DGGS grid conversions

This notebook introduces and demonstrates conversions of spatial data into two prominent Discrete Global Grid Systems (DGGS): H3 and HEALPix, using the PixCDust library.

DGGS are spatial reference systems that partition the globe into hierarchical grids. These grids divide the Earth into regions that can be easily indexed, offering advantages for spatial analysis, data integration, and visualization. DGGS are increasingly used in applications like environmental monitoring, geospatial analysis, and big data processing, because they provide a standardized way to manage spatial data across different scales.

# imports
from pixcdust.downloaders.hydroweb_next import PixCDownloader
from pixcdust.readers import NcSimpleReader
import geopandas as gpd
from datetime import datetime
import xarray as xr
import glob

Download

# reading the area of interest
gdf_geom = gpd.read_file("../data/aoi.gpkg")

# Limiting time period
dates = (
    datetime(2023,4,6),
    datetime(2023,4,8),
)

pixcdownloader = PixCDownloader(
    gdf_geom,
    dates,
    verbose=1,
    path_download='/tmp/pixc',
    )
pixcdownloader.search_download()

Reading nc files and reprojecting into dggs grids

# Search swot nc files
swot_nc_files = glob.glob('/tmp/pixc'+'/*/*nc')
print(swot_nc_files)

['/tmp/pixc/SWOT_L2_HR_PIXC_483_016_078L_20230407T093656_20230407T093707_PGC0_01_swot_d476ce69-7756-3de7-99b7-4c70671461cb_V1-SWOT_L2_HR_PIXC_483_016_078L_20230407T093656_20230407T093707_PGC0_01_swot_d476ce69-7756-3de7-99b7-4c70671461cb_V1/SWOT_L2_HR_PIXC_483_016_078L_20230407T093656_20230407T093707_PGC0_01.nc', '/tmp/pixc/SWOT_L2_HR_PIXC_482_016_078L_20230406T094618_20230406T094629_PGC0_01_swot_b067e476-1232-363f-9ca8-2ff9791fa7b3_V1-SWOT_L2_HR_PIXC_482_016_078L_20230406T094618_20230406T094629_PGC0_01_swot_b067e476-1232-363f-9ca8-2ff9791fa7b3_V1/SWOT_L2_HR_PIXC_482_016_078L_20230406T094618_20230406T094629_PGC0_01.nc']

# Read one swot nc file
path = swot_nc_files[0]
reader = NcSimpleReader(path)
reader.read()

# Print variables
reader.data.data_vars

Data variables:
    azimuth_index                          (points) float64 49MB ...
    range_index                            (points) float64 49MB ...
    interferogram                          (points, complex_depth) float32 49MB ...
    power_plus_y                           (points) float32 25MB ...
    power_minus_y                          (points) float32 25MB ...
    coherent_power                         (points) float32 25MB ...
    x_factor_plus_y                        (points) float32 25MB ...
    x_factor_minus_y                       (points) float32 25MB ...
    water_frac                             (points) float32 25MB ...
    water_frac_uncert                      (points) float32 25MB ...
    classification                         (points) float32 25MB ...
    false_detection_rate                   (points) float32 25MB ...
    missed_detection_rate                  (points) float32 25MB ...
    prior_water_prob                       (points) float32 25MB ...
    bright_land_flag                       (points) float32 25MB ...
    layover_impact                         (points) float32 25MB ...
    eff_num_rare_looks                     (points) float32 25MB ...
    height                                 (points) float32 25MB ...
    cross_track                            (points) float32 25MB ...
    pixel_area                             (points) float32 25MB ...
    inc                                    (points) float32 25MB ...
    phase_noise_std                        (points) float32 25MB ...
    dlatitude_dphase                       (points) float32 25MB ...
    dlongitude_dphase                      (points) float32 25MB ...
    dheight_dphase                         (points) float32 25MB ...
    dheight_droll                          (points) float32 25MB ...
    dheight_dbaseline                      (points) float32 25MB ...
    dheight_drange                         (points) float32 25MB ...
    darea_dheight                          (points) float32 25MB ...
    illumination_time                      (points) datetime64[ns] 49MB ...
    illumination_time_tai                  (points) datetime64[ns] 49MB ...
    eff_num_medium_looks                   (points) float32 25MB ...
    sig0                                   (points) float32 25MB ...
    sig0_uncert                            (points) float32 25MB ...
    phase_unwrapping_region                (points) float64 49MB ...
    ambiguity_cost1                        (points) float32 25MB ...
    ambiguity_cost2                        (points) float32 25MB ...
    instrument_range_cor                   (points) float32 25MB ...
    instrument_phase_cor                   (points) float32 25MB ...
    instrument_baseline_cor                (points) float32 25MB ...
    sig0_cor_atmos_model                   (points) float32 25MB ...
    height_cor_xover                       (points) float32 25MB ...
    model_dry_tropo_cor                    (points) float32 25MB ...
    model_wet_tropo_cor                    (points) float32 25MB ...
    iono_cor_gim_ka                        (points) float32 25MB ...
    geoid                                  (points) float32 25MB ...
    solid_earth_tide                       (points) float32 25MB ...
    load_tide_fes                          (points) float32 25MB ...
    load_tide_got                          (points) float32 25MB ...
    pole_tide                              (points) float32 25MB ...
    ancillary_surface_classification_flag  (points) float32 25MB ...
    interferogram_qual                     (points) float64 49MB ...
    classification_qual                    (points) float64 49MB ...
    geolocation_qual                       (points) float64 49MB ...
    sig0_qual                              (points) float64 49MB ...
    pixc_line_qual                         (num_pixc_lines) float64 26kB ...
    pixc_line_to_tvp                       (num_pixc_lines) float32 13kB ...
    data_window_first_valid                (num_pixc_lines) float64 26kB ...
    data_window_last_valid                 (num_pixc_lines) float64 26kB ...
    data_window_first_cross_track          (num_pixc_lines) float32 13kB ...
    data_window_last_cross_track           (num_pixc_lines) float32 13kB ...

# Chose one or more variables
reader.data['height']

<xarray.DataArray 'height' (points: 6137280)> Size: 25MB
[6137280 values with dtype=float32]
Coordinates:
    latitude   (points) float64 49MB 43.77 43.76 43.77 ... 43.35 43.35 43.35
    longitude  (points) float64 49MB 0.7255 0.7249 0.7254 ... 1.728 1.727 1.728
  * points     (points) object 49MB POINT (0.7255257239487491 43.765118151867...
Indexes:
    points   GeometryIndex (crs=EPSG:4326)
Attributes:
    long_name:     height above reference ellipsoid
    units:         m
    quality_flag:  geolocation_qual
    valid_min:     -1500.0
    valid_max:     15000.0
    comment:       Height of the pixel above the reference ellipsoid.

xarray.DataArray

'height'

• points: 6137280

• ...

  [6137280 values with dtype=float32]

• Coordinates: (3)
  • latitude
    (points)
    float64
    43.77 43.76 43.77 ... 43.35 43.35

    long_name :

    latitude (positive N, negative S)

    standard_name :

    latitude

    units :

    degrees_north

    quality_flag :

    geolocation_qual

    valid_min :

    -80.0

    valid_max :

    80.0

    comment :

    Geodetic latitude [-80,80] (degrees north of equator) of the pixel.

    array([43.765118, 43.764995, 43.765092, ..., 43.353043, 43.353   , 43.353046])

  • longitude
    (points)
    float64
    0.7255 0.7249 ... 1.727 1.728

    long_name :

    longitude (degrees East)

    standard_name :

    longitude

    units :

    degrees_east

    quality_flag :

    geolocation_qual

    valid_min :

    -180.0

    valid_max :

    180.0

    comment :

    Longitude [-180,180) (east of the Greenwich meridian) of the pixel.

    array([0.725526, 0.724866, 0.725384, ..., 1.72764 , 1.7274  , 1.727659])

  • points
    (points)
    object
    POINT (0.7255257239487491 43.765...

    crs :

    EPSG:4326

    array([<POINT (0.726 43.765)>, <POINT (0.725 43.765)>, <POINT (0.725 43.765)>,
           ..., <POINT (1.728 43.353)>, <POINT (1.727 43.353)>,
           <POINT (1.728 43.353)>], dtype=object)

• Indexes: (1)
  • points
    GeometryIndex (crs=EPSG:4326)

    GeometryIndex(
        [<POINT (0.726 43.765)>
         <POINT (0.725 43.765)>
         <POINT (0.725 43.765)>
         <POINT (0.726 43.765)>
         ...
         <POINT (1.719 43.351)>
         <POINT (1.728 43.353)>
         <POINT (1.727 43.353)>
         <POINT (1.728 43.353)>],
        crs=EPSG:4326)

• Attributes: (6)

  long_name :

  height above reference ellipsoid

  units :

  m

  quality_flag :

  geolocation_qual

  valid_min :

  -1500.0

  valid_max :

  15000.0

  comment :

  Height of the pixel above the reference ellipsoid.

# Reproject variables into h3 grid
ds_h3 = reader.to_h3(variables = 'height', resolution = 8) # Modify the resolution if needeed
ds_h3

<xarray.Dataset> Size: 178kB
Dimensions:   (cell_ids: 6351)
Coordinates:
  * cell_ids  (cell_ids) int64 51kB 613498900998782975 ... 613499360254099455
    h3_lon    (cell_ids) float64 51kB 1.613 1.624 1.606 ... 0.7561 0.7668 0.7491
    h3_lat    (cell_ids) float64 51kB 43.58 43.58 43.58 ... 43.72 43.71 43.71
Data variables:
    height    (cell_ids) float32 25kB 236.5 240.1 285.0 ... 265.5 250.5 254.3
Indexes:
    cell_ids  H3Index(level=8)
Attributes:
    description:                 cloud of geolocated interferogram pixels
    interferogram_size_azimuth:  3244
    interferogram_size_range:    4856
    looks_to_efflooks:           1.5337356015127774
    num_azimuth_looks:           7.0
    azimuth_offset:              6

xarray.Dataset

• Dimensions:
  • cell_ids: 6351
• Coordinates: (3)
  • cell_ids
    (cell_ids)
    int64
    613498900998782975 ... 613499360...

    grid_name :

    h3

    resolution :

    8

    array([613498900998782975, 613498901000880127, 613498901002977279, ...,
           613499360249905151, 613499360252002303, 613499360254099455])

  • h3_lon
    (cell_ids)
    float64
    1.613 1.624 1.606 ... 0.7668 0.7491

    array([1.6129412 , 1.62370928, 1.60591681, ..., 0.75612773, 0.76677435,
           0.74910544])

  • h3_lat
    (cell_ids)
    float64
    43.58 43.58 43.58 ... 43.71 43.71

    array([43.58472689, 43.58383407, 43.57864903, ..., 43.7152204 ,
           43.71336856, 43.70907088])

• Data variables: (1)
  • height
    (cell_ids)
    float32
    236.5 240.1 285.0 ... 250.5 254.3

    array([236.46721, 240.10999, 284.98608, ..., 265.51453, 250.46234,
           254.27176], dtype=float32)

• Indexes: (1)
  • cell_ids
    H3Index(level=8)

    <xdggs.h3.H3Index object at 0x72bc1babcc10>

• Attributes: (6)

  description :

  cloud of geolocated interferogram pixels

  interferogram_size_azimuth :

  3244

  interferogram_size_range :

  4856

  looks_to_efflooks :

  1.5337356015127774

  num_azimuth_looks :

  7.0

  azimuth_offset :

  6

# Show
ds_h3['height'].dggs.explore()

# Reproject variables into healpix grid
ds_healpix = reader.to_healpix(variables = 'height', resolution = 13) # modify resolution if needed
ds_healpix

<xarray.Dataset> Size: 197kB
Dimensions:      (cell_ids: 7047)
Coordinates:
  * cell_ids     (cell_ids) int64 56kB 123386469 123386471 ... 126962196
    healpix_lon  (cell_ids) float64 56kB 1.484 1.507 1.518 ... 0.9431 0.9544
    healpix_lat  (cell_ids) float64 56kB 43.91 43.91 43.91 ... 43.21 43.21 43.21
Data variables:
    height       (cell_ids) float32 28kB 238.4 213.9 244.6 ... 426.7 434.2 412.5
Indexes:
    cell_ids  HealpixIndex(nside=13, indexing_scheme=ring)
Attributes:
    description:                 cloud of geolocated interferogram pixels
    interferogram_size_azimuth:  3244
    interferogram_size_range:    4856
    looks_to_efflooks:           1.5337356015127774
    num_azimuth_looks:           7.0
    azimuth_offset:              6

xarray.Dataset

• Dimensions:
  • cell_ids: 7047
• Coordinates: (3)
  • cell_ids
    (cell_ids)
    int64
    123386469 123386471 ... 126962196

    grid_name :

    healpix

    nside :

    8192

    nest :

    False

    array([123386469, 123386471, 123386472, ..., 126962194, 126962195, 126962196])

  • healpix_lon
    (cell_ids)
    float64
    1.484 1.507 1.518 ... 0.9431 0.9544

    array([1.4837683 , 1.50668364, 1.51814131, ..., 0.93185241, 0.94314759,
           0.95444277])

  • healpix_lat
    (cell_ids)
    float64
    43.91 43.91 43.91 ... 43.21 43.21

    array([43.91000922, 43.91000922, 43.91000922, ..., 43.20782108,
           43.20782108, 43.20782108])

• Data variables: (1)
  • height
    (cell_ids)
    float32
    238.4 213.9 244.6 ... 434.2 412.5

    array([238.41127, 213.90572, 244.6037 , ..., 426.6898 , 434.17267,
           412.48456], dtype=float32)

• Indexes: (1)
  • cell_ids
    HealpixIndex(nside=13, indexing_scheme=ring)

    <xdggs.healpix.HealpixIndex object at 0x72bc1babe710>

• Attributes: (6)

  description :

  cloud of geolocated interferogram pixels

  interferogram_size_azimuth :

  3244

  interferogram_size_range :

  4856

  looks_to_efflooks :

  1.5337356015127774

  num_azimuth_looks :

  7.0

  azimuth_offset :

  6

# Show
ds_healpix['height'].dggs.explore()

Enjoy !