Search ga_ls7e_ard_3 across 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:

Add field

First 100 results (query limit)

Time (local)	Label
2019-11-05 09:52:50	ga_ls7e_ard_3-1-0_099067_2019-11-05_final
2019-11-05 09:53:13	ga_ls7e_ard_3-1-0_099068_2019-11-05_final
2019-11-05 09:53:37	ga_ls7e_ard_3-1-0_099069_2019-11-05_final
2019-11-05 09:54:01	ga_ls7e_ard_3-1-0_099070_2019-11-05_final
2019-11-05 09:54:25	ga_ls7e_ard_3-1-0_099071_2019-11-05_final
2019-11-05 09:54:49	ga_ls7e_ard_3-1-0_099072_2019-11-05_final
2019-11-05 09:55:13	ga_ls7e_ard_3-1-0_099073_2019-11-05_final
2019-11-05 09:55:37	ga_ls7e_ard_3-1-0_099074_2019-11-05_final
2019-11-05 09:56:01	ga_ls7e_ard_3-1-0_099075_2019-11-05_final
2019-11-05 09:56:25	ga_ls7e_ard_3-1-0_099076_2019-11-05_final
2019-11-05 09:56:49	ga_ls7e_ard_3-1-0_099077_2019-11-05_final
2019-11-05 09:57:13	ga_ls7e_ard_3-1-0_099078_2019-11-05_final
2019-11-05 09:57:37	ga_ls7e_ard_3-1-0_099079_2019-11-05_final
2019-11-05 09:58:01	ga_ls7e_ard_3-1-0_099080_2019-11-05_final
2019-11-05 09:58:25	ga_ls7e_ard_3-1-0_099081_2019-11-05_final
2019-11-05 09:58:49	ga_ls7e_ard_3-1-0_099082_2019-11-05_final
2019-11-05 09:59:13	ga_ls7e_ard_3-1-0_099083_2019-11-05_final
2019-11-05 09:59:36	ga_ls7e_ard_3-1-0_099084_2019-11-05_final
2019-11-05 11:34:30	ga_ls7e_ard_3-1-0_115074_2019-11-05_final
2019-11-05 11:34:54	ga_ls7e_ard_3-1-0_115075_2019-11-05_final
2019-11-05 11:35:18	ga_ls7e_ard_3-1-0_115076_2019-11-05_final
2019-11-05 11:35:42	ga_ls7e_ard_3-1-0_115077_2019-11-05_final
2019-11-05 11:36:06	ga_ls7e_ard_3-1-0_115078_2019-11-05_final
2019-11-05 11:36:30	ga_ls7e_ard_3-1-0_115079_2019-11-05_final
2019-11-06 09:01:09	ga_ls7e_ard_3-1-0_090077_2019-11-05_final
2019-11-06 09:01:33	ga_ls7e_ard_3-1-0_090078_2019-11-05_final
2019-11-06 09:01:57	ga_ls7e_ard_3-1-0_090079_2019-11-05_final
2019-11-06 09:02:21	ga_ls7e_ard_3-1-0_090080_2019-11-05_final
2019-11-06 09:02:45	ga_ls7e_ard_3-1-0_090081_2019-11-05_final
2019-11-06 09:03:09	ga_ls7e_ard_3-1-0_090082_2019-11-05_final
2019-11-06 09:03:33	ga_ls7e_ard_3-1-0_090083_2019-11-05_final
2019-11-06 09:03:57	ga_ls7e_ard_3-1-0_090084_2019-11-05_final
2019-11-06 09:04:21	ga_ls7e_ard_3-1-0_090085_2019-11-05_final
2019-11-06 09:04:44	ga_ls7e_ard_3-1-0_090086_2019-11-05_final
2019-11-06 09:05:32	ga_ls7e_ard_3-1-0_090088_2019-11-05_final
2019-11-06 09:05:56	ga_ls7e_ard_3-1-0_090089_2019-11-05_final
2019-11-06 09:06:20	ga_ls7e_ard_3-1-0_090090_2019-11-05_final
2019-11-06 10:36:26	ga_ls7e_ard_3-1-0_106068_2019-11-06_final
2019-11-06 10:36:50	ga_ls7e_ard_3-1-0_106069_2019-11-06_final
2019-11-06 10:37:14	ga_ls7e_ard_3-1-0_106070_2019-11-06_final
2019-11-06 10:37:38	ga_ls7e_ard_3-1-0_106071_2019-11-06_final
2019-11-06 10:38:02	ga_ls7e_ard_3-1-0_106072_2019-11-06_final
2019-11-06 10:38:26	ga_ls7e_ard_3-1-0_106073_2019-11-06_final
2019-11-06 10:38:50	ga_ls7e_ard_3-1-0_106074_2019-11-06_final
2019-11-06 10:39:14	ga_ls7e_ard_3-1-0_106075_2019-11-06_final
2019-11-06 10:39:38	ga_ls7e_ard_3-1-0_106076_2019-11-06_final
2019-11-06 10:40:02	ga_ls7e_ard_3-1-0_106077_2019-11-06_final
2019-11-06 10:40:26	ga_ls7e_ard_3-1-0_106078_2019-11-06_final
2019-11-06 10:40:50	ga_ls7e_ard_3-1-0_106079_2019-11-06_final
2019-11-06 10:41:14	ga_ls7e_ard_3-1-0_106080_2019-11-06_final
2019-11-06 10:41:38	ga_ls7e_ard_3-1-0_106081_2019-11-06_final
2019-11-06 10:42:01	ga_ls7e_ard_3-1-0_106082_2019-11-06_final
2019-11-06 10:42:25	ga_ls7e_ard_3-1-0_106083_2019-11-06_final
2019-11-07 09:41:34	ga_ls7e_ard_3-1-0_097070_2019-11-07_final
2019-11-07 09:41:58	ga_ls7e_ard_3-1-0_097071_2019-11-07_final
2019-11-07 09:42:22	ga_ls7e_ard_3-1-0_097072_2019-11-07_final
2019-11-07 09:42:46	ga_ls7e_ard_3-1-0_097073_2019-11-07_final
2019-11-07 09:43:10	ga_ls7e_ard_3-1-0_097074_2019-11-07_final
2019-11-07 09:43:34	ga_ls7e_ard_3-1-0_097075_2019-11-07_final
2019-11-07 09:43:58	ga_ls7e_ard_3-1-0_097076_2019-11-07_final
2019-11-07 09:44:22	ga_ls7e_ard_3-1-0_097077_2019-11-07_final
2019-11-07 09:44:46	ga_ls7e_ard_3-1-0_097078_2019-11-07_final
2019-11-07 09:45:10	ga_ls7e_ard_3-1-0_097079_2019-11-07_final
2019-11-07 09:45:34	ga_ls7e_ard_3-1-0_097080_2019-11-07_final
2019-11-07 09:45:58	ga_ls7e_ard_3-1-0_097081_2019-11-07_final
2019-11-07 09:46:22	ga_ls7e_ard_3-1-0_097082_2019-11-07_final
2019-11-07 09:46:46	ga_ls7e_ard_3-1-0_097083_2019-11-07_final
2019-11-07 09:47:33	ga_ls7e_ard_3-1-0_097085_2019-11-07_final
2019-11-07 11:19:15	ga_ls7e_ard_3-1-0_113067_2019-11-07_final
2019-11-07 11:22:03	ga_ls7e_ard_3-1-0_113074_2019-11-07_final
2019-11-07 11:22:27	ga_ls7e_ard_3-1-0_113075_2019-11-07_final
2019-11-07 11:22:51	ga_ls7e_ard_3-1-0_113076_2019-11-07_final
2019-11-07 11:23:15	ga_ls7e_ard_3-1-0_113077_2019-11-07_final
2019-11-07 11:23:39	ga_ls7e_ard_3-1-0_113078_2019-11-07_final
2019-11-07 11:24:03	ga_ls7e_ard_3-1-0_113079_2019-11-07_final
2019-11-07 11:24:27	ga_ls7e_ard_3-1-0_113080_2019-11-07_final
2019-11-07 11:24:50	ga_ls7e_ard_3-1-0_113081_2019-11-07_final
2019-11-07 11:25:14	ga_ls7e_ard_3-1-0_113082_2019-11-07_final
2019-11-07 11:25:38	ga_ls7e_ard_3-1-0_113083_2019-11-07_final
2019-11-07 11:26:02	ga_ls7e_ard_3-1-0_113084_2019-11-07_final
2019-11-08 10:23:59	ga_ls7e_ard_3-1-0_104068_2019-11-08_final
2019-11-08 10:24:23	ga_ls7e_ard_3-1-0_104069_2019-11-08_final
2019-11-08 10:24:47	ga_ls7e_ard_3-1-0_104070_2019-11-08_final
2019-11-08 10:25:11	ga_ls7e_ard_3-1-0_104071_2019-11-08_final
2019-11-08 10:25:35	ga_ls7e_ard_3-1-0_104072_2019-11-08_final
2019-11-08 10:25:59	ga_ls7e_ard_3-1-0_104073_2019-11-08_final
2019-11-08 10:26:23	ga_ls7e_ard_3-1-0_104074_2019-11-08_final
2019-11-08 10:26:47	ga_ls7e_ard_3-1-0_104075_2019-11-08_final
2019-11-08 10:27:11	ga_ls7e_ard_3-1-0_104076_2019-11-08_final
2019-11-08 10:27:35	ga_ls7e_ard_3-1-0_104077_2019-11-08_final
2019-11-08 10:27:59	ga_ls7e_ard_3-1-0_104078_2019-11-08_final
2019-11-08 10:28:23	ga_ls7e_ard_3-1-0_104079_2019-11-08_final
2019-11-08 10:28:47	ga_ls7e_ard_3-1-0_104080_2019-11-08_final
2019-11-08 10:29:10	ga_ls7e_ard_3-1-0_104081_2019-11-08_final
2019-12-22 09:10:29	ga_ls7e_ard_3-1-0_092075_2019-12-21_final
2019-12-22 09:10:53	ga_ls7e_ard_3-1-0_092076_2019-12-21_final
2019-12-22 09:11:17	ga_ls7e_ard_3-1-0_092077_2019-12-21_final
2019-12-22 09:11:41	ga_ls7e_ard_3-1-0_092078_2019-12-21_final
2019-12-22 09:12:05	ga_ls7e_ard_3-1-0_092079_2019-12-21_final
2019-12-22 09:12:29	ga_ls7e_ard_3-1-0_092080_2019-12-21_final

Return to ga_ls7e_ard_3 across 2019 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

2019

All months