SHIFT V2 Reflectance Data Guide

This guide explains how to use the SHIFT V2 Reflectance data. The data is stored in an S3 bucket, which is mounted to your SMCE computing environment. You can access the data at the following location: s3/dh-shift-curated/aviris/v2

The SHIFT V2 Reflectance data has transitioned from ENVI files to NetCDF format. I have written a few functions to facilitate interaction with this new format. Below are some examples of how to work with the data.

import sys
sys.path.append('s3/dh-shift-curated/aviris/utils')
from shift_v2_utils import find_overlap_shift_v2, open_shift_v2_data

You can use the find overlap function to find flightlines that overlap a shapefile formated as a geopandas dataframe

import geopandas as gpd
from shapely.geometry import Polygon

polygon_1 = Polygon([
    (-119.8853015 , 34.42277795),
    (-119.86975941, 34.42312643),
    (-119.86921817, 34.4066284 ),
    (-119.88476322, 34.40623869),
    (-119.8853015 , 34.42277795)
])

polygon_2 = Polygon([
    (-119.8753015 , 34.41777795),
    (-119.85975941, 34.41812643),
    (-119.85921817, 34.4016284 ),
    (-119.87476322, 34.40123869),
    (-119.8753015 , 34.41777795)
])

# Create a GeoDataFrame
gdf = gpd.GeoDataFrame({
    'id': [1, 2],
    'name': ['Polygon 1', 'Polygon 2'],
    'geometry': [polygon_1, polygon_2]
}, crs="EPSG:4326")

The overlap function takes a GeoDataFrame as an argument. The GeoDataFrame must have its CRS set to EPSG:4326. You can filter the overlapping flightlines by date. The date argument can be provided as a single string or as a list of strings,

# all dates
find_overlap_shift_v2(gdf)

# single date filter
find_overlap_shift_v2(gdf, dates='20220316')

# multiple dates filter
flightlines = find_overlap_shift_v2(gdf, dates=['20220316', '20220914'])
flightlines

	index	flightline	date	path	geometry	index_right	id	name
0	306	ang20220316t184402_001	20220316	s3/dh-shift-curated/aviris/v2/L2a/20220316/ang...	POLYGON ((-119.77003 34.38405, -119.76854 34.4...	1	2	Polygon 2
1	307	ang20220316t184402_002	20220316	s3/dh-shift-curated/aviris/v2/L2a/20220316/ang...	POLYGON ((-119.85591 34.39932, -119.85424 34.4...	1	2	Polygon 2
2	1418	ang20220914t170915_000	20220914	s3/dh-shift-curated/aviris/v2/L2a/20220914/ang...	POLYGON ((-119.80109 34.37997, -119.79822 34.4...	1	2	Polygon 2
3	1426	ang20220914t171806_003	20220914	s3/dh-shift-curated/aviris/v2/L2a/20220914/ang...	POLYGON ((-119.80085 34.31877, -119.79571 34.4...	1	2	Polygon 2
4	1428	ang20220914t172925_001	20220914	s3/dh-shift-curated/aviris/v2/L2a/20220914/ang...	POLYGON ((-119.77325 34.38317, -119.77163 34.4...	1	2	Polygon 2
5	1429	ang20220914t172925_002	20220914	s3/dh-shift-curated/aviris/v2/L2a/20220914/ang...	POLYGON ((-119.86728 34.39939, -119.86555 34.4...	1	2	Polygon 2
6	307	ang20220316t184402_002	20220316	s3/dh-shift-curated/aviris/v2/L2a/20220316/ang...	POLYGON ((-119.85591 34.39932, -119.85424 34.4...	0	1	Polygon 1
7	1426	ang20220914t171806_003	20220914	s3/dh-shift-curated/aviris/v2/L2a/20220914/ang...	POLYGON ((-119.80085 34.31877, -119.79571 34.4...	0	1	Polygon 1
8	1428	ang20220914t172925_001	20220914	s3/dh-shift-curated/aviris/v2/L2a/20220914/ang...	POLYGON ((-119.77325 34.38317, -119.77163 34.4...	0	1	Polygon 1
9	1429	ang20220914t172925_002	20220914	s3/dh-shift-curated/aviris/v2/L2a/20220914/ang...	POLYGON ((-119.86728 34.39939, -119.86555 34.4...	0	1	Polygon 1

Using the output of the overlap function, you can use the path to open the dataset of interest. By default, the function will return the reflectance group. However, if you want to load additional groups, you can specify them by name: [‘reflectance’, ‘water_vapor’, ‘aerosol_optical_thickness’].

The mask_and_scale argument controls how Xarray handles “NoData” values. By default, mask_and_scale is set to False, meaning Xarray will use the coded NoData value of -9999. If you set mask_and_scale to True, Xarray will fill NoData values with np.nan instead.

path = flightlines.iloc[5].path
ds = open_shift_v2_data(path)
ds = open_shift_v2_data(path, groups=['reflectance', 'water_vapor', 'aerosol_optical_thickness'], mask_and_scale=False)
ds

<xarray.Dataset>
Dimensions:                    (easting: 2259, northing: 991, wavelength: 425)
Coordinates:
  * easting                    (easting) float64 7.762e+05 ... 7.88e+05
  * northing                   (northing) float64 3.816e+06 ... 3.811e+06
    transverse_mercator        int64 0
  * wavelength                 (wavelength) float32 377.2 382.2 ... 2.501e+03
Data variables:
    fwhm                       (wavelength) float32 dask.array<chunksize=(425,), meta=np.ndarray>
    reflectance                (wavelength, northing, easting) float32 dask.array<chunksize=(425, 991, 2259), meta=np.ndarray>
    water_vapor                (northing, easting) float32 dask.array<chunksize=(991, 2259), meta=np.ndarray>
    aerosol_optical_thickness  (northing, easting) float32 dask.array<chunksize=(991, 2259), meta=np.ndarray>
Attributes: (12/23)
    Conventions:                       CF-1.6
    date_created:                      2024-12-30T18:52:40Z
    summary:                           The Airborne Visible / Infrared Imagin...
    keywords:                          Imaging Spectroscopy, AVIRIS, AVIRIS-NG
    sensor:                            Airborne Visible / Infrared Imaging Sp...
    instrument:                        AVIRIS-NG
    ...                                ...
    ncei_template_version:             NCEI_NetCDF_Grid_Template_v2.0
    title:                             AVIRIS-NG L2A Surface reflectance (fli...
    processing_level:                  L2A
    time_coverage_start:               2022-09-14T17:33:37Z
    time_coverage_end:                 2022-09-14T17:33:37Z
    product_version:                   0

xarray.Dataset

• Dimensions:
  • easting: 2259
  • northing: 991
  • wavelength: 425
• Coordinates: (4)
  • easting
    (easting)
    float64
    7.762e+05 7.763e+05 ... 7.88e+05

    description :

    UTM easting coordinate for center of grid cell for orthocorrected pixel location

    standard_name :

    projection_x_coordinate

    long_name :

    X coordinate of projection

    units :

    m

    axis :

    X

    array([776247.179458, 776252.379458, 776257.579458, ..., 787978.379458,
           787983.579458, 787988.779458])

  • northing
    (northing)
    float64
    3.816e+06 3.816e+06 ... 3.811e+06

    description :

    UTM northing coordinate for center of grid cell for orthocorrected pixel location

    standard_name :

    projection_y_coordinate

    long_name :

    Y coordinate of projection

    units :

    m

    axis :

    Y

    array([3816042.428581, 3816037.228581, 3816032.028581, ..., 3810904.828581,
           3810899.628581, 3810894.428581])

  • transverse_mercator
    ()
    int64
    0

    crs_wkt :

    PROJCS["WGS 84 / UTM zone 10N",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"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    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

    projected_crs_name :

    WGS 84 / UTM zone 10N

    grid_mapping_name :

    transverse_mercator

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    -123.0

    false_easting :

    500000.0

    false_northing :

    0.0

    scale_factor_at_central_meridian :

    0.9996

    spatial_ref :

    PROJCS["WGS 84 / UTM zone 10N",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"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    array(0)

  • wavelength
    (wavelength)
    float32
    377.2 382.2 ... 2.496e+03 2.501e+03

    standard_name :

    radiation_wavelength

    long_name :

    Wavelength centers

    units :

    nm

    array([ 377.19565,  382.20566,  387.21564, ..., 2490.8557 , 2495.8657 ,
           2500.8757 ], dtype=float32)

• Data variables: (4)
  • fwhm
    (wavelength)
    float32
    dask.array<chunksize=(425,), meta=np.ndarray>

    long_name :

    Full width at half maximum

    units :

    nm

    Array Chunk Bytes 1.66 kiB 1.66 kiB Shape (425,) (425,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • reflectance
    (wavelength, northing, easting)
    float32
    dask.array<chunksize=(425, 991, 2259), meta=np.ndarray>

    _QuantizeBitGroomNumberOfSignificantDigits :

    5

    _FillValue :

    -9999.0

    long_name :

    Surface hemispherical directional reflectance factor

    grid_mapping :

    transverse_mercator

    orthorectified :

    True

    Array Chunk Bytes 3.54 GiB 3.54 GiB Shape (425, 991, 2259) (425, 991, 2259) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • water_vapor
    (northing, easting)
    float32
    dask.array<chunksize=(991, 2259), meta=np.ndarray>

    _FillValue :

    -9999.0

    standard_name :

    lwe_thickness_of_atmosphere_mass_content_of_water_vapor

    units :

    cm

    grid_mapping :

    transverse_mercator

    orthorectified :

    True

    Array Chunk Bytes 8.54 MiB 8.54 MiB Shape (991, 2259) (991, 2259) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • aerosol_optical_thickness
    (northing, easting)
    float32
    dask.array<chunksize=(991, 2259), meta=np.ndarray>

    _FillValue :

    -9999.0

    standard_name :

    optical_thickness_of_atmosphere_layer_due_to_ambient_aerosol_particles

    units :

    grid_mapping :

    transverse_mercator

    orthorectified :

    True

    Array Chunk Bytes 8.54 MiB 8.54 MiB Shape (991, 2259) (991, 2259) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • easting
    PandasIndex

    PandasIndex(Float64Index([ 776247.179457851, 776252.3794578509,  776257.579457851,
                   776262.779457851,  776267.979457851,  776273.179457851,
                  776278.3794578509,  776283.579457851,  776288.779457851,
                   776293.979457851,
                  ...
                   787941.979457851,  787947.179457851, 787952.3794578509,
                   787957.579457851,  787962.779457851,  787967.979457851,
                   787973.179457851, 787978.3794578509,  787983.579457851,
                   787988.779457851],
                 dtype='float64', name='easting', length=2259))

  • northing
    PandasIndex

    PandasIndex(Float64Index([3816042.42858074, 3816037.22858074, 3816032.02858074,
                  3816026.82858074, 3816021.62858074, 3816016.42858074,
                  3816011.22858074, 3816006.02858074, 3816000.82858074,
                  3815995.62858074,
                  ...
                  3810941.22858074, 3810936.02858074, 3810930.82858074,
                  3810925.62858074, 3810920.42858074, 3810915.22858074,
                  3810910.02858074, 3810904.82858074, 3810899.62858074,
                  3810894.42858074],
                 dtype='float64', name='northing', length=991))

  • wavelength
    PandasIndex

    PandasIndex(Float64Index([ 377.1956481933594,  382.2056579589844, 387.21563720703125,
                  392.22564697265625, 397.22564697265625, 402.23565673828125,
                   407.2456359863281,  412.2556457519531,  417.2656555175781,
                    422.275634765625,
                  ...
                   2455.795654296875,    2460.8056640625,  2465.815673828125,
                   2470.815673828125,   2475.82568359375,  2480.835693359375,
                      2485.845703125,  2490.855712890625,   2495.86572265625,
                   2500.875732421875],
                 dtype='float64', name='wavelength', length=425))

• Attributes: (23)

  Conventions :

  CF-1.6

  date_created :

  2024-12-30T18:52:40Z

  summary :

  The Airborne Visible / Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) is part of NASA’s Airborne Science Program (ASP) and the Jet Propulsion Laboratory’s (JPL) Earth Science Airborne Program. AVIRIS-NG is the successor to AVIRIS-Classic and provides high signal-to-noise ratio imaging spectroscopy measurements in 425 contiguous spectral channels with wavelengths in the solar reflected spectral range (380-2510 nm).

  keywords :

  Imaging Spectroscopy, AVIRIS, AVIRIS-NG

  sensor :

  Airborne Visible / Infrared Imaging Spectrometer Next Generation

  instrument :

  AVIRIS-NG

  platform :

  Beechcraft Super King Air 200

  institution :

  NASA Jet Propulsion Laboratory/California Institute of Technology

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  creator_name :

  Jet Propulsion Laboratory/California Institute of Technology

  creator_url :

  aviris.jpl.nasa.gov

  publisher_type :

  institution

  publisher_institution :

  Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)

  identifier_product_doi_authority :

  https://doi.org

  flight_line :

  ang20220914t172925

  scene :

  002

  software_build_version :

  main

  ncei_template_version :

  NCEI_NetCDF_Grid_Template_v2.0

  title :

  AVIRIS-NG L2A Surface reflectance (flight line: ang20220914t172925, scene: 002)

  processing_level :

  L2A

  time_coverage_start :

  2022-09-14T17:33:37Z

  time_coverage_end :

  2022-09-14T17:33:37Z

  product_version :

  0

The data is lazily loaded using Dask, meaning it is not fully loaded into memory until necessary. You can perform operations, such as clipping the data, using tools like rioxarray without actually loading the data into memory.

clipped = ds.rio.clip(gdf.to_crs(ds.rio.crs).geometry)
clipped

<xarray.Dataset>
Dimensions:                    (wavelength: 425, easting: 334, northing: 470)
Coordinates:
    transverse_mercator        int64 0
  * wavelength                 (wavelength) float32 377.2 382.2 ... 2.501e+03
  * easting                    (easting) float64 7.863e+05 ... 7.88e+05
  * northing                   (northing) float64 3.814e+06 ... 3.811e+06
Data variables:
    fwhm                       (wavelength) float32 dask.array<chunksize=(425,), meta=np.ndarray>
    reflectance                (wavelength, northing, easting) float32 dask.array<chunksize=(425, 470, 334), meta=np.ndarray>
    water_vapor                (northing, easting) float32 dask.array<chunksize=(470, 334), meta=np.ndarray>
    aerosol_optical_thickness  (northing, easting) float32 dask.array<chunksize=(470, 334), meta=np.ndarray>
Attributes: (12/23)
    Conventions:                       CF-1.6
    date_created:                      2024-12-30T18:52:40Z
    summary:                           The Airborne Visible / Infrared Imagin...
    keywords:                          Imaging Spectroscopy, AVIRIS, AVIRIS-NG
    sensor:                            Airborne Visible / Infrared Imaging Sp...
    instrument:                        AVIRIS-NG
    ...                                ...
    ncei_template_version:             NCEI_NetCDF_Grid_Template_v2.0
    title:                             AVIRIS-NG L2A Surface reflectance (fli...
    processing_level:                  L2A
    time_coverage_start:               2022-09-14T17:33:37Z
    time_coverage_end:                 2022-09-14T17:33:37Z
    product_version:                   0

xarray.Dataset

• Dimensions:
  • wavelength: 425
  • easting: 334
  • northing: 470
• Coordinates: (4)
  • transverse_mercator
    ()
    int64
    0

    crs_wkt :

    PROJCS["WGS 84 / UTM zone 10N",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"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    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

    projected_crs_name :

    WGS 84 / UTM zone 10N

    grid_mapping_name :

    transverse_mercator

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    -123.0

    false_easting :

    500000.0

    false_northing :

    0.0

    scale_factor_at_central_meridian :

    0.9996

    spatial_ref :

    PROJCS["WGS 84 / UTM zone 10N",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"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    array(0)

  • wavelength
    (wavelength)
    float32
    377.2 382.2 ... 2.496e+03 2.501e+03

    standard_name :

    radiation_wavelength

    long_name :

    Wavelength centers

    units :

    nm

    array([ 377.19565,  382.20566,  387.21564, ..., 2490.8557 , 2495.8657 ,
           2500.8757 ], dtype=float32)

  • easting
    (easting)
    float64
    7.863e+05 7.863e+05 ... 7.88e+05

    description :

    UTM easting coordinate for center of grid cell for orthocorrected pixel location

    standard_name :

    projection_x_coordinate

    long_name :

    x coordinate of projection

    units :

    metre

    axis :

    X

    array([786257.179458, 786262.379458, 786267.579458, ..., 787978.379458,
           787983.579458, 787988.779458])

  • northing
    (northing)
    float64
    3.814e+06 3.814e+06 ... 3.811e+06

    description :

    UTM northing coordinate for center of grid cell for orthocorrected pixel location

    standard_name :

    projection_y_coordinate

    long_name :

    y coordinate of projection

    units :

    metre

    axis :

    Y

    array([3813515.228581, 3813510.028581, 3813504.828581, ..., 3811086.828581,
           3811081.628581, 3811076.428581])

• Data variables: (4)
  • fwhm
    (wavelength)
    float32
    dask.array<chunksize=(425,), meta=np.ndarray>

    long_name :

    Full width at half maximum

    units :

    nm

    Array Chunk Bytes 1.66 kiB 1.66 kiB Shape (425,) (425,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • reflectance
    (wavelength, northing, easting)
    float32
    dask.array<chunksize=(425, 470, 334), meta=np.ndarray>

    _QuantizeBitGroomNumberOfSignificantDigits :

    5

    long_name :

    Surface hemispherical directional reflectance factor

    grid_mapping :

    transverse_mercator

    orthorectified :

    True

    _FillValue :

    -9999.0

    Array Chunk Bytes 254.50 MiB 254.50 MiB Shape (425, 470, 334) (425, 470, 334) Dask graph 1 chunks in 8 graph layers Data type float32 numpy.ndarray

  • water_vapor
    (northing, easting)
    float32
    dask.array<chunksize=(470, 334), meta=np.ndarray>

    standard_name :

    lwe_thickness_of_atmosphere_mass_content_of_water_vapor

    units :

    cm

    grid_mapping :

    transverse_mercator

    orthorectified :

    True

    _FillValue :

    -9999.0

    Array Chunk Bytes 613.20 kiB 613.20 kiB Shape (470, 334) (470, 334) Dask graph 1 chunks in 8 graph layers Data type float32 numpy.ndarray

  • aerosol_optical_thickness
    (northing, easting)
    float32
    dask.array<chunksize=(470, 334), meta=np.ndarray>

    standard_name :

    optical_thickness_of_atmosphere_layer_due_to_ambient_aerosol_particles

    units :

    grid_mapping :

    transverse_mercator

    orthorectified :

    True

    _FillValue :

    -9999.0

    Array Chunk Bytes 613.20 kiB 613.20 kiB Shape (470, 334) (470, 334) Dask graph 1 chunks in 8 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • wavelength
    PandasIndex

    PandasIndex(Float64Index([ 377.1956481933594,  382.2056579589844, 387.21563720703125,
                  392.22564697265625, 397.22564697265625, 402.23565673828125,
                   407.2456359863281,  412.2556457519531,  417.2656555175781,
                    422.275634765625,
                  ...
                   2455.795654296875,    2460.8056640625,  2465.815673828125,
                   2470.815673828125,   2475.82568359375,  2480.835693359375,
                      2485.845703125,  2490.855712890625,   2495.86572265625,
                   2500.875732421875],
                 dtype='float64', name='wavelength', length=425))

  • easting
    PandasIndex

    PandasIndex(Float64Index([ 786257.179457851, 786262.3794578509,  786267.579457851,
                   786272.779457851,  786277.979457851,  786283.179457851,
                  786288.3794578509,  786293.579457851,  786298.779457851,
                   786303.979457851,
                  ...
                   787941.979457851,  787947.179457851, 787952.3794578509,
                   787957.579457851,  787962.779457851,  787967.979457851,
                   787973.179457851, 787978.3794578509,  787983.579457851,
                   787988.779457851],
                 dtype='float64', name='easting', length=334))

  • northing
    PandasIndex

    PandasIndex(Float64Index([3813515.22858074, 3813510.02858074, 3813504.82858074,
                  3813499.62858074, 3813494.42858074, 3813489.22858074,
                  3813484.02858074, 3813478.82858074, 3813473.62858074,
                  3813468.42858074,
                  ...
                  3811123.22858074, 3811118.02858074, 3811112.82858074,
                  3811107.62858074, 3811102.42858074, 3811097.22858074,
                  3811092.02858074, 3811086.82858074, 3811081.62858074,
                  3811076.42858074],
                 dtype='float64', name='northing', length=470))

• Attributes: (23)

  Conventions :

  CF-1.6

  date_created :

  2024-12-30T18:52:40Z

  summary :

  The Airborne Visible / Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) is part of NASA’s Airborne Science Program (ASP) and the Jet Propulsion Laboratory’s (JPL) Earth Science Airborne Program. AVIRIS-NG is the successor to AVIRIS-Classic and provides high signal-to-noise ratio imaging spectroscopy measurements in 425 contiguous spectral channels with wavelengths in the solar reflected spectral range (380-2510 nm).

  keywords :

  Imaging Spectroscopy, AVIRIS, AVIRIS-NG

  sensor :

  Airborne Visible / Infrared Imaging Spectrometer Next Generation

  instrument :

  AVIRIS-NG

  platform :

  Beechcraft Super King Air 200

  institution :

  NASA Jet Propulsion Laboratory/California Institute of Technology

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  creator_name :

  Jet Propulsion Laboratory/California Institute of Technology

  creator_url :

  aviris.jpl.nasa.gov

  publisher_type :

  institution

  publisher_institution :

  Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)

  identifier_product_doi_authority :

  https://doi.org

  flight_line :

  ang20220914t172925

  scene :

  002

  software_build_version :

  main

  ncei_template_version :

  NCEI_NetCDF_Grid_Template_v2.0

  title :

  AVIRIS-NG L2A Surface reflectance (flight line: ang20220914t172925, scene: 002)

  processing_level :

  L2A

  time_coverage_start :

  2022-09-14T17:33:37Z

  time_coverage_end :

  2022-09-14T17:33:37Z

  product_version :

  0

Once you have clipped the data you can load it into memory using compute. Loading the data into memory might take some time. Once the data has been accessed once following access attempts should be much quicker.

%%time
clipped.reflectance.compute()

CPU times: user 14.6 s, sys: 1.72 s, total: 16.3 s
Wall time: 16.4 s

<xarray.DataArray 'reflectance' (wavelength: 425, northing: 470, easting: 334)>
array([[[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        ...,
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],

       [[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        ...,
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],

       [[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        ...,
...
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],

       [[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        ...,
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],

       [[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        ...,
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
        [-9999., -9999., -9999., ..., -9999., -9999., -9999.]]],
      dtype=float32)
Coordinates:
    transverse_mercator  int64 0
  * wavelength           (wavelength) float32 377.2 382.2 ... 2.501e+03
  * easting              (easting) float64 7.863e+05 7.863e+05 ... 7.88e+05
  * northing             (northing) float64 3.814e+06 3.814e+06 ... 3.811e+06
Attributes:
    _QuantizeBitGroomNumberOfSignificantDigits:  5
    long_name:                                   Surface hemispherical direct...
    grid_mapping:                                transverse_mercator
    orthorectified:                              True
    _FillValue:                                  -9999.0

xarray.DataArray

'reflectance'

• wavelength: 425
• northing: 470
• easting: 334

• -9.999e+03 -9.999e+03 -9.999e+03 ... -9.999e+03 -9.999e+03 -9.999e+03

  array([[[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          ...,
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],
  
         [[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          ...,
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],
  
         [[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          ...,
  ...
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],
  
         [[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          ...,
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],
  
         [[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          ...,
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
          [-9999., -9999., -9999., ..., -9999., -9999., -9999.]]],
        dtype=float32)

• Coordinates: (4)
  • transverse_mercator
    ()
    int64
    0

    crs_wkt :

    PROJCS["WGS 84 / UTM zone 10N",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"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    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

    projected_crs_name :

    WGS 84 / UTM zone 10N

    grid_mapping_name :

    transverse_mercator

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    -123.0

    false_easting :

    500000.0

    false_northing :

    0.0

    scale_factor_at_central_meridian :

    0.9996

    spatial_ref :

    PROJCS["WGS 84 / UTM zone 10N",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"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    array(0)

  • wavelength
    (wavelength)
    float32
    377.2 382.2 ... 2.496e+03 2.501e+03

    standard_name :

    radiation_wavelength

    long_name :

    Wavelength centers

    units :

    nm

    array([ 377.19565,  382.20566,  387.21564, ..., 2490.8557 , 2495.8657 ,
           2500.8757 ], dtype=float32)

  • easting
    (easting)
    float64
    7.863e+05 7.863e+05 ... 7.88e+05

    description :

    UTM easting coordinate for center of grid cell for orthocorrected pixel location

    standard_name :

    projection_x_coordinate

    long_name :

    x coordinate of projection

    units :

    metre

    axis :

    X

    array([786257.179458, 786262.379458, 786267.579458, ..., 787978.379458,
           787983.579458, 787988.779458])

  • northing
    (northing)
    float64
    3.814e+06 3.814e+06 ... 3.811e+06

    description :

    UTM northing coordinate for center of grid cell for orthocorrected pixel location

    standard_name :

    projection_y_coordinate

    long_name :

    y coordinate of projection

    units :

    metre

    axis :

    Y

    array([3813515.228581, 3813510.028581, 3813504.828581, ..., 3811086.828581,
           3811081.628581, 3811076.428581])

• Indexes: (3)
  • wavelength
    PandasIndex

    PandasIndex(Float64Index([ 377.1956481933594,  382.2056579589844, 387.21563720703125,
                  392.22564697265625, 397.22564697265625, 402.23565673828125,
                   407.2456359863281,  412.2556457519531,  417.2656555175781,
                    422.275634765625,
                  ...
                   2455.795654296875,    2460.8056640625,  2465.815673828125,
                   2470.815673828125,   2475.82568359375,  2480.835693359375,
                      2485.845703125,  2490.855712890625,   2495.86572265625,
                   2500.875732421875],
                 dtype='float64', name='wavelength', length=425))

  • easting
    PandasIndex

    PandasIndex(Float64Index([ 786257.179457851, 786262.3794578509,  786267.579457851,
                   786272.779457851,  786277.979457851,  786283.179457851,
                  786288.3794578509,  786293.579457851,  786298.779457851,
                   786303.979457851,
                  ...
                   787941.979457851,  787947.179457851, 787952.3794578509,
                   787957.579457851,  787962.779457851,  787967.979457851,
                   787973.179457851, 787978.3794578509,  787983.579457851,
                   787988.779457851],
                 dtype='float64', name='easting', length=334))

  • northing
    PandasIndex

    PandasIndex(Float64Index([3813515.22858074, 3813510.02858074, 3813504.82858074,
                  3813499.62858074, 3813494.42858074, 3813489.22858074,
                  3813484.02858074, 3813478.82858074, 3813473.62858074,
                  3813468.42858074,
                  ...
                  3811123.22858074, 3811118.02858074, 3811112.82858074,
                  3811107.62858074, 3811102.42858074, 3811097.22858074,
                  3811092.02858074, 3811086.82858074, 3811081.62858074,
                  3811076.42858074],
                 dtype='float64', name='northing', length=470))

• Attributes: (5)

  _QuantizeBitGroomNumberOfSignificantDigits :

  5

  long_name :

  Surface hemispherical directional reflectance factor

  grid_mapping :

  transverse_mercator

  orthorectified :

  True

  _FillValue :

  -9999.0