SEVIRI Level 1.5

SEVIRI Level 1.5#

Wildfires Exploration Standard Python

Context#

Purpose#

Explore SEVIRI satellite imagery and wildfire data that is open and free for scientific use.

Sensor description#

The SEVIRI Level 1.5 Image Data product contains provides 12 spectral channels. Level 1.5 image data corresponds to the geolocated and radiometrically pre-processed image data, ready for further processing, e.g. the extraction of meteorological products.

Highlights#

Use satpy to load, visualise, and regrid SEVIRI level 1.5 data.
Fetch a fire database containing some 8080 fires from September 1st, 2020.
Visualisation of fire pixels from the database.
Visualisation of the fire pixels alongside bands from the SEVIRI satellite data.
Demonstration of how to write a custom intake driver for satpy.

Contributions#

Notebook#

Samuel Jackson (author), Science & Technology Facilities Council, @samueljackson92
Alejandro Coca-Castro (reviewer), The Alan Turing Institute, @acocac

Dataset originator/creator#

SEVIRI Level 1.5 Image Data - MSG - 0 degree#

European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)

FRPPIXEL#

Land Surface Analysis, Satellite Application Facility on Land Surface Analysis (LSA SAF)

Dataset authors#

SEVIRI Level 1.5 Image Data - MSG - 0 degree#

European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)

FRPPIXEL#

Land Surface Analysis, Satellite Application Facility on Land Surface Analysis (LSA SAF)

Dataset documentation#

Martin Wooster, Jiangping He, Weidong Xu, and Alessio Lattanzio. Frp - product user manual. URL: https://nextcloud.lsasvcs.ipma.pt/s/pnDEepeq8zqRyrq (visited on 2021-11-18).
MJ Wooster, G Roberts, PH Freeborn, W Xu, Y Govaerts, R Beeby, J He, A Lattanzio, D Fisher, and R Mullen. Lsa saf meteosat frp products–part 1: algorithms, product contents, and analysis. Atmospheric Chemistry and Physics, 15(22):13217–13239, 2015.

Note

The author acknowledges EUMETSAT.

Install and load libraries#

WARNING: The directory '/home/jovyan/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: The directory '/home/jovyan/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: The directory '/home/jovyan/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: The directory '/home/jovyan/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
WARNING: The directory '/home/jovyan/.cache/pip' or its parent directory is not owned or is not writable by the current user. The cache has been disabled. Check the permissions and owner of that directory. If executing pip with sudo, you should use sudo's -H flag.
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

Set project structure#

notebook_folder = Path('./notebook')
if not notebook_folder.exists():
    notebook_folder.mkdir(exist_ok=True)

Fetch Data#

Note

To download data from the EUMETSAT’s Data site you must have a valid account. Please register with EUMETSAT’s data sevices if you do not already have an account. Then provide your consumer key and consumer secret when prompted in the cell below. Your consumer key and consumer secret can be found at the following url: https://api.eumetsat.int/api-key/

Now you should successfully be able to download SEVIRI data.

file_path = notebook_folder / 'MSG4-SEVI-MSG15-0100-NA-20200901104243.230000000Z-NA.nat'

if not file_path.is_file() or file_path.stat().st_size  == 0:
    load_dotenv()
    consumer_key = os.environ.get("EUMESAT_API_KEY") #replace for your EUMESAT consumer key if run local or in Binder
    consumer_secret = os.environ.get("EUMESAT_API_SECRET") #replace for your EUMESAT consumer secret if run local or in Binder

    token_url = 'https://api.eumetsat.int/token'
    response = requests.post(
            token_url,
            auth=requests.auth.HTTPBasicAuth(consumer_key, consumer_secret),
            data = {'grant_type': 'client_credentials'},
            headers = {"Content-Type" : "application/x-www-form-urlencoded"}
        )

    access_token = response.json()['access_token']

    filename = 'MSG4-SEVI-MSG15-0100-NA-20200901104243.230000000Z-NA.nat'

    product_url = 'https://api.eumetsat.int/data/download/collections/EO%3AEUM%3ADAT%3AMSG%3AHRSEVIRI/products/MSG4-SEVI-MSG15-0100-NA-20200901104243.230000000Z-NA/entry'
    product_url += f'?name={filename}'
    product_url += f'&access_token={access_token}'

    urllib.request.urlretrieve(product_url, str(file_path))

Download the fire pixel data for this day from Zenodo. This data is not directly downloadable from the internet, so we share a subset of fires for this imagery here.

filename = 'HDF5_LSASAF_MSG_FRP-PIXEL-ListProduct_MSG-Disk_202009011045'
file_path = notebook_folder / filename
url = f'https://zenodo.org/record/5717106/files/{filename}?download=1'

if not file_path.is_file() or file_path.stat().st_size  == 0:
    urllib.request.urlretrieve(url, file_path)

Load an intake catalog for the downloaded data

catalog_file = notebook_folder / 'catalog.yaml'

with catalog_file.open('w') as f:
    f.write('''
sources:
    seviri_l1b:
      args:
        urlpath: 'notebook/MSG4-SEVI-MSG15-0100-NA-20200901104243.230000000Z-NA.nat'
        reader: 'seviri_l1b_native'
      description: "SEVIRI Level 1.5 Products"
      driver: SatpySource
      metadata: {}
    seviri_fires:
      args:
        urlpath: 'notebook/HDF5_LSASAF_MSG_FRP-PIXEL-ListProduct_MSG-Disk_202009011045'
      description: "SEVIRI Level 2 Fires"
      driver: netcdf
      metadata: {}
''')

from intake.source.base import PatternMixin
from intake.source.base import DataSource, Schema

import glob

class SatpySource(DataSource, PatternMixin):
    """Intake driver for data supported by satpy.Scene"""
    
    container = 'python'
    name = 'satpy'
    version = '0.0.1'
    partition_access = False

    def __init__(self, urlpath, reader=None, metadata=None, path_as_pattern=True):
        """
        Parameters
        ----------
        path: str, location of model pkl file
        Either the absolute or relative path to the file
        opened. Some examples:
          - ``{{ CATALOG_DIR }}/file.nat``
          - ``{{ CATALOG_DIR }}/*.nc``
        reader: str, optional
        Name of the satpy reader to use when loading data (ie. ``modis_l1b``)
        metadata: dict, optional
        Additional metadata to pass to the intake catalog
        path_as_pattern: bool or str, optional
        Whether to treat the path as a pattern (ie. ``data_{field}.nc``)
        and create new coodinates in the output corresponding to pattern
        fields. If str, is treated as pattern to match on. Default is True.
        """

        self.path_as_pattern = path_as_pattern
        self.urlpath = urlpath
        self._reader = reader
        super(SatpySource, self).__init__(metadata=metadata)

    def _load(self):
        files = self.urlpath
        files = list(glob.glob(files))
        return satpy.Scene(files, reader=self._reader)
    
    def _get_schema(self):
        self._schema = Schema(
            npartitions=1,
            extra_metadata={}
        )
        return self._schema

    def read(self):
        self._load_metadata()
        return self._load()

intake.register_driver('SatpySource', SatpySource)
cat = intake.open_catalog(catalog_file)

Load SEVIRI Satellite Data#

Here we use satpy to load the SEVIRI level 1b data into memory. As well as loading the image data, satpy provides a easy way to regrid the data to different coordinate systems.

scn = cat['seviri_l1b'].read()
scn

<satpy.scene.Scene at 0x7f2ffae6d850>

../../../_images/e77b17da5b248df792cdda324c28fa426c3cec80c11c780bcfa395a06bb98c50.png

Resample to a different projection#

In the plot above you can see that the raw SEVIRI data has distortion towards edge of the image. By regridding the data we can avoid some of this distortion.

area_id = "Southern Africa"
description = "Southern Africa in Mercator-Projection"
proj_id = "Southern Africa"
proj_dict = {"proj": "eqc"}

width = 1000    # width of the result domain in pixels
height = 1000   # height of the result domain in pixels

llx =  0    # projection x coordinate of lower left corner of lower left pixel
lly =  -30e5  # projection y coordinate of lower left corner of lower left pixel
urx =  55e5   # projection x coordinate of upper right corner of upper right pixel
ury =  10e5   # projection y coordinate of upper right corner of upper right pixel

area_extent = (llx,lly,urx,ury)

resolution=3000
center =(0,0)
area_def = create_area_def(area_id, proj_dict, resolution=resolution, area_extent=area_extent)

seviri_scn = scn.resample(area_def, radius_of_influence=10000)

WARNING:root:shape found from radius and resolution does not contain only integers: (1333.3333333333333, 1833.3333333333333)
Rounding shape to (1334, 1834) and resolution from (3000.0, 3000.0) meters to (2998.909487459106, 2998.5007496251874) meters

../../../_images/8c7d9a7eabd0f23deb902690176087ddf797d8ae4a424048266ea9e81a6e1f60.png

Load SEVIRI Fire Database#

Now we’re going to load the SEVIRI fire database from HDF file. This contains the longitude, latitude, and time of where fires have been detected to occur. It also includes an estimate of the Fire Radiative Power (FRP), a measure of the intensity of the fire, for each fire detected.

Show code cell source Hide code cell source

# Scale factors for the SEVIRI fire product from:
# https://nextcloud.lsasvcs.ipma.pt/s/CZa8RwDxjGqYezS?dir=undefined&openfile=105793

SCALE_FACTORS = dict(
    FRP=10,
    FRP_UNCERTAINTY=100,
    ERR_FRP_COEFF=10000,
    ERR_BACKGROUND=10000,
    ERR_ATM_TRANS=10000,
    ERR_VERT_COMP=10000,
    ERR_RADIOMETRIC=10000,
    LATITUDE=100,
    LONGITUDE=100,
    FIRE_CONFIDENCE=100,
    BT_MIR=10,
    BT_TIR=10,
    BW_BT_MIR=10,
    BW_BTD=10,
    PIXEL_SIZE=100,
    PIXEL_VZA=100,
    PIXEL_ATM_TRANS=10000,
    RAD_PIX=10000,
    STD_BCK=10000
)

def process_fire_product(fire_pixels):
    # Hack: xarray randomly loads some columns as coordinates, which don't get converted to a dataframe
    # Correct here by removing them as coords and renaming them back to their original name
    coords = list(fire_pixels.coords.keys())
    fire_pixels = fire_pixels.reset_index(coords).reset_coords()
    fire_pixels = fire_pixels.swap_dims({key: f'phony_dim_0' for key in list(fire_pixels.dims.keys())})
    fire_pixels = fire_pixels.rename({f"{name}_": name for name in coords})
    fire_pixels = fire_pixels.to_dataframe()

    for c in fire_pixels.columns:
        if c in SCALE_FACTORS:
            fire_pixels[c] = fire_pixels[c] / SCALE_FACTORS[c]
        
    fire_pixels['ABS_LINE_1KM'] = fire_pixels.ABS_LINE // 2
    fire_pixels['ABS_PIXEL_1KM'] = fire_pixels.ABS_PIXEL // 2
    fire_pixels.index.name = 'index'
    return fire_pixels

def parse_l2_timestamp(product_name):
    """Parse the timestamp from the SEVIRI L2 product name"""
    timestamp = product_name.split('_')[-1]
    timestamp = pd.to_datetime(timestamp, format='%Y%m%d%H%M')
    return timestamp

# Read in fires, scale and rename dimensions
fires = cat['seviri_fires'].read()
fires = process_fire_product(fires)
fires = fires.rename({'LONGITUDE': 'longitude', 'LATITUDE': 'latitude', 'FRP': 'frp'}, axis=1)

# Grab the timestamp of the product
urlpath = cat['seviri_fires'].describe()['args']['urlpath']
fires['timestamp'] = parse_l2_timestamp(urlpath)

# Convert to geopandas
fires = geopandas.GeoDataFrame(
    fires, geometry=geopandas.points_from_xy(fires.longitude, fires.latitude))

# We're only interested in data from Southern Africa for now
llx =  0    # projection x coordinate of lower left corner of lower left pixel
lly =  -30  # projection y coordinate of lower left corner of lower left pixel
urx =  55   # projection x coordinate of upper right corner of upper right pixel
ury =  10   # projection y coordinate of upper right corner of upper right pixel

fires = fires.cx[llx:urx, lly:ury]
fires = fires.sort_index()
fires

	ABS_LINE	ABS_PIXEL	ACQTIME	BT_MIR	BT_TIR	BW_BTD	BW_BT_MIR	BW_NUMPIX	BW_SIZE	ERR_ATM_TRANS	...	PIXEL_ATM_TRANS	PIXEL_SIZE	PIXEL_VZA	RAD_BCK	RAD_PIX	STD_BCK	ABS_LINE_1KM	ABS_PIXEL_1KM	timestamp	geometry
index
6	1942	3065	1051	321.7	313.8	48.9	313.4	10	5	0.1098	...	0.6517	11.97	41.26	16239	0.219	0.1575	971	1532	2020-09-01 10:45:00	POINT (36.19000 -2.40000)
7	1953	3110	1051	316.8	306.2	39.9	309.0	10	5	0.1071	...	0.6518	12.35	43.23	13830	0.184	0.1477	976	1555	2020-09-01 10:45:00	POINT (37.93000 -2.72000)
8	1955	3110	1051	321.8	311.5	46.6	312.6	13	5	0.1078	...	0.6509	12.35	43.23	15938	0.219	0.2502	977	1555	2020-09-01 10:45:00	POINT (37.93000 -2.78000)
9	1955	3111	1051	323.5	305.7	38.4	311.9	14	5	0.1083	...	0.6502	12.36	43.28	15542	0.233	0.2599	977	1555	2020-09-01 10:45:00	POINT (37.97000 -2.78000)
10	2009	3069	1051	316.0	311.5	19.2	311.8	16	5	0.1020	...	0.6612	12.05	41.68	15268	0.179	0.0447	1004	1534	2020-09-01 10:45:00	POINT (36.44000 -4.30000)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
904	2566	3166	1049	318.7	312.2	12.4	313.6	16	5	0.1476	...	0.5852	15.49	54.49	16312	0.196	0.0326	1283	1583	2020-09-01 10:45:00	POINT (44.54000 -20.99000)
905	2597	2398	1049	315.7	310.0	21.6	312.2	16	5	0.0843	...	0.7036	10.35	29.65	15512	0.176	0.0800	1298	1199	2020-09-01 10:45:00	POINT (16.11000 -20.95000)
906	2597	2399	1049	319.1	310.3	16.4	311.9	16	5	0.0844	...	0.7032	10.35	29.68	15337	0.199	0.0679	1298	1199	2020-09-01 10:45:00	POINT (16.14000 -20.95000)
907	2598	2398	1049	320.8	310.4	18.2	311.6	16	5	0.0842	...	0.7037	10.36	29.69	15187	0.211	0.0534	1299	1199	2020-09-01 10:45:00	POINT (16.12000 -20.98000)
908	2598	2399	1049	325.8	310.3	12.6	311.4	16	5	0.0843	...	0.7035	10.36	29.71	15018	0.252	0.0381	1299	1199	2020-09-01 10:45:00	POINT (16.14000 -20.98000)

880 rows × 29 columns

Geographical distribution of Fires#

Here we plot the geographical distribution of fires detected by SEVIRI over Southern Africa.

Visualising Fire Pixels with Satellite Imagery#

Visualise a color image of the SEVIRI region using hvplot.

Now overlay the fire pixels ontop of the SEVIRI image, along with the FRP for each fire pixel. Try zooming in with rasterize=False, you should be able to see clear smoke trails at the locations of some of the fires!

We can also overlay the fire pixels ontop of the band difference between SEVIRI 3.9 and 10.8 micron wavelength bands. The 3.9 and 10.8 bands are thermal bands. Fires will appear as very bright pixels in the difference between these two bands. Try zooming in with rasterize=False, you should be able to clearly see bright spots at the location of each fire pixel.

Summary#

This notebook has demonstrated the use of:

The satpy package to easily load, plot and regrid satellite data from the SEVIRI sensor.
hvplot to interatively visualise wildfire pixels detected from the SEVIRI sensor.
geopandas to load, filter, and manipulate historical wildfire pixel data.
intake the load data, including using a custom plugin for obsecure file formats.

Citing this Notebook#

Please see CITATION.cff for the full citation information. The citation file can be exported to APA or BibTex formats (learn more here).

Additional information#

License: The code in this notebook is licensed under the MIT License. The Environmental Data Science book is licensed under the Creative Commons by Attribution 4.0 license. See further details here.

Contact: If you have any suggestion or report an issue with this notebook, feel free to create an issue or send a direct message to environmental.ds.book@gmail.com.

Notebook repository version: v1.0.5
Last tested: 2024-03-11

SEVIRI Level 1.5

Contents

SEVIRI Level 1.5#

Context#

Purpose#

Sensor description#

Highlights#

Contributions#

Notebook#

Dataset originator/creator#

SEVIRI Level 1.5 Image Data - MSG - 0 degree#

FRPPIXEL#

Dataset authors#

SEVIRI Level 1.5 Image Data - MSG - 0 degree#

FRPPIXEL#

Dataset documentation#

Install and load libraries#

Set project structure#

Fetch Data#

Load SEVIRI Satellite Data#

Resample to a different projection#

Load SEVIRI Fire Database#

Geographical distribution of Fires#

Visualising Fire Pixels with Satellite Imagery#

Summary#

Citing this Notebook#

Additional information#