Search ga_ls7e_ard_3 across June 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
2018-06-09 10:22:54	ga_ls7e_ard_3-0-0_101071_2018-06-09_final
2018-06-09 10:23:18	ga_ls7e_ard_3-0-0_101072_2018-06-09_final
2018-06-09 10:23:42	ga_ls7e_ard_3-0-0_101073_2018-06-09_final
2018-06-09 10:24:06	ga_ls7e_ard_3-0-0_101074_2018-06-09_final
2018-06-09 10:24:30	ga_ls7e_ard_3-1-0_101075_2018-06-09_final
2018-06-09 10:24:54	ga_ls7e_ard_3-0-0_101076_2018-06-09_final
2018-06-09 10:25:18	ga_ls7e_ard_3-0-0_101077_2018-06-09_final
2018-06-09 10:25:42	ga_ls7e_ard_3-0-0_101078_2018-06-09_final
2018-06-09 10:26:06	ga_ls7e_ard_3-0-0_101079_2018-06-09_final
2018-06-09 10:27:18	ga_ls7e_ard_3-0-0_101082_2018-06-09_final
2018-06-09 10:27:42	ga_ls7e_ard_3-0-0_101083_2018-06-09_final
2018-06-10 09:28:52	ga_ls7e_ard_3-0-0_092075_2018-06-09_final
2018-06-10 09:29:16	ga_ls7e_ard_3-0-0_092076_2018-06-09_final
2018-06-10 09:29:40	ga_ls7e_ard_3-0-0_092077_2018-06-09_final
2018-06-10 09:30:03	ga_ls7e_ard_3-1-0_092078_2018-06-10_final
2018-06-10 09:30:27	ga_ls7e_ard_3-0-0_092079_2018-06-10_final
2018-06-10 09:30:51	ga_ls7e_ard_3-0-0_092080_2018-06-10_final
2018-06-10 09:31:15	ga_ls7e_ard_3-0-0_092081_2018-06-10_final
2018-06-10 09:31:39	ga_ls7e_ard_3-0-0_092082_2018-06-10_final
2018-06-10 09:32:03	ga_ls7e_ard_3-0-0_092083_2018-06-10_final
2018-06-10 09:32:27	ga_ls7e_ard_3-0-0_092084_2018-06-10_final
2018-06-10 09:32:51	ga_ls7e_ard_3-0-0_092085_2018-06-10_final
2018-06-10 09:33:15	ga_ls7e_ard_3-0-0_092086_2018-06-10_final
2018-06-10 09:33:39	ga_ls7e_ard_3-0-0_092087_2018-06-10_final
2018-06-10 09:34:03	ga_ls7e_ard_3-0-0_092088_2018-06-10_final
2018-06-10 09:34:27	ga_ls7e_ard_3-0-0_092089_2018-06-10_final
2018-06-10 09:34:51	ga_ls7e_ard_3-1-0_092090_2018-06-10_final
2018-06-10 11:05:21	ga_ls7e_ard_3-0-0_108069_2018-06-10_final
2018-06-10 11:05:45	ga_ls7e_ard_3-0-0_108070_2018-06-10_final
2018-06-10 11:06:09	ga_ls7e_ard_3-0-0_108071_2018-06-10_final
2018-06-10 11:06:33	ga_ls7e_ard_3-0-0_108072_2018-06-10_final
2018-06-10 11:06:57	ga_ls7e_ard_3-0-0_108073_2018-06-10_final
2018-06-10 11:07:21	ga_ls7e_ard_3-0-0_108074_2018-06-10_final
2018-06-10 11:07:45	ga_ls7e_ard_3-1-0_108075_2018-06-10_final
2018-06-10 11:08:09	ga_ls7e_ard_3-0-0_108076_2018-06-10_final
2018-06-10 11:08:32	ga_ls7e_ard_3-0-0_108077_2018-06-10_final
2018-06-10 11:08:56	ga_ls7e_ard_3-0-0_108078_2018-06-10_final
2018-06-10 11:09:20	ga_ls7e_ard_3-0-0_108079_2018-06-10_final
2018-06-10 11:09:44	ga_ls7e_ard_3-0-0_108080_2018-06-10_final
2018-06-10 11:10:08	ga_ls7e_ard_3-0-0_108081_2018-06-10_final
2018-06-10 11:10:32	ga_ls7e_ard_3-1-0_108082_2018-06-10_final
2018-06-10 11:10:56	ga_ls7e_ard_3-0-0_108083_2018-06-10_final
2018-06-11 10:08:55	ga_ls7e_ard_3-0-0_099067_2018-06-11_final
2018-06-11 10:09:19	ga_ls7e_ard_3-0-0_099068_2018-06-11_final
2018-06-11 10:09:43	ga_ls7e_ard_3-0-0_099069_2018-06-11_final
2018-06-11 10:10:07	ga_ls7e_ard_3-0-0_099070_2018-06-11_final
2018-06-11 10:10:30	ga_ls7e_ard_3-0-0_099071_2018-06-11_final
2018-06-11 10:10:54	ga_ls7e_ard_3-0-0_099072_2018-06-11_final
2018-06-11 10:11:18	ga_ls7e_ard_3-0-0_099073_2018-06-11_final
2018-06-11 10:11:42	ga_ls7e_ard_3-0-0_099074_2018-06-11_final
2018-06-11 10:12:06	ga_ls7e_ard_3-0-0_099075_2018-06-11_final
2018-06-11 10:12:30	ga_ls7e_ard_3-0-0_099076_2018-06-11_final
2018-06-11 10:12:54	ga_ls7e_ard_3-0-0_099077_2018-06-11_final
2018-06-11 10:13:18	ga_ls7e_ard_3-0-0_099078_2018-06-11_final
2018-06-11 10:13:42	ga_ls7e_ard_3-0-0_099079_2018-06-11_final
2018-06-11 10:14:06	ga_ls7e_ard_3-0-0_099080_2018-06-11_final
2018-06-11 10:14:30	ga_ls7e_ard_3-0-0_099081_2018-06-11_final
2018-06-11 10:14:54	ga_ls7e_ard_3-0-0_099082_2018-06-11_final
2018-06-11 10:15:18	ga_ls7e_ard_3-0-0_099083_2018-06-11_final
2018-06-11 10:15:42	ga_ls7e_ard_3-0-0_099084_2018-06-11_final
2018-06-11 11:50:35	ga_ls7e_ard_3-0-0_115074_2018-06-11_final
2018-06-11 11:50:59	ga_ls7e_ard_3-0-0_115075_2018-06-11_final
2018-06-11 11:51:23	ga_ls7e_ard_3-0-0_115076_2018-06-11_final
2018-06-11 11:51:47	ga_ls7e_ard_3-1-0_115077_2018-06-11_final
2018-06-11 11:52:11	ga_ls7e_ard_3-0-0_115078_2018-06-11_final
2018-06-11 11:52:35	ga_ls7e_ard_3-0-0_115079_2018-06-11_final
2018-06-12 09:22:27	ga_ls7e_ard_3-0-0_090090_2018-06-11_final
2018-06-12 10:52:33	ga_ls7e_ard_3-0-0_106068_2018-06-12_final
2018-06-12 10:52:57	ga_ls7e_ard_3-0-0_106069_2018-06-12_final
2018-06-12 10:53:21	ga_ls7e_ard_3-0-0_106070_2018-06-12_final
2018-06-12 10:53:45	ga_ls7e_ard_3-0-0_106071_2018-06-12_final
2018-06-12 10:54:09	ga_ls7e_ard_3-0-0_106072_2018-06-12_final
2018-06-12 10:54:33	ga_ls7e_ard_3-0-0_106073_2018-06-12_final
2018-06-12 10:54:57	ga_ls7e_ard_3-0-0_106074_2018-06-12_final
2018-06-12 10:55:21	ga_ls7e_ard_3-1-0_106075_2018-06-12_final
2018-06-12 10:55:45	ga_ls7e_ard_3-0-0_106076_2018-06-12_final
2018-06-12 10:56:09	ga_ls7e_ard_3-0-0_106077_2018-06-12_final
2018-06-12 10:56:33	ga_ls7e_ard_3-0-0_106078_2018-06-12_final
2018-06-12 10:56:57	ga_ls7e_ard_3-0-0_106079_2018-06-12_final
2018-06-12 10:57:20	ga_ls7e_ard_3-0-0_106080_2018-06-12_final
2018-06-12 10:57:44	ga_ls7e_ard_3-0-0_106081_2018-06-12_final
2018-06-12 10:58:08	ga_ls7e_ard_3-0-0_106082_2018-06-12_final
2018-06-12 10:58:32	ga_ls7e_ard_3-0-0_106083_2018-06-12_final
2018-06-19 09:22:55	ga_ls7e_ard_3-0-0_091076_2018-06-18_final
2018-06-19 09:23:19	ga_ls7e_ard_3-0-0_091077_2018-06-18_final
2018-06-19 09:23:43	ga_ls7e_ard_3-0-0_091078_2018-06-18_final
2018-06-19 09:24:07	ga_ls7e_ard_3-0-0_091079_2018-06-18_final
2018-06-19 09:24:31	ga_ls7e_ard_3-0-0_091080_2018-06-18_final
2018-06-19 09:24:55	ga_ls7e_ard_3-0-0_091081_2018-06-18_final
2018-06-19 09:25:18	ga_ls7e_ard_3-0-0_091082_2018-06-18_final
2018-06-19 09:25:42	ga_ls7e_ard_3-0-0_091083_2018-06-18_final
2018-06-19 09:26:06	ga_ls7e_ard_3-0-0_091084_2018-06-18_final
2018-06-19 09:26:30	ga_ls7e_ard_3-0-0_091085_2018-06-18_final
2018-06-19 09:26:54	ga_ls7e_ard_3-1-0_091086_2018-06-18_final
2018-06-19 09:27:18	ga_ls7e_ard_3-0-0_091087_2018-06-18_final
2018-06-19 09:27:42	ga_ls7e_ard_3-0-0_091088_2018-06-18_final
2018-06-19 09:28:06	ga_ls7e_ard_3-0-0_091089_2018-06-18_final
2018-06-19 09:28:30	ga_ls7e_ard_3-0-0_091090_2018-06-18_final
2018-06-20 10:02:58	ga_ls7e_ard_3-0-0_098068_2018-06-20_final
2018-06-20 10:03:22	ga_ls7e_ard_3-0-0_098069_2018-06-20_final

Return to ga_ls7e_ard_3 across June 2018 overview

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

Baseline satellite data

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

2018

June

All days