Search ga_ls7e_ard_3 across June 2019

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels. For these ARD products, this value comes from the Fmask algorithm. from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels. For these ARD products, this value comes from the Fmask algorithm. to (exclusive):

Time when dataset was created (processed) From

Time when dataset was created (processed) to (exclusive)

The raw CRS string as it appears in metadata:

One of - final|interim|nrt (near real time):

ID of a dataset type:

Ground sampling distance of the sensor’s best resolution band in metres; represents the size (or spatial resolution) of one pixel. from:

Ground sampling distance of the sensor’s best resolution band in metres; represents the size (or spatial resolution) of one pixel. to (exclusive):

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north from:

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north to (exclusive):

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon. from:

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon. to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm to (exclusive):

File format (GeoTiff, NetCDF):

GQA Circular error probable (90%) from:

GQA Circular error probable (90%) to (exclusive):

Mean of the absolute values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the absolute values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the absolute values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the absolute values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the absolute values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the absolute values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Absolute value of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Absolute value of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Absolute value of the total GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Absolute value of the total GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Absolute value of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Absolute value of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Circular error probable (90%) of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Circular error probable (90%) of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Dataset UUID:

User who indexed the dataset:

When dataset was indexed:

Label:

A Landsat Collection 1 Level-1 product identifier including the Collection processing levels, processing date, collection number, and collection tier category (e.g. ‘LE07_L1TP_098084_20210307_20210308_02_RT’) :

A Landsat scene ID including WRS path and row, year and day, ground station identifier and archive version number (e.g. ‘LC80900852021066LGN00’) :

Latitude range from:

Latitude range to (exclusive):

Longitude range from:

Longitude range to (exclusive):

Full metadata document:

Metadata type name of dataset:

ID of a metadata type:

Spatial reference code from the provider. For Landsat region_code is a scene path row: '{:03d}{:03d}.format(path,row)' For Sentinel it is MGRS code. In general it is a unique string identifier that datasets covering roughly the same spatial region share. :

Acquisition time range From

Acquisition time range to (exclusive)

Dataset URI:

Add field

First 100 results (query limit)

Time (local)	Label
2019-06-01 10:30:33	ga_ls7e_ard_3-0-0_104068_2019-06-01_final
2019-06-01 10:30:57	ga_ls7e_ard_3-0-0_104069_2019-06-01_final
2019-06-01 10:31:21	ga_ls7e_ard_3-1-0_104070_2019-06-01_final
2019-06-01 10:31:45	ga_ls7e_ard_3-0-0_104071_2019-06-01_final
2019-06-01 10:32:09	ga_ls7e_ard_3-0-0_104072_2019-06-01_final
2019-06-01 10:32:33	ga_ls7e_ard_3-0-0_104073_2019-06-01_final
2019-06-01 10:32:57	ga_ls7e_ard_3-0-0_104074_2019-06-01_final
2019-06-01 10:33:21	ga_ls7e_ard_3-0-0_104075_2019-06-01_final
2019-06-01 10:33:45	ga_ls7e_ard_3-0-0_104076_2019-06-01_final
2019-06-01 10:34:08	ga_ls7e_ard_3-0-0_104077_2019-06-01_final
2019-06-01 10:34:32	ga_ls7e_ard_3-0-0_104078_2019-06-01_final
2019-06-01 10:34:56	ga_ls7e_ard_3-0-0_104079_2019-06-01_final
2019-06-01 10:35:20	ga_ls7e_ard_3-0-0_104080_2019-06-01_final
2019-06-01 10:35:44	ga_ls7e_ard_3-0-0_104081_2019-06-01_final
2019-06-01 10:36:08	ga_ls7e_ard_3-0-0_104082_2019-06-01_final
2019-06-02 09:34:30	ga_ls7e_ard_3-0-0_095067_2019-06-02_final
2019-06-02 09:36:53	ga_ls7e_ard_3-1-0_095073_2019-06-02_final
2019-06-02 09:37:17	ga_ls7e_ard_3-0-0_095074_2019-06-02_final
2019-06-02 09:37:41	ga_ls7e_ard_3-0-0_095075_2019-06-02_final
2019-06-02 09:38:05	ga_ls7e_ard_3-0-0_095076_2019-06-02_final
2019-06-02 09:38:29	ga_ls7e_ard_3-0-0_095077_2019-06-02_final
2019-06-02 09:38:53	ga_ls7e_ard_3-0-0_095078_2019-06-02_final
2019-06-02 09:39:17	ga_ls7e_ard_3-1-0_095079_2019-06-02_final
2019-06-02 09:39:41	ga_ls7e_ard_3-0-0_095080_2019-06-02_final
2019-06-02 09:40:05	ga_ls7e_ard_3-0-0_095081_2019-06-02_final
2019-06-02 09:40:29	ga_ls7e_ard_3-0-0_095082_2019-06-02_final
2019-06-02 09:40:53	ga_ls7e_ard_3-0-0_095083_2019-06-02_final
2019-06-02 11:13:23	ga_ls7e_ard_3-0-0_111067_2019-06-02_final
2019-06-02 11:13:47	ga_ls7e_ard_3-0-0_111068_2019-06-02_final
2019-06-02 11:14:58	ga_ls7e_ard_3-0-0_111071_2019-06-02_final
2019-06-02 11:15:22	ga_ls7e_ard_3-0-0_111072_2019-06-02_final
2019-06-02 11:15:46	ga_ls7e_ard_3-0-0_111073_2019-06-02_final
2019-06-02 11:16:10	ga_ls7e_ard_3-0-0_111074_2019-06-02_final
2019-06-02 11:16:34	ga_ls7e_ard_3-0-0_111075_2019-06-02_final
2019-06-02 11:16:58	ga_ls7e_ard_3-0-0_111076_2019-06-02_final
2019-06-02 11:17:22	ga_ls7e_ard_3-0-0_111077_2019-06-02_final
2019-06-02 11:17:46	ga_ls7e_ard_3-0-0_111078_2019-06-02_final
2019-06-02 11:18:10	ga_ls7e_ard_3-0-0_111079_2019-06-02_final
2019-06-02 11:18:34	ga_ls7e_ard_3-0-0_111080_2019-06-02_final
2019-06-02 11:18:58	ga_ls7e_ard_3-0-0_111081_2019-06-02_final
2019-06-02 11:19:22	ga_ls7e_ard_3-1-0_111082_2019-06-02_final
2019-06-02 11:19:46	ga_ls7e_ard_3-0-0_111083_2019-06-02_final
2019-06-02 11:20:10	ga_ls7e_ard_3-0-0_111084_2019-06-02_final
2019-06-03 10:18:55	ga_ls7e_ard_3-0-0_102070_2019-06-03_final
2019-06-03 10:19:19	ga_ls7e_ard_3-0-0_102071_2019-06-03_final
2019-06-03 10:19:43	ga_ls7e_ard_3-0-0_102072_2019-06-03_final
2019-06-03 10:20:07	ga_ls7e_ard_3-0-0_102073_2019-06-03_final
2019-06-03 10:20:31	ga_ls7e_ard_3-0-0_102074_2019-06-03_final
2019-06-03 10:20:55	ga_ls7e_ard_3-0-0_102075_2019-06-03_final
2019-06-03 10:21:19	ga_ls7e_ard_3-0-0_102076_2019-06-03_final
2019-06-03 10:21:43	ga_ls7e_ard_3-0-0_102077_2019-06-03_final
2019-06-03 10:22:07	ga_ls7e_ard_3-0-0_102078_2019-06-03_final
2019-06-03 10:22:31	ga_ls7e_ard_3-0-0_102079_2019-06-03_final
2019-06-04 09:24:51	ga_ls7e_ard_3-0-0_093074_2019-06-03_final
2019-06-04 09:25:15	ga_ls7e_ard_3-0-0_093075_2019-06-03_final
2019-06-04 09:25:39	ga_ls7e_ard_3-0-0_093076_2019-06-03_final
2019-06-04 09:26:03	ga_ls7e_ard_3-0-0_093077_2019-06-03_final
2019-06-04 09:26:27	ga_ls7e_ard_3-0-0_093078_2019-06-03_final
2019-06-04 09:26:51	ga_ls7e_ard_3-0-0_093079_2019-06-03_final
2019-06-04 09:27:15	ga_ls7e_ard_3-0-0_093080_2019-06-03_final
2019-06-04 09:27:39	ga_ls7e_ard_3-0-0_093081_2019-06-03_final
2019-06-04 09:28:03	ga_ls7e_ard_3-0-0_093082_2019-06-03_final
2019-06-04 09:28:27	ga_ls7e_ard_3-0-0_093083_2019-06-03_final
2019-06-04 09:28:51	ga_ls7e_ard_3-0-0_093084_2019-06-03_final
2019-06-04 09:29:15	ga_ls7e_ard_3-0-0_093085_2019-06-03_final
2019-06-04 09:29:39	ga_ls7e_ard_3-0-0_093086_2019-06-03_final
2019-06-04 09:30:27	ga_ls7e_ard_3-0-0_093088_2019-06-04_final
2019-06-28 10:13:49	ga_ls7e_ard_3-1-0_101075_2019-06-28_final
2019-06-28 10:14:13	ga_ls7e_ard_3-0-0_101076_2019-06-28_final
2019-06-28 10:16:13	ga_ls7e_ard_3-0-0_101081_2019-06-28_final
2019-06-29 09:18:09	ga_ls7e_ard_3-0-0_092075_2019-06-28_final
2019-06-29 09:18:33	ga_ls7e_ard_3-0-0_092076_2019-06-28_final
2019-06-29 09:18:57	ga_ls7e_ard_3-0-0_092077_2019-06-28_final
2019-06-29 09:19:21	ga_ls7e_ard_3-0-0_092078_2019-06-28_final
2019-06-29 09:19:45	ga_ls7e_ard_3-0-0_092079_2019-06-28_final
2019-06-29 09:20:09	ga_ls7e_ard_3-0-0_092080_2019-06-28_final
2019-06-29 09:20:33	ga_ls7e_ard_3-0-0_092081_2019-06-28_final
2019-06-29 09:20:57	ga_ls7e_ard_3-0-0_092082_2019-06-28_final
2019-06-29 09:21:21	ga_ls7e_ard_3-0-0_092083_2019-06-28_final
2019-06-29 09:21:45	ga_ls7e_ard_3-0-0_092084_2019-06-28_final
2019-06-29 10:55:27	ga_ls7e_ard_3-1-0_108071_2019-06-29_final
2019-06-29 10:57:02	ga_ls7e_ard_3-1-0_108075_2019-06-29_final
2019-06-30 09:58:11	ga_ls7e_ard_3-0-0_099067_2019-06-30_final
2019-06-30 09:58:35	ga_ls7e_ard_3-0-0_099068_2019-06-30_final
2019-06-30 09:58:59	ga_ls7e_ard_3-0-0_099069_2019-06-30_final
2019-06-30 09:59:23	ga_ls7e_ard_3-0-0_099070_2019-06-30_final
2019-06-30 09:59:47	ga_ls7e_ard_3-0-0_099071_2019-06-30_final
2019-06-30 10:00:11	ga_ls7e_ard_3-0-0_099072_2019-06-30_final
2019-06-30 10:00:35	ga_ls7e_ard_3-0-0_099073_2019-06-30_final
2019-06-30 10:00:59	ga_ls7e_ard_3-0-0_099074_2019-06-30_final
2019-06-30 10:01:23	ga_ls7e_ard_3-0-0_099075_2019-06-30_final
2019-06-30 10:01:47	ga_ls7e_ard_3-0-0_099076_2019-06-30_final
2019-06-30 10:02:11	ga_ls7e_ard_3-0-0_099077_2019-06-30_final
2019-06-30 10:02:35	ga_ls7e_ard_3-0-0_099078_2019-06-30_final
2019-06-30 10:02:59	ga_ls7e_ard_3-0-0_099079_2019-06-30_final
2019-06-30 10:03:23	ga_ls7e_ard_3-0-0_099080_2019-06-30_final
2019-06-30 10:03:46	ga_ls7e_ard_3-0-0_099081_2019-06-30_final
2019-06-30 10:04:10	ga_ls7e_ard_3-0-0_099082_2019-06-30_final
2019-06-30 10:04:34	ga_ls7e_ard_3-0-0_099083_2019-06-30_final
2019-06-30 10:04:58	ga_ls7e_ard_3-0-0_099084_2019-06-30_final

Return to ga_ls7e_ard_3 across June 2019 overview

ga_ls7e_ard_3: Geoscience Australia Landsat 7 Enhanced Thematic Mapper Plus Analysis Ready Data Collection 3

GA Collection 3

USGS Products

Collection 2 NBAR

Collection 2 NBAR Tiled

Pixel Quality

Collection 2 Level1

Tidal

Water Observations

Fractional Cover

Sentinel 2

Ingested Pixel Quality

Geomedians

TMAD

Telemetry data

Summary

Other Products

2019

June

All days