Search ga_ls7e_ard_3 across September 2018

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
2018-09-02 10:38:38	ga_ls7e_ard_3-0-0_104068_2018-09-02_final
2018-09-02 10:39:02	ga_ls7e_ard_3-0-0_104069_2018-09-02_final
2018-09-02 10:39:26	ga_ls7e_ard_3-1-0_104070_2018-09-02_final
2018-09-02 10:39:50	ga_ls7e_ard_3-0-0_104071_2018-09-02_final
2018-09-02 10:40:14	ga_ls7e_ard_3-0-0_104072_2018-09-02_final
2018-09-02 10:40:38	ga_ls7e_ard_3-0-0_104073_2018-09-02_final
2018-09-02 10:41:02	ga_ls7e_ard_3-0-0_104074_2018-09-02_final
2018-09-02 10:41:26	ga_ls7e_ard_3-0-0_104075_2018-09-02_final
2018-09-02 10:41:50	ga_ls7e_ard_3-0-0_104076_2018-09-02_final
2018-09-02 10:42:14	ga_ls7e_ard_3-0-0_104077_2018-09-02_final
2018-09-02 10:42:38	ga_ls7e_ard_3-0-0_104078_2018-09-02_final
2018-09-02 10:43:01	ga_ls7e_ard_3-0-0_104079_2018-09-02_final
2018-09-02 10:43:25	ga_ls7e_ard_3-0-0_104080_2018-09-02_final
2018-09-02 10:43:49	ga_ls7e_ard_3-0-0_104081_2018-09-02_final
2018-09-02 10:44:13	ga_ls7e_ard_3-0-0_104082_2018-09-02_final
2018-09-03 09:42:36	ga_ls7e_ard_3-0-0_095067_2018-09-03_final
2018-09-03 09:44:35	ga_ls7e_ard_3-0-0_095072_2018-09-03_final
2018-09-03 09:44:59	ga_ls7e_ard_3-1-0_095073_2018-09-03_final
2018-09-03 09:45:23	ga_ls7e_ard_3-0-0_095074_2018-09-03_final
2018-09-03 09:45:47	ga_ls7e_ard_3-0-0_095075_2018-09-03_final
2018-09-03 09:46:11	ga_ls7e_ard_3-0-0_095076_2018-09-03_final
2018-09-03 09:46:35	ga_ls7e_ard_3-0-0_095077_2018-09-03_final
2018-09-03 09:46:59	ga_ls7e_ard_3-0-0_095078_2018-09-03_final
2018-09-03 09:47:23	ga_ls7e_ard_3-0-0_095079_2018-09-03_final
2018-09-03 09:47:47	ga_ls7e_ard_3-0-0_095080_2018-09-03_final
2018-09-03 09:48:11	ga_ls7e_ard_3-0-0_095081_2018-09-03_final
2018-09-03 09:48:35	ga_ls7e_ard_3-0-0_095082_2018-09-03_final
2018-09-03 09:48:59	ga_ls7e_ard_3-0-0_095083_2018-09-03_final
2018-09-03 09:49:23	ga_ls7e_ard_3-0-0_095084_2018-09-03_final
2018-09-03 09:49:47	ga_ls7e_ard_3-0-0_095085_2018-09-03_final
2018-09-03 09:50:11	ga_ls7e_ard_3-0-0_095086_2018-09-03_final
2018-09-03 11:21:29	ga_ls7e_ard_3-0-0_111067_2018-09-03_final
2018-09-03 11:21:53	ga_ls7e_ard_3-0-0_111068_2018-09-03_final
2018-09-03 11:23:04	ga_ls7e_ard_3-0-0_111071_2018-09-03_final
2018-09-03 11:23:28	ga_ls7e_ard_3-0-0_111072_2018-09-03_final
2018-09-03 11:23:52	ga_ls7e_ard_3-0-0_111073_2018-09-03_final
2018-09-03 11:24:16	ga_ls7e_ard_3-0-0_111074_2018-09-03_final
2018-09-03 11:24:40	ga_ls7e_ard_3-0-0_111075_2018-09-03_final
2018-09-03 11:25:04	ga_ls7e_ard_3-0-0_111076_2018-09-03_final
2018-09-03 11:25:28	ga_ls7e_ard_3-0-0_111077_2018-09-03_final
2018-09-03 11:25:52	ga_ls7e_ard_3-0-0_111078_2018-09-03_final
2018-09-03 11:26:16	ga_ls7e_ard_3-0-0_111079_2018-09-03_final
2018-09-03 11:26:40	ga_ls7e_ard_3-0-0_111080_2018-09-03_final
2018-09-03 11:27:04	ga_ls7e_ard_3-0-0_111081_2018-09-03_final
2018-09-03 11:27:28	ga_ls7e_ard_3-1-0_111082_2018-09-03_final
2018-09-03 11:27:52	ga_ls7e_ard_3-0-0_111083_2018-09-03_final
2018-09-03 11:28:16	ga_ls7e_ard_3-0-0_111084_2018-09-03_final
2018-09-04 10:26:14	ga_ls7e_ard_3-0-0_102068_2018-09-04_final
2018-09-04 10:26:38	ga_ls7e_ard_3-0-0_102069_2018-09-04_final
2018-09-04 10:27:02	ga_ls7e_ard_3-0-0_102070_2018-09-04_final
2018-09-04 10:27:26	ga_ls7e_ard_3-0-0_102071_2018-09-04_final
2018-09-04 10:27:50	ga_ls7e_ard_3-0-0_102072_2018-09-04_final
2018-09-04 10:28:14	ga_ls7e_ard_3-0-0_102073_2018-09-04_final
2018-09-04 10:28:38	ga_ls7e_ard_3-0-0_102074_2018-09-04_final
2018-09-04 10:29:02	ga_ls7e_ard_3-0-0_102075_2018-09-04_final
2018-09-04 10:29:25	ga_ls7e_ard_3-0-0_102076_2018-09-04_final
2018-09-04 10:29:49	ga_ls7e_ard_3-0-0_102077_2018-09-04_final
2018-09-04 10:30:13	ga_ls7e_ard_3-0-0_102078_2018-09-04_final
2018-09-04 10:30:37	ga_ls7e_ard_3-0-0_102079_2018-09-04_final
2018-09-04 10:31:01	ga_ls7e_ard_3-0-0_102080_2018-09-04_final
2018-09-04 10:31:25	ga_ls7e_ard_3-0-0_102081_2018-09-04_final
2018-09-04 10:31:49	ga_ls7e_ard_3-0-0_102082_2018-09-04_final
2018-09-05 09:32:59	ga_ls7e_ard_3-0-0_093074_2018-09-05_final
2018-09-05 09:33:47	ga_ls7e_ard_3-0-0_093076_2018-09-05_final
2018-09-05 09:34:11	ga_ls7e_ard_3-0-0_093077_2018-09-05_final
2018-09-05 09:34:35	ga_ls7e_ard_3-0-0_093078_2018-09-05_final
2018-09-05 09:34:59	ga_ls7e_ard_3-0-0_093079_2018-09-05_final
2018-09-05 09:35:23	ga_ls7e_ard_3-0-0_093080_2018-09-05_final
2018-09-05 09:35:47	ga_ls7e_ard_3-0-0_093081_2018-09-05_final
2018-09-05 09:36:11	ga_ls7e_ard_3-0-0_093082_2018-09-05_final
2018-09-05 09:36:35	ga_ls7e_ard_3-0-0_093083_2018-09-05_final
2018-09-05 09:36:59	ga_ls7e_ard_3-0-0_093084_2018-09-05_final
2018-09-05 09:37:22	ga_ls7e_ard_3-0-0_093085_2018-09-05_final
2018-09-05 09:37:46	ga_ls7e_ard_3-0-0_093086_2018-09-05_final
2018-09-05 09:38:10	ga_ls7e_ard_3-0-0_093087_2018-09-05_final
2018-09-05 11:10:16	ga_ls7e_ard_3-0-0_109070_2018-09-05_final
2018-09-05 11:10:40	ga_ls7e_ard_3-0-0_109071_2018-09-05_final
2018-09-05 11:11:04	ga_ls7e_ard_3-0-0_109072_2018-09-05_final
2018-09-05 11:11:28	ga_ls7e_ard_3-0-0_109073_2018-09-05_final
2018-09-05 11:11:52	ga_ls7e_ard_3-0-0_109074_2018-09-05_final
2018-09-05 11:12:16	ga_ls7e_ard_3-0-0_109075_2018-09-05_final
2018-09-05 11:12:40	ga_ls7e_ard_3-0-0_109076_2018-09-05_final
2018-09-05 11:13:04	ga_ls7e_ard_3-0-0_109077_2018-09-05_final
2018-09-05 11:13:28	ga_ls7e_ard_3-0-0_109078_2018-09-05_final
2018-09-05 11:13:52	ga_ls7e_ard_3-0-0_109079_2018-09-05_final
2018-09-05 11:14:16	ga_ls7e_ard_3-0-0_109080_2018-09-05_final
2018-09-05 11:14:40	ga_ls7e_ard_3-1-0_109081_2018-09-05_final
2018-09-05 11:15:04	ga_ls7e_ard_3-0-0_109082_2018-09-05_final
2018-09-05 11:15:27	ga_ls7e_ard_3-0-0_109083_2018-09-05_final
2018-09-05 11:15:51	ga_ls7e_ard_3-0-0_109084_2018-09-05_final
2018-09-21 09:32:38	ga_ls7e_ard_3-0-0_093074_2018-09-21_final
2018-09-21 09:33:02	ga_ls7e_ard_3-0-0_093075_2018-09-21_final
2018-09-21 09:33:26	ga_ls7e_ard_3-0-0_093076_2018-09-21_final
2018-09-21 09:33:49	ga_ls7e_ard_3-0-0_093077_2018-09-21_final
2018-09-21 09:34:13	ga_ls7e_ard_3-0-0_093078_2018-09-21_final
2018-09-21 09:34:37	ga_ls7e_ard_3-0-0_093079_2018-09-21_final
2018-09-21 09:35:01	ga_ls7e_ard_3-0-0_093080_2018-09-21_final
2018-09-21 09:35:25	ga_ls7e_ard_3-0-0_093081_2018-09-21_final
2018-09-21 09:35:49	ga_ls7e_ard_3-0-0_093082_2018-09-21_final
2018-09-21 09:36:13	ga_ls7e_ard_3-1-0_093083_2018-09-21_final

Return to ga_ls7e_ard_3 across September 2018 overview

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

Baseline satellite data

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

2018

September

All days