This is an old version of the notebook, see the latest here.

From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.

SWOT Simulated North American Continent Hydrology Dataset Exploration in the Cloud

Accessing and Visualizing SWOT Simulated Datasets

Requirement:

This tutorial can only be run in an AWS cloud instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via earthaccess python library; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.

Learning Objectives:

• Access all 5 products of SWOT HR sample data (archived in NASA Earthdata Cloud) within the AWS cloud, without downloading to local machine
• Visualize accessed data

SWOT Simulated Level 2 North America Continent KaRIn High Rate Version 1 Datasets:

1. River Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1

DOI: https://doi.org/10.5067/KARIN-2RSP1

2. Lake Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1

DOI: https://doi.org/10.5067/KARIN-2LSP1

3. Water Mask Pixel Cloud NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1

DOI: https://doi.org/10.5067/KARIN-2PIX1

4. Water Mask Pixel Cloud Vector Attribute NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1

DOI: https://doi.org/10.5067/KARIN-2PXV1

5. Raster NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1

DOI: https://doi.org/10.5067/KARIN-2RAS1

Notebook Author: Cassie Nickles, NASA PO.DAAC (Aug 2022)

Libraries Needed

import glob
import os
import requests
import s3fs
import fiona
import netCDF4 as nc
import h5netcdf
import xarray as xr
import pandas as pd
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
import hvplot.xarray
import earthaccess
from earthaccess import Auth, DataCollections, DataGranules, Store

Earthdata Login

An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up. We use earthaccess to authenticate your login credentials below.

#auth = earthaccess.login(strategy="interactive", persist=True) #if you do not have a netrc created, this line will do so with your credentials
auth = earthaccess.login(strategy="netrc")

We are already authenticated with NASA EDL

Set up an s3fs session for Direct Access

s3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the data access information.

fs_s3 = earthaccess.get_s3fs_session(daac='PODAAC', provider='POCLOUD')

Single File Access

The s3 access link can be found using earthaccess data search. Since this collection consists of Reach and Node files, we need to extract only the granule for the Reach file. We do this by filtering for the ‘Reach’ title in the data link.

Alternatively, Earthdata Search (see tutorial) can be used to manually search for a single file.

1. River Vector Shapefiles

#retrieves granule from the day we want
river_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1', temporal = ('2022-08-22 19:24:41', '2022-08-22 19:30:37'))

Granules found: 2

#finds the s3 link of the one granule we want (The collection contains both Reaches and Nodes, but here we want only the Reach)
river_data_urls = []
for g in river_results:
    for l in earthaccess.results.DataGranule.data_links(g, access='direct'):
        if "Reach" in l:
            river_data_urls.append(l)
print(river_data_urls[0])

s3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1/SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.zip

The native format for this data is a .zip file, and we want the .shp file within the .zip file, so we will create a Fiona AWS session using the credentials from setting up the s3fs session above to access the shapefiles within the shp files. If we don’t do this, the alternative would be to download the data to the cloud environment and extract the .zip file there.

fiona_session=fiona.session.AWSSession(
        aws_access_key_id=fs_s3.storage_options["key"],
        aws_secret_access_key=fs_s3.storage_options["secret"],
        aws_session_token=fs_s3.storage_options["token"]
    )

# We use the zip+ prefix so fiona knows that we are operating on a zip file
river_shp_url = f"zip+{river_data_urls[0]}"

with fiona.Env(session=fiona_session):
    SWOT_HR_shp1 = gpd.read_file(river_shp_url) 

#view the attribute table
SWOT_HR_shp1 

	reach_id	time	time_tai	time_str	p_lat	p_lon	river_name	wse	wse_u	wse_r_u	...	p_width	p_wid_var	p_n_nodes	p_dist_out	p_length	p_maf	p_dam_id	p_n_ch_max	p_n_ch_mod	geometry
0	71224300241	7.145115e+08	7.145114e+08	2022-08-22T19:2441Z	49.364818	-94.879318	no_data	3.472248e+01	-1.000000e+12	1.511000e-02	...	7294.5	3.265803e+06	15	390935.258	3008.959150	-1.000000e+12	0	15	8	LINESTRING (-94.86483 49.37485, -94.86515 49.3...
1	71224300253	7.145115e+08	7.145115e+08	2022-08-22T19:2446Z	49.049486	-94.899554	no_data	3.439994e+01	-1.000000e+12	7.600000e-03	...	394.5	7.876447e+06	42	444613.943	8411.845753	-1.000000e+12	0	8	1	LINESTRING (-94.92557 49.08401, -94.92556 49.0...
2	71224300263	7.145115e+08	7.145115e+08	2022-08-22T19:2448Z	48.977915	-94.869598	no_data	3.434701e+01	-1.000000e+12	9.620000e-03	...	6365.5	2.935181e+06	42	453020.631	8406.687501	-1.000000e+12	0	3	1	LINESTRING (-94.88015 49.01512, -94.88006 49.0...
3	71224300273	7.145115e+08	7.145115e+08	2022-08-22T19:2449Z	48.902998	-94.854720	no_data	3.416786e+01	-1.000000e+12	1.372000e-02	...	4650.0	3.770782e+06	43	461636.940	8616.309267	-1.000000e+12	0	1	1	LINESTRING (-94.86229 48.94092, -94.86228 48.9...
4	71224300283	7.145115e+08	7.145115e+08	2022-08-22T19:2450Z	48.883377	-94.783621	no_data	3.426341e+01	-1.000000e+12	5.050000e-03	...	10439.0	2.952077e+07	46	470821.047	9184.106587	-1.000000e+12	0	5	1	LINESTRING (-94.73002 48.90430, -94.72952 48.9...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
666	74291700141	7.145116e+08	7.145116e+08	2022-08-22T19:2729Z	39.811344	-92.684233	no_data	3.869180e+01	-1.000000e+12	3.840000e-01	...	42.0	1.060619e+02	48	2463828.961	9553.659853	-1.000000e+12	0	1	1	LINESTRING (-92.68784 39.77391, -92.68804 39.7...
667	74291700151	7.145116e+08	7.145116e+08	2022-08-22T19:2727Z	39.888856	-92.683047	no_data	3.687848e+01	-1.000000e+12	1.897500e-01	...	42.0	1.208373e+02	48	2473386.489	9557.528221	-1.000000e+12	0	1	1	LINESTRING (-92.68497 39.84931, -92.68497 39.8...
668	74291700161	-1.000000e+12	-1.000000e+12	no_data	39.962507	-92.671510	no_data	-1.000000e+12	-1.000000e+12	-1.000000e+12	...	42.0	9.634731e+01	48	2482916.982	9530.492810	-1.000000e+12	0	1	1	LINESTRING (-92.66461 39.92629, -92.66425 39.9...
669	74291700171	7.145116e+08	7.145116e+08	2022-08-22T19:2725Z	40.045903	-92.690485	no_data	3.647532e+01	-1.000000e+12	1.394000e-01	...	54.0	6.557368e+02	59	2494780.714	11863.732260	-1.000000e+12	0	2	1	LINESTRING (-92.68041 40.00013, -92.68006 40.0...
670	74291700181	7.145116e+08	7.145116e+08	2022-08-22T19:2723Z	40.133798	-92.687305	no_data	3.384407e+01	-1.000000e+12	1.019290e+00	...	42.0	1.250056e+02	58	2506424.880	11644.165636	-1.000000e+12	0	1	1	LINESTRING (-92.68547 40.09150, -92.68519 40.0...

671 rows × 111 columns

fig, ax = plt.subplots(figsize=(11,7))
SWOT_HR_shp1.plot(ax=ax, color='black')

2. Lake Vector Shapefiles

The lake vector shapefiles can be accessed in the same way as the river shapefiles above.

lake_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1', temporal = ('2022-08-22 19:24:18', '2022-08-22 19:30:50'))

Granules found: 3

#find the s3 link of the desired granule (This collection has three options: Obs, Unassigned, and Prior - we want Obs)
lake_data_urls = []
for g in lake_results:
    for l in earthaccess.results.DataGranule.data_links(g, access='direct'):
        if "Obs" in l:
            lake_data_urls.append(l)
print(lake_data_urls[0])

s3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1/SWOT_L2_HR_LakeSP_Obs_007_522_NA_20220822T192415_20220822T193051_Dx0000_01.zip

The native format for this data is a .zip file, and we want the .shp file within the .zip file, so we will create a Fiona AWS session using the credentials from setting up the s3fs session above to access the shapefiles within the shp files. If we don’t do this, the alternative would be to download the data to the cloud environment and extract the .zip file there.

fiona_session=fiona.session.AWSSession(
        aws_access_key_id=fs_s3.storage_options["key"],
        aws_secret_access_key=fs_s3.storage_options["secret"],
        aws_session_token=fs_s3.storage_options["token"]
    )

# We use the zip+ prefix so fiona knows that we are operating on a zip file
lake_shp_url = f"zip+{lake_data_urls[0]}"

with fiona.Env(session=fiona_session):
    SWOT_HR_shp2 = gpd.read_file(lake_shp_url) 

#view the attribute table
SWOT_HR_shp2

	obs_id	lake_id	overlap	time	time_tai	time_str	wse	wse_u	wse_r_u	wse_std	...	iono_c	xovr_cal_c	p_name	p_grand_id	p_max_wse	p_max_area	p_ref_date	p_ref_ds	p_storage	geometry
0	742081R000002	7420470702	93	7.145116e+08	7.145116e+08	2022-08-22T19:26:51	36.934	0.051	0.051	0.159	...	0.0	0.0	no_data	-99999999	-1.000000e+12	1.35	-9999	-9999.0	-1.000000e+12	POLYGON ((-92.75926 42.04142, -92.75977 42.041...
1	742081R000003	7420472462	75	7.145116e+08	7.145116e+08	2022-08-22T19:26:51	37.037	0.080	0.080	0.143	...	0.0	0.0	no_data	-99999999	-1.000000e+12	1.62	-9999	-9999.0	-1.000000e+12	POLYGON ((-92.91651 42.01167, -92.91681 42.011...
2	742081R000008	7420473212	58	7.145116e+08	7.145116e+08	2022-08-22T19:26:51	36.578	0.181	0.181	0.058	...	0.0	0.0	HENDRICKSON MARSH LAKE	-99999999	-1.000000e+12	45.94	-9999	-9999.0	-1.000000e+12	POLYGON ((-93.24060 41.93319, -93.24066 41.933...
3	742081R000009	7420470712	73	7.145116e+08	7.145116e+08	2022-08-22T19:26:51	36.910	0.110	0.110	0.136	...	0.0	0.0	no_data	-99999999	-1.000000e+12	4.50	-9999	-9999.0	-1.000000e+12	POLYGON ((-92.72557 42.03424, -92.72560 42.034...
4	742081R000011	7420470582	76	7.145116e+08	7.145116e+08	2022-08-22T19:26:51	36.904	0.109	0.109	0.628	...	0.0	0.0	no_data	-99999999	-1.000000e+12	1.89	-9999	-9999.0	-1.000000e+12	POLYGON ((-93.39929 41.90871, -93.39945 41.908...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
17240	742070R000729	7420857422	92	7.145115e+08	7.145115e+08	2022-08-22T19:25:10	33.186	0.098	0.098	0.056	...	0.0	0.0	no_data	-99999999	-1.000000e+12	14.94	-9999	-9999.0	-1.000000e+12	POLYGON ((-95.07581 47.57853, -95.07587 47.578...
17241	742070R000730	7420848152	96	7.145115e+08	7.145115e+08	2022-08-22T19:25:10	33.071	0.038	0.038	0.174	...	0.0	0.0	no_data	-99999999	-1.000000e+12	3.06	-9999	-9999.0	-1.000000e+12	POLYGON ((-94.88532 47.61669, -94.88577 47.616...
17242	712070R000731	7120812272	74	7.145115e+08	7.145115e+08	2022-08-22T19:25:10	33.104	0.077	0.077	0.085	...	0.0	0.0	no_data	-99999999	-1.000000e+12	4.41	-9999	-9999.0	-1.000000e+12	POLYGON ((-95.08313 47.57179, -95.08341 47.571...
17243	712070R000732	7120816202	67	7.145115e+08	7.145115e+08	2022-08-22T19:25:10	32.713	0.106	0.106	0.198	...	0.0	0.0	MUD LAKE	-99999999	-1.000000e+12	6.30	-9999	-9999.0	-1.000000e+12	POLYGON ((-95.39968 47.51004, -95.39975 47.510...
17244	742070R000733	7420857422	95	7.145115e+08	7.145115e+08	2022-08-22T19:25:10	32.725	0.093	0.093	0.119	...	0.0	0.0	no_data	-99999999	-1.000000e+12	14.94	-9999	-9999.0	-1.000000e+12	POLYGON ((-95.07228 47.57473, -95.07257 47.574...

17245 rows × 43 columns

fig, ax = plt.subplots(figsize=(7,12))
SWOT_HR_shp2.plot(ax=ax, color='black')

3. Water Mask Pixel Cloud NetCDF

Accessing the remaining files is different than the shp files above. We do not need to unzip the files because they are stored in native netCDF files in the cloud. For the rest of the products, we will open via xarray.

watermask_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1', temporal = ('2022-08-22 19:29:00', '2022-08-22 19:29:11'), point = ('-90', '35'))

Granules found: 1

The pixel cloud netCDF files are formatted with three groups titled, “pixel cloud”, “tvp”, or “noise” (more detail here). In order to access the coordinates and variables within the file, a group must be specified when calling xarray open_dataset.

ds_PIXC = xr.open_mfdataset(earthaccess.open([watermask_results[0]]), group = 'pixel_cloud', engine='h5netcdf')
ds_PIXC

 Opening 1 granules, approx size: 0.0 GB

<xarray.Dataset>
Dimensions:                                (points: 489673, complex_depth: 2)
Coordinates:
    latitude                               (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
    longitude                              (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
Dimensions without coordinates: points, complex_depth
Data variables: (12/49)
    azimuth_index                          (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
    range_index                            (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
    interferogram                          (points, complex_depth) float32 dask.array<chunksize=(489673, 2), meta=np.ndarray>
    power_plus_y                           (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    power_minus_y                          (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    coherent_power                         (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    ...                                     ...
    solid_earth_tide                       (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    load_tide_fes                          (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    load_tide_got                          (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    pole_tide                              (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    ancillary_surface_classification_flag  (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    pixc_qual                              (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
Attributes:
    description:                 cloud of geolocated interferogram pixels
    interferogram_size_azimuth:  2923
    interferogram_size_range:    4575
    looks_to_efflooks:           1.75

xarray.Dataset

• Dimensions:
  • points: 489673
  • complex_depth: 2
• Coordinates: (2)
  • latitude
    (points)
    float64
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    latitude (positive N, negative S)

    standard_name :

    latitude

    units :

    degrees_north

    valid_min :

    -80.0

    valid_max :

    80.0

    comment :

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

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • longitude
    (points)
    float64
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    longitude (degrees East)

    standard_name :

    longitude

    units :

    degrees_east

    valid_min :

    -180.0

    valid_max :

    180.0

    comment :

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

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Data variables: (49)
  • azimuth_index
    (points)
    float64
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    rare interferogram azimuth index

    units :

    1

    valid_min :

    0

    valid_max :

    999999

    comment :

    Rare interferogram azimuth index (indexed from 0).

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • range_index
    (points)
    float64
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    rare interferogram range index

    units :

    1

    valid_min :

    0

    valid_max :

    999999

    comment :

    Rare interferogram range index (indexed from 0).

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • interferogram
    (points, complex_depth)
    float32
    dask.array<chunksize=(489673, 2), meta=np.ndarray>

    long_name :

    rare interferogram

    units :

    1

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Complex unflattened rare interferogram.

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673, 2) (489673, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • power_plus_y
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    power for plus_y channel

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Power for the plus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • power_minus_y
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    power for minus_y channel

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Power for the minus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • coherent_power
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    coherent power combination of minus_y and plus_y channels

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Power computed by combining the plus_y and minus_y channels coherently by co-aligning the phases (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • x_factor_plus_y
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    X factor for plus_y channel power

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    X factor for the plus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • x_factor_minus_y
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    X factor for minus_y channel power

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    X factor for the minus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • water_frac
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    water fraction

    units :

    1

    valid_min :

    -1000.0

    valid_max :

    10000.0

    comment :

    Noisy estimate of the fraction of the pixel that is water.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • water_frac_uncert
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    water fraction uncertainty

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Uncertainty estimate of the water fraction estimate (width of noisy water frac estimate distribution).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • classification
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    classification

    flag_meanings :

    land land_near_water water_near_land open_water land_near_dark_water dark_water_edge dark_water

    flag_values :

    [ 1 2 3 4 22 23 24]

    valid_min :

    1

    valid_max :

    24

    comment :

    Flags indicating water detection results.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • false_detection_rate
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    false detection rate

    units :

    1

    valid_min :

    0.0

    valid_max :

    1.0

    comment :

    Probability of falsely detecting water when there is none.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • missed_detection_rate
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    missed detection rate

    units :

    1

    valid_min :

    0.0

    valid_max :

    1.0

    comment :

    Probability of falsely detecting no water when there is water.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • prior_water_prob
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    prior water probability

    units :

    1

    valid_min :

    0.0

    valid_max :

    1.0

    comment :

    Prior probability of water occurring.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • bright_land_flag
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    bright land flag

    standard_name :

    status_flag

    flag_meanings :

    not_bright_land bright_land bright_land_or_water

    flag_values :

    [0 1 2]

    valid_min :

    0

    valid_max :

    2

    comment :

    Flag indicating areas that are not typically water but are expected to be bright (e.g., urban areas, ice). Flag value 2 indicates cases where prior data indicate land, but where prior_water_prob indicates possible water.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • layover_impact
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    layover impact

    units :

    m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Estimate of the height error caused by layover, which may not be reliable on a pixel by pixel basis, but may be useful to augment aggregated height uncertainties.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • eff_num_rare_looks
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    effective number of rare looks

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Effective number of independent looks taken to form the rare interferogram.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • height
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    height above reference ellipsoid

    units :

    m

    valid_min :

    -1500.0

    valid_max :

    15000.0

    comment :

    Height of the pixel above the reference ellipsoid.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • cross_track
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    approximate cross-track location

    units :

    m

    valid_min :

    -75000.0

    valid_max :

    75000.0

    comment :

    Approximate cross-track location of the pixel.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pixel_area
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    pixel area

    units :

    m^2

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Pixel area.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • inc
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    incidence angle

    units :

    degrees

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Incidence angle.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • phase_noise_std
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    phase noise standard deviation

    units :

    radians

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Estimate of the phase noise standard deviation.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dlatitude_dphase
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    sensitivity of latitude estimate to interferogram phase

    units :

    degrees/radian

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the latitude estimate to the interferogram phase.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dlongitude_dphase
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    sensitivity of longitude estimate to interferogram phase

    units :

    degrees/radian

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the longitude estimate to the interferogram phase.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dheight_dphase
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    sensitivity of height estimate to interferogram phase

    units :

    m/radian

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the height estimate to the interferogram phase.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dheight_droll
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    sensitivity of height estimate to spacecraft roll

    units :

    m/degrees

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the height estimate to the spacecraft roll.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dheight_dbaseline
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    sensitivity of height estimate to interferometric baseline

    units :

    m/m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the height estimate to the interferometric baseline.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • dheight_drange
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    sensitivity of height estimate to range (delay)

    units :

    m/m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the height estimate to the range (delay).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • darea_dheight
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    sensitivity of pixel area to reference height

    units :

    m^2/m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the pixel area to the reference height.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • illumination_time
    (points)
    datetime64[ns]
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    time of illumination of each pixel (UTC)

    standard_name :

    time

    tai_utc_difference :

    [Value of TAI-UTC at time of first record]

    leap_second :

    YYYY-MM-DD hh:mm:ss

    comment :

    Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • illumination_time_tai
    (points)
    datetime64[ns]
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    time of illumination of each pixel (TAI)

    standard_name :

    time

    comment :

    Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • eff_num_medium_looks
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    effective number of medium looks

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Effective number of independent looks taken in forming the medium interferogram (after adaptive averaging).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sig0
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    sigma0

    units :

    1

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • phase_unwrapping_region
    (points)
    float64
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    phase unwrapping region index

    units :

    1

    valid_min :

    -1

    valid_max :

    99999999

    comment :

    Phase unwrapping region index.

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • instrument_range_cor
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    instrument range correction

    units :

    m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Term that incorporates all calibration corrections applied to range before geolocation.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • instrument_phase_cor
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    instrument phase correction

    units :

    radians

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Term that incorporates all calibration corrections applied to phase before geolocation.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • instrument_baseline_cor
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    instrument baseline correction

    units :

    m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Term that incorporates all calibration corrections applied to baseline before geolocation.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • instrument_attitude_cor
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    instrument attitude correction

    units :

    degrees

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Term that incorporates all calibration corrections applied to attitude before geolocation.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • model_dry_tropo_cor
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    dry troposphere vertical correction

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    m

    valid_min :

    -3.0

    valid_max :

    -1.5

    comment :

    Equivalent vertical correction due to dry troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • model_wet_tropo_cor
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    wet troposphere vertical correction

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    m

    valid_min :

    -1.0

    valid_max :

    0.0

    comment :

    Equivalent vertical correction due to wet troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • iono_cor_gim_ka
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    ionosphere vertical correction

    source :

    Global Ionosphere Maps

    institution :

    JPL

    units :

    m

    valid_min :

    -0.5

    valid_max :

    0.0

    comment :

    Equivalent vertical correction due to ionosphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • height_cor_xover
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    height correction from KaRIn crossovers

    units :

    m

    valid_min :

    -10.0

    valid_max :

    10.0

    comment :

    Height correction from KaRIn crossover calibration. The correction is applied before geolocation but reported as an equivalent height correction.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geoid
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    geoid height

    standard_name :

    geoid_height_above_reference_ellipsoid

    source :

    EGM2008 (Pavlis et al., 2012)

    units :

    m

    valid_min :

    -150.0

    valid_max :

    150.0

    comment :

    Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • solid_earth_tide
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    solid Earth tide height

    source :

    Cartwright and Taylor (1971) and Cartwright and Edden (1973)

    units :

    m

    valid_min :

    -1.0

    valid_max :

    1.0

    comment :

    Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • load_tide_fes
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    geocentric load tide height (FES)

    source :

    FES2014b (Carrere et al., 2016)

    institution :

    LEGOS/CNES

    units :

    m

    valid_min :

    -0.2

    valid_max :

    0.2

    comment :

    Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • load_tide_got
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    geocentric load tide height (GOT)

    source :

    GOT4.10c (Ray, 2013)

    institution :

    GSFC

    units :

    m

    valid_min :

    -0.2

    valid_max :

    0.2

    comment :

    Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pole_tide
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    geocentric pole tide height

    source :

    Wahr (1985) and Desai et al. (2015)

    units :

    m

    valid_min :

    -0.2

    valid_max :

    0.2

    comment :

    Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth's crust).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ancillary_surface_classification_flag
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    surface classification

    standard_name :

    status_flag

    source :

    MODIS/GlobCover

    institution :

    European Space Agency

    flag_meanings :

    open_ocean land continental_water aquatic_vegetation continental_ice_snow floating_ice salted_basin

    flag_values :

    [0 1 2 3 4 5 6]

    valid_min :

    0

    valid_max :

    6

    comment :

    7-state surface type classification computed from a mask built with MODIS and GlobCover data.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • pixc_qual
    (points)
    float32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    standard_name :

    status_flag

    flag_meanings :

    good bad

    flag_values :

    [0 1]

    valid_min :

    0

    valid_max :

    1

    comment :

    Quality flag for pixel cloud data

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

Indexes: (0)

Attributes: (4)

description :

cloud of geolocated interferogram pixels

interferogram_size_azimuth :

2923

interferogram_size_range :

4575

looks_to_efflooks :

1.75