GOSAT-based Top-down Total and Natural Methane Emissions

Total and wetland yearly methane emissions derived using the GEOS-Chem global chemistry transport model with inclusion of GOSAT data for 2010 to 2022 on a 4 x 5 degree (lat/lon) grid
Author

Siddharth Chaudhary, Vishal Gaur

Published

November 15, 2024

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 gridded methane emissions 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

The NASA Carbon Monitoring System Flux (CMS-Flux) team analyzed remote sensing observations from Japan’s Greenhouse gases Observing SATellite (GOSAT) to produce the global Committee on Earth Observation Satellites (CEOS) CH₄ Emissions data product. They used an analytic Bayesian inversion approach and the GEOS-Chem global chemistry transport model to quantify annual methane (CH₄) emissions and their uncertainties at a spatial resolution of 1° by 1° and then projected these to each country for 2019.

For more information regarding this dataset, please visit the GOSAT-based Top-down Total and Natural Methane Emissions 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

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.

# 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
# Provide STAC and RASTER API endpoints
STAC_API_URL = "https://earth.gov/ghgcenter/api/stac"
RASTER_API_URL = "https://earth.gov/ghgcenter/api/raster"

# Please use the collection name similar to the one used in STAC collection.

# Name of the collection for gosat budget methane. 
collection_name = "gosat-based-ch4budget-yeargrid-v1"
# Fetching the collection from STAC collections using appropriate endpoint.
collection = requests.get(f"{STAC_API_URL}/collections/{collection_name}").json()
collection

Examining the contents of our collection under the temporal variable, we see that the data is available from January 2010 to December 2022. By looking at the dashboard:time density, we observe that the data is available for thirteen years.

def get_item_count(collection_id):
    count = 0
    items_url = f"{STAC_API_URL}/collections/{collection_id}/items"

    while True:
        response = requests.get(items_url)

        if not response.ok:
            print("error getting items")
            exit()

        stac = response.json()
        count += int(stac["context"].get("returned", 0))
        next = [link for link in stac["links"] if link["rel"] == "next"]

        if not next:
            break
        items_url = next[0]["href"]

    return count
# Check total number of items available
number_of_items = get_item_count(collection_name)
items = requests.get(f"{STAC_API_URL}/collections/{collection_name}/items?limit={number_of_items}").json()["features"]
print(f"Found {len(items)} items")
Found 13 items
# Examining the first item in the collection
items[0]
{'id': 'gosat-based-ch4budget-yeargrid-v1-test-2022',
 'bbox': [-182.5, -90.9777777777778, 177.5, 90.97777777777777],
 'type': 'Feature',
 'links': [{'rel': 'collection',
   'type': 'application/json',
   'href': 'https://dev.ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1-test'},
  {'rel': 'parent',
   'type': 'application/json',
   'href': 'https://dev.ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1-test'},
  {'rel': 'root',
   'type': 'application/json',
   'href': 'https://dev.ghg.center/api/stac/'},
  {'rel': 'self',
   'type': 'application/geo+json',
   'href': 'https://dev.ghg.center/api/stac/collections/gosat-based-ch4budget-yeargrid-v1-test/items/gosat-based-ch4budget-yeargrid-v1-test-2022'},
  {'title': 'Map of Item',
   'href': 'https://dev.ghg.center/api/raster/collections/gosat-based-ch4budget-yeargrid-v1-test/items/gosat-based-ch4budget-yeargrid-v1-test-2022/map?assets=post-total&rescale=0%2C0.3&colormap_name=spectral_r',
   'rel': 'preview',
   'type': 'text/html'}],
 'assets': {'post-anth': {'href': 's3://ghgc-data-store-develop/transformed_cogs/gosat_ch4/Post_Total_ANTH_CH4_emissions_2022.tif',
   'type': 'image/tiff; application=geotiff',
   'roles': ['data', 'layer'],
   'title': 'Anthropogenic Posterior Methane Emissions',
   'proj:bbox': [-182.5, -90.9777777777778, 177.5, 90.97777777777777],
   'proj:epsg': 4326,
   'proj:wkt2': 'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]',
   'proj:shape': [46, 72],
   'description': 'Estimated methane emissions per grid cell from anthropogenic informed by GOSAT satellite total column methane data.',
   'raster:bands': [{'scale': 1.0,
     'nodata': -9999.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float64',
     'histogram': {'max': 7.221596690004756,
      'min': -4.446999177355341,
      'count': 11,
      'buckets': [1, 1, 3, 2954, 284, 41, 15, 6, 5, 2]},
     'statistics': {'mean': 0.11414124629900693,
      'stddev': 0.4784569130920799,
      'maximum': 7.221596690004756,
      'minimum': -4.446999177355341,
      'valid_percent': 100.0}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-182.5, -90.9777777777778],
      [177.5, -90.9777777777778],
      [177.5, 90.97777777777777],
      [-182.5, 90.97777777777777],
      [-182.5, -90.9777777777778]]]},
   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},
    'name': 'WGS 84',
    'type': 'GeographicCRS',
    'datum': {'name': 'World Geodetic System 1984',
     'type': 'GeodeticReferenceFrame',
     'ellipsoid': {'name': 'WGS 84',
      'semi_major_axis': 6378137,
      'inverse_flattening': 298.257223563}},
    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',
    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
       'unit': 'degree',
       'direction': 'north',
       'abbreviation': 'Lat'},
      {'name': 'Geodetic longitude',
       'unit': 'degree',
       'direction': 'east',
       'abbreviation': 'Lon'}],
     'subtype': 'ellipsoidal'}},
   'proj:transform': [5.0,
    0.0,
    -182.5,
    0.0,
    -3.9555555555555557,
    90.97777777777777,
    0.0,
    0.0,
    1.0]},
  'post-total': {'href': 's3://ghgc-data-store-develop/transformed_cogs/gosat_ch4/Post_Total_TOTAL_CH4_emissions_2022.tif',
   'type': 'image/tiff; application=geotiff',
   'roles': ['data', 'layer'],
   'title': 'Posterior Total Methane Emissions',
   'proj:bbox': [-182.5, -90.9777777777778, 177.5, 90.97777777777777],
   'proj:epsg': 4326,
   'proj:wkt2': 'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]',
   'proj:shape': [46, 72],
   'description': 'Estimated total methane emissions per grid cell informed by GOSAT satellite total column methane data.',
   'raster:bands': [{'scale': 1.0,
     'nodata': -9999.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float64',
     'histogram': {'max': 8.355603568623742,
      'min': -4.4208666115704,
      'count': 11,
      'buckets': [1, 1, 2, 3064, 176, 36, 15, 11, 4, 2]},
     'statistics': {'mean': 0.17184094844139713,
      'stddev': 0.609173348963156,
      'maximum': 8.355603568623742,
      'minimum': -4.4208666115704,
      'valid_percent': 100.0}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-182.5, -90.9777777777778],
      [177.5, -90.9777777777778],
      [177.5, 90.97777777777777],
      [-182.5, 90.97777777777777],
      [-182.5, -90.9777777777778]]]},
   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},
    'name': 'WGS 84',
    'type': 'GeographicCRS',
    'datum': {'name': 'World Geodetic System 1984',
     'type': 'GeodeticReferenceFrame',
     'ellipsoid': {'name': 'WGS 84',
      'semi_major_axis': 6378137,
      'inverse_flattening': 298.257223563}},
    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',
    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
       'unit': 'degree',
       'direction': 'north',
       'abbreviation': 'Lat'},
      {'name': 'Geodetic longitude',
       'unit': 'degree',
       'direction': 'east',
       'abbreviation': 'Lon'}],
     'subtype': 'ellipsoidal'}},
   'proj:transform': [5.0,
    0.0,
    -182.5,
    0.0,
    -3.9555555555555557,
    90.97777777777777,
    0.0,
    0.0,
    1.0]},
  'prior-anth': {'href': 's3://ghgc-data-store-develop/transformed_cogs/gosat_ch4/Post_Total_ANTHP_CH4_emissions_2022.tif',
   'type': 'image/tiff; application=geotiff',
   'roles': ['data', 'layer'],
   'title': 'Anthropogenic Prior Methane Emissions',
   'proj:bbox': [-182.5, -90.9777777777778, 177.5, 90.97777777777777],
   'proj:epsg': 4326,
   'proj:wkt2': 'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]',
   'proj:shape': [46, 72],
   'description': 'Estimated methane emissions per grid cell from anthropogenic informed by GOSAT satellite total column methane data.',
   'raster:bands': [{'scale': 1.0,
     'nodata': -9999.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float64',
     'histogram': {'max': 6.610429486472943,
      'min': 0.0,
      'count': 11,
      'buckets': [3158, 95, 31, 13, 3, 4, 5, 1, 0, 2]},
     'statistics': {'mean': 0.10766109705438384,
      'stddev': 0.39201492749800015,
      'maximum': 6.610429486472943,
      'minimum': 0.0,
      'valid_percent': 100.0}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-182.5, -90.9777777777778],
      [177.5, -90.9777777777778],
      [177.5, 90.97777777777777],
      [-182.5, 90.97777777777777],
      [-182.5, -90.9777777777778]]]},
   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},
    'name': 'WGS 84',
    'type': 'GeographicCRS',
    'datum': {'name': 'World Geodetic System 1984',
     'type': 'GeodeticReferenceFrame',
     'ellipsoid': {'name': 'WGS 84',
      'semi_major_axis': 6378137,
      'inverse_flattening': 298.257223563}},
    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',
    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
       'unit': 'degree',
       'direction': 'north',
       'abbreviation': 'Lat'},
      {'name': 'Geodetic longitude',
       'unit': 'degree',
       'direction': 'east',
       'abbreviation': 'Lon'}],
     'subtype': 'ellipsoidal'}},
   'proj:transform': [5.0,
    0.0,
    -182.5,
    0.0,
    -3.9555555555555557,
    90.97777777777777,
    0.0,
    0.0,
    1.0]},
  'post-wetland': {'href': 's3://ghgc-data-store-develop/transformed_cogs/gosat_ch4/Post_Total_WET_CH4_emissions_2022.tif',
   'type': 'image/tiff; application=geotiff',
   'roles': ['data', 'layer'],
   'title': 'Wetland Posterior Methane Emissions',
   'proj:bbox': [-182.5, -90.9777777777778, 177.5, 90.97777777777777],
   'proj:epsg': 4326,
   'proj:wkt2': 'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]',
   'proj:shape': [46, 72],
   'description': 'Estimated methane emissions per grid cell from wetlands informed by GOSAT satellite total column methane data.',
   'raster:bands': [{'scale': 1.0,
     'nodata': -9999.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float64',
     'histogram': {'max': 8.256931476495279,
      'min': -0.0016259006808810538,
      'count': 11,
      'buckets': [3263, 30, 8, 4, 4, 1, 0, 1, 0, 1]},
     'statistics': {'mean': 0.057699702142390175,
      'stddev': 0.30538996981278377,
      'maximum': 8.256931476495279,
      'minimum': -0.0016259006808810538,
      'valid_percent': 100.0}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-182.5, -90.9777777777778],
      [177.5, -90.9777777777778],
      [177.5, 90.97777777777777],
      [-182.5, 90.97777777777777],
      [-182.5, -90.9777777777778]]]},
   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},
    'name': 'WGS 84',
    'type': 'GeographicCRS',
    'datum': {'name': 'World Geodetic System 1984',
     'type': 'GeodeticReferenceFrame',
     'ellipsoid': {'name': 'WGS 84',
      'semi_major_axis': 6378137,
      'inverse_flattening': 298.257223563}},
    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',
    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
       'unit': 'degree',
       'direction': 'north',
       'abbreviation': 'Lat'},
      {'name': 'Geodetic longitude',
       'unit': 'degree',
       'direction': 'east',
       'abbreviation': 'Lon'}],
     'subtype': 'ellipsoidal'}},
   'proj:transform': [5.0,
    0.0,
    -182.5,
    0.0,
    -3.9555555555555557,
    90.97777777777777,
    0.0,
    0.0,
    1.0]},
  'prior-wetland': {'href': 's3://ghgc-data-store-develop/transformed_cogs/gosat_ch4/Post_Total_WETP_CH4_emissions_2022.tif',
   'type': 'image/tiff; application=geotiff',
   'roles': ['data', 'layer'],
   'title': 'Wetland Prior Methane Emissions',
   'proj:bbox': [-182.5, -90.9777777777778, 177.5, 90.97777777777777],
   'proj:epsg': 4326,
   'proj:wkt2': 'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]',
   'proj:shape': [46, 72],
   'description': 'Methane emissions per grid cell from wetlands estimated by various inventories or models, excluding satellite based observations from GOSAT.',
   'raster:bands': [{'scale': 1.0,
     'nodata': -9999.0,
     'offset': 0.0,
     'sampling': 'area',
     'data_type': 'float64',
     'histogram': {'max': 6.639171063899994,
      'min': 0.0,
      'count': 11,
      'buckets': [3270, 24, 7, 4, 2, 1, 2, 1, 0, 1]},
     'statistics': {'mean': 0.044958096379183715,
      'stddev': 0.25345413829587626,
      'maximum': 6.639171063899994,
      'minimum': 0.0,
      'valid_percent': 100.0}}],
   'proj:geometry': {'type': 'Polygon',
    'coordinates': [[[-182.5, -90.9777777777778],
      [177.5, -90.9777777777778],
      [177.5, 90.97777777777777],
      [-182.5, 90.97777777777777],
      [-182.5, -90.9777777777778]]]},
   'proj:projjson': {'id': {'code': 4326, 'authority': 'EPSG'},
    'name': 'WGS 84',
    'type': 'GeographicCRS',
    'datum': {'name': 'World Geodetic System 1984',
     'type': 'GeodeticReferenceFrame',
     'ellipsoid': {'name': 'WGS 84',
      'semi_major_axis': 6378137,
      'inverse_flattening': 298.257223563}},
    '$schema': 'https://proj.org/schemas/v0.7/projjson.schema.json',
    'coordinate_system': {'axis': [{'name': 'Geodetic latitude',
       'unit': 'degree',
       'direction': 'north',
       'abbreviation': 'Lat'},
      {'name': 'Geodetic longitude',
       'unit': 'degree',
       'direction': 'east',
       'abbreviation': 'Lon'}],
     'subtype': 'ellipsoidal'}},
   'proj:transform': [5.0,
    0.0,
    -182.5,
    0.0,
    -3.9555555555555557,
    90.97777777777777,
    0.0,
    0.0,
    1.0]},
  'rendered_preview': {'title': 'Rendered preview',
   'href': 'https://dev.ghg.center/api/raster/collections/gosat-based-ch4budget-yeargrid-v1-test/items/gosat-based-ch4budget-yeargrid-v1-test-2022/preview.png?assets=post-total&rescale=0%2C0.3&colormap_name=spectral_r',
   'rel': 'preview',
   'roles': ['overview'],
   'type': 'image/png'}},
 'geometry': {'type': 'Polygon',
  'coordinates': [[[-182.5, -90.9777777777778],
    [177.5, -90.9777777777778],
    [177.5, 90.97777777777777],
    [-182.5, 90.97777777777777],
    [-182.5, -90.9777777777778]]]},
 'collection': 'gosat-based-ch4budget-yeargrid-v1-test',
 'properties': {'datetime': '2022-01-01T00:00:00+00:00'},
 'stac_version': '1.0.0',
 'stac_extensions': ['https://stac-extensions.github.io/raster/v1.1.0/schema.json',
  'https://stac-extensions.github.io/projection/v1.1.0/schema.json']}

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

Exploring Changes in GOSAT Methane budgets (CH4) Levels Using the Raster API

In this notebook, we will explore the impacts of methane emissions and by examining changes over time in urban regions. We will visualize the outputs on a map using folium.

# To access the year value from each item more easily, this will let us query more explicity by year and month (e.g., 2020-02)
items = {item["properties"]["datetime"][:10]: item for item in items} 
asset_name = "post-total"
# 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"]}
items.keys()

Now, we will pass the item id, collection name, and rescaling_factor to the Raster API endpoint. We will do this for first January 2019.

color_map = "rainbow" # please select the color ramp from matplotlib library.
january_2019_tile = requests.get(
    f"{RASTER_API_URL}/collections/{items['2019-01-01']['collection']}/items/{items['2019-01-01']['id']}/tilejson.json?"
    f"&assets={asset_name}"
    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
    f"&rescale={rescale_values['min']},{rescale_values['max']}", 
).json()
january_2019_tile

Visualizing CH₄ Emissions

# Set initial zoom and center of map for CH₄ Layer
# Centre of map [latitude,longitude]
map_ = folium.Map(location=(34, -118), zoom_start=2)

# January 2019
map_layer_2019 = TileLayer(
    tiles=january_2019_tile["tiles"][0],
    attr="GHG",
    opacity=0.7,
)
map_layer_2019.add_to(map_)

# # January 2012
# map_layer_2012 = TileLayer(
#     tiles=january_2012_tile["tiles"][0],
#     attr="GHG",
#     opacity=0.7,
# )
# map_layer_2012.add_to(map_.m2)

# visualising the 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).

# Texas, USA
texas_aoi = {
    "type": "Feature",
    "properties": {},
    "geometry": {
        "coordinates": [
            [
                # [13.686159004559698, -21.700046934333145],
                # [13.686159004559698, -23.241974326585833],
                # [14.753560168039911, -23.241974326585833],
                # [14.753560168039911, -21.700046934333145],
                # [13.686159004559698, -21.700046934333145],
                [-95, 29],
                [-95, 33],
                [-104, 33],
                [-104,29],
                [-95, 29]
            ]
        ],
        "type": "Polygon",
    },
}
# We will plug in the coordinates for a location inside the the polygon and a zoom level

aoi_map = Map(
    tiles="OpenStreetMap",
    location=[
        30,-100
    ],
    zoom_start=6,
)

folium.GeoJson(texas_aoi, name="Texas, USA").add_to(aoi_map)
aoi_map
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# Check total number of items available
items = requests.get(
    f"{STAC_API_URL}/collections/{collection_name}/items?limit=300"
).json()["features"]
print(f"Found {len(items)} items")
Found 13 items
# Explore the first item
items[0]
# The bounding box should be passed to the geojson param as a geojson Feature or FeatureCollection
def generate_stats(item, geojson):
    result = requests.post(
        f"{RASTER_API_URL}/cog/statistics",
        params={"url": item["assets"][asset_name]["href"]},
        json=geojson,
    ).json()
    return {
        **result["properties"],
        "datetime": item["properties"]["datetime"][:10],
    }

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

%%time
stats = [generate_stats(item, texas_aoi) for item in items]
CPU times: user 160 ms, sys: 22.7 ms, total: 182 ms
Wall time: 7.4 s
stats[0]
{'statistics': {'b1': {'min': 0.45840875428297767,
   'max': 2.563434123331244,
   'mean': 1.3501146985384829,
   'count': 1.9799998998641968,
   'sum': 2.6732269679113765,
   'std': 0.5669339642223294,
   'median': 1.0878720382884925,
   'majority': 0.45840875428297767,
   'minority': 0.45840875428297767,
   'unique': 6.0,
   'histogram': [[2.0, 0.0, 1.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 1.0],
    [0.45840875428297767,
     0.6689112911878043,
     0.8794138280926309,
     1.0899163649974577,
     1.300418901902284,
     1.5109214388071108,
     1.7214239757119376,
     1.931926512616764,
     2.1424290495215907,
     2.352931586426417,
     2.563434123331244]],
   'valid_percent': 100.0,
   'masked_pixels': 0.0,
   'valid_pixels': 6.0,
   'percentile_2': 0.45840875428297767,
   'percentile_98': 2.563434123331244}},
 'datetime': '2022-01-01'}
def clean_stats(stats_json) -> pd.DataFrame:
    df = pd.json_normalize(stats_json)
    df.columns = [col.replace("statistics.b1.", "") for col in df.columns]
    df["date"] = pd.to_datetime(df["datetime"])
    return df


df = clean_stats(stats)
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 2022-01-01 0.458409 2.563434 1.350115 1.98 2.673227 0.566934 1.087872 0.458409 0.458409 6.0 [[2.0, 0.0, 1.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0,... 100.0 0.0 6.0 0.458409 2.563434 2022-01-01
1 2021-01-01 0.417406 2.977322 1.409865 1.98 2.791533 0.719124 1.029306 0.417406 0.417406 6.0 [[2.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0,... 100.0 0.0 6.0 0.417406 2.977322 2021-01-01
2 2020-01-01 0.392606 2.619608 1.301333 1.98 2.576640 0.623242 0.978943 0.392606 0.392606 6.0 [[2.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0,... 100.0 0.0 6.0 0.392606 2.619608 2020-01-01
3 2019-01-01 0.521856 2.678034 1.409689 1.98 2.791184 0.667938 0.953206 0.521856 0.521856 6.0 [[1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0,... 100.0 0.0 6.0 0.521856 2.678034 2019-01-01
4 2018-01-01 0.552891 2.624957 1.427609 1.98 2.826666 0.582425 1.086807 0.552891 0.552891 6.0 [[1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0,... 100.0 0.0 6.0 0.552891 2.624957 2018-01-01 
print(items[0]["properties"]["datetime"][:10])
2022-01-01
tile_2016 = requests.get(
    f"{RASTER_API_URL}/collections/{items[0]['collection']}/items/{items[0]['id']}/tilejson.json?"
    f"&assets={asset_name}"
    f"&color_formula=gamma+r+1.05&colormap_name={color_map}"
    f"&rescale={rescale_values['min']},{rescale_values['max']}",
).json()
tile_2016
{'tilejson': '2.2.0',
 'version': '1.0.0',
 'scheme': 'xyz',
 'tiles': ['https://dev.ghg.center/api/raster/collections/gosat-based-ch4budget-yeargrid-v1-test/items/gosat-based-ch4budget-yeargrid-v1-test-2022/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=post-total&color_formula=gamma+r+1.05&colormap_name=rainbow&rescale=-4.4208666115704%2C8.355603568623742'],
 'minzoom': 0,
 'maxzoom': 24,
 'bounds': [-182.5, -90.9777777777778, 177.5, 90.97777777777777],
 'center': [-2.5, -1.4210854715202004e-14, 0]}
# Use bbox initial zoom and map
# Set up a map located w/in event bounds

aoi_map_bbox = Map(
    tiles="OpenStreetMap",
    location=[
        30,-100
    ],
    zoom_start=2,
)

map_layer = TileLayer(
    tiles=tile_2016["tiles"][0],
    attr="GHG", opacity = 0.5
)

map_layer.add_to(aoi_map_bbox)

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

Summary

In this notebook we have successfully completed the following steps for the STAC collection for the GOSAT-based Top-down Total and Natural Methane Emissions 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 the methane emission levels
  5. Generate zonal statistics for the area of interest (AOI)

If you have any questions regarding this user notebook, please contact us using the feedback form.

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