VIIRS Level 1B - Calibrated Radiances - True Color and Dust RGB - 1km¶

About¶

This module shows the structure of VIIRS Level 1B data and what information of the data files can be used in order to load, browse and visualize the data.

According to NASA, "VIIRS is aboard two satellites: Suomi National Polar-orbiting Partnership (Suomi NPP) and National Oceanic and Atmospheric Administration-20 (NOAA-20).

VIIRS observes the entire Earth’s surface twice each day crossing equator approximately 14 times each day per satellite. Suomi NPP crosses the equator about 1:30 a.m. and 1:30 p.m. and NOAA-20 crossing at 2:20 am and 2:20 pm, local time; from NPP’s polar orbit 824 kilometers (km) (512 miles) above the Earth’s surface. [...] The VIIRS instrument provides 22 spectral bands from 412 nanometers (nm) to 12 micrometers (µm) at two spatial resolutions, 375 meters (m) and 750 m, which are resampled to 500 m, 1 km, and 0.05 degrees in the NASA produced data products to promote consistency with the MODIS heritage."

Basic Facts¶

Spatial resolution: 375m or 750m at nadir
Spatial coverage: Global
Revisit time: Daily
Data availability: since 2012

How to access the data¶

This notebook uses VIIRS Level 1B data from the SNPP platform. This data can be ordered via the LAADS DAAC and are distributed in netcdf format.

Note that you need to also search for and download the 6 data files in order to get all the data you need to geolocate the data and create the full set of composites:

• VNP02DNB - VIIRS/NPP Day/Night Band 6-Min L1B Swath 750m
• VVNP02IMG - VIIRS/NPP Imagery Resolution 6-Min L1B Swath 375m
• VNP02MOD - VIIRS/NPP Moderate Resolution 6-Min L1B Swath 750m
• VNP03DNB - VIIRS/NPP Day/Night Band Moderate Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 750m
• VNP03IMG - VIIRS/NPP Imagery Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 375m
• VNP03MOD - VIIRS/NPP Moderate Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 750m

The filenames for the same granule will have a common middle portion which reflects the date and time. For example, A2021037.1306. This means the file date and time is 2021-02-06 13:06:00.

You need to register for an Earthdata account in order to be able to download data. Read this webpage for more information on downloading data programmatically using wget.

Module outline¶

• 1 - Load and browse VIIRS VVNP02IMG Level 1B data files
• 2 - Browse and visualize RGB composite IDs
• 3 - Generate a geographical subset around Sardinia, Italy
• 4 - Visualize VIIRS 'natural_color_raw' RGB composite with Cartopy features
• 5 - Visualize MODIS 'dust' RGB composite with Cartopy features

Load required libraries¶

Load helper functions¶

1. Load and browse VIIRS Level 1B Calibrated Radiances data¶

From the LAADS DAAC, we downloaded one tile of Level-1B Image data on 6 February 2021. The data is available in the folder ../../eodata/1_satellite/viirs/level_1b/2021/02/06/. Let us load the data. First, we specify the file path and create a variable with the name file_name.

In a next step, we use the Scene constructor from the satpy library. Once loaded, a Scene object represents a single geographic region of data, typically at a single continuous time range.

You have to specify the two keyword arguments reader and filenames in order to successfully load a scene. As mentioned above, for VIIRS Level-1B data, you can use the viirs_l1b reader.

A Scene object is a collection of different bands, with the function available_dataset_names(), you can see the available bands of the scene. To learn more about the bands of VIIRS, visit this website.

The underlying container for data in satpy is the xarray.DataArray. With the function load(), you can specify an individual band by name, e.g. I01 and load the data. If you then select the loaded band, you see that the band object is a xarray.DataArray. Band I01 has a bandwidth or wavelength of 0.6 - 0.68μm.

With an xarray data structure, you can handle the object as a xarray.DataArray. For example, you can print a list of available attributes with the function attrs.keys().

With the attrs() function, you can also access individual metadata information, e.g. start_time and end_time.

2. Browse and visualize RGB composite IDs¶

RGB composites combine different channels of satellite data in order to get e.g. a true-color image of the scene. Depending on which channel combination is used, different features can be highlighted in the composite, e.g. dust. SatPy offers several predefined RGB composite options. The function available_composite_ids() returns a list of available composite IDs.

Let us define a list with the composite ID natural_color and dust. This list (composite_id) can then be passed to the function load(). Per default, scenes are loaded with the north pole facing downwards. You can specify the keyword argument upper_right_corner='NE' in order to turn the image around and have the north pole facing upwards.

A print of the Scene object scn shows you that three bands / composites are available: natural_color, dust and band I01.

3. Generate a geographical subset around Sardinia, Italy¶

Often, you might want to highlight a specific geographical region. Let us generate a geographical subset around Sardinia, Italy where a dust cloud is visible. You can do this with the function stored in the coord2area_def.py script, which converts human coordinates (longitude and latitude) to an area definition.

We need to define the following arguments:

• name :the name of the area definition, set this to sardinia_area_1km
• proj : the projection, set this to laea which stands for the Lambert azimuthal equal-area projection
• min_lat: the minimum latitude value, set this to 35
• max_lat: the maximum latitude value, set this to 45
• min_lon: the minimum longitude value, set this to 0
• max_lon: the maximum longitude value, set this to 15
• resolution(km): the resolution in kilometres, set this to 1

Afterwards, you can visualize the resampled image with the function show().

From the values generated by coord2area_def.py, we copy and paste several into the template below.

We need to define the following arguments in the code block template below:

• area_id (string): the name of the area definition, set this to sardinia_area_1km
• x_size (integer): the number of values for the width, set this to the value of the shape width, which is 1368
• y_size (integer): the number of values for the height, set this to the value of the shape height, which is 1107
• area_extent (set of coordinates in brackets): the extent of the map is defined by 2 sets of coordinates, within a set of brackets () paste in the values of the lower_left_xy from the area_extent above, followed by the upper_right_xy values. You should end up with (-684216.045232, -526823.964278, 684216.045232, 580660.441353).
• projection (string): the projection, paste in the first line after ### starting with +proj
• description (string): Give this a generic name for the region,
• proj_id (string): A recommended format is the projection short code followed by lat_0 and lon_0, e.g. laea_40.0_7.5

You should end up with the following code block.

Next, you can use the area definition above in order to resample the loaded Scene object. You can use the function resample() to do so.

Afterwards, you can visualize the resampled natural_color RGB with the function show(). You see see dust intrusions over the Mediterranean sea.

4. Visualize VIIRS natural_color RGB composite with Cartopy features¶

SatPy's built-in visualization function is nice, but often you want to make use of additonal features, such as country borders. The library Cartopy offers powerful functions that enable the visualization of geospatial data in different projections and to add additional features to a plot. Below, we will show you how you can visualize the natural_color composite with the two Python packages matplotlib and Cartopy.

As a first step, we have to convert the Scene object into a numpy array. The numpy array additionally needs to be transposed to a shape that can be interpreted by matplotlib's function imshow(): (M,N,3). You can convert a Scene object into a numpy.array object with the function np.asarray(). We have to transpose the array and add index=0 on index position 3.

The next step is then to replace all nan values with 0. You can do this with the numpy function nan_to_num(). In a subsequent step, we then scale the values to the range between 0 and 1, clipping the lower and upper percentiles so that a potential contrast decrease caused by outliers is eliminated.

Let us now also define a variable for the coordinate reference system. We take the area attribute from she scn_resample_nc Scene and convert it with the function to_cartopy_crs() into a format Cartopy can read. We will use the crs information for plotting.

Now, we can visualize the natural_color composite. The plotting code can be divided in four main parts:

• Initiate a matplotlib figure: Initiate a matplotlib plot and define the size of the plot
• Specify coastlines and a grid: specify additional features to be added to the plot
• Plotting function: plot the numpy array with the plotting function imshow()
• Set plot title: specify title of the plot

5. Visualize and interpret the 'dust' RGB composite¶

In a final step, we would like to have a closer look at the Dust RGB composite, whose primary aim is to support the detection of dust in the atmosphere. The dust composite makes use of three window channels:

• Red: IR12.0-IR10.8
• Green: IR10.8-IR.8.7
• Blue: IR10.8

Since we already loaded the dust composite to our Scene object scn_resample_nc, we can visualize the dust composite with Satpy's built-in visualization function show(). The colours of the dust RGB can be interpreted as follows:

• Magenta: Dust or ash clouds
• Black: Cirrus clouds
• Dark red: Thick, high and cold ice clouds
• Yellow: Thick mid-level clouds
• Darkblue: Humid air in lower levels
• Lilac: Dry air in lower levels

Get more information about the Dust RGB in the SEVIRI Dust RGB Quick Guide.

The dust composite of 6 February at 13:06 UTC clearly show a dust cloud below the thicker cold ice cloud over northern Italy.

References¶

• VNP02IMG - VIIRS/NPP Imagery Resolution 6-Min L1B Swath 375m distributed from NASA LAADS DAAC. Available on-line https://ladsweb.modaps.eosdis.nasa.gov/search/order/2/VNP02IMG--5200. doi:10.5067/VIIRS/VNP02IMG.002

• VNP02DNB - VIIRS/NPP Day/Night Band 6-Min L1B Swath 750m distributed from NASA LAADS DAAC. Available on-line https://ladsweb.modaps.eosdis.nasa.gov/search/order/2/VNP02DNB--5200. doi:10.5067/VIIRS/VNP02IMG.002

• VNP02MOD - VIIRS/NPP Moderate Resolution 6-Min L1B Swath 750m distributed from NASA LAADS DAAC. Available on-line https://ladsweb.modaps.eosdis.nasa.gov/search/order/2/VNP02MOD--5200. doi:10.5067/VIIRS/VNP02MOD.002

• VNP03IMG - VIIRS/NPP Imagery Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 375m distributed from NASA LAADS DAAC. Available on-line https://ladsweb.modaps.eosdis.nasa.gov/search/order/2/VNP03IMG--5200. doi:10.5067/VIIRS/VNP03IMG.002

• VNP03DNB - VIIRS/NPP Day/Night Band Moderate Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 750m distributed from NASA LAADS DAAC. Available on-line https://ladsweb.modaps.eosdis.nasa.gov/search/order/2/VNP02DNB--5200. doi:10.5067/VIIRS/VNP02DNB.002

• VNP03MOD - VIIRS/NPP Moderate Resolution Terrain-Corrected Geolocation 6-Min L1 Swath 750m distributed from NASA LAADS DAAC. Available on-line https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VNP03MOD. doi:10.5067/VIIRS/VNP03MOD.002

• Some code in this notebook was adapted from the following sources:

  • origin: https://python-kurs.github.io/sommersemester_2019/units/S01E07.html
  • copyright: 2019, Marburg University
  • license: CC BY-SA 4.0
  • retrieved: 2023-01-26 Sabrina Szeto

This project is licensed under GNU General Public License v3.0 only and is developed under a Copernicus contract.

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