4.2.1.4. ESA CCI Toolbox Vector Data Access

The ESA CCI Toolbox also provides access to data that is not provided on a structured grid. This data is provided in the form of geopandas geodataframes rather than xarray datasets.

To run this Notebook, make sure the ESA CCI Toolbox is setup correctly.

For this notebook, we start as before, by opening the standard esa-cci data store.

from xcube.core.store import new_data_store

cci_store = new_data_store('esa-cci')

<frozen abc>:106: FutureWarning: xarray subclass VectorDataCube should explicitly define __slots__

The data in question are GHG, SEALEVEL, and SEAICE datasets in satellite-orbit-frequency. Let’s search for GHG datasets.

descriptors = cci_store.search_data(
    data_type="geodataframe",
    cci_attrs=dict(
        ecv="GHG"
    )
)
[descriptor.data_id for descriptor in descriptors]

Dataset is not accessible via Opendap or Download
Dataset is not accessible via Opendap or Download
Dataset is not accessible via Opendap or Download
/home/tonio-bc/projects/esa-cci/xcube-cci/xcube_cci/cciodp.py:1960: CciOdpWarning: Variable "gain" has no fill value, cannot set one. For parts where no data is available you will see random values. This is usually the case when data is missing for a time step.
  warnings.warn(f'Variable "{fixed_key}" has no fill value, '
/home/tonio-bc/projects/esa-cci/xcube-cci/xcube_cci/cciodp.py:1960: CciOdpWarning: Variable "l1b_name" has no fill value, cannot set one. For parts where no data is available you will see random values. This is usually the case when data is missing for a time step.
  warnings.warn(f'Variable "{fixed_key}" has no fill value, '
/home/tonio-bc/projects/esa-cci/xcube-cci/xcube_cci/cciodp.py:1960: CciOdpWarning: Variable "exposure_id" has no fill value, cannot set one. For parts where no data is available you will see random values. This is usually the case when data is missing for a time step.
  warnings.warn(f'Variable "{fixed_key}" has no fill value, '

['esacci.GHG.satellite-orbit-frequency.L2.CH4.SCIAMACHY.Envisat.IMAP.v7-2.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.SCIAMACHY.Envisat.WFMD.v4-0.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS-2.GOSAT-2.SRFP.v2-0-2.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS-2.GOSAT-2.SRPR.v2-0-2.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS.GOSAT.EMMA.ch4_v1-2.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS.GOSAT.OCFP.v2-1.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS.GOSAT.OCPR.v7-0.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS.GOSAT.SRFP.v2-3-8.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS.GOSAT.SRPR.v2-3-8.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TROPOMI.Sentinel-5P.WFMD.v1-8.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.OCO.OCO-2.FOCAL.v10-1.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.SCIAMACHY.Envisat.BESD.v02-01-02.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.SCIAMACHY.Envisat.WFMD.v4-0.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.TANSO-FTS-2.GOSAT-2.SRFP.v2-0-2.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.TANSO-FTS.GOSAT.EMMA.v2-2c.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.TANSO-FTS.GOSAT.OCFP.v7-0-1.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.TANSO-FTS.GOSAT.SRFP.v2-3-8.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.multi-sensor.multi-platform.EMMA.v2-2a.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.multi-sensor.multi-platform.EMMA.v2-2b.r1']

We have a closer look at the first one.

descriptors[0]

<xcube.core.store.descriptor.GeoDataFrameDescriptor at 0x7efeac4baf60>

It makes sense to not open the whole dataframe but read in a subset. We ask for two variables for one day (the variable names are listed under feature_schema:properties).

var_names = ["xch4", "xco2_retrieved"]
time_range = ["2010-07-04", "2010-07-04"]

ghg_df = cci_store.open_data(
    "esacci.GHG.satellite-orbit-frequency.L2.CH4.SCIAMACHY.Envisat.IMAP.v7-2.r1",
    variable_names=var_names,
    time_range=time_range
)
ghg_df

	geometry	xch4	xco2_retrieved	time
0	POINT (21.12531 66.15704)	1835.639282	350.589783	2010-07-04T00:00:00
1	POINT (17.33319 54.23273)	1848.246216	350.940857	2010-07-04T00:00:00
2	POINT (21.32617 53.16351)	1834.837891	361.529053	2010-07-04T00:00:00
3	POINT (17.02777 53.6568)	1767.532104	357.012085	2010-07-04T00:00:00
4	POINT (15.31268 53.61804)	1846.445312	384.051636	2010-07-04T00:00:00
...	...	...	...	...
3546	POINT (176.04736 62.59637)	1756.709839	379.857666	2010-07-04T00:00:00
3547	POINT (173.65729 62.86865)	1774.327759	357.646545	2010-07-04T00:00:00
3548	POINT (174.71912 62.44507)	1805.762573	374.800110	2010-07-04T00:00:00
3549	POINT (173.18512 62.00031)	1841.642090	350.549011	2010-07-04T00:00:00
3550	POINT (171.62189 61.54938)	1752.140625	350.487610	2010-07-04T00:00:00

3551 rows × 4 columns

ghg_df.plot(column="xch4")

<Axes: >