Search ga_ls7e_ard_3 across May 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
2018-05-20 09:11:28	ga_ls7e_ard_3-0-0_089077_2018-05-19_final
2018-05-20 09:11:52	ga_ls7e_ard_3-0-0_089078_2018-05-19_final
2018-05-20 09:12:16	ga_ls7e_ard_3-0-0_089079_2018-05-19_final
2018-05-20 09:12:40	ga_ls7e_ard_3-0-0_089080_2018-05-19_final
2018-05-20 09:13:04	ga_ls7e_ard_3-1-0_089081_2018-05-19_final
2018-05-20 09:13:28	ga_ls7e_ard_3-0-0_089082_2018-05-19_final
2018-05-20 09:13:52	ga_ls7e_ard_3-0-0_089083_2018-05-19_final
2018-05-20 09:14:16	ga_ls7e_ard_3-0-0_089084_2018-05-19_final
2018-05-20 09:14:40	ga_ls7e_ard_3-0-0_089085_2018-05-19_final
2018-05-20 09:15:03	ga_ls7e_ard_3-0-0_089086_2018-05-19_final
2018-05-21 09:50:43	ga_ls7e_ard_3-0-0_096067_2018-05-21_final
2018-05-21 09:51:55	ga_ls7e_ard_3-0-0_096070_2018-05-21_final
2018-05-21 09:52:19	ga_ls7e_ard_3-0-0_096071_2018-05-21_final
2018-05-21 09:52:43	ga_ls7e_ard_3-0-0_096072_2018-05-21_final
2018-05-21 09:53:07	ga_ls7e_ard_3-0-0_096073_2018-05-21_final
2018-05-21 09:53:31	ga_ls7e_ard_3-0-0_096074_2018-05-21_final
2018-05-21 09:53:55	ga_ls7e_ard_3-0-0_096075_2018-05-21_final
2018-05-21 09:54:19	ga_ls7e_ard_3-0-0_096076_2018-05-21_final
2018-05-21 09:54:43	ga_ls7e_ard_3-0-0_096077_2018-05-21_final
2018-05-26 10:09:11	ga_ls7e_ard_3-0-0_099067_2018-05-26_final
2018-05-26 10:09:35	ga_ls7e_ard_3-0-0_099068_2018-05-26_final
2018-05-26 10:09:59	ga_ls7e_ard_3-0-0_099069_2018-05-26_final
2018-05-26 10:10:23	ga_ls7e_ard_3-0-0_099070_2018-05-26_final
2018-05-26 10:10:47	ga_ls7e_ard_3-0-0_099071_2018-05-26_final
2018-05-26 10:11:11	ga_ls7e_ard_3-0-0_099072_2018-05-26_final
2018-05-26 10:11:35	ga_ls7e_ard_3-0-0_099073_2018-05-26_final
2018-05-26 10:11:59	ga_ls7e_ard_3-0-0_099074_2018-05-26_final
2018-05-26 10:12:22	ga_ls7e_ard_3-0-0_099075_2018-05-26_final
2018-05-26 10:12:46	ga_ls7e_ard_3-0-0_099076_2018-05-26_final
2018-05-26 10:13:10	ga_ls7e_ard_3-0-0_099077_2018-05-26_final
2018-05-26 10:13:34	ga_ls7e_ard_3-0-0_099078_2018-05-26_final
2018-05-26 10:13:58	ga_ls7e_ard_3-0-0_099079_2018-05-26_final
2018-05-26 10:14:22	ga_ls7e_ard_3-0-0_099080_2018-05-26_final
2018-05-26 10:14:46	ga_ls7e_ard_3-0-0_099081_2018-05-26_final
2018-05-26 10:15:10	ga_ls7e_ard_3-0-0_099082_2018-05-26_final
2018-05-26 10:15:34	ga_ls7e_ard_3-0-0_099083_2018-05-26_final
2018-05-26 10:15:58	ga_ls7e_ard_3-0-0_099084_2018-05-26_final
2018-05-26 11:50:52	ga_ls7e_ard_3-0-0_115074_2018-05-26_final
2018-05-26 11:51:16	ga_ls7e_ard_3-0-0_115075_2018-05-26_final
2018-05-26 11:51:39	ga_ls7e_ard_3-0-0_115076_2018-05-26_final
2018-05-26 11:52:03	ga_ls7e_ard_3-0-0_115077_2018-05-26_final
2018-05-26 11:52:27	ga_ls7e_ard_3-0-0_115078_2018-05-26_final
2018-05-26 11:52:51	ga_ls7e_ard_3-0-0_115079_2018-05-26_final
2018-05-27 09:17:32	ga_ls7e_ard_3-0-0_090077_2018-05-26_final
2018-05-27 09:17:56	ga_ls7e_ard_3-0-0_090078_2018-05-26_final
2018-05-27 09:18:20	ga_ls7e_ard_3-0-0_090079_2018-05-26_final
2018-05-27 09:18:44	ga_ls7e_ard_3-0-0_090080_2018-05-26_final
2018-05-27 09:19:08	ga_ls7e_ard_3-0-0_090081_2018-05-26_final
2018-05-27 09:19:32	ga_ls7e_ard_3-0-0_090082_2018-05-26_final
2018-05-27 09:19:56	ga_ls7e_ard_3-0-0_090083_2018-05-26_final
2018-05-27 09:20:20	ga_ls7e_ard_3-0-0_090084_2018-05-26_final
2018-05-27 09:20:44	ga_ls7e_ard_3-1-0_090085_2018-05-26_final
2018-05-27 09:21:08	ga_ls7e_ard_3-0-0_090086_2018-05-26_final
2018-05-27 09:21:56	ga_ls7e_ard_3-0-0_090088_2018-05-26_final
2018-05-27 09:22:20	ga_ls7e_ard_3-0-0_090089_2018-05-26_final
2018-05-27 09:22:44	ga_ls7e_ard_3-0-0_090090_2018-05-26_final
2018-05-27 10:52:50	ga_ls7e_ard_3-0-0_106068_2018-05-27_final
2018-05-27 10:53:14	ga_ls7e_ard_3-0-0_106069_2018-05-27_final
2018-05-27 10:53:38	ga_ls7e_ard_3-0-0_106070_2018-05-27_final
2018-05-27 10:54:01	ga_ls7e_ard_3-0-0_106071_2018-05-27_final
2018-05-27 10:54:25	ga_ls7e_ard_3-0-0_106072_2018-05-27_final
2018-05-27 10:54:49	ga_ls7e_ard_3-0-0_106073_2018-05-27_final
2018-05-27 10:55:13	ga_ls7e_ard_3-0-0_106074_2018-05-27_final
2018-05-27 10:55:37	ga_ls7e_ard_3-1-0_106075_2018-05-27_final
2018-05-27 10:56:01	ga_ls7e_ard_3-0-0_106076_2018-05-27_final
2018-05-27 10:56:25	ga_ls7e_ard_3-0-0_106077_2018-05-27_final
2018-05-27 10:56:49	ga_ls7e_ard_3-0-0_106078_2018-05-27_final
2018-05-27 10:57:13	ga_ls7e_ard_3-0-0_106079_2018-05-27_final
2018-05-27 10:57:37	ga_ls7e_ard_3-0-0_106080_2018-05-27_final
2018-05-27 10:58:01	ga_ls7e_ard_3-0-0_106081_2018-05-27_final
2018-05-27 10:58:25	ga_ls7e_ard_3-0-0_106082_2018-05-27_final
2018-05-27 10:58:49	ga_ls7e_ard_3-0-0_106083_2018-05-27_final
2018-05-28 09:57:35	ga_ls7e_ard_3-0-0_097069_2018-05-28_final
2018-05-28 09:57:59	ga_ls7e_ard_3-0-0_097070_2018-05-28_final
2018-05-28 09:58:23	ga_ls7e_ard_3-0-0_097071_2018-05-28_final
2018-05-28 09:58:47	ga_ls7e_ard_3-0-0_097072_2018-05-28_final
2018-05-28 09:59:11	ga_ls7e_ard_3-0-0_097073_2018-05-28_final
2018-05-28 09:59:35	ga_ls7e_ard_3-0-0_097074_2018-05-28_final
2018-05-28 09:59:59	ga_ls7e_ard_3-0-0_097075_2018-05-28_final
2018-05-28 10:00:23	ga_ls7e_ard_3-1-0_097076_2018-05-28_final
2018-05-28 10:00:47	ga_ls7e_ard_3-0-0_097077_2018-05-28_final
2018-05-28 10:01:11	ga_ls7e_ard_3-0-0_097078_2018-05-28_final
2018-05-28 10:01:35	ga_ls7e_ard_3-0-0_097079_2018-05-28_final
2018-05-28 10:01:59	ga_ls7e_ard_3-0-0_097080_2018-05-28_final
2018-05-28 10:02:23	ga_ls7e_ard_3-0-0_097081_2018-05-28_final
2018-05-28 10:02:47	ga_ls7e_ard_3-0-0_097082_2018-05-28_final
2018-05-28 10:03:11	ga_ls7e_ard_3-0-0_097083_2018-05-28_final
2018-05-28 10:03:34	ga_ls7e_ard_3-0-0_097084_2018-05-28_final
2018-05-28 10:03:58	ga_ls7e_ard_3-0-0_097085_2018-05-28_final
2018-05-28 11:35:40	ga_ls7e_ard_3-0-0_113067_2018-05-28_final
2018-05-28 11:38:28	ga_ls7e_ard_3-0-0_113074_2018-05-28_final
2018-05-28 11:38:52	ga_ls7e_ard_3-0-0_113075_2018-05-28_final
2018-05-28 11:39:16	ga_ls7e_ard_3-0-0_113076_2018-05-28_final
2018-05-28 11:39:40	ga_ls7e_ard_3-0-0_113077_2018-05-28_final
2018-05-28 11:40:04	ga_ls7e_ard_3-0-0_113078_2018-05-28_final
2018-05-28 11:40:28	ga_ls7e_ard_3-0-0_113079_2018-05-28_final
2018-05-28 11:40:52	ga_ls7e_ard_3-0-0_113080_2018-05-28_final
2018-05-28 11:41:16	ga_ls7e_ard_3-0-0_113081_2018-05-28_final
2018-05-28 11:41:40	ga_ls7e_ard_3-0-0_113082_2018-05-28_final
2018-05-28 11:42:04	ga_ls7e_ard_3-0-0_113083_2018-05-28_final

Return to ga_ls7e_ard_3 across May 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

May

All days