.8.0 :artificial_satellite:

• Fixed concatenation error when c-factor had been reprojected.

sen2nbar v2023.7.2 :artificial_satellite:

• Raise ValueError events when not all bands include angle values.
• Exclude timesteps in nbar_stac when not all bands include angle values.

sen2nbar v2023.7.1 :artificial_satellite:

• Catch RuntimeWarning events when doing np.nanmean of detector angles.
• Added description to tqdm progress bar and set leave=False.
• Replaced get_all_items() by item_collection().

sen2nbar v2023.7.0 :artificial_satellite:

• Fixed path separators by os (https://github.com/ESDS-Leipzig/sen2nbar/issues/4).
• Pinned latest versions: :code:cubo>=2023.7.0.
• Fixed the required datatype of the EPSG code for stackstac (https://github.com/ESDS-Leipzig/sen2nbar/issues/5)

sen2nbar v2023.3.0 :artificial_satellite: (First Release!)

Nadir BRDF Adjusted Reflectance (NBAR) for Sentinel-2 in Python

SAFE

You can use sen2nbar to convert complete images via SAFE:

from sen2nbar.nbar import nbar_SAFE

# Converted images are saved inside the SAFE path
nbar_SAFE("S2A_MSIL2A_20230223T075931_N0509_R035_T35HLC_20230223T120656.SAFE")

Note

Note that sen2nbar automatically shifts the DN of images with a processing baseline >= 04.00. This includes data cubes obtained via stackstac or cubo.

stackstac

Or, if you are using STAC and retrieving images via stackstac:

import pystac_client
import stackstac
import planetary_computer as pc
from sen2nbar.nbar import nbar_stackstac

# Important infor for later
endpoint = "https://planetarycomputer.microsoft.com/api/stac/v1"
collection = "sentinel-2-l2a"
bounds = (-148.565368, 60.800723, -147.443389, 61.183638)

# Open the STAC
catalog = pystac_client.Client.open(endpoint, modifier=pc.sign_inplace)

# Define your area
area_of_interest = {
    "type": "Polygon",
    "coordinates": [
        [
            [bounds[0], bounds[1]],
            [bounds[2], bounds[1]],
            [bounds[2], bounds[3]],
            [bounds[0], bounds[3]],
            [bounds[0], bounds[1]],
        ]
    ],
}

# Search the items
items = catalog.search(
    collections=[collection],
    intersects=area_of_interest,
    datetime="2019-06-01/2019-08-01",
    query={"eo:cloud_cover": {"lt": 10}},
).get_all_items()

# Retrieve all items as a xr.DataArray
stack = stackstac.stack(
    items,
    assets=["B05","B06","B07"], # Red Edge here, but you can use more!
    bounds_latlon=bounds,
    resolution=20
)

# Convert it to NBAR!
da = nbar_stackstac(
    stack,
    stac=endpoint,
    collection=collection
)

Warning

These examples are done using Planetary Computer. If you are using data cubes retrieved via STAC (e.g., by using stackstac or cubo), we recommend you to use this provider. The provider Element84 is not supported at the moment.

cubo

And going deeper, if you are using cubo:

import cubo
import xarray as xr
from sen2nbar.nbar import nbar_cubo

# Get your cube
da = cubo.create(
    lat=47.84815,
    lon=13.37949,
    collection="sentinel-2-l2a",
    bands=["B02","B03","B04"], # RGB here, but you can add more bands!
    start_date="2020-01-01",
    end_date="2021-01-01",
    edge_size=64,
    resolution=10,
    query={"eo:cloud_cover": {"lt": 3}}
)

# Convert it to NBAR (This a xr.DataArray)
da = nbar_cubo(da)