Calculate timeseries from COGs

Demonstrates how to aggregate COGs using pystac_client, rioxarray, and stackstac

Run this notebook

You can launch this notebook in VEDA JupyterHub by clicking the link below.

Launch in VEDA JupyterHub (requires access)

Learn more

Inside the Hub

This notebook was written on the VEDA JupyterHub and as such is designed to be run on a jupyterhub which is associated with an AWS IAM role which has been granted permissions to the VEDA data store via its bucket policy. The instance used provided 16GB of RAM.

See (VEDA Analytics JupyterHub Access)[https://nasa-impact.github.io/veda-docs/veda-jh-access.html] for information about how to gain access.

Outside the Hub

The data is in a protected bucket. Please request access by emailng aimee@developmentseed.org or alexandra@developmentseed.org and providing your affiliation, interest in or expected use of the dataset and an AWS IAM role or user Amazon Resource Name (ARN). The team will help you configure the cognito client.

You should then run:

%run -i 'cognito_login.py'

Approach

1. Use pystac_client to open the STAC catalog and retrieve the items in the collection
2. Use stackstac to create an xarray dataset containing all the items cropped to AOI
3. Calculate the mean for each timestep over the AOI

from pystac_client import Client
import pandas as pd
import stackstac

import rioxarray  # noqa
import hvplot.xarray  # noqa

Declare your collection of interest

You can discover available collections the following ways:

• Programmatically: see example in the list-collections.ipynb notebook
• JSON API: https://staging-stac.delta-backend.com/collections
• STAC Browser: http://veda-staging-stac-browser.s3-website-us-west-2.amazonaws.com

STAC_API_URL = "https://staging-stac.delta-backend.com/"
collection = "no2-monthly"

Discover items in collection for region and time of interest

Use pystac_client to search the STAC collection for a particular area of interest within specified datetime bounds.

china_bbox = [
    73.675,
    18.198,
    135.026,
    53.459,
]
datetime = "2000-01-01/2022-01-02"

catalog = Client.open(STAC_API_URL)
search = catalog.search(
    bbox=china_bbox, datetime=datetime, collections=[collection], limit=1000
)
item_collection = search.item_collection()
print(f"Found {len(item_collection)} items")

Found 73 items

Read data

Read in data using xarray using a combination of xpystac, stackstac, and rasterio.

da = stackstac.stack(item_collection, epsg=4326)
da = da.assign_coords({"time": pd.to_datetime(da.start_datetime)}).squeeze()
da

/srv/conda/envs/notebook/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
  times = pd.to_datetime(

<xarray.DataArray 'stackstac-501e9e9badc173d84c999a59abafa101' (time: 73,
                                                                y: 1800, x: 3600)>
dask.array<getitem, shape=(73, 1800, 3600), dtype=float64, chunksize=(1, 1024, 1024), chunktype=numpy.ndarray>
Coordinates: (12/15)
    id              (time) <U37 'OMI_trno2_0.10x0.10_202201_Col3_V4.nc' ... '...
    band            <U11 'cog_default'
  * x               (x) float64 -180.0 -179.9 -179.8 ... 179.7 179.8 179.9
  * y               (y) float64 90.0 89.9 89.8 89.7 ... -89.6 -89.7 -89.8 -89.9
    proj:shape      object {1800.0, 3600.0}
    start_datetime  (time) <U19 '2022-01-01T00:00:00' ... '2016-01-01T00:00:00'
    ...              ...
    end_datetime    (time) <U19 '2022-01-31T00:00:00' ... '2016-01-31T00:00:00'
    proj:geometry   object {'type': 'Polygon', 'coordinates': [[[-180.0, -90....
    title           <U17 'Default COG Layer'
    description     <U47 'Cloud optimized default layer to display on map'
    epsg            int64 4326
  * time            (time) datetime64[ns] 2022-01-01 2021-12-01 ... 2016-01-01
Attributes:
    spec:        RasterSpec(epsg=4326, bounds=(-180.0, -90.0, 180.0, 90.0), r...
    crs:         epsg:4326
    transform:   | 0.10, 0.00,-180.00|\n| 0.00,-0.10, 90.00|\n| 0.00, 0.00, 1...
    resolution:  0.1

xarray.DataArray

'stackstac-501e9e9badc173d84c999a59abafa101'

• time: 73
• y: 1800
• x: 3600

• dask.array<chunksize=(1, 1024, 1024), meta=np.ndarray>

  Array Chunk Bytes 3.52 GiB 8.00 MiB Shape (73, 1800, 3600) (1, 1024, 1024) Dask graph 584 chunks in 4 graph layers Data type float64 numpy.ndarray

• Coordinates: (15)
  • id
    (time)
    <U37
    'OMI_trno2_0.10x0.10_202201_Col3...

    array(['OMI_trno2_0.10x0.10_202201_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202112_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202111_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202110_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202109_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202108_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202107_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202106_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202105_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202104_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202103_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202102_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202101_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202012_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202011_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202010_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202009_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202008_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202007_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202006_Col3_V4.nc',
    ...
           'OMI_trno2_0.10x0.10_201708_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201707_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201706_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201705_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201704_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201703_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201702_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201701_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201612_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201611_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201610_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201609_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201608_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201607_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201606_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201605_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201604_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201603_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201602_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201601_Col3_V4.nc'], dtype='<U37')

  • band
    ()
    <U11
    'cog_default'

    array('cog_default', dtype='<U11')

  • x
    (x)
    float64
    -180.0 -179.9 ... 179.8 179.9

    array([-180. , -179.9, -179.8, ...,  179.7,  179.8,  179.9])

  • y
    (y)
    float64
    90.0 89.9 89.8 ... -89.8 -89.9

    array([ 90. ,  89.9,  89.8, ..., -89.7, -89.8, -89.9])

  • proj:shape
    ()
    object
    {1800.0, 3600.0}

    array({1800.0, 3600.0}, dtype=object)

  • start_datetime
    (time)
    <U19
    '2022-01-01T00:00:00' ... '2016-...

    array(['2022-01-01T00:00:00', '2021-12-01T00:00:00',
           '2021-11-01T00:00:00', '2021-10-01T00:00:00',
           '2021-09-01T00:00:00', '2021-08-01T00:00:00',
           '2021-07-01T00:00:00', '2021-06-01T00:00:00',
           '2021-05-01T00:00:00', '2021-04-01T00:00:00',
           '2021-03-01T00:00:00', '2021-02-01T00:00:00',
           '2021-01-01T00:00:00', '2020-12-01T00:00:00',
           '2020-11-01T00:00:00', '2020-10-01T00:00:00',
           '2020-09-01T00:00:00', '2020-08-01T00:00:00',
           '2020-07-01T00:00:00', '2020-06-01T00:00:00',
           '2020-05-01T00:00:00', '2020-04-01T00:00:00',
           '2020-03-01T00:00:00', '2020-02-01T00:00:00',
           '2020-01-01T00:00:00', '2019-12-01T00:00:00',
           '2019-11-01T00:00:00', '2019-10-01T00:00:00',
           '2019-09-01T00:00:00', '2019-08-01T00:00:00',
           '2019-07-01T00:00:00', '2019-06-01T00:00:00',
           '2019-05-01T00:00:00', '2019-04-01T00:00:00',
           '2019-03-01T00:00:00', '2019-02-01T00:00:00',
           '2019-01-01T00:00:00', '2018-12-01T00:00:00',
           '2018-11-01T00:00:00', '2018-10-01T00:00:00',
           '2018-09-01T00:00:00', '2018-08-01T00:00:00',
           '2018-07-01T00:00:00', '2018-06-01T00:00:00',
           '2018-05-01T00:00:00', '2018-04-01T00:00:00',
           '2018-03-01T00:00:00', '2018-02-01T00:00:00',
           '2018-01-01T00:00:00', '2017-12-01T00:00:00',
           '2017-11-01T00:00:00', '2017-10-01T00:00:00',
           '2017-09-01T00:00:00', '2017-08-01T00:00:00',
           '2017-07-01T00:00:00', '2017-06-01T00:00:00',
           '2017-05-01T00:00:00', '2017-04-01T00:00:00',
           '2017-03-01T00:00:00', '2017-02-01T00:00:00',
           '2017-01-01T00:00:00', '2016-12-01T00:00:00',
           '2016-11-01T00:00:00', '2016-10-01T00:00:00',
           '2016-09-01T00:00:00', '2016-08-01T00:00:00',
           '2016-07-01T00:00:00', '2016-06-01T00:00:00',
           '2016-05-01T00:00:00', '2016-04-01T00:00:00',
           '2016-03-01T00:00:00', '2016-02-01T00:00:00',
           '2016-01-01T00:00:00'], dtype='<U19')

  • proj:bbox
    ()
    object
    {90.0, 180.0, -90.0, -180.0}

    array({90.0, 180.0, -90.0, -180.0}, dtype=object)

  • proj:transform
    ()
    object
    {0.1, 0.0, 1.0, -0.1, -180.0, 90.0}

    array({0.1, 0.0, 1.0, -0.1, -180.0, 90.0}, dtype=object)

  • proj:epsg
    ()
    float64
    4.326e+03

    array(4326.)

  • end_datetime
    (time)
    <U19
    '2022-01-31T00:00:00' ... '2016-...

    array(['2022-01-31T00:00:00', '2021-12-31T00:00:00',
           '2021-11-30T00:00:00', '2021-10-31T00:00:00',
           '2021-09-30T00:00:00', '2021-08-31T00:00:00',
           '2021-07-31T00:00:00', '2021-06-30T00:00:00',
           '2021-05-31T00:00:00', '2021-04-30T00:00:00',
           '2021-03-31T00:00:00', '2021-02-28T00:00:00',
           '2021-01-31T00:00:00', '2020-12-31T00:00:00',
           '2020-11-30T00:00:00', '2020-10-31T00:00:00',
           '2020-09-30T00:00:00', '2020-08-31T00:00:00',
           '2020-07-31T00:00:00', '2020-06-30T00:00:00',
           '2020-05-31T00:00:00', '2020-04-30T00:00:00',
           '2020-03-31T00:00:00', '2020-02-29T00:00:00',
           '2020-01-31T00:00:00', '2019-12-31T00:00:00',
           '2019-11-30T00:00:00', '2019-10-31T00:00:00',
           '2019-09-30T00:00:00', '2019-08-31T00:00:00',
           '2019-07-31T00:00:00', '2019-06-30T00:00:00',
           '2019-05-31T00:00:00', '2019-04-30T00:00:00',
           '2019-03-31T00:00:00', '2019-02-28T00:00:00',
           '2019-01-31T00:00:00', '2018-12-31T00:00:00',
           '2018-11-30T00:00:00', '2018-10-31T00:00:00',
           '2018-09-30T00:00:00', '2018-08-31T00:00:00',
           '2018-07-31T00:00:00', '2018-06-30T00:00:00',
           '2018-05-31T00:00:00', '2018-04-30T00:00:00',
           '2018-03-31T00:00:00', '2018-02-28T00:00:00',
           '2018-01-31T00:00:00', '2017-12-31T00:00:00',
           '2017-11-30T00:00:00', '2017-10-31T00:00:00',
           '2017-09-30T00:00:00', '2017-08-31T00:00:00',
           '2017-07-31T00:00:00', '2017-06-30T00:00:00',
           '2017-05-31T00:00:00', '2017-04-30T00:00:00',
           '2017-03-31T00:00:00', '2017-02-28T00:00:00',
           '2017-01-31T00:00:00', '2016-12-31T00:00:00',
           '2016-11-30T00:00:00', '2016-10-31T00:00:00',
           '2016-09-30T00:00:00', '2016-08-31T00:00:00',
           '2016-07-31T00:00:00', '2016-06-30T00:00:00',
           '2016-05-31T00:00:00', '2016-04-30T00:00:00',
           '2016-03-31T00:00:00', '2016-02-29T00:00:00',
           '2016-01-31T00:00:00'], dtype='<U19')

  • proj:geometry
    ()
    object
    {'type': 'Polygon', 'coordinates...

    array({'type': 'Polygon', 'coordinates': [[[-180.0, -90.0], [180.0, -90.0], [180.0, 90.0], [-180.0, 90.0], [-180.0, -90.0]]]},
          dtype=object)

  • title
    ()
    <U17
    'Default COG Layer'

    array('Default COG Layer', dtype='<U17')

  • description
    ()
    <U47
    'Cloud optimized default layer t...

    array('Cloud optimized default layer to display on map', dtype='<U47')

  • epsg
    ()
    int64
    4326

    array(4326)

  • time
    (time)
    datetime64[ns]
    2022-01-01 ... 2016-01-01

    array(['2022-01-01T00:00:00.000000000', '2021-12-01T00:00:00.000000000',
           '2021-11-01T00:00:00.000000000', '2021-10-01T00:00:00.000000000',
           '2021-09-01T00:00:00.000000000', '2021-08-01T00:00:00.000000000',
           '2021-07-01T00:00:00.000000000', '2021-06-01T00:00:00.000000000',
           '2021-05-01T00:00:00.000000000', '2021-04-01T00:00:00.000000000',
           '2021-03-01T00:00:00.000000000', '2021-02-01T00:00:00.000000000',
           '2021-01-01T00:00:00.000000000', '2020-12-01T00:00:00.000000000',
           '2020-11-01T00:00:00.000000000', '2020-10-01T00:00:00.000000000',
           '2020-09-01T00:00:00.000000000', '2020-08-01T00:00:00.000000000',
           '2020-07-01T00:00:00.000000000', '2020-06-01T00:00:00.000000000',
           '2020-05-01T00:00:00.000000000', '2020-04-01T00:00:00.000000000',
           '2020-03-01T00:00:00.000000000', '2020-02-01T00:00:00.000000000',
           '2020-01-01T00:00:00.000000000', '2019-12-01T00:00:00.000000000',
           '2019-11-01T00:00:00.000000000', '2019-10-01T00:00:00.000000000',
           '2019-09-01T00:00:00.000000000', '2019-08-01T00:00:00.000000000',
           '2019-07-01T00:00:00.000000000', '2019-06-01T00:00:00.000000000',
           '2019-05-01T00:00:00.000000000', '2019-04-01T00:00:00.000000000',
           '2019-03-01T00:00:00.000000000', '2019-02-01T00:00:00.000000000',
           '2019-01-01T00:00:00.000000000', '2018-12-01T00:00:00.000000000',
           '2018-11-01T00:00:00.000000000', '2018-10-01T00:00:00.000000000',
           '2018-09-01T00:00:00.000000000', '2018-08-01T00:00:00.000000000',
           '2018-07-01T00:00:00.000000000', '2018-06-01T00:00:00.000000000',
           '2018-05-01T00:00:00.000000000', '2018-04-01T00:00:00.000000000',
           '2018-03-01T00:00:00.000000000', '2018-02-01T00:00:00.000000000',
           '2018-01-01T00:00:00.000000000', '2017-12-01T00:00:00.000000000',
           '2017-11-01T00:00:00.000000000', '2017-10-01T00:00:00.000000000',
           '2017-09-01T00:00:00.000000000', '2017-08-01T00:00:00.000000000',
           '2017-07-01T00:00:00.000000000', '2017-06-01T00:00:00.000000000',
           '2017-05-01T00:00:00.000000000', '2017-04-01T00:00:00.000000000',
           '2017-03-01T00:00:00.000000000', '2017-02-01T00:00:00.000000000',
           '2017-01-01T00:00:00.000000000', '2016-12-01T00:00:00.000000000',
           '2016-11-01T00:00:00.000000000', '2016-10-01T00:00:00.000000000',
           '2016-09-01T00:00:00.000000000', '2016-08-01T00:00:00.000000000',
           '2016-07-01T00:00:00.000000000', '2016-06-01T00:00:00.000000000',
           '2016-05-01T00:00:00.000000000', '2016-04-01T00:00:00.000000000',
           '2016-03-01T00:00:00.000000000', '2016-02-01T00:00:00.000000000',
           '2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Indexes: (3)
  • x
    PandasIndex

    PandasIndex(Index([            -180.0,             -179.9,             -179.8,
                       -179.7,             -179.6,             -179.5,
                       -179.4,             -179.3,             -179.2,
                       -179.1,
           ...
                        179.0, 179.10000000000002, 179.20000000000005,
                        179.3, 179.40000000000003,              179.5,
           179.60000000000002, 179.70000000000005,              179.8,
           179.90000000000003],
          dtype='float64', name='x', length=3600))

  • y
    PandasIndex

    PandasIndex(Index([              90.0,               89.9,               89.8,
                         89.7,               89.6,               89.5,
                         89.4,               89.3,               89.2,
                         89.1,
           ...
                        -89.0, -89.10000000000002, -89.20000000000002,
           -89.30000000000001,              -89.4,              -89.5,
           -89.60000000000002, -89.70000000000002, -89.80000000000001,
                        -89.9],
          dtype='float64', name='y', length=1800))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-01-01', '2021-12-01', '2021-11-01', '2021-10-01',
                   '2021-09-01', '2021-08-01', '2021-07-01', '2021-06-01',
                   '2021-05-01', '2021-04-01', '2021-03-01', '2021-02-01',
                   '2021-01-01', '2020-12-01', '2020-11-01', '2020-10-01',
                   '2020-09-01', '2020-08-01', '2020-07-01', '2020-06-01',
                   '2020-05-01', '2020-04-01', '2020-03-01', '2020-02-01',
                   '2020-01-01', '2019-12-01', '2019-11-01', '2019-10-01',
                   '2019-09-01', '2019-08-01', '2019-07-01', '2019-06-01',
                   '2019-05-01', '2019-04-01', '2019-03-01', '2019-02-01',
                   '2019-01-01', '2018-12-01', '2018-11-01', '2018-10-01',
                   '2018-09-01', '2018-08-01', '2018-07-01', '2018-06-01',
                   '2018-05-01', '2018-04-01', '2018-03-01', '2018-02-01',
                   '2018-01-01', '2017-12-01', '2017-11-01', '2017-10-01',
                   '2017-09-01', '2017-08-01', '2017-07-01', '2017-06-01',
                   '2017-05-01', '2017-04-01', '2017-03-01', '2017-02-01',
                   '2017-01-01', '2016-12-01', '2016-11-01', '2016-10-01',
                   '2016-09-01', '2016-08-01', '2016-07-01', '2016-06-01',
                   '2016-05-01', '2016-04-01', '2016-03-01', '2016-02-01',
                   '2016-01-01'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (4)

  spec :

  RasterSpec(epsg=4326, bounds=(-180.0, -90.0, 180.0, 90.0), resolutions_xy=(0.1, 0.1))

  crs :

  epsg:4326

  transform :

  | 0.10, 0.00,-180.00| | 0.00,-0.10, 90.00| | 0.00, 0.00, 1.00|

  resolution :

  0.1

Clip the data to the bounding box for China

# Subset to Bounding Box for China
subset = da.rio.clip_box(*china_bbox)
subset

<xarray.DataArray 'stackstac-501e9e9badc173d84c999a59abafa101' (time: 73,
                                                                y: 354, x: 614)>
dask.array<getitem, shape=(73, 354, 614), dtype=float64, chunksize=(1, 354, 535), chunktype=numpy.ndarray>
Coordinates: (12/16)
    id              (time) <U37 'OMI_trno2_0.10x0.10_202201_Col3_V4.nc' ... '...
    band            <U11 'cog_default'
  * x               (x) float64 73.7 73.8 73.9 74.0 ... 134.7 134.8 134.9 135.0
  * y               (y) float64 53.5 53.4 53.3 53.2 53.1 ... 18.5 18.4 18.3 18.2
    proj:shape      object {1800.0, 3600.0}
    start_datetime  (time) <U19 '2022-01-01T00:00:00' ... '2016-01-01T00:00:00'
    ...              ...
    proj:geometry   object {'type': 'Polygon', 'coordinates': [[[-180.0, -90....
    title           <U17 'Default COG Layer'
    description     <U47 'Cloud optimized default layer to display on map'
    epsg            int64 4326
  * time            (time) datetime64[ns] 2022-01-01 2021-12-01 ... 2016-01-01
    spatial_ref     int64 0
Attributes:
    spec:        RasterSpec(epsg=4326, bounds=(-180.0, -90.0, 180.0, 90.0), r...
    resolution:  0.1

xarray.DataArray

'stackstac-501e9e9badc173d84c999a59abafa101'

• time: 73
• y: 354
• x: 614

• dask.array<chunksize=(1, 354, 535), meta=np.ndarray>

  Array Chunk Bytes 121.06 MiB 1.44 MiB Shape (73, 354, 614) (1, 354, 535) Dask graph 146 chunks in 5 graph layers Data type float64 numpy.ndarray

• Coordinates: (16)
  • id
    (time)
    <U37
    'OMI_trno2_0.10x0.10_202201_Col3...

    array(['OMI_trno2_0.10x0.10_202201_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202112_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202111_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202110_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202109_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202108_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202107_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202106_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202105_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202104_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202103_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202102_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202101_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202012_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202011_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202010_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202009_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202008_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202007_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_202006_Col3_V4.nc',
    ...
           'OMI_trno2_0.10x0.10_201708_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201707_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201706_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201705_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201704_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201703_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201702_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201701_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201612_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201611_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201610_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201609_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201608_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201607_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201606_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201605_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201604_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201603_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201602_Col3_V4.nc',
           'OMI_trno2_0.10x0.10_201601_Col3_V4.nc'], dtype='<U37')

  • band
    ()
    <U11
    'cog_default'

    array('cog_default', dtype='<U11')

  • x
    (x)
    float64
    73.7 73.8 73.9 ... 134.9 135.0

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([ 73.7,  73.8,  73.9, ..., 134.8, 134.9, 135. ])

  • y
    (y)
    float64
    53.5 53.4 53.3 ... 18.4 18.3 18.2

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([53.5, 53.4, 53.3, ..., 18.4, 18.3, 18.2])

  • proj:shape
    ()
    object
    {1800.0, 3600.0}

    array({1800.0, 3600.0}, dtype=object)

  • start_datetime
    (time)
    <U19
    '2022-01-01T00:00:00' ... '2016-...

    array(['2022-01-01T00:00:00', '2021-12-01T00:00:00',
           '2021-11-01T00:00:00', '2021-10-01T00:00:00',
           '2021-09-01T00:00:00', '2021-08-01T00:00:00',
           '2021-07-01T00:00:00', '2021-06-01T00:00:00',
           '2021-05-01T00:00:00', '2021-04-01T00:00:00',
           '2021-03-01T00:00:00', '2021-02-01T00:00:00',
           '2021-01-01T00:00:00', '2020-12-01T00:00:00',
           '2020-11-01T00:00:00', '2020-10-01T00:00:00',
           '2020-09-01T00:00:00', '2020-08-01T00:00:00',
           '2020-07-01T00:00:00', '2020-06-01T00:00:00',
           '2020-05-01T00:00:00', '2020-04-01T00:00:00',
           '2020-03-01T00:00:00', '2020-02-01T00:00:00',
           '2020-01-01T00:00:00', '2019-12-01T00:00:00',
           '2019-11-01T00:00:00', '2019-10-01T00:00:00',
           '2019-09-01T00:00:00', '2019-08-01T00:00:00',
           '2019-07-01T00:00:00', '2019-06-01T00:00:00',
           '2019-05-01T00:00:00', '2019-04-01T00:00:00',
           '2019-03-01T00:00:00', '2019-02-01T00:00:00',
           '2019-01-01T00:00:00', '2018-12-01T00:00:00',
           '2018-11-01T00:00:00', '2018-10-01T00:00:00',
           '2018-09-01T00:00:00', '2018-08-01T00:00:00',
           '2018-07-01T00:00:00', '2018-06-01T00:00:00',
           '2018-05-01T00:00:00', '2018-04-01T00:00:00',
           '2018-03-01T00:00:00', '2018-02-01T00:00:00',
           '2018-01-01T00:00:00', '2017-12-01T00:00:00',
           '2017-11-01T00:00:00', '2017-10-01T00:00:00',
           '2017-09-01T00:00:00', '2017-08-01T00:00:00',
           '2017-07-01T00:00:00', '2017-06-01T00:00:00',
           '2017-05-01T00:00:00', '2017-04-01T00:00:00',
           '2017-03-01T00:00:00', '2017-02-01T00:00:00',
           '2017-01-01T00:00:00', '2016-12-01T00:00:00',
           '2016-11-01T00:00:00', '2016-10-01T00:00:00',
           '2016-09-01T00:00:00', '2016-08-01T00:00:00',
           '2016-07-01T00:00:00', '2016-06-01T00:00:00',
           '2016-05-01T00:00:00', '2016-04-01T00:00:00',
           '2016-03-01T00:00:00', '2016-02-01T00:00:00',
           '2016-01-01T00:00:00'], dtype='<U19')

  • proj:bbox
    ()
    object
    {90.0, 180.0, -90.0, -180.0}

    array({90.0, 180.0, -90.0, -180.0}, dtype=object)

  • proj:transform
    ()
    object
    {0.1, 0.0, 1.0, -0.1, -180.0, 90.0}

    array({0.1, 0.0, 1.0, -0.1, -180.0, 90.0}, dtype=object)

  • proj:epsg
    ()
    float64
    4.326e+03

    array(4326.)

  • end_datetime
    (time)
    <U19
    '2022-01-31T00:00:00' ... '2016-...

    array(['2022-01-31T00:00:00', '2021-12-31T00:00:00',
           '2021-11-30T00:00:00', '2021-10-31T00:00:00',
           '2021-09-30T00:00:00', '2021-08-31T00:00:00',
           '2021-07-31T00:00:00', '2021-06-30T00:00:00',
           '2021-05-31T00:00:00', '2021-04-30T00:00:00',
           '2021-03-31T00:00:00', '2021-02-28T00:00:00',
           '2021-01-31T00:00:00', '2020-12-31T00:00:00',
           '2020-11-30T00:00:00', '2020-10-31T00:00:00',
           '2020-09-30T00:00:00', '2020-08-31T00:00:00',
           '2020-07-31T00:00:00', '2020-06-30T00:00:00',
           '2020-05-31T00:00:00', '2020-04-30T00:00:00',
           '2020-03-31T00:00:00', '2020-02-29T00:00:00',
           '2020-01-31T00:00:00', '2019-12-31T00:00:00',
           '2019-11-30T00:00:00', '2019-10-31T00:00:00',
           '2019-09-30T00:00:00', '2019-08-31T00:00:00',
           '2019-07-31T00:00:00', '2019-06-30T00:00:00',
           '2019-05-31T00:00:00', '2019-04-30T00:00:00',
           '2019-03-31T00:00:00', '2019-02-28T00:00:00',
           '2019-01-31T00:00:00', '2018-12-31T00:00:00',
           '2018-11-30T00:00:00', '2018-10-31T00:00:00',
           '2018-09-30T00:00:00', '2018-08-31T00:00:00',
           '2018-07-31T00:00:00', '2018-06-30T00:00:00',
           '2018-05-31T00:00:00', '2018-04-30T00:00:00',
           '2018-03-31T00:00:00', '2018-02-28T00:00:00',
           '2018-01-31T00:00:00', '2017-12-31T00:00:00',
           '2017-11-30T00:00:00', '2017-10-31T00:00:00',
           '2017-09-30T00:00:00', '2017-08-31T00:00:00',
           '2017-07-31T00:00:00', '2017-06-30T00:00:00',
           '2017-05-31T00:00:00', '2017-04-30T00:00:00',
           '2017-03-31T00:00:00', '2017-02-28T00:00:00',
           '2017-01-31T00:00:00', '2016-12-31T00:00:00',
           '2016-11-30T00:00:00', '2016-10-31T00:00:00',
           '2016-09-30T00:00:00', '2016-08-31T00:00:00',
           '2016-07-31T00:00:00', '2016-06-30T00:00:00',
           '2016-05-31T00:00:00', '2016-04-30T00:00:00',
           '2016-03-31T00:00:00', '2016-02-29T00:00:00',
           '2016-01-31T00:00:00'], dtype='<U19')

  • proj:geometry
    ()
    object
    {'type': 'Polygon', 'coordinates...

    array({'type': 'Polygon', 'coordinates': [[[-180.0, -90.0], [180.0, -90.0], [180.0, 90.0], [-180.0, 90.0], [-180.0, -90.0]]]},
          dtype=object)

  • title
    ()
    <U17
    'Default COG Layer'

    array('Default COG Layer', dtype='<U17')

  • description
    ()
    <U47
    'Cloud optimized default layer t...

    array('Cloud optimized default layer to display on map', dtype='<U47')

  • epsg
    ()
    int64
    4326

    array(4326)

  • time
    (time)
    datetime64[ns]
    2022-01-01 ... 2016-01-01

    array(['2022-01-01T00:00:00.000000000', '2021-12-01T00:00:00.000000000',
           '2021-11-01T00:00:00.000000000', '2021-10-01T00:00:00.000000000',
           '2021-09-01T00:00:00.000000000', '2021-08-01T00:00:00.000000000',
           '2021-07-01T00:00:00.000000000', '2021-06-01T00:00:00.000000000',
           '2021-05-01T00:00:00.000000000', '2021-04-01T00:00:00.000000000',
           '2021-03-01T00:00:00.000000000', '2021-02-01T00:00:00.000000000',
           '2021-01-01T00:00:00.000000000', '2020-12-01T00:00:00.000000000',
           '2020-11-01T00:00:00.000000000', '2020-10-01T00:00:00.000000000',
           '2020-09-01T00:00:00.000000000', '2020-08-01T00:00:00.000000000',
           '2020-07-01T00:00:00.000000000', '2020-06-01T00:00:00.000000000',
           '2020-05-01T00:00:00.000000000', '2020-04-01T00:00:00.000000000',
           '2020-03-01T00:00:00.000000000', '2020-02-01T00:00:00.000000000',
           '2020-01-01T00:00:00.000000000', '2019-12-01T00:00:00.000000000',
           '2019-11-01T00:00:00.000000000', '2019-10-01T00:00:00.000000000',
           '2019-09-01T00:00:00.000000000', '2019-08-01T00:00:00.000000000',
           '2019-07-01T00:00:00.000000000', '2019-06-01T00:00:00.000000000',
           '2019-05-01T00:00:00.000000000', '2019-04-01T00:00:00.000000000',
           '2019-03-01T00:00:00.000000000', '2019-02-01T00:00:00.000000000',
           '2019-01-01T00:00:00.000000000', '2018-12-01T00:00:00.000000000',
           '2018-11-01T00:00:00.000000000', '2018-10-01T00:00:00.000000000',
           '2018-09-01T00:00:00.000000000', '2018-08-01T00:00:00.000000000',
           '2018-07-01T00:00:00.000000000', '2018-06-01T00:00:00.000000000',
           '2018-05-01T00:00:00.000000000', '2018-04-01T00:00:00.000000000',
           '2018-03-01T00:00:00.000000000', '2018-02-01T00:00:00.000000000',
           '2018-01-01T00:00:00.000000000', '2017-12-01T00:00:00.000000000',
           '2017-11-01T00:00:00.000000000', '2017-10-01T00:00:00.000000000',
           '2017-09-01T00:00:00.000000000', '2017-08-01T00:00:00.000000000',
           '2017-07-01T00:00:00.000000000', '2017-06-01T00:00:00.000000000',
           '2017-05-01T00:00:00.000000000', '2017-04-01T00:00:00.000000000',
           '2017-03-01T00:00:00.000000000', '2017-02-01T00:00:00.000000000',
           '2017-01-01T00:00:00.000000000', '2016-12-01T00:00:00.000000000',
           '2016-11-01T00:00:00.000000000', '2016-10-01T00:00:00.000000000',
           '2016-09-01T00:00:00.000000000', '2016-08-01T00:00:00.000000000',
           '2016-07-01T00:00:00.000000000', '2016-06-01T00:00:00.000000000',
           '2016-05-01T00:00:00.000000000', '2016-04-01T00:00:00.000000000',
           '2016-03-01T00:00:00.000000000', '2016-02-01T00:00:00.000000000',
           '2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    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"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    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"]]

    GeoTransform :

    73.65000000000002 0.09999999999999998 0.0 53.55 0.0 -0.09999999999999999

    array(0)

• Indexes: (3)
  • x
    PandasIndex

    PandasIndex(Index([ 73.70000000000002,  73.80000000000001,               73.9,
                         74.0,  74.10000000000002,  74.20000000000002,
            74.30000000000001,               74.4,               74.5,
            74.60000000000002,
           ...
           134.10000000000002, 134.20000000000005,              134.3,
           134.40000000000003,              134.5, 134.60000000000002,
           134.70000000000005,              134.8, 134.90000000000003,
                        135.0],
          dtype='float64', name='x', length=614))

  • y
    PandasIndex

    PandasIndex(Index([              53.5,               53.4,               53.3,
           53.199999999999996,               53.1,               53.0,
                         52.9,               52.8, 52.699999999999996,
                         52.6,
           ...
           19.099999999999994,               19.0,  18.89999999999999,
           18.799999999999997, 18.700000000000003, 18.599999999999994,
                         18.5,  18.39999999999999, 18.299999999999997,
           18.200000000000003],
          dtype='float64', name='y', length=354))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2022-01-01', '2021-12-01', '2021-11-01', '2021-10-01',
                   '2021-09-01', '2021-08-01', '2021-07-01', '2021-06-01',
                   '2021-05-01', '2021-04-01', '2021-03-01', '2021-02-01',
                   '2021-01-01', '2020-12-01', '2020-11-01', '2020-10-01',
                   '2020-09-01', '2020-08-01', '2020-07-01', '2020-06-01',
                   '2020-05-01', '2020-04-01', '2020-03-01', '2020-02-01',
                   '2020-01-01', '2019-12-01', '2019-11-01', '2019-10-01',
                   '2019-09-01', '2019-08-01', '2019-07-01', '2019-06-01',
                   '2019-05-01', '2019-04-01', '2019-03-01', '2019-02-01',
                   '2019-01-01', '2018-12-01', '2018-11-01', '2018-10-01',
                   '2018-09-01', '2018-08-01', '2018-07-01', '2018-06-01',
                   '2018-05-01', '2018-04-01', '2018-03-01', '2018-02-01',
                   '2018-01-01', '2017-12-01', '2017-11-01', '2017-10-01',
                   '2017-09-01', '2017-08-01', '2017-07-01', '2017-06-01',
                   '2017-05-01', '2017-04-01', '2017-03-01', '2017-02-01',
                   '2017-01-01', '2016-12-01', '2016-11-01', '2016-10-01',
                   '2016-09-01', '2016-08-01', '2016-07-01', '2016-06-01',
                   '2016-05-01', '2016-04-01', '2016-03-01', '2016-02-01',
                   '2016-01-01'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (2)

  spec :

  RasterSpec(epsg=4326, bounds=(-180.0, -90.0, 180.0, 90.0), resolutions_xy=(0.1, 0.1))

  resolution :

  0.1

Aggregate the data

Calculate the mean at each time across regional data. Note this is the first time that the data is actually loaded.

means = subset.mean(dim=("x", "y")).compute()

Plot the mean monthly NO2 using hvplot

means.hvplot.line(x="time", ylabel="NO2", title="Mean Monthly NO2 in China")