CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes

Global, monthly 1 degree resolution methane emission estimates from microbial, fossil and pyrogenic sources derived using inverse modeling, version 2023.

Run this notebook

You can launch this notebook in the US GHG Center JupyterHub by clicking the link below.

Launch in the US GHG Center JupyterHub (requires access)

Approach

1. Identify available dates and temporal frequency of observations for the given collection using the GHGC API /stac endpoint. The collection processed in this notebook is the CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes Data product.
2. Pass the STAC item into the raster API /collections/{collection_id}/items/{item_id}/tilejson.jsonendpoint.
3. Using folium.plugins.DualMap, we will visualize two tiles (side-by-side), allowing us to compare time points.
4. After the visualization, we will perform zonal statistics for a given polygon.

About the Data

Surface methane (CH₄) emissions are derived from atmospheric measurements of methane and its ¹³C carbon isotope content. Different sources of methane contain different ratios of the two stable isotopologues, ¹²CH₄ and ¹³CH₄. This makes normally indistinguishable collocated sources of methane, say from agriculture and oil and gas exploration, distinguishable. The National Oceanic and Atmospheric Administration (NOAA) collects whole air samples from its global cooperative network of flasks (https://gml.noaa.gov/ccgg/about.html), which are then analyzed for methane and other trace gasses. A subset of those flasks are also analyzed for ¹³C of methane in collaboration with the Institute of Arctic and Alpine Research at the University of Colorado Boulder. Scientists at the National Aeronautics and Space Administration (NASA) and NOAA used those measurements of methane and ¹³C of methane in conjunction with a model of atmospheric circulation to estimate emissions of methane separated by three source types, microbial, fossil and pyrogenic.

For more information regarding this dataset, please visit the CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes data overview page.

Install the Required Libraries

Required libraries are pre-installed on the GHG Center Hub. If you need to run this notebook elsewhere, please install them with this line in a code cell:

%pip install requests folium rasterstats pystac_client pandas matplotlib –quiet

# Import the following libraries
import requests
import folium
import folium.plugins
from folium import Map, TileLayer
from pystac_client import Client
import branca
import pandas as pd
import matplotlib.pyplot as plt

Querying the STAC API

First, we are going to import the required libraries. Once imported, they allow better executing a query in the GHG Center Spatio Temporal Asset Catalog (STAC) Application Programming Interface (API) where the granules for this collection are stored.

# Provide the STAC and RASTER API endpoints
# The endpoint is referring to a location within the API that executes a request on a data collection nesting on the server.

# The STAC API is a catalog of all the existing data collections that are stored in the GHG Center.
STAC_API_URL = "https://earth.gov/ghgcenter/api/stac"

# The RASTER API is used to fetch collections for visualization
RASTER_API_URL = "https://earth.gov/ghgcenter/api/raster"

# The collection name is used to fetch the dataset from the STAC API. First, we define the collection name as a variable
# Name of the collection for TM5 CH₄ inverse flux dataset 
collection_name = "ct-ch4-monthgrid-v2023"

# Fetch the collection from the STAC API using the appropriate endpoint
# The 'requests' library allows a HTTP request possible
collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()

# Print the properties of the collection to the console
collection

{'id': 'ct-ch4-monthgrid-v2023',
 'type': 'Collection',
 'links': [{'rel': 'items',
   'type': 'application/geo+json',
   'href': 'https://dev.ghg.center/api/stac/collections/ct-ch4-monthgrid-v2023/items'},
  {'rel': 'parent',
   'type': 'application/json',
   'href': 'https://dev.ghg.center/api/stac/'},
  {'rel': 'root',
   'type': 'application/json',
   'href': 'https://dev.ghg.center/api/stac/'},
  {'rel': 'self',
   'type': 'application/json',
   'href': 'https://dev.ghg.center/api/stac/collections/ct-ch4-monthgrid-v2023'}],
 'title': 'CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes v2023',
 'extent': {'spatial': {'bbox': [[-180, -90, 180, 90]]},
  'temporal': {'interval': [['1998-01-01 00:00:00+00',
     '2021-12-31 00:00:00+00']]}},
 'license': 'CC0-1.0',
 'renders': {'total': {'assets': ['total'],
   'rescale': [[0, 50]],
   'colormap_name': 'purd'},
  'fossil': {'assets': ['fossil'],
   'rescale': [[0, 50]],
   'colormap_name': 'purd'},
  'dashboard': {'assets': ['total'],
   'rescale': [[0, 50]],
   'colormap_name': 'purd'},
  'microbial': {'assets': ['microbial'],
   'rescale': [[0, 30]],
   'colormap_name': 'purd'},
  'pyrogenic': {'assets': ['pyrogenic'],
   'rescale': [[0, 8]],
   'colormap_name': 'purd'}},
 'summaries': {'datetime': ['1998-01-01T00:00:00Z', '2021-12-31T00:00:00Z']},
 'description': 'Surface methane (CH₄) emissions are derived from atmospheric measurements of methane and its ¹³C carbon isotope content. Different sources of methane contain different ratios of the two stable isotopologues, ¹²CH₄ and ¹³CH₄. This makes normally indistinguishable collocated sources of methane, say from agriculture and oil and gas exploration, distinguishable. The National Oceanic and Atmospheric Administration (NOAA) collects whole air samples from its global cooperative network of flasks (https://gml.noaa.gov/ccgg/about.html), which are then analyzed for methane and other trace gases. A subset of those flasks are also analyzed for ¹³C of methane in collaboration with the Institute of Arctic and Alpine Research at the University of Colorado Boulder. Scientists at NOAA and the National Aeronautics and Space Administration (NASA) used those measurements of methane and ¹³C of methane in conjunction with a model of atmospheric circulation to estimate emissions of methane separated by three source types: microbial, fossil and pyrogenic. This dataset presents monthly methane emissions in units of grams of methane per square meter per year (g CH₄/m²/year) from microbial, fossil and pyrogenic sources, along with a layer of total methane emissions from all three sources combined, at 1° resolution from 1998 to 2021. The source data can be found at https://doi.org/10.25925/40jt-qd67',
 'item_assets': {'total': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Total CH₄ Emission',
   'description': 'Total methane emission from microbial, fossil, and pyrogenic sources.'},
  'fossil': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Fossil CH₄ Emission',
   'description': 'Emission of methane from all fossil sources, such as oil and gas activities and coal mining.'},
  'microbial': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Microbial CH₄ Emission',
   'description': 'Emission of methane from all microbial sources, such as wetlands, ruminants, agriculture, and termites.'},
  'pyrogenic': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
   'roles': ['data', 'layer'],
   'title': 'Pyrogenic CH₄ Emission',
   'description': 'Emission of methane from all sources of biomass burning, such as wildfires and crop residue burning.'}},
 'stac_version': '1.0.0',
 'stac_extensions': ['https://stac-extensions.github.io/render/v1.0.0/schema.json',
  'https://stac-extensions.github.io/item-assets/v1.0.0/schema.json'],
 'dashboard:is_periodic': True,
 'dashboard:time_density': 'month'}

Examining the contents of our collection under the temporal variable, we see that the data is available from January 1999 to December 2016. By looking at the dashboard:time density, we observe that the data is periodic with monthly time density.

# Create a function that would search for a data collection in the US GHG Center STAC API

# First, we need to define the function
# The name of the function = "get_item_count"
# The argument that will be passed through the defined function = "collection_id"
def get_item_count(collection_id):

    # Set a counter for the number of items existing in the collection
    count = 0

    # Define the path to retrieve the granules (items) of the collection of interest in the STAC API
    items_url = f"{STAC_API_URL}/collections/{collection_id}/items"

    # Run a while loop to make HTTP requests until there are no more URLs associated with the collection in the STAC API
    while True:

        # Retrieve information about the granules by sending a "get" request to the STAC API using the defined collection path
        response = requests.get(items_url)

        # If the items do not exist, print an error message and quit the loop
        if not response.ok:
            print("error getting items")
            exit()

        # Return the results of the HTTP response as JSON
        stac = response.json()

        # Increase the "count" by the number of items (granules) returned in the response
        count += int(stac["context"].get("returned", 0))

        # Retrieve information about the next URL associated with the collection in the STAC API (if applicable)
        next = [link for link in stac["links"] if link["rel"] == "next"]

        # Exit the loop if there are no other URLs
        if not next:
            break
        
        # Ensure the information gathered by other STAC API links associated with the collection are added to the original path
        # "href" is the identifier for each of the tiles stored in the STAC API
        items_url = next[0]["href"]

    # Return the information about the total number of granules found associated with the collection
    return count

# Apply the function created above "get_item_count" to the data collection
number_of_items = get_item_count(collection_name)

# Get the information about the number of granules found in the collection
items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]

# Print the total number of items (granules) found
print(f"Found {len(items)} items")

Found 288 items

# Examine the first item in the collection
# Keep in mind that a list starts from 0, 1, 2... therefore items[0] is referring to the first item in the list/collection
items[0]

Exploring Changes in CH₄ flux Levels Using the Raster API

In this notebook, we will explore the global changes of CH₄ flux over time in urban regions. We will visualize the outputs on a map using folium.

# Now we create a dictionary where the start datetime values for each granule is queried more explicitly by year and month (e.g., 2020-02)
items = {item["properties"]["start_datetime"][:10]: item for item in items} 

# Next, we need to specify the asset name for this collection
# The asset name is referring to the raster band containing the pixel values for the parameter of interest
# For the case of the TM5-4DVar Isotopic CH₄ Inverse Fluxes collection, the parameter of interest is “fossil”
asset_name = "fossil" #fossil fuel

Below, we are entering the minimum and maximum values to provide our upper and lower bounds in the rescale_values.

# Fetching the min and max values for a specific item
rescale_values = {"max":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["max"], "min":items[list(items.keys())[0]]["assets"][asset_name]["raster:bands"][0]["histogram"]["min"]}

Now, we will pass the item id, collection name, asset name, and the rescaling factor to the Raster API endpoint. We will do this twice, once for 2016 and again for 1999, so that we can visualize each event independently.

# Choose a color map for displaying the first observation (event)
# Please refer to matplotlib library if you'd prefer choosing a different color ramp.
# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html
color_map = "purd"

# Make a GET request to retrieve information for the 2016 tile
ch4_flux_1 = requests.get(

    # Pass the collection name, the item number in the list, and its ID
    f"{RASTER_API_URL}/collections/{items['2016-12-01']['collection']}/items/{items['2016-12-01']['id']}/tilejson.json?"

    # Pass the asset name
    f"&assets={asset_name}"

    # Pass the color formula and colormap for custom visualization
    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"

    # Pass the minimum and maximum values for rescaling
    f"&rescale={rescale_values['min']},{rescale_values['max']}", 

# Return the response in JSON format
).json()

# Print the properties of the retrieved granule to the console
ch4_flux_1

# Make a GET request to retrieve information for the 1999 tile
ch4_flux_2 = requests.get(

    # Pass the collection name, the item number in the list, and its ID
    f"{RASTER_API_URL}/collections/{items['1999-12-01']['collection']}/items/{items['1999-12-01']['id']}/tilejson.json?"

    # Pass the asset name
    f"&assets={asset_name}"

    # Pass the color formula and colormap for custom visualization
    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"

    # Pass the minimum and maximum values for rescaling
    f"&rescale={rescale_values['min']},{rescale_values['max']}", 

# Return the response in JSON format
).json()

# Print the properties of the retrieved granule to the console
ch4_flux_2

Visualizing CH₄ flux Emissions from Fossil Fuel

# For this study we are going to compare CH4 fluxes from fossil fuels in 2016 and 1999 along the coast of California
# To change the location, you can simply insert the latitude and longitude of the area of your interest in the "location=(LAT, LONG)" statement

# Set the initial zoom level and center of map for both tiles
# 'folium.plugins' allows mapping side-by-side
map_ = folium.plugins.DualMap(location=(34, -118), zoom_start=6)

# Define the first map layer (2016)
map_layer_2016 = TileLayer(
    tiles=ch4_flux_1["tiles"][0], # Path to retrieve the tile
    attr="GHG", # Set the attribution
    opacity=0.8, # Adjust the transparency of the layer
)
# Add the first layer to the Dual Map
map_layer_2016.add_to(map_.m1)


# Define the second map layer (1999)
map_layer_1999 = TileLayer(
    tiles=ch4_flux_2["tiles"][0], # Path to retrieve the tile
    attr="GHG", # Set the attribution
    opacity=0.8, # Adjust the transparency of the layer
)

# Add the second layer to the Dual Map
map_layer_1999.add_to(map_.m2)

# Visualize the Dual Map
map_

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Calculating Zonal Statistics

To perform zonal statistics, first we need to create a polygon. In this use case we are creating a polygon in Texas (USA).

# Create a polygon for the area of interest (aoi)
texas_aoi = {
    "type": "Feature", # Create a feature object
    "properties": {},
    "geometry": { # Set the bounding coordinates for the polygon
        "coordinates": [
            [
                [-95, 29], # South-east bounding coordinate
                [-95, 33], # North-east bounding coordinate
                [-104,33], # North-west bounding coordinate
                [-104,29], # South-west bounding coordinate
                [-95, 29]  # South-east bounding coordinate (closing the polygon)
            ]
        ],
        "type": "Polygon",
    },
}

# Create a new map to display the generated polygon
# We'll plug in the coordinates for a location
# Central to the study area and a reasonable zoom level
aoi_map = Map(

    # Base map is set to OpenStreetMap
    tiles="OpenStreetMap",

    # Define the spatial properties for the map
    location=[
        30,-100
    ],

    # Set the zoom value
    zoom_start=6,
)

# Insert the polygon to the map
folium.GeoJson(texas_aoi, name="Texas, USA").add_to(aoi_map)

# Visualize the map
aoi_map

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# Check total number of items available within the collection
items = requests.get(
    f"{STAC_API_URL}/collections/{collection_name}/items?limit=600"
).json()["features"]

# Print the total number of items (granules) found
print(f"Found {len(items)} items")

Found 288 items

# Examine the first item in the collection
items[0]

Now that we created the polygon for the area of interest, we need to develop a function that runs through the data collection and generates the statistics for a specific item (granule) within the boundaries of the AOI polygon.

# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
# Create a function that retrieves information regarding a specific granule using its asset name and raster identifier and generates the statistics for it

# The function takes an item (granule) and a JSON (polygon) as input parameters
def generate_stats(item, geojson):

    # A POST request is made to submit the data associated with the item of interest (specific observation) within the boundaries of the polygon to compute its statistics
    result = requests.post(

        # Raster API Endpoint for computing statistics
        f"{RASTER_API_URL}/cog/statistics",

        # Pass the URL to the item, asset name, and raster identifier as parameters
        params={"url": item["assets"][asset_name]["href"]},

        # Send the GeoJSON object (polygon) along with the request
        json=geojson,

    # Return the response in JSON format
    ).json()

    # Print the result
    print(result)

    # Return a dictionary containing the computed statistics along with the item's datetime information.
    return {
        **result["properties"],
        "datetime": item["properties"]["start_datetime"][:10],
    }

# Generate a for loop that iterates over all the existing items in the collection
for item in items:

    # The loop will then retrieve the information for the start datetime of each item in the list
    print(item["properties"]["start_datetime"][:10])

    # Exit the loop after printing the start datetime for the first item in the collection
    break

2021-12-01

With the function above we can generate the statistics for the AOI.

%%time
# %%time = Wall time (execution time) for running the code below

# Generate statistics using the created function "generate_stats" within the bounding box defined by the polygon
stats = [generate_stats(item, texas_aoi) for item in items]

{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.042764596042364544, 'max': 45.46200998420616, 'mean': 9.320847132019729, 'count': 36.0, 'sum': 335.5504967527103, 'std': 11.191973503272377, 'median': 5.158028075849703, 'majority': 0.042764596042364544, 'minority': 0.042764596042364544, 'unique': 36.0, 'histogram': [[17.0, 7.0, 4.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0, 1.0], [0.042764596042364544, 4.584689134858745, 9.126613673675124, 13.668538212491503, 18.210462751307883, 22.752387290124265, 27.294311828940643, 31.83623636775702, 36.378160906573406, 40.92008544538979, 45.46200998420616]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.042764596042364544, 'percentile_98': 45.46200998420616}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.04496430558583199, 'max': 47.79265560178876, 'mean': 9.881737424028517, 'count': 36.0, 'sum': 355.7425472650266, 'std': 11.811205784197897, 'median': 5.539923377905558, 'majority': 0.04496430558583199, 'minority': 0.04496430558583199, 'unique': 36.0, 'histogram': [[17.0, 7.0, 4.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0, 1.0], [0.04496430558583199, 4.819733435206126, 9.594502564826419, 14.369271694446713, 19.144040824067005, 23.918809953687298, 28.693579083307593, 33.46834821292788, 38.243117342548175, 43.01788647216847, 47.79265560178876]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.04496430558583199, 'percentile_98': 47.79265560178876}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.047353506784757395, 'max': 50.74800679056948, 'mean': 10.482134112053338, 'count': 36.0, 'sum': 377.3568280339202, 'std': 12.54485108480068, 'median': 5.875716232313681, 'majority': 0.047353506784757395, 'minority': 0.047353506784757395, 'unique': 36.0, 'histogram': [[17.0, 8.0, 3.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0, 1.0], [0.047353506784757395, 5.117418835163229, 10.1874841635417, 15.257549491920171, 20.327614820298642, 25.397680148677114, 30.467745477055583, 35.53781080543406, 40.60787613381253, 45.677941462191, 50.74800679056948]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.047353506784757395, 'percentile_98': 50.74800679056948}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.04978520091128141, 'max': 53.757996760862326, 'mean': 11.038499105114045, 'count': 36.0, 'sum': 397.3859677841056, 'std': 13.261091061726859, 'median': 6.160317590083132, 'majority': 0.04978520091128141, 'minority': 0.04978520091128141, 'unique': 36.0, 'histogram': [[17.0, 8.0, 3.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0, 1.0], [0.04978520091128141, 5.420606356906386, 10.79142751290149, 16.162248668896595, 21.533069824891697, 26.9038909808868, 32.27471213688191, 37.64553329287701, 43.016354448872114, 48.38717560486722, 53.757996760862326]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.04978520091128141, 'percentile_98': 53.757996760862326}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.05131693335817921, 'max': 55.69460264477133, 'mean': 11.387664949927311, 'count': 36.0, 'sum': 409.9559381973832, 'std': 13.717379362207868, 'median': 6.33717365693283, 'majority': 0.05131693335817921, 'minority': 0.05131693335817921, 'unique': 36.0, 'histogram': [[17.0, 8.0, 3.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0, 1.0], [0.05131693335817921, 5.615645504499494, 11.17997407564081, 16.744302646782124, 22.30863121792344, 27.87295978906476, 33.43728836020607, 39.001616931347385, 44.5659455024887, 50.13027407363002, 55.69460264477133]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.05131693335817921, 'percentile_98': 55.69460264477133}}}}
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{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0686255813758811, 'max': 24.690525636929134, 'mean': 4.173745320994119, 'count': 36.0, 'sum': 150.25483155578826, 'std': 4.44969290970702, 'median': 2.3369262781076605, 'majority': 0.0686255813758811, 'minority': 0.0686255813758811, 'unique': 36.0, 'histogram': [[18.0, 7.0, 7.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0686255813758811, 2.530815586931207, 4.993005592486532, 7.455195598041858, 9.917385603597184, 12.379575609152509, 14.841765614707835, 17.30395562026316, 19.766145625818485, 22.22833563137381, 24.690525636929134]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.0686255813758811, 'percentile_98': 24.690525636929134}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.06832142617437467, 'max': 25.085595462842768, 'mean': 4.161943629365855, 'count': 36.0, 'sum': 149.8299706571708, 'std': 4.484970490384918, 'median': 2.325909418796101, 'majority': 0.06832142617437467, 'minority': 0.06832142617437467, 'unique': 36.0, 'histogram': [[18.0, 7.0, 7.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.06832142617437467, 2.570048829841214, 5.071776233508054, 7.573503637174893, 10.075231040841732, 12.576958444508572, 15.078685848175411, 17.58041325184225, 20.08214065550909, 22.58386805917593, 25.085595462842768]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.06832142617437467, 'percentile_98': 25.085595462842768}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.06617992025238292, 'max': 24.017015738382582, 'mean': 4.054095928694071, 'count': 36.0, 'sum': 145.94745343298655, 'std': 4.327463830662909, 'median': 2.269274487008467, 'majority': 0.06617992025238292, 'minority': 0.06617992025238292, 'unique': 36.0, 'histogram': [[18.0, 7.0, 7.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.06617992025238292, 2.4612635020654023, 4.856347083878422, 7.251430665691442, 9.646514247504461, 12.04159782931748, 14.4366814111305, 16.831764992943523, 19.22684857475654, 21.62193215656956, 24.017015738382582]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.06617992025238292, 'percentile_98': 24.017015738382582}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.060558375102073604, 'max': 21.07280855113715, 'mean': 3.7315461992759547, 'count': 36.0, 'sum': 134.33566317393436, 'std': 3.892521288204617, 'median': 2.096809586353803, 'majority': 0.060558375102073604, 'minority': 0.060558375102073604, 'unique': 36.0, 'histogram': [[18.0, 6.0, 7.0, 2.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.060558375102073604, 2.1617833927055816, 4.263008410309089, 6.364233427912597, 8.465458445516106, 10.566683463119613, 12.667908480723122, 14.76913349832663, 16.870358515930135, 18.971583533533643, 21.07280855113715]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.060558375102073604, 'percentile_98': 21.07280855113715}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.05721962050373909, 'max': 19.375131537714612, 'mean': 3.555183613347703, 'count': 36.0, 'sum': 127.98661008051731, 'std': 3.6509729273629197, 'median': 2.012218174307557, 'majority': 0.05721962050373909, 'minority': 0.05721962050373909, 'unique': 36.0, 'histogram': [[17.0, 7.0, 6.0, 2.0, 3.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.05721962050373909, 1.9890108122248262, 3.920802003945913, 5.852593195667, 7.784384387388087, 9.716175579109175, 11.64796677083026, 13.579757962551348, 15.511549154272435, 17.443340345993523, 19.375131537714612]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.05721962050373909, 'percentile_98': 19.375131537714612}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.0544682680950732, 'max': 17.971370719457198, 'mean': 3.4159328748277336, 'count': 36.0, 'sum': 122.9735834937984, 'std': 3.458012240984571, 'median': 1.951103711911926, 'majority': 0.0544682680950732, 'minority': 0.0544682680950732, 'unique': 36.0, 'histogram': [[16.0, 7.0, 6.0, 3.0, 1.0, 2.0, 0.0, 0.0, 0.0, 1.0], [0.0544682680950732, 1.8461585132312859, 3.6378487583674985, 5.429539003503711, 7.221229248639924, 9.012919493776137, 10.80460973891235, 12.596299984048562, 14.387990229184775, 16.179680474320985, 17.971370719457198]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.0544682680950732, 'percentile_98': 17.971370719457198}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.054682258126349595, 'max': 18.258597626033986, 'mean': 3.4594496490126185, 'count': 36.0, 'sum': 124.54018736445427, 'std': 3.5018897035469814, 'median': 1.9985026283006802, 'majority': 0.054682258126349595, 'minority': 0.054682258126349595, 'unique': 36.0, 'histogram': [[16.0, 7.0, 6.0, 3.0, 1.0, 2.0, 0.0, 0.0, 0.0, 1.0], [0.054682258126349595, 1.8750737949171132, 3.695465331707877, 5.51585686849864, 7.336248405289404, 9.156639942080169, 10.977031478870932, 12.797423015661696, 14.61781455245246, 16.438206089243224, 18.258597626033986]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.054682258126349595, 'percentile_98': 18.258597626033986}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.052018797181943, 'max': 16.928742111497318, 'mean': 3.30331065274379, 'count': 36.0, 'sum': 118.91918349877643, 'std': 3.311028800407719, 'median': 1.9138274284105172, 'majority': 0.052018797181943, 'minority': 0.052018797181943, 'unique': 36.0, 'histogram': [[15.0, 8.0, 6.0, 3.0, 1.0, 2.0, 0.0, 0.0, 0.0, 1.0], [0.052018797181943, 1.7396911286134804, 3.427363460045018, 5.115035791476555, 6.802708122908093, 8.490380454339629, 10.178052785771166, 11.865725117202704, 13.553397448634241, 15.241069780065779, 16.928742111497318]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.052018797181943, 'percentile_98': 16.928742111497318}}}}
{'type': 'Feature', 'geometry': {'type': 'Polygon', 'coordinates': [[[-95.0, 29.0], [-95.0, 33.0], [-104.0, 33.0], [-104.0, 29.0], [-95.0, 29.0]]]}, 'properties': {'statistics': {'b1': {'min': 0.04866568769103654, 'max': 15.40446170465091, 'mean': 3.0925303410411136, 'count': 36.0, 'sum': 111.3310922774801, 'std': 3.080247911241435, 'median': 1.7905992166111768, 'majority': 0.04866568769103654, 'minority': 0.04866568769103654, 'unique': 36.0, 'histogram': [[15.0, 8.0, 3.0, 6.0, 1.0, 2.0, 0.0, 0.0, 0.0, 1.0], [0.04866568769103654, 1.5842452893870238, 3.1198248910830113, 4.655404492778998, 6.190984094474985, 7.726563696170973, 9.26214329786696, 10.797722899562947, 12.333302501258935, 13.868882102954922, 15.40446170465091]], 'valid_percent': 100.0, 'masked_pixels': 0.0, 'valid_pixels': 36.0, 'percentile_2': 0.04866568769103654, 'percentile_98': 15.40446170465091}}}}
CPU times: user 2.77 s, sys: 366 ms, total: 3.14 s
Wall time: 3min 35s

Create a function that goes through every single item in the collection and populates their properties - including the minimum, maximum, and sum of their values - in a table.

# Print the stats for the first item in the collection
stats[0]

{'statistics': {'b1': {'min': 0.042764596042364544,
   'max': 45.46200998420616,
   'mean': 9.320847132019729,
   'count': 36.0,
   'sum': 335.5504967527103,
   'std': 11.191973503272377,
   'median': 5.158028075849703,
   'majority': 0.042764596042364544,
   'minority': 0.042764596042364544,
   'unique': 36.0,
   'histogram': [[17.0, 7.0, 4.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0, 1.0],
    [0.042764596042364544,
     4.584689134858745,
     9.126613673675124,
     13.668538212491503,
     18.210462751307883,
     22.752387290124265,
     27.294311828940643,
     31.83623636775702,
     36.378160906573406,
     40.92008544538979,
     45.46200998420616]],
   'valid_percent': 100.0,
   'masked_pixels': 0.0,
   'valid_pixels': 36.0,
   'percentile_2': 0.042764596042364544,
   'percentile_98': 45.46200998420616}},
 'datetime': '2021-12-01'}

# Create a function that converts statistics in JSON format into a pandas DataFrame
def clean_stats(stats_json) -> pd.DataFrame:

    # Normalize the JSON data
    df = pd.json_normalize(stats_json)

    # Replace the naming "statistics.b1" in the columns
    df.columns = [col.replace("statistics.b1.", "") for col in df.columns]

    # Set the datetime format
    df["date"] = pd.to_datetime(df["datetime"])

    # Return the cleaned format
    return df

# Apply the generated function on the stats data
df = clean_stats(stats)

# Display the stats for the first 5 granules in the collection in the table
# Change the value in the parenthesis to show more or a smaller number of rows in the table
df.head(5)

	datetime	min	max	mean	count	sum	std	median	majority	minority	unique	histogram	valid_percent	masked_pixels	valid_pixels	percentile_2	percentile_98	date
0	2021-12-01	0.042765	45.462010	9.320847	36.0	335.550497	11.191974	5.158028	0.042765	0.042765	36.0	[[17.0, 7.0, 4.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0...	100.0	0.0	36.0	0.042765	45.462010	2021-12-01
1	2021-11-01	0.044964	47.792656	9.881737	36.0	355.742547	11.811206	5.539923	0.044964	0.044964	36.0	[[17.0, 7.0, 4.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0...	100.0	0.0	36.0	0.044964	47.792656	2021-11-01
2	2021-10-01	0.047354	50.748007	10.482134	36.0	377.356828	12.544851	5.875716	0.047354	0.047354	36.0	[[17.0, 8.0, 3.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0...	100.0	0.0	36.0	0.047354	50.748007	2021-10-01
3	2021-09-01	0.049785	53.757997	11.038499	36.0	397.385968	13.261091	6.160318	0.049785	0.049785	36.0	[[17.0, 8.0, 3.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0...	100.0	0.0	36.0	0.049785	53.757997	2021-09-01
4	2021-08-01	0.051317	55.694603	11.387665	36.0	409.955938	13.717379	6.337174	0.051317	0.051317	36.0	[[17.0, 8.0, 3.0, 0.0, 3.0, 3.0, 0.0, 0.0, 1.0...	100.0	0.0	36.0	0.051317	55.694603	2021-08-01

Visualizing the Data as a Time Series

We can now explore the fossil fuel emission time series (January 1999 -December 2016) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:

# Figure size: 20 representing the width, 10 representing the height
df = df.sort_values(by="datetime")
fig = plt.figure(figsize=(20, 10))

plt.plot(
    df["datetime"], # X-axis: sorted datetime
    df["max"], # Y-axis: maximum CH4 flux
    color="red", # Line color
    linestyle="-", # Line style
    linewidth=0.5, # Line width
    label="CH4 emissions", # Legend label
)

# Display legend
plt.legend()

# Insert label for the X-axis
plt.xlabel("Years")

# Insert label for the Y-axis
plt.ylabel("g CH₄/m²/year")
plt.xticks(rotation = 90)

interval = 12
ticks = range(0, len(df), interval)  # Tick positions
plt.xticks(ticks, df["datetime"].iloc[ticks])  # Use the corresponding datetime values


# Insert title for the plot
plt.title("CH4 emission Values for Texas, Dallas (1999-2016)")

# Add data citation
plt.text(
    df["datetime"].iloc[0],           # X-coordinate of the text
    df["max"].min(),                  # Y-coordinate of the text




    # Text to be displayed
    "Source: NASA/NOAA CarbonTracker-CH₄ Isotopic Methane Inverse Fluxes",                  
    fontsize=12,                             # Font size
    horizontalalignment="left",              # Horizontal alignment
    verticalalignment="top",                 # Vertical alignment
    color="blue",                            # Text color
)


# Plot the time series
plt.show()

# Print the properties for the 3rd item in the collection
print(items[2]["properties"]["start_datetime"])

2021-10-01T00:00:00+00:00

# A GET request is made for the 3rd granule
ch4_flux_3 = requests.get(

    # Pass the collection name, the item number in the list, and its ID
    f"{RASTER_API_URL}/collections/{items[2]['collection']}/items/{items[2]['id']}/tilejson.json?"

    # Pass the asset name
    f"&assets={asset_name}"

    # Pass the color formula and colormap for custom visualization
    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"

    # Pass the minimum and maximum values for rescaling
    f"&rescale={rescale_values['min']},{rescale_values['max']}",

# Return the response in JSON format
).json()

# Print the properties of the retrieved granule to the console
ch4_flux_3

{'tilejson': '2.2.0',
 'version': '1.0.0',
 'scheme': 'xyz',
 'tiles': ['https://dev.ghg.center/api/raster/collections/ct-ch4-monthgrid-v2023/items/ct-ch4-monthgrid-v2023-202110/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=fossil&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C198.25427154645362'],
 'minzoom': 0,
 'maxzoom': 24,
 'bounds': [-180.0, -90.0, 180.0, 90.0],
 'center': [0.0, 0.0, 0]}

# Create a new map to display the tile
aoi_map_bbox = Map(

    # Base map is set to OpenStreetMap
    tiles="OpenStreetMap",

    # Set the center of the map
    location=[
        30,-100
    ],

    # Set the zoom value
    zoom_start=6.8,
)

# Define the map layer
map_layer = TileLayer(

    # Path to retrieve the tile
    tiles=ch4_flux_3["tiles"][0],

    # Set the attribution and adjust the transparency of the layer
    attr="GHG", opacity = 0.7
)

# Add the layer to the map
map_layer.add_to(aoi_map_bbox)

# Visualize the map
aoi_map_bbox

Make this Notebook Trusted to load map: File -> Trust Notebook

Summary

In this notebook we have successfully explored, analyzed, and visualized the STAC collection for TM5-4DVar Isotopic CH₄ Inverse Fluxes dataset.

1. Install and import the necessary libraries
2. Fetch the collection from STAC collections using the appropriate endpoints
3. Count the number of existing granules within the collection
4. Map and compare the CH₄ inverse fluxes for two distinctive months/years
5. Generate zonal statistics for the area of interest (AOI)
6. Visualizing the Data as a Time Series

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