Search ga_ls7e_ard_3 across May 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:

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First 100 results (query limit)

Time (local)	Label
2019-05-06 09:54:03	ga_ls7e_ard_3-0-0_098067_2019-05-06_final
2019-05-06 09:54:27	ga_ls7e_ard_3-0-0_098068_2019-05-06_final
2019-05-06 09:54:51	ga_ls7e_ard_3-0-0_098069_2019-05-06_final
2019-05-06 09:55:15	ga_ls7e_ard_3-0-0_098070_2019-05-06_final
2019-05-06 09:55:39	ga_ls7e_ard_3-0-0_098071_2019-05-06_final
2019-05-06 09:56:03	ga_ls7e_ard_3-0-0_098072_2019-05-06_final
2019-05-06 09:56:27	ga_ls7e_ard_3-0-0_098073_2019-05-06_final
2019-05-06 09:56:50	ga_ls7e_ard_3-0-0_098074_2019-05-06_final
2019-05-06 09:57:14	ga_ls7e_ard_3-0-0_098075_2019-05-06_final
2019-05-06 09:57:38	ga_ls7e_ard_3-0-0_098076_2019-05-06_final
2019-05-06 09:58:02	ga_ls7e_ard_3-0-0_098077_2019-05-06_final
2019-05-06 09:58:26	ga_ls7e_ard_3-0-0_098078_2019-05-06_final
2019-05-06 09:58:50	ga_ls7e_ard_3-0-0_098079_2019-05-06_final
2019-05-06 09:59:14	ga_ls7e_ard_3-1-0_098080_2019-05-06_final
2019-05-06 09:59:38	ga_ls7e_ard_3-0-0_098081_2019-05-06_final
2019-05-06 10:00:02	ga_ls7e_ard_3-0-0_098082_2019-05-06_final
2019-05-06 10:00:26	ga_ls7e_ard_3-0-0_098083_2019-05-06_final
2019-05-06 10:00:50	ga_ls7e_ard_3-0-0_098084_2019-05-06_final
2019-05-06 10:01:14	ga_ls7e_ard_3-0-0_098085_2019-05-06_final
2019-05-06 11:32:56	ga_ls7e_ard_3-0-0_114067_2019-05-06_final
2019-05-06 11:35:43	ga_ls7e_ard_3-0-0_114074_2019-05-06_final
2019-05-06 11:36:07	ga_ls7e_ard_3-0-0_114075_2019-05-06_final
2019-05-06 11:36:31	ga_ls7e_ard_3-0-0_114076_2019-05-06_final
2019-05-06 11:36:55	ga_ls7e_ard_3-0-0_114077_2019-05-06_final
2019-05-06 11:37:19	ga_ls7e_ard_3-0-0_114078_2019-05-06_final
2019-05-06 11:37:43	ga_ls7e_ard_3-0-0_114079_2019-05-06_final
2019-05-06 11:38:07	ga_ls7e_ard_3-0-0_114080_2019-05-06_final
2019-05-07 09:02:23	ga_ls7e_ard_3-0-0_089077_2019-05-06_final
2019-05-07 09:02:47	ga_ls7e_ard_3-0-0_089078_2019-05-06_final
2019-05-07 09:03:11	ga_ls7e_ard_3-0-0_089079_2019-05-06_final
2019-05-07 09:03:35	ga_ls7e_ard_3-0-0_089080_2019-05-06_final
2019-05-07 09:03:59	ga_ls7e_ard_3-1-0_089081_2019-05-06_final
2019-05-07 09:04:22	ga_ls7e_ard_3-0-0_089082_2019-05-06_final
2019-05-07 09:04:46	ga_ls7e_ard_3-0-0_089083_2019-05-06_final
2019-05-07 09:05:10	ga_ls7e_ard_3-0-0_089084_2019-05-06_final
2019-05-07 09:05:34	ga_ls7e_ard_3-0-0_089085_2019-05-06_final
2019-05-07 09:05:58	ga_ls7e_ard_3-0-0_089086_2019-05-06_final
2019-05-07 10:37:40	ga_ls7e_ard_3-0-0_105068_2019-05-07_final
2019-05-07 10:38:04	ga_ls7e_ard_3-0-0_105069_2019-05-07_final
2019-05-07 10:38:28	ga_ls7e_ard_3-0-0_105070_2019-05-07_final
2019-05-07 10:38:52	ga_ls7e_ard_3-0-0_105071_2019-05-07_final
2019-05-07 10:39:16	ga_ls7e_ard_3-0-0_105072_2019-05-07_final
2019-05-07 10:39:40	ga_ls7e_ard_3-0-0_105073_2019-05-07_final
2019-05-07 10:40:04	ga_ls7e_ard_3-0-0_105074_2019-05-07_final
2019-05-07 10:40:28	ga_ls7e_ard_3-0-0_105075_2019-05-07_final
2019-05-07 10:40:52	ga_ls7e_ard_3-0-0_105076_2019-05-07_final
2019-05-07 10:41:16	ga_ls7e_ard_3-0-0_105077_2019-05-07_final
2019-05-07 10:41:40	ga_ls7e_ard_3-0-0_105078_2019-05-07_final
2019-05-07 10:42:03	ga_ls7e_ard_3-0-0_105079_2019-05-07_final
2019-05-07 10:42:27	ga_ls7e_ard_3-1-0_105080_2019-05-07_final
2019-05-07 10:42:51	ga_ls7e_ard_3-0-0_105081_2019-05-07_final
2019-05-07 10:43:15	ga_ls7e_ard_3-0-0_105082_2019-05-07_final
2019-05-08 09:41:37	ga_ls7e_ard_3-0-0_096067_2019-05-08_final
2019-05-08 09:42:48	ga_ls7e_ard_3-0-0_096070_2019-05-08_final
2019-05-08 09:43:12	ga_ls7e_ard_3-0-0_096071_2019-05-08_final
2019-05-08 09:43:36	ga_ls7e_ard_3-0-0_096072_2019-05-08_final
2019-05-08 09:44:00	ga_ls7e_ard_3-0-0_096073_2019-05-08_final
2019-05-08 09:44:24	ga_ls7e_ard_3-0-0_096074_2019-05-08_final
2019-05-08 09:44:48	ga_ls7e_ard_3-0-0_096075_2019-05-08_final
2019-05-08 09:45:12	ga_ls7e_ard_3-0-0_096076_2019-05-08_final
2019-05-08 09:45:36	ga_ls7e_ard_3-0-0_096077_2019-05-08_final
2019-05-08 09:46:00	ga_ls7e_ard_3-0-0_096078_2019-05-08_final
2019-05-08 09:46:24	ga_ls7e_ard_3-0-0_096079_2019-05-08_final
2019-05-08 09:46:48	ga_ls7e_ard_3-0-0_096080_2019-05-08_final
2019-05-08 09:47:12	ga_ls7e_ard_3-0-0_096081_2019-05-08_final
2019-05-08 09:47:36	ga_ls7e_ard_3-0-0_096082_2019-05-08_final
2019-05-08 09:48:00	ga_ls7e_ard_3-0-0_096083_2019-05-08_final
2019-05-08 09:48:24	ga_ls7e_ard_3-0-0_096084_2019-05-08_final
2019-05-08 09:48:48	ga_ls7e_ard_3-0-0_096085_2019-05-08_final
2019-05-08 11:20:30	ga_ls7e_ard_3-0-0_112067_2019-05-08_final
2019-05-08 11:23:17	ga_ls7e_ard_3-0-0_112074_2019-05-08_final
2019-05-08 11:23:41	ga_ls7e_ard_3-0-0_112075_2019-05-08_final
2019-05-08 11:24:05	ga_ls7e_ard_3-0-0_112076_2019-05-08_final
2019-05-08 11:24:29	ga_ls7e_ard_3-0-0_112077_2019-05-08_final
2019-05-08 11:24:53	ga_ls7e_ard_3-0-0_112078_2019-05-08_final
2019-05-08 11:25:17	ga_ls7e_ard_3-1-0_112079_2019-05-08_final
2019-05-08 11:25:41	ga_ls7e_ard_3-0-0_112080_2019-05-08_final
2019-05-08 11:26:05	ga_ls7e_ard_3-0-0_112081_2019-05-08_final
2019-05-08 11:26:29	ga_ls7e_ard_3-0-0_112082_2019-05-08_final
2019-05-08 11:26:53	ga_ls7e_ard_3-0-0_112083_2019-05-08_final
2019-05-08 11:27:17	ga_ls7e_ard_3-0-0_112084_2019-05-08_final
2019-05-09 10:25:14	ga_ls7e_ard_3-0-0_103068_2019-05-09_final
2019-05-09 10:25:38	ga_ls7e_ard_3-0-0_103069_2019-05-09_final
2019-05-09 10:26:02	ga_ls7e_ard_3-0-0_103070_2019-05-09_final
2019-05-09 10:26:26	ga_ls7e_ard_3-0-0_103071_2019-05-09_final
2019-05-09 10:26:50	ga_ls7e_ard_3-0-0_103072_2019-05-09_final
2019-05-09 10:27:14	ga_ls7e_ard_3-0-0_103073_2019-05-09_final
2019-05-09 10:27:38	ga_ls7e_ard_3-0-0_103074_2019-05-09_final
2019-05-09 10:28:01	ga_ls7e_ard_3-0-0_103075_2019-05-09_final
2019-05-09 10:28:25	ga_ls7e_ard_3-0-0_103076_2019-05-09_final
2019-05-09 10:28:49	ga_ls7e_ard_3-0-0_103077_2019-05-09_final
2019-05-09 10:29:13	ga_ls7e_ard_3-0-0_103078_2019-05-09_final
2019-05-09 10:29:37	ga_ls7e_ard_3-0-0_103079_2019-05-09_final
2019-05-09 10:30:25	ga_ls7e_ard_3-0-0_103081_2019-05-09_final
2019-05-09 10:30:49	ga_ls7e_ard_3-0-0_103082_2019-05-09_final
2019-05-10 09:29:10	ga_ls7e_ard_3-1-0_094067_2019-05-09_final
2019-05-19 09:25:26	ga_ls7e_ard_3-0-0_093074_2019-05-18_final
2019-05-19 09:25:50	ga_ls7e_ard_3-0-0_093075_2019-05-18_final
2019-05-19 09:26:14	ga_ls7e_ard_3-0-0_093076_2019-05-18_final
2019-05-19 09:26:38	ga_ls7e_ard_3-0-0_093077_2019-05-18_final

Return to ga_ls7e_ard_3 across May 2019 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

2019

May

All days