Downsample zarr

Demonstrates downsampling zarr using xarray and dask with data from SMAP

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

This notebook demonstrates 2 strategies for resampling data from a Zarr dataset in order to visualize within the memory limits of a notebook.

1. Downsample the temporal resolution of the data using xarray.DataArray.resample
2. Coarsening the spatial resolution of the data using xarray.DataArray.coarsen

A strategy for visualizing any large amount of data is Datashader which bins data into a fixed 2-D array. Using the rasterize argument within hvplot calls ensures the use of the datashader library to bin the data. Optionally an external Dask cluster is used to parallelize and distribute these large downsampling operations across compute nodes.

About the data

The SMAP mission is an orbiting observatory that measures the amount of water in the surface soil everywhere on Earth.

Load libraries

import xarray as xr
import hvplot.xarray
import cartopy.crs as ccrs

Optional: Create and Scale a Dask Cluster

We create a separate Dask cluster to speed up reprojecting the data (and other potential computations which could be required and are parallelizable).

Note if you skip this cell you will still be using Dask, you’ll just be using the machine where you are running this notebook.

from dask_gateway import GatewayCluster, Gateway

gateway = Gateway()
clusters = gateway.list_clusters()

# connect to an existing cluster - this is useful when the kernel shutdown in the middle of an interactive session
if clusters:
    cluster = gateway.connect(clusters[0].name)
else:
    cluster = GatewayCluster(shutdown_on_close=True)

cluster.scale(16)
client = cluster.get_client()
client

Client

Client-f633df20-5c0c-11ef-92ac-860c0d05aa65

Connection method: Cluster object	Cluster type: dask_gateway.GatewayCluster
Dashboard: /services/dask-gateway/clusters/prod.f63d365675734e2ba2dee10ae8769a7c/status

Cluster Info

GatewayCluster

• Name: prod.f63d365675734e2ba2dee10ae8769a7c
• Dashboard: /services/dask-gateway/clusters/prod.f63d365675734e2ba2dee10ae8769a7c/status

Open the dataset from S3

ds = xr.open_zarr("s3:///veda-data-store-staging/EIS/zarr/SPL3SMP.zarr")
ds

<xarray.Dataset> Size: 68GB
Dimensions:                        (northing_m: 406, easting_m: 964,
                                    datetime: 1679)
Coordinates:
  * datetime                       (datetime) datetime64[ns] 13kB 2018-01-01 ...
  * easting_m                      (easting_m) float64 8kB -1.735e+07 ... 1.7...
  * northing_m                     (northing_m) float64 3kB 7.297e+06 ... -7....
Data variables: (12/26)
    albedo                         (northing_m, easting_m, datetime) float32 3GB dask.array<chunksize=(100, 100, 100), meta=np.ndarray>
    albedo_pm                      (northing_m, easting_m, datetime) float32 3GB dask.array<chunksize=(100, 100, 100), meta=np.ndarray>
    bulk_density                   (northing_m, easting_m, datetime) float32 3GB dask.array<chunksize=(100, 100, 100), meta=np.ndarray>
    bulk_density_pm                (northing_m, easting_m, datetime) float32 3GB dask.array<chunksize=(100, 100, 100), meta=np.ndarray>
    clay_fraction                  (northing_m, easting_m, datetime) float32 3GB dask.array<chunksize=(100, 100, 100), meta=np.ndarray>
    clay_fraction_pm               (northing_m, easting_m, datetime) float32 3GB dask.array<chunksize=(100, 100, 100), meta=np.ndarray>
    ...                             ...
    static_water_body_fraction     (northing_m, easting_m, datetime) float32 3GB dask.array<chunksize=(100, 100, 100), meta=np.ndarray>
    static_water_body_fraction_pm  (northing_m, easting_m, datetime) float32 3GB dask.array<chunksize=(100, 100, 100), meta=np.ndarray>
    surface_flag                   (northing_m, easting_m, datetime) float32 3GB dask.array<chunksize=(100, 100, 100), meta=np.ndarray>
    surface_flag_pm                (northing_m, easting_m, datetime) float32 3GB dask.array<chunksize=(100, 100, 100), meta=np.ndarray>
    surface_temperature            (northing_m, easting_m, datetime) float32 3GB dask.array<chunksize=(100, 100, 100), meta=np.ndarray>
    surface_temperature_pm         (northing_m, easting_m, datetime) float32 3GB dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • northing_m: 406
  • easting_m: 964
  • datetime: 1679
• Coordinates: (3)
  • datetime
    (datetime)
    datetime64[ns]
    2018-01-01 ... 2022-09-09

    array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
           '2018-01-03T00:00:00.000000000', ..., '2022-09-07T00:00:00.000000000',
           '2022-09-08T00:00:00.000000000', '2022-09-09T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • easting_m
    (easting_m)
    float64
    -1.735e+07 -1.731e+07 ... 1.735e+07

    array([-17349514.34, -17313482.12, -17277449.9 , ...,  17277449.08,
            17313481.3 ,  17349513.52])

  • northing_m
    (northing_m)
    float64
    7.297e+06 7.26e+06 ... -7.297e+06

    array([ 7296524.72,  7260492.5 ,  7224460.28, ..., -7224459.94, -7260492.16,
           -7296524.38])

• Data variables: (26)
  • albedo
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    Diffuse reflecting power of the Earth&apos;s surface used in DCA within the grid cell.

    valid_max :

    1.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • albedo_pm
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    Diffuse reflecting power of the Earth&apos;s surface used in DCA retrievals within the grid cell.

    valid_max :

    1.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • bulk_density
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    A unitless value that is indicative of aggregated bulk_density within the 36 km grid cell.

    valid_max :

    2.6500000953674316

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • bulk_density_pm
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    A unitless value that is indicative of aggregated bulk density within the 36 km grid cell.

    valid_max :

    2.6500000953674316

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • clay_fraction
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    A unitless value that is indicative of aggregated clay fraction within the 36 km grid cell.

    valid_max :

    1.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • clay_fraction_pm
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    A unitless value that is indicative of aggregated clay fraction within the 36 km grid cell.

    valid_max :

    1.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • freeze_thaw_fraction
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    Fraction of the 36 km grid cell that is denoted as frozen. Based on binary flag that specifies freeze thaw conditions in each of the component 3 km grid cells.

    valid_max :

    1.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • freeze_thaw_fraction_pm
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    Fraction of the 36 km grid cell that is denoted as frozen. Based on binary flag that specifies freeze thaw conditions in each of the component 3 km grid cells.

    valid_max :

    1.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • grid_surface_status
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    Indicates if the grid point lies on land (0) or water (1).

    valid_max :

    1

    valid_min :

    0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • grid_surface_status_pm
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    Indicates if the grid point lies on land (0) or water (1).

    valid_max :

    1

    valid_min :

    0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • radar_water_body_fraction
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    The fraction of the area of the 36 km grid cell that is covered by water based on the radar detection algorithm.

    valid_max :

    1.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • radar_water_body_fraction_pm
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    The fraction of the area of the 36 km grid cell that is covered by water based on the radar detection algorithm.

    valid_max :

    1.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • retrieval_qual_flag
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    flag_masks :

    1s, 2s, 4s, 8s

    flag_meanings :

    Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success

    long_name :

    Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • retrieval_qual_flag_pm
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    flag_masks :

    1s, 2s, 4s, 8s

    flag_meanings :

    Retrieval_recommended Retrieval_attempted Retrieval_success FT_retrieval_success

    long_name :

    Bit flags that record the conditions and the quality of the DCA retrieval algorithms that generate soil moisture for the grid cell.

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • roughness_coefficient
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    A unitless value that is indicative of bare soil roughness used in DCA within the 36 km grid cell.

    valid_max :

    1.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • roughness_coefficient_pm
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    A unitless value that is indicative of bare soil roughness used in DCA retrievals within the 36 km grid cell.

    valid_max :

    1.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soil_moisture
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    Representative DCA soil moisture measurement for the Earth based grid cell.

    units :

    cm**3/cm**3

    valid_max :

    0.5

    valid_min :

    0.019999999552965164

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soil_moisture_error
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    Net uncertainty measure of soil moisture measure for the Earth based grid cell. - Calculation method is TBD.

    units :

    cm**3/cm**3

    valid_max :

    0.20000000298023224

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soil_moisture_error_pm
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    Net uncertainty measure of soil moisture measure for the Earth based grid cell. - Calculation method is TBD.

    units :

    cm**3/cm**3

    valid_max :

    0.20000000298023224

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soil_moisture_pm
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    Representative DCA soil moisture measurement for the Earth based grid cell.

    units :

    cm**3/cm**3

    valid_max :

    0.5

    valid_min :

    0.019999999552965164

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • static_water_body_fraction
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    The fraction of the area of the 36 km grid cell that is covered by static water based on a Digital Elevation Map.

    valid_max :

    1.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • static_water_body_fraction_pm
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    The fraction of the area of the 36 km grid cell that is covered by static water based on a Digital Elevation Map.

    valid_max :

    1.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • surface_flag
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    flag_masks :

    1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s

    flag_meanings :

    36_km_static_water_body 36_km_radar_water_body_detection 36_km_coastal_proximity 36_km_urban_area 36_km_precipitation 36_km_snow_or_ice 36_km_permanent_snow_or_ice 36_km_radiometer_frozen_ground 36_km_model_frozen_ground 36_km_mountainous_terrain 36_km_dense_vegetation 36_km_nadir_region

    long_name :

    Bit flags that record ambient surface conditions for the grid cell

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • surface_flag_pm
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    flag_masks :

    1s, 2s, 4s, 8s, 16s, 32s, 64s, 128s, 256s, 512s, 1024s, 2048s

    flag_meanings :

    36_km_static_water_body 36_km_radar_water_body_detection 36_km_coastal_proximity 36_km_urban_area 36_km_precipitation 36_km_snow_or_ice 36_km_permanent_snow_or_ice 36_km_radar_frozen_ground 36_km_model_frozen_ground 36_km_mountainous_terrain 36_km_dense_vegetation 36_km_nadir_region

    long_name :

    Bit flags that record ambient surface conditions for the grid cell

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • surface_temperature
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    Temperature at land surface based on GMAO GEOS-5 data.

    units :

    Kelvins

    valid_max :

    350.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

  • surface_temperature_pm
    (northing_m, easting_m, datetime)
    float32
    dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

    long_name :

    Temperature at land surface based on GMAO GEOS-5 data.

    units :

    Kelvins

    valid_max :

    350.0

    valid_min :

    0.0

    Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • datetime
    PandasIndex

    PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
                   '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',
                   '2018-01-09', '2018-01-10',
                   ...
                   '2022-08-31', '2022-09-01', '2022-09-02', '2022-09-03',
                   '2022-09-04', '2022-09-05', '2022-09-06', '2022-09-07',
                   '2022-09-08', '2022-09-09'],
                  dtype='datetime64[ns]', name='datetime', length=1679, freq=None))

  • easting_m
    PandasIndex

    PandasIndex(Index([      -17349514.34,       -17313482.12,        -17277449.9,
                 -17241417.68,       -17205385.46,       -17169353.24,
                 -17133321.02,        -17097288.8,       -17061256.58,
                 -17025224.36,
           ...
           17025223.540000003,        17061255.76,        17097287.98,
                   17133320.2, 17169352.419999998, 17205384.640000004,
           17241416.860000003, 17277449.080000002,         17313481.3,
                  17349513.52],
          dtype='float64', name='easting_m', length=964))

  • northing_m
    PandasIndex

    PandasIndex(Index([        7296524.72,          7260492.5,  7224460.279999999,
                   7188428.06,         7152395.84,         7116363.62,
            7080331.399999999,         7044299.18,         7008266.96,
                   6972234.74,
           ...
           -6972234.400000001,        -7008266.62, -7044298.840000001,
           -7080331.060000001,        -7116363.28, -7152395.500000001,
           -7188427.720000002,        -7224459.94, -7260492.160000001,
                  -7296524.38],
          dtype='float64', name='northing_m', length=406))

• Attributes: (0)

Select the variable of interest (soil moisture for this example).

soil_moisture = ds.soil_moisture
soil_moisture

<xarray.DataArray 'soil_moisture' (northing_m: 406, easting_m: 964,
                                   datetime: 1679)> Size: 3GB
dask.array<open_dataset-soil_moisture, shape=(406, 964, 1679), dtype=float32, chunksize=(100, 100, 100), chunktype=numpy.ndarray>
Coordinates:
  * datetime    (datetime) datetime64[ns] 13kB 2018-01-01 ... 2022-09-09
  * easting_m   (easting_m) float64 8kB -1.735e+07 -1.731e+07 ... 1.735e+07
  * northing_m  (northing_m) float64 3kB 7.297e+06 7.26e+06 ... -7.297e+06
Attributes:
    long_name:  Representative DCA soil moisture measurement for the Earth ba...
    units:      cm**3/cm**3
    valid_max:  0.5
    valid_min:  0.019999999552965164

xarray.DataArray

'soil_moisture'

• northing_m: 406
• easting_m: 964
• datetime: 1679

• dask.array<chunksize=(100, 100, 100), meta=np.ndarray>

  Array Chunk Bytes 2.45 GiB 3.81 MiB Shape (406, 964, 1679) (100, 100, 100) Dask graph 850 chunks in 2 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • datetime
    (datetime)
    datetime64[ns]
    2018-01-01 ... 2022-09-09

    array(['2018-01-01T00:00:00.000000000', '2018-01-02T00:00:00.000000000',
           '2018-01-03T00:00:00.000000000', ..., '2022-09-07T00:00:00.000000000',
           '2022-09-08T00:00:00.000000000', '2022-09-09T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • easting_m
    (easting_m)
    float64
    -1.735e+07 -1.731e+07 ... 1.735e+07

    array([-17349514.34, -17313482.12, -17277449.9 , ...,  17277449.08,
            17313481.3 ,  17349513.52])

  • northing_m
    (northing_m)
    float64
    7.297e+06 7.26e+06 ... -7.297e+06

    array([ 7296524.72,  7260492.5 ,  7224460.28, ..., -7224459.94, -7260492.16,
           -7296524.38])

• Indexes: (3)
  • datetime
    PandasIndex

    PandasIndex(DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
                   '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08',
                   '2018-01-09', '2018-01-10',
                   ...
                   '2022-08-31', '2022-09-01', '2022-09-02', '2022-09-03',
                   '2022-09-04', '2022-09-05', '2022-09-06', '2022-09-07',
                   '2022-09-08', '2022-09-09'],
                  dtype='datetime64[ns]', name='datetime', length=1679, freq=None))

  • easting_m
    PandasIndex

    PandasIndex(Index([      -17349514.34,       -17313482.12,        -17277449.9,
                 -17241417.68,       -17205385.46,       -17169353.24,
                 -17133321.02,        -17097288.8,       -17061256.58,
                 -17025224.36,
           ...
           17025223.540000003,        17061255.76,        17097287.98,
                   17133320.2, 17169352.419999998, 17205384.640000004,
           17241416.860000003, 17277449.080000002,         17313481.3,
                  17349513.52],
          dtype='float64', name='easting_m', length=964))

  • northing_m
    PandasIndex

    PandasIndex(Index([        7296524.72,          7260492.5,  7224460.279999999,
                   7188428.06,         7152395.84,         7116363.62,
            7080331.399999999,         7044299.18,         7008266.96,
                   6972234.74,
           ...
           -6972234.400000001,        -7008266.62, -7044298.840000001,
           -7080331.060000001,        -7116363.28, -7152395.500000001,
           -7188427.720000002,        -7224459.94, -7260492.160000001,
                  -7296524.38],
          dtype='float64', name='northing_m', length=406))

• Attributes: (4)

  long_name :

  Representative DCA soil moisture measurement for the Earth based grid cell.

  units :

  cm**3/cm**3

  valid_max :

  0.5

  valid_min :

  0.019999999552965164

Strategy 1: Downsample the temporal resolution of the data

To plot one day from every month, resample the data to 1 observation a month.

somo_one_month = soil_moisture.resample(datetime="1ME").nearest()

Quick plot

We can generate a quick plot using hvplot and datashader.

# workaround to avoid warnings that are triggered within Dask.
import warnings

warnings.filterwarnings(
    "ignore", message="All-NaN slice encountered", category=RuntimeWarning
)

somo_one_month.hvplot(
    x="easting_m",
    y="northing_m",
    groupby="datetime",
    crs=ccrs.epsg(6933),  # this is a workaround for https://github.com/holoviz/hvplot/issues/1329
    rasterize=True,
    coastline=True,
    aggregator="mean",
    frame_height=400,
    widget_location="bottom",
)