Nadir BRDF Adjusted Reflectance (NBAR) for Sentinel-2 in Python
---
**GitHub**: [https://github.com/ESDS-Leipzig/sen2nbar](https://github.com/ESDS-Leipzig/sen2nbar)
**Documentation**: [https://sen2nbar.readthedocs.io/](https://sen2nbar.readthedocs.io/)
**PyPI**: [https://pypi.org/project/sen2nbar/](https://pypi.org/project/sen2nbar/)
**Conda-forge**: [https://anaconda.org/conda-forge/sen2nbar](https://anaconda.org/conda-forge/sen2nbar)
**Tutorials**: [https://sen2nbar.readthedocs.io/en/latest/tutorials.html](https://sen2nbar.readthedocs.io/en/latest/tutorials.html)
**Paper**: [https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-105-2024](https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-105-2024)
---
> [!IMPORTANT]
> Our `sen2nbar` paper is out! Check it here: **[Montero, D., Mahecha, M.D., Aybar, C., Mosig, C., & Wieneke, S. (2024). Facilitating Advanced Sentinel-2 Analysis Through a Simplified Computation of Nadir BRDF Adjusted Reflectance.](https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-105-2024)**
## Overview
First, a super small glossary:
- **BRDF**: Bidirectional Reflectance Distribution Function.
- **DN**: Digital Number.
- **NBAR**: Nadir BRDF Adjusted Reflectance.
- **SR**: Surface Reflectance.
- **STAC**: SpatioTemporal Assets Catalogs.
Second, the amazing bibliography by David P. Roy et al., used to create this package:
- [Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data.](https://doi.org/10.1016/j.rse.2008.03.009)
- [A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance.](https://doi.org/10.1016/j.rse.2016.01.023)
- [Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance.](https://doi.org/10.1016/j.rse.2017.06.019)
- [Adjustment of sentinel-2 multi-spectral instrument (MSI) red-edge band reflectance to nadir BRDF adjusted reflectance (NBAR) and quantification of red-edge band BRDF effects.](https://doi.org/10.3390/rs9121325)
Third, the super useful bibliography by Lucht et al.,:
- [An algorithm for the retrieval of albedo from space using semiempirical BRDF models.](https://doi.org/10.1109/36.841980)
Given this, and in a few words, `sen2nbar` converts the **Sentinel-2 SR** (i.e., L2A) to **Sentinel-2 NBAR** via the **_c_-factor** method.
### SAFE
You can use `sen2nbar` to convert complete images via SAFE:
```python
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`:
```python
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`:
```python
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)
```
## Bands
`sen2nbar` converts the following bands (if available in the input data):
- **RGB Bands**: 02, 03, 04.
- **Red Edge Bands**: 05, 06, 07.
- **Broad NIR Band**: 08.
- **SWIR Bands**: 11, 12.
## Installation
Install the latest version from PyPI:
```
pip install sen2nbar
```
Upgrade `sen2nbar` by running:
```
pip install -U sen2nbar
```
Install the latest version from conda-forge:
```
conda install -c conda-forge sen2nbar
```
Install the latest dev version from GitHub by running:
```
pip install git+https://github.com/davemlz/sen2nbar
```
## Citation
If you use this work, please consider citing the following paper:
```bibtex
@article{montero2024sen2nbar,
title = {Facilitating advanced Sentinel-2 analysis through a simplified computation of Nadir BRDF Adjusted Reflectance},
volume = {XLVIII-4/W12-2024},
ISSN = {2194-9034},
url = {http://dx.doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-105-2024},
DOI = {10.5194/isprs-archives-xlviii-4-w12-2024-105-2024},
journal = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
publisher = {Copernicus GmbH},
author = {Montero, David and Mahecha, Miguel D. and Aybar, César and Mosig, Clemens and Wieneke, Sebastian},
year = {2024},
month = jun,
pages = {105–112}
}
```
## License
The project is licensed under the MIT license.
[](https://rsc4earth.de/authors/esds/)