# Import the following libraries
# For fetching from the Raster API
import requests
# For making maps
import folium
import folium.plugins
from folium import Map, TileLayer
# For talking to the STAC API
from pystac_client import Client
# For working with data
import pandas as pd
# For making time series
import matplotlib.pyplot as plt
# For formatting date/time data
import datetime
# Custom functions for working with GHGC data via the API
import ghgc_utils
OCO-2 MIP Top-Down CO₂ Budgets
Access this Notebook
You can launch this notebook in the US GHG Center JupyterHub by clicking the link below. If you are a new user, you should first sign up for the hub by filling out this request form and providing the required information.
Access the OCO-2 MIP Top-down CO2 Budgets notebook in the US GHG Center JupyterHub.
Table of Contents
Data Summary and Application
- Spatial coverage: Global
- Spatial resolution: 1° x 1°
- Temporal extent: 2015 – 2020
- Temporal resolution: Annual
- Unit: Grams of carbon dioxide per square meter per year
- Utility: Climate Research
For more, visit the OCO-2 MIP Top-down CO2 Budgets data overview page.
About the Data
OCO-2 MIP Top-Down CO₂ Budgets
The Committee on Earth Observation Satellites (CEOS) Atmospheric Composition - Virtual Constellation (AC-VC) Greenhouse Gas (GHG) team has generated the CEOS CO₂ Budgets dataset, which provides annual top-down carbon dioxide (CO2) emissions and removals from 2015 - 2020 gridded globally at 1° resolution, and as national totals. Data is provided in units of grams of carbon dioxide per square meter per year (g CO2/m2/yr). Only a subset of the full dataset is displayed in the GHG Center explore view.
For more information regarding this dataset, please visit the OCO-2 MIP Top-Down CO₂ Budgets data overview page.
Approach
- 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 OCO-2 MIP Top-Down CO₂ Budgets data product. - Pass the STAC item into the raster API
/collections/{collection_id}/items/{item_id}/tilejson.json
endpoint. - Using
folium.plugins.DualMap
, we will visualize two tiles (side-by-side), allowing us to compare time points. - After the visualization, we will perform zonal statistics for a given polygon.
Terminology
Navigating data via the GHGC API, you will encounter terminology that is different from browsing in a typical filesystem. We’ll define some terms here which are used throughout this notebook. - catalog
: All datasets available at the /stac
endpoint - collection
: A specific dataset, e.g. OCO-2 MIP Top-Down CO2 Budgets - item
: One granule in the dataset, e.g. one year of CO2 fluxes - asset
: A variable available within the granule, e.g. CO2 fluxes from crops, wood, or rivers - STAC API
: SpatioTemporal Asset Catalogs - Endpoint for fetching metadata about available datasets - Raster API
: Endpoint for fetching data itself, for imagery and statistics
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
Query the STAC API
STAC API Collection Names
Now, you must fetch the dataset from the STAC API by defining its associated STAC API collection ID as a variable. The collection ID, also known as the collection name, for the OCO-2 MIP Top-Down CO2 Budgets dataset is oco2-mip-co2budget-yeargrid-v1.*
**You can find the collection name of any dataset on the GHGC data portal by navigating to the dataset landing page within the data catalog. The collection name is the last portion of the dataset landing page’s URL, and is also listed in the pop-up box after clicking “ACCESS DATA.”*
# 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.
= "https://earth.gov/ghgcenter/api/stac"
STAC_API_URL
# The RASTER API is used to fetch collections for visualization
= "https://earth.gov/ghgcenter/api/raster"
RASTER_API_URL
# Define the collection name, used to fetch the dataset from the STAC API
= "oco2-mip-co2budget-yeargrid-v1" collection_name
# Using PySTAC client
# Fetch the collection from the STAC API using the appropriate endpoint
# The 'pystac' library allows a HTTP request possible
= Client.open(STAC_API_URL)
catalog = catalog.get_collection(collection_name)
collection
# Print the properties of the collection to the console
collection
- type "Collection"
- id "oco2-mip-co2budget-yeargrid-v1"
- stac_version "1.0.0"
- description "This dataset contains top-down annual net land-atmosphere fluxes of CO₂ on a 1-degree grid for a six-year period (2015-2020) from an ensamble of atmospheric CO₂ inversions, as well as annual bottom-up estimates of fossil fuel emissions and lateral carbon fluxes due to crop trade, wood trade, river export and cement production. Annual changes (loss) in terrestrial carbon stocks are obtained by combining top-down and bottom-up estimates. The standard deviation for each variable is provided (std). IS layers assimilates in situ CO₂ mole fraction measurements from an international observational network. LNLG layers assimilates CO₂ measurements from the ACOS v10 algorithm which uses land nadir and land glint total column dry-air mole fractions observed by the OCO-2 satellite. LNLGIS layers assimilates both in situ and ACOS v10 OCO-2 land nadir and glint retrievals together. OG layers assimilates CO₂ measurements from the ACOS v10 algorithm using OCO-2 ocean glint retrievals. LNLGOGIS assimilates all sources together (in situ (IS), OCO-2 ACOS v10 land nadir and land glint (LNLG), OCO-2 ACOS v10 ocean glint (OG)). Data is provided in units of grams of carbon dioxide per square meter per year (g CO₂/m2/yr). The source dataset is titled Pilot top-down CO₂ budget constrained by the v10 OCO-2 MIP Version 1 and can be found at https://doi.org/10.48588/npf6-sw92"
links[] 5 items
0
- rel "items"
- href "https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1/items"
- type "application/geo+json"
1
- rel "parent"
- href "https://earth.gov/ghgcenter/api/stac/"
- type "application/json"
2
- rel "root"
- href "https://earth.gov/ghgcenter/api/stac"
- type "application/json"
- title "US GHG Center STAC API"
3
- rel "self"
- href "https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1"
- type "application/json"
4
- rel "http://www.opengis.net/def/rel/ogc/1.0/queryables"
- href "https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1/queryables"
- type "application/schema+json"
- title "Queryables"
stac_extensions[] 2 items
- 0 "https://stac-extensions.github.io/render/v1.0.0/schema.json"
- 1 "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json"
renders
ff
assets[] 1 items
- 0 "ff"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 0
- 1 450
- colormap_name "purd"
crop
assets[] 1 items
- 0 "crop"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 -100
- 1 100
- colormap_name "coolwarm"
wood
assets[] 1 items
- 0 "wood"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 -100
- 1 100
- colormap_name "coolwarm"
river
assets[] 1 items
- 0 "river"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 -50
- 1 50
- colormap_name "coolwarm"
ff-std
assets[] 1 items
- 0 "ff-std"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 0
- 1 100
- colormap_name "purd"
crop-std
assets[] 1 items
- 0 "crop-std"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 0
- 1 50
- colormap_name "purd"
wood-std
assets[] 1 items
- 0 "wood-std"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 0
- 1 50
- colormap_name "purd"
dashboard
assets[] 1 items
- 0 "lnlgis-nbe"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 -1200
- 1 1200
- colormap_name "coolwarm"
river-std
assets[] 1 items
- 0 "river-std"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 0
- 1 60
- colormap_name "purd"
lnlgis-nbe
assets[] 1 items
- 0 "lnlgis-nbe"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 -1200
- 1 1200
- colormap_name "coolwarm"
lnlgis-nce
assets[] 1 items
- 0 "lnlgis-nce"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 -1200
- 1 1200
- colormap_name "coolwarm"
lnlgis-dc-loss
assets[] 1 items
- 0 "lnlgis-dc-loss"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 -600
- 1 600
- colormap_name "coolwarm"
lnlgis-nbe-std
assets[] 1 items
- 0 "lnlgis-nbe-std"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 0
- 1 1000
- colormap_name "purd"
lnlgis-nce-std
assets[] 1 items
- 0 "lnlgis-nce-std"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 0
- 1 1200
- colormap_name "purd"
lnlgis-dc-loss-std
assets[] 1 items
- 0 "lnlgis-dc-loss-std"
- nodata 0
rescale[] 1 items
0[] 2 items
- 0 0
- 1 800
- colormap_name "purd"
item_assets
ff
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Fossil Fuel and Cement Emissions"
- description "The burning of fossil fuels and release of carbon due to cement production, representing a flux of carbon from the land surface (geologic reservoir) to the atmosphere."
crop
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Lateral Crop Flux"
- description "The lateral flux of carbon in (positive) or out (negative) of a region due to agriculture."
wood
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Lateral Wood Flux"
- description "The lateral flux of carbon in (positive) or out (negative) of a region due to wood product harvesting and usage."
river
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Lateral River Flux"
- description "The lateral flux of carbon in (positive) or out (negative) of a region transported by the water cycle."
ff-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Fossil Fuel and Cement Emissions std"
- description "The burning of fossil fuels and release of carbon due to cement production, representing a flux of carbon from the land surface (geologic reservoir) to the atmosphere."
is-nbe
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "In Situ Net Biosphere Exchange"
- description "Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances)."
is-nce
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "In Situ Terrestrial Net Carbon Exchange"
- description "Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions."
crop-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Lateral Crop Flux std"
- description "The lateral flux of carbon in (positive) or out (negative) of a region due to agriculture."
lnlg-nbe
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir and Land Glint Net Biosphere Exchange"
- description "Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances."
lnlg-nce
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir and Land Glint Terrestrial Net Carbon Exchange"
- description "Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions."
wood-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Lateral Wood Flux std"
- description "The lateral flux of carbon in (positive) or out (negative) of a region due to wood product harvesting and usage."
river-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Lateral River Flux std"
- description "The lateral flux of carbon in (positive) or out (negative) of a region transported by the water cycle."
is-dc-loss
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "In Situ Net Terrestrial Carbon Stock Loss"
- description "Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.)."
is-nbe-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "In Situ Net Biosphere Exchange std"
- description "Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances."
is-nce-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "In Situ Terrestrial Net Carbon Exchange std"
- description "Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions."
lnlgis-nbe
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir, Land Glint and In Situ Net Biosphere Exchange"
- description "Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances."
lnlgis-nce
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir, Land Glint and In Situ Terrestrial Net Carbon Exchange"
- description "Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions."
lnlg-dc-loss
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir and Land Glint Net Terrestrial Carbon Stock Loss"
- description "Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.)."
lnlg-nbe-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir and Land Glint Net Biosphere Exchange std"
- description "Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances."
lnlg-nce-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir and Land Glint Terrestrial Net Carbon Exchange std"
- description "Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions."
lnlgogis-nbe
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir, Land Glint, Ocean Glint and In Situ Net Biosphere Exchange"
- description "Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances."
lnlgogis-nce
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir, Land Glint, Ocean Glint and In Situ Terrestrial Net Carbon Exchange"
- description "Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions."
is-dc-loss-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "In Situ Net Terrestrial Carbon Stock Loss std"
- description "Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.)."
lnlgis-dc-loss
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir, Land Glint and In Situ Net Terrestrial Carbon Stock Loss"
- description "Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.)."
lnlgis-nbe-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir, Land Glint and In Situ Net Biosphere Exchange std"
- description "Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances."
lnlgis-nce-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir, Land Glint and In Situ Terrestrial Net Carbon Exchange std"
- description "Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions."
lnlg-dc-loss-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir and Land Glint Net Terrestrial Carbon Stock Loss std"
- description "Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.)."
lnlgogis-dc-loss
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir, Land Glint, Ocean Glint and In Situ Net Terrestrial Carbon Stock Loss"
- description "Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.)."
lnlgogis-nbe-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir, Land Glint, Ocean Glint and In Situ Net Biosphere Exchange"
- description "Net flux of carbon from the terrestrial biosphere to the atmosphere due to biomass burning (BB) and Reco minus gross primary production (GPP). It includes both anthropogenic processes (e.g., deforestation, reforestation, farming) and natural processes (e.g., climate-variability-induced carbon fluxes, disturbances, recovery from disturbances."
lnlgogis-nce-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir, Land Glint, Ocean Glint and In Situ Terrestrial Net Carbon Exchange std"
- description "Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions."
lnlgis-dc-loss-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir, Land Glint and In Situ Net Terrestrial Carbon Stock Loss std"
- description "Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.)."
lnlgogis-dc-loss-std
- type "image/tiff; application=geotiff; profile=cloud-optimized"
roles[] 2 items
- 0 "data"
- 1 "layer"
- title "Land Nadir, Land Glint, Ocean Glint and In Situ Net Terrestrial Carbon Stock Loss std"
- description "Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.)."
- dashboard:is_periodic True
- dashboard:time_density "year"
- title "OCO-2 MIP Top-down CO₂ Budgets v1"
extent
spatial
bbox[] 1 items
0[] 4 items
- 0 -180.0
- 1 -90.0
- 2 180.0
- 3 90.0
temporal
interval[] 1 items
0[] 2 items
- 0 "2015-01-01T00:00:00Z"
- 1 "2020-01-01T00:00:00Z"
- license "CC-BY-4.0"
summaries
datetime[] 2 items
- 0 "2015-01-01T00:00:00+00:00"
- 1 "2020-01-01T00:00:00+00:00"
Examining the contents of our collection
under the temporal
variable, we see that the data is available from January 2015 to December 2020. By looking at the dashboard:time density
, we observe that the periodic frequency of these observations is yearly.
= list(collection.get_items()) # Convert the iterator to a list
items print(f"Found {len(items)} items")
Found 6 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
0] items[
- type "Feature"
- stac_version "1.0.0"
stac_extensions[] 0 items
- id "oco2-mip-co2budget-yeargrid-v1-2020"
geometry
- type "Polygon"
coordinates[] 1 items
0[] 5 items
0[] 2 items
- 0 -180
- 1 -90
1[] 2 items
- 0 180
- 1 -90
2[] 2 items
- 0 180
- 1 90
3[] 2 items
- 0 -180
- 1 90
4[] 2 items
- 0 -180
- 1 -90
bbox[] 4 items
- 0 -180.0
- 1 -90.0
- 2 180.0
- 3 90.0
properties
- end_datetime "2020-12-31T00:00:00+00:00"
- start_datetime "2020-01-01T00:00:00+00:00"
- datetime None
links[] 5 items
0
- rel "collection"
- href "https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1"
- type "application/json"
1
- rel "parent"
- href "https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1"
- type "application/json"
2
- rel "root"
- href "https://earth.gov/ghgcenter/api/stac/"
- type "application/json"
3
- rel "self"
- href "https://earth.gov/ghgcenter/api/stac/collections/oco2-mip-co2budget-yeargrid-v1/items/oco2-mip-co2budget-yeargrid-v1-2020"
- type "application/geo+json"
4
- rel "preview"
- href "https://earth.gov/ghgcenter/api/raster/collections/oco2-mip-co2budget-yeargrid-v1/items/oco2-mip-co2budget-yeargrid-v1-2020/map?assets=lnlgis-nbe&nodata=0&rescale=-1200%2C1200&colormap_name=coolwarm"
- type "text/html"
- title "Map of Item"
assets
ff
- href "s3://ghgc-data-store/oco2-mip-co2budget-yeargrid-v1/pilot_topdown_FF_CO2_Budget_grid_v1_2020.tif"
- type "image/tiff; application=geotiff; profile=cloud-optimized"
- title "Fossil Fuel and Cement Emissions"
- description "The burning of fossil fuels and release of carbon due to cement production, representing a flux of carbon from the land surface (geologic reservoir) to the atmosphere."
proj:bbox[] 4 items
- 0 -180.0
- 1 -90.0
- 2 180.0
- 3 90.0
- proj:epsg 4326.0
proj:shape[] 2 items
- 0 180.0
- 1 360.0
raster:bands[] 1 items
0
- scale 1.0
- offset 0.0
- sampling "area"
- data_type "float64"
histogram
- max 25762.3125
- min 0.0
- count 11.0
buckets[] 10 items
- 0 64516.0
- 1 199.0
- 2 48.0
- 3 17.0
- 4 10.0
- 5 4.0
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- type "image/tiff; application=geotiff; profile=cloud-optimized"
- title "Land Nadir, Land Glint, Ocean Glint and In Situ Terrestrial Net Carbon Exchange std"
- description "Net flux of carbon from the surface to the atmosphere. For land, NCE can be defined as sum of NBE and fossil fuel and cement emissions."
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- type "image/tiff; application=geotiff; profile=cloud-optimized"
- title "Land Nadir, Land Glint and In Situ Net Terrestrial Carbon Stock Loss std"
- description "Positive values indicate a loss (decrease) of terrestrial carbon stocks (organic matter stored on land), including above- and below-ground biomass in ecosystems and biomass contained in anthropogenic products (lumber, cattle, etc.)."
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- type "image/tiff; application=geotiff; profile=cloud-optimized"
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# Restructure our items into a dictionary where keys are the datetime items
# Then we can query more easily by date/time, e.g. "2020"
= {item.properties["start_datetime"][:4]: item for item in collection.get_items()} items_dict
# Before we go further, let's pick which asset to focus on for the remainder of the notebook.
# We'll focus CO2 fluxes from fossil fuels, so our asset of interest is:
= "ff" asset_name
Creating Maps Using Folium
You will now explore changes in the microbial CH₄ flux for two different dates/times. You will visualize the outputs on a map using folium
.
Fetch Imagery Using Raster API
Here we get information from the Raster API
which we will add to our map in the next section.
# Specify two date/times that you would like to visualize, using the format of items_dict.keys()
=["2015","2020"] dates
Below, we use some statistics of the raster data to set upper and lower limits for our color bar. These are saved as the rescale_values
, and will be passed to the Raster API in the following step(s).
# Extract collection name and item ID for the first date
= items_dict[dates[0]]
first_date = first_date.collection_id
collection_id = first_date.id
item_id # Select relevant asset
object = first_date.assets[asset_name]
= object.extra_fields.get("raster:bands", [{}])
raster_bands # Print raster bands' information
raster_bands
[{'scale': 1.0,
'offset': 0.0,
'sampling': 'area',
'data_type': 'float64',
'histogram': {'max': 26214.189453125,
'min': 0.0,
'count': 11.0,
'buckets': [64514.0, 196.0, 50.0, 25.0, 6.0, 3.0, 1.0, 3.0, 1.0, 1.0]},
'statistics': {'mean': 56.14299174917896,
'stddev': 442.02880191302614,
'maximum': 26214.189453125,
'minimum': 0.0,
'valid_percent': 0.00154320987654321}}]
# Use statistics to generate an appropriate colorbar range.
= {
rescale_values "max": raster_bands[0]['statistics']['mean'] + 4.0*raster_bands[0]['statistics']['stddev'],
"min": raster_bands[0]['statistics']['minimum'],
}
print(rescale_values)
{'max': 1824.2581994012835, 'min': 0.0}
Now, you will pass the item id
, collection name
, asset name
, and the rescale values
to the Raster API endpoint, along with a colormap. This step is done twice, one for each date/time you will visualize, and tells the Raster API which collection, item, and asset you want to view, specifying the colormap and colorbar ranges to use for visualization. The API returns a JSON with information about the requested image. Each image will be referred to as a tile.
# Choose a colormap for displaying the data
# Make sure to capitalize per Matplotlib standard colormap names
# For more information on Colormaps in Matplotlib, please visit https://matplotlib.org/stable/users/explain/colors/colormaps.html
= "PuRd" color_map
# Make a GET request to retrieve information for your first date/time
= requests.get(
co2_flux_1 f"{RASTER_API_URL}/collections/{collection_id}/items/{item_id}/tilejson.json?"
f"&assets={asset_name}"
f"&color_formula=gamma+r+1.05&colormap_name={color_map.lower()}"
f"&rescale={rescale_values['min']},{rescale_values['max']}"
).json()
# Print the properties of the retrieved granule to the console
co2_flux_1
{'tilejson': '2.2.0',
'version': '1.0.0',
'scheme': 'xyz',
'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/oco2-mip-co2budget-yeargrid-v1/items/oco2-mip-co2budget-yeargrid-v1-2015/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ff&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C1824.2581994012835'],
'minzoom': 0,
'maxzoom': 24,
'bounds': [-180.0, -90.0, 180.0, 90.0],
'center': [0.0, 0.0, 0]}
# Repeat the above for your second date/time
# Note that we do not calculate new rescale_values for this tile, because we want date tiles 1 and 2 to have the same colorbar range for best visual comparison.
= items_dict[dates[1]]
second_date
# Extract collection name and item ID
= second_date.collection_id
collection_id = second_date.id
item_id
= requests.get(
co2_flux_2 f"{RASTER_API_URL}/collections/{collection_id}/items/{item_id}/tilejson.json?"
f"&assets={asset_name}"
f"&color_formula=gamma+r+1.05&colormap_name={color_map.lower()}"
f"&rescale={rescale_values['min']},{rescale_values['max']}"
).json()
# Print the properties of the retrieved granule to the console
co2_flux_2
{'tilejson': '2.2.0',
'version': '1.0.0',
'scheme': 'xyz',
'tiles': ['https://earth.gov/ghgcenter/api/raster/collections/oco2-mip-co2budget-yeargrid-v1/items/oco2-mip-co2budget-yeargrid-v1-2020/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?assets=ff&color_formula=gamma+r+1.05&colormap_name=purd&rescale=0.0%2C1824.2581994012835'],
'minzoom': 0,
'maxzoom': 24,
'bounds': [-180.0, -90.0, 180.0, 90.0],
'center': [0.0, 0.0, 0]}
Generate Map
# Initialize the map, specifying the center of the map and the starting zoom level.
# 'folium.plugins' allows mapping side-by-side via 'DualMap'
# Map is centered on the position specified by "location=(lat,lon)"
= folium.plugins.DualMap(location=(34, -103), zoom_start=3)
map_
# Define the first map layer
= TileLayer(
map_layer_1=co2_flux_1["tiles"][0], # Path to retrieve the tile
tiles="GHG", # Set the attribution
attr=0.85, # Adjust the transparency of the layer
opacity=f"{collection.title}, {dates[0]}",
name=True
overlay
)
# Add the first layer to the Dual Map
map_layer_1.add_to(map_.m1)
# Define the second map layer
= TileLayer(
map_layer_2 =co2_flux_2["tiles"][0], # Path to retrieve the tile
tiles="GHG", # Set the attribution
attr=0.85, # Adjust the transparency of the layer,
opacity=f"{collection.title}, {dates[1]}",
name=True
overlay
)
# Add the second layer to the Dual Map
map_layer_2.add_to(map_.m2)
# Add a layer control to switch between map layers
=False).add_to(map_)
folium.LayerControl(collapsed
# Add colorbar
# We can use 'generate_html_colorbar' from the 'ghgc_utils' module
# to create an HTML colorbar representation.
= ghgc_utils.generate_html_colorbar(color_map,rescale_values,label=f'{items[0].assets[asset_name].title} (g CO2/m2/year)',dark=True)
legend_html
# Add colorbar to the map
map_.get_root().html.add_child(folium.Element(legend_html))
# Visualize the Dual Map
map_
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).
# Give the AOI a name to use in plotting later
= "Dallas-Fort Worth, Texas"
aoi_name # Define AOI as a GeoJSON
= {
aoi "type": "Feature", # Create a feature object
"properties": {},
"geometry": { # Set the bounding coordinates for the polygon
"coordinates": [
[-99, 33.7], # South-east bounding coordinate
[-99, 31.85], # North-east bounding coordinate
[-94.75,31.85], # North-west bounding coordinate
[-94.75,33.7], # South-west bounding coordinate
[-99, 33.7] # South-east bounding coordinate (closing the polygon)
[
]
],"type": "Polygon",
}, }
# Quick Folium map to visualize this AOI
= folium.Map(location=(32.5, -96.5), zoom_start=7)
map_ # Add AOI to map
=aoi_name).add_to(map_)
folium.GeoJson(aoi, name# Add data layer to visualize number of grid cells within AOI
map_layer_2.add_to(map_)# Add a quick colorbar
= ghgc_utils.generate_html_colorbar(color_map,rescale_values,label=f'{items[0].assets[asset_name].title} (g CO2/m2/year)',dark=True)
legend_html
map_.get_root().html.add_child(folium.Element(legend_html)) map_
Generate the statistics for the AOI using a function from the ghgc_utils
module, which fetches the data and its statistics from the Raster API.
%%time
# %%time = Wall time (execution time) for running the code below
# Statistics will be returned as a Pandas DataFrame
= ghgc_utils.generate_stats(items,aoi,url=RASTER_API_URL,asset=asset_name)
df # Print first five rows of stats from our DataFrame
5) df.head(
Generating stats...
Done!
CPU times: user 26.2 ms, sys: 0 ns, total: 26.2 ms
Wall time: 1.74 s
datetime | min | max | mean | count | sum | std | median | majority | minority | unique | histogram | valid_percent | masked_pixels | valid_pixels | percentile_2 | percentile_98 | date | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2020-01-01T00:00:00+00:00 | 38.34401321411132812500 | 2750.13330078125000000000 | 1165.56741810753874233342 | 8.17000007629394531250 | 9522.68589486432756530121 | 1022.80657612162599434669 | 1091.07080078125000000000 | 38.34401321411132812500 | 38.34401321411132812500 | 15.00000000000000000000 | [[6, 2, 0, 1, 1, 1, 0, 0, 1, 3], [38.344013214... | 100.00000000000000000000 | 0.00000000000000000000 | 15.00000000000000000000 | 38.34401321411132812500 | 2750.13330078125000000000 | 2020-01-01 00:00:00+00:00 |
1 | 2019-01-01T00:00:00+00:00 | 44.85459899902343750000 | 3046.95703125000000000000 | 1312.26374809024650858191 | 8.17000007629394531250 | 10721.19492201509274309501 | 1150.11834215528142522089 | 1226.69079589843750000000 | 44.85459899902343750000 | 44.85459899902343750000 | 15.00000000000000000000 | [[6, 2, 0, 1, 1, 1, 0, 0, 1, 3], [44.854598999... | 100.00000000000000000000 | 0.00000000000000000000 | 15.00000000000000000000 | 44.85459899902343750000 | 3046.95703125000000000000 | 2019-01-01 00:00:00+00:00 |
2 | 2018-01-01T00:00:00+00:00 | 45.94244003295898437500 | 3129.08129882812500000000 | 1346.56139881486274134659 | 8.17000007629394531250 | 11001.40673105190944625065 | 1180.23846463163772568805 | 1258.82971191406250000000 | 45.94244003295898437500 | 45.94244003295898437500 | 15.00000000000000000000 | [[6, 2, 0, 1, 1, 1, 0, 0, 1, 3], [45.942440032... | 100.00000000000000000000 | 0.00000000000000000000 | 15.00000000000000000000 | 45.94244003295898437500 | 3129.08129882812500000000 | 2018-01-01 00:00:00+00:00 |
3 | 2017-01-01T00:00:00+00:00 | 44.66185760498046875000 | 3033.76367187500000000000 | 1306.59445668284115527058 | 8.17000007629394531250 | 10674.87681078405876178294 | 1145.14884120936176259420 | 1221.38989257812500000000 | 44.66185760498046875000 | 44.66185760498046875000 | 15.00000000000000000000 | [[6, 2, 0, 1, 1, 1, 0, 0, 1, 3], [44.661857604... | 100.00000000000000000000 | 0.00000000000000000000 | 15.00000000000000000000 | 44.66185760498046875000 | 3033.76367187500000000000 | 2017-01-01 00:00:00+00:00 |
4 | 2016-01-01T00:00:00+00:00 | 45.04702377319335937500 | 3074.26977539062500000000 | 1322.02282230678315499972 | 8.17000007629394531250 | 10800.92655910875510016922 | 1158.77874239572884107474 | 1235.76232910156250000000 | 45.04702377319335937500 | 45.04702377319335937500 | 15.00000000000000000000 | [[6, 2, 0, 1, 1, 1, 0, 0, 1, 3], [45.047023773... | 100.00000000000000000000 | 0.00000000000000000000 | 15.00000000000000000000 | 45.04702377319335937500 | 3074.26977539062500000000 | 2016-01-01 00:00:00+00:00 |
Time-Series Analysis
We can now explore the fossil fuel emission time series (January 2015 -December 2020) available for the Dallas, Texas area of the U.S. We can plot the data set using the code below:
# Figure size: 10 is width, 5 is height
= plt.figure(figsize=(10,5))
fig
# Sort our df by datetime
= df.sort_values(by="datetime")
df
# Change 'which_stat' below if you would rather look at a different statistic, like minimum or mean.
= "mean"
which_stat
plt.plot(0:4] for d in df["datetime"]], # X-axis: sorted datetime
[d[# Y-axis: maximum CO₂ emission
df[which_stat], ="red", # Line color
color="-", # Line style
linestyle=2, # Line width
linewidth="CO$_2$ emissions", # Legend label
label
)
# Display legend
plt.legend()
# Insert label for the X-axis
"Years")
plt.xlabel(
# Insert label for the Y-axis
"gC/$m^2$/year")
plt.ylabel(
# Insert title for the plot
f"{items[0].assets[asset_name].title} for {aoi_name} (2015-2020)")
plt.title(
# Add data citation
plt.text(min([d[0:4] for d in df["datetime"]]), # X-coordinate of the text
min(), # Y-coordinate of the text
df[which_stat].# Text to be displayed
f"Source: {collection.title}",
=9, # Font size
fontsize="left", # Horizontal alignment
horizontalalignment="top", # Vertical alignment
verticalalignment="blue", # Text color
color
)
# Plot the time series
plt.show()
Summary
In this notebook we have successfully explored, analyzed, and visualized the STAC collection for OCO-2 MIP Top-Down CO₂ Budgets.
- Install and import the necessary libraries
- Fetch the collection from STAC collections using the appropriate endpoints
- Count the number of existing granules within the collection
- Visualizing CO₂ Emissions for two distinctive months/years
- Generate zonal statistics for a specified region
- Generate a time-series graph
If you have any questions regarding this user notebook, please contact us using the feedback form.