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
2018-05-05 09:50:58	ga_ls7e_ard_3-0-0_096067_2018-05-05_final
2018-05-05 09:52:10	ga_ls7e_ard_3-0-0_096070_2018-05-05_final
2018-05-05 09:52:34	ga_ls7e_ard_3-0-0_096071_2018-05-05_final
2018-05-05 09:52:58	ga_ls7e_ard_3-0-0_096072_2018-05-05_final
2018-05-05 09:53:22	ga_ls7e_ard_3-0-0_096073_2018-05-05_final
2018-05-05 09:53:46	ga_ls7e_ard_3-0-0_096074_2018-05-05_final
2018-05-05 09:54:10	ga_ls7e_ard_3-0-0_096075_2018-05-05_final
2018-05-05 09:54:34	ga_ls7e_ard_3-0-0_096076_2018-05-05_final
2018-05-05 09:54:58	ga_ls7e_ard_3-0-0_096077_2018-05-05_final
2018-05-05 09:55:22	ga_ls7e_ard_3-0-0_096078_2018-05-05_final
2018-05-05 09:55:46	ga_ls7e_ard_3-0-0_096079_2018-05-05_final
2018-05-05 09:56:10	ga_ls7e_ard_3-0-0_096080_2018-05-05_final
2018-05-05 09:56:33	ga_ls7e_ard_3-0-0_096081_2018-05-05_final
2018-05-05 09:56:57	ga_ls7e_ard_3-0-0_096082_2018-05-05_final
2018-05-05 09:57:21	ga_ls7e_ard_3-0-0_096083_2018-05-05_final
2018-05-05 09:57:45	ga_ls7e_ard_3-0-0_096084_2018-05-05_final
2018-05-05 09:58:09	ga_ls7e_ard_3-0-0_096085_2018-05-05_final
2018-05-05 11:29:51	ga_ls7e_ard_3-0-0_112067_2018-05-05_final
2018-05-05 11:32:39	ga_ls7e_ard_3-0-0_112074_2018-05-05_final
2018-05-05 11:33:03	ga_ls7e_ard_3-0-0_112075_2018-05-05_final
2018-05-05 11:33:27	ga_ls7e_ard_3-0-0_112076_2018-05-05_final
2018-05-05 11:33:51	ga_ls7e_ard_3-0-0_112077_2018-05-05_final
2018-05-05 11:34:15	ga_ls7e_ard_3-0-0_112078_2018-05-05_final
2018-05-05 11:34:39	ga_ls7e_ard_3-0-0_112079_2018-05-05_final
2018-05-05 11:35:03	ga_ls7e_ard_3-0-0_112080_2018-05-05_final
2018-05-05 11:35:26	ga_ls7e_ard_3-0-0_112081_2018-05-05_final
2018-05-05 11:35:50	ga_ls7e_ard_3-0-0_112082_2018-05-05_final
2018-05-05 11:36:14	ga_ls7e_ard_3-0-0_112083_2018-05-05_final
2018-05-05 11:36:38	ga_ls7e_ard_3-1-0_112084_2018-05-05_final
2018-05-06 10:34:37	ga_ls7e_ard_3-0-0_103068_2018-05-06_final
2018-05-06 10:35:01	ga_ls7e_ard_3-0-0_103069_2018-05-06_final
2018-05-06 10:35:25	ga_ls7e_ard_3-0-0_103070_2018-05-06_final
2018-05-06 10:35:49	ga_ls7e_ard_3-0-0_103071_2018-05-06_final
2018-05-06 10:36:12	ga_ls7e_ard_3-0-0_103072_2018-05-06_final
2018-05-06 10:36:36	ga_ls7e_ard_3-0-0_103073_2018-05-06_final
2018-05-06 10:37:00	ga_ls7e_ard_3-0-0_103074_2018-05-06_final
2018-05-06 10:37:24	ga_ls7e_ard_3-0-0_103075_2018-05-06_final
2018-05-06 10:37:48	ga_ls7e_ard_3-0-0_103076_2018-05-06_final
2018-05-06 10:38:12	ga_ls7e_ard_3-0-0_103077_2018-05-06_final
2018-05-06 10:38:36	ga_ls7e_ard_3-0-0_103078_2018-05-06_final
2018-05-06 10:39:00	ga_ls7e_ard_3-0-0_103079_2018-05-06_final
2018-05-06 10:39:24	ga_ls7e_ard_3-0-0_103080_2018-05-06_final
2018-05-06 10:39:48	ga_ls7e_ard_3-0-0_103081_2018-05-06_final
2018-05-06 10:40:12	ga_ls7e_ard_3-0-0_103082_2018-05-06_final
2018-05-07 09:38:34	ga_ls7e_ard_3-1-0_094067_2018-05-07_final
2018-05-07 09:40:58	ga_ls7e_ard_3-0-0_094073_2018-05-07_final
2018-05-07 09:41:22	ga_ls7e_ard_3-0-0_094074_2018-05-07_final
2018-05-07 09:41:46	ga_ls7e_ard_3-0-0_094075_2018-05-07_final
2018-05-07 09:42:10	ga_ls7e_ard_3-0-0_094076_2018-05-07_final
2018-05-07 09:42:34	ga_ls7e_ard_3-0-0_094077_2018-05-07_final
2018-05-07 09:42:58	ga_ls7e_ard_3-0-0_094078_2018-05-07_final
2018-05-07 09:43:22	ga_ls7e_ard_3-0-0_094079_2018-05-07_final
2018-05-07 09:43:46	ga_ls7e_ard_3-0-0_094080_2018-05-07_final
2018-05-07 09:44:10	ga_ls7e_ard_3-0-0_094081_2018-05-07_final
2018-05-07 09:44:33	ga_ls7e_ard_3-0-0_094082_2018-05-07_final
2018-05-07 09:44:57	ga_ls7e_ard_3-0-0_094083_2018-05-07_final
2018-05-07 09:45:21	ga_ls7e_ard_3-0-0_094084_2018-05-07_final
2018-05-07 09:45:45	ga_ls7e_ard_3-0-0_094085_2018-05-07_final
2018-05-07 09:46:33	ga_ls7e_ard_3-0-0_094087_2018-05-07_final
2018-05-07 11:17:27	ga_ls7e_ard_3-0-0_110067_2018-05-07_final
2018-05-07 11:18:39	ga_ls7e_ard_3-0-0_110070_2018-05-07_final
2018-05-07 11:19:03	ga_ls7e_ard_3-0-0_110071_2018-05-07_final
2018-05-07 11:19:27	ga_ls7e_ard_3-1-0_110072_2018-05-07_final
2018-05-07 11:19:51	ga_ls7e_ard_3-0-0_110073_2018-05-07_final
2018-05-07 11:20:15	ga_ls7e_ard_3-0-0_110074_2018-05-07_final
2018-05-07 11:20:39	ga_ls7e_ard_3-0-0_110075_2018-05-07_final
2018-05-07 11:21:03	ga_ls7e_ard_3-0-0_110076_2018-05-07_final
2018-05-07 11:21:27	ga_ls7e_ard_3-0-0_110077_2018-05-07_final
2018-05-07 11:21:51	ga_ls7e_ard_3-0-0_110078_2018-05-07_final
2018-05-07 11:22:15	ga_ls7e_ard_3-0-0_110079_2018-05-07_final
2018-05-07 11:22:39	ga_ls7e_ard_3-0-0_110080_2018-05-07_final
2018-05-07 11:23:02	ga_ls7e_ard_3-0-0_110081_2018-05-07_final
2018-05-07 11:23:26	ga_ls7e_ard_3-0-0_110082_2018-05-07_final
2018-05-07 11:23:50	ga_ls7e_ard_3-0-0_110083_2018-05-07_final
2018-05-07 11:24:14	ga_ls7e_ard_3-0-0_110084_2018-05-07_final
2018-05-08 10:23:25	ga_ls7e_ard_3-0-0_101071_2018-05-08_final
2018-05-08 10:23:48	ga_ls7e_ard_3-0-0_101072_2018-05-08_final
2018-05-08 10:24:12	ga_ls7e_ard_3-0-0_101073_2018-05-08_final
2018-05-08 10:24:36	ga_ls7e_ard_3-0-0_101074_2018-05-08_final
2018-05-08 10:25:00	ga_ls7e_ard_3-1-0_101075_2018-05-08_final
2018-05-08 10:25:24	ga_ls7e_ard_3-0-0_101076_2018-05-08_final
2018-05-08 10:25:48	ga_ls7e_ard_3-0-0_101077_2018-05-08_final
2018-05-08 10:26:12	ga_ls7e_ard_3-0-0_101078_2018-05-08_final
2018-05-08 10:26:36	ga_ls7e_ard_3-0-0_101079_2018-05-08_final
2018-05-08 10:27:00	ga_ls7e_ard_3-0-0_101080_2018-05-08_final
2018-05-08 10:27:24	ga_ls7e_ard_3-0-0_101081_2018-05-08_final
2018-05-08 10:27:48	ga_ls7e_ard_3-0-0_101082_2018-05-08_final
2018-05-09 09:29:22	ga_ls7e_ard_3-0-0_092075_2018-05-08_final
2018-05-09 09:29:46	ga_ls7e_ard_3-0-0_092076_2018-05-08_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

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

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

2018

May

All days