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:

Add field

First 100 results (query limit)

Time (local)	Label
2019-05-10 09:29:10	ga_ls7e_ard_3-1-0_094067_2019-05-09_final
2019-05-10 09:31:58	ga_ls7e_ard_3-0-0_094074_2019-05-10_final
2019-05-10 09:32:22	ga_ls7e_ard_3-0-0_094075_2019-05-10_final
2019-05-10 09:32:46	ga_ls7e_ard_3-0-0_094076_2019-05-10_final
2019-05-10 09:33:10	ga_ls7e_ard_3-0-0_094077_2019-05-10_final
2019-05-10 09:33:34	ga_ls7e_ard_3-0-0_094078_2019-05-10_final
2019-05-10 09:33:58	ga_ls7e_ard_3-0-0_094079_2019-05-10_final
2019-05-10 09:34:22	ga_ls7e_ard_3-0-0_094080_2019-05-10_final
2019-05-12 09:19:56	ga_ls7e_ard_3-0-0_092075_2019-05-11_final
2019-05-12 09:20:20	ga_ls7e_ard_3-0-0_092076_2019-05-11_final
2019-05-12 09:20:43	ga_ls7e_ard_3-0-0_092077_2019-05-11_final
2019-05-12 09:21:07	ga_ls7e_ard_3-0-0_092078_2019-05-11_final
2019-05-12 09:21:31	ga_ls7e_ard_3-0-0_092079_2019-05-11_final
2019-05-12 09:21:55	ga_ls7e_ard_3-0-0_092080_2019-05-11_final
2019-05-12 09:22:19	ga_ls7e_ard_3-0-0_092081_2019-05-11_final
2019-05-12 09:22:43	ga_ls7e_ard_3-0-0_092082_2019-05-11_final
2019-05-12 09:23:07	ga_ls7e_ard_3-0-0_092083_2019-05-11_final
2019-05-12 09:23:31	ga_ls7e_ard_3-0-0_092084_2019-05-11_final
2019-05-12 09:23:55	ga_ls7e_ard_3-0-0_092085_2019-05-11_final
2019-05-12 09:24:19	ga_ls7e_ard_3-0-0_092086_2019-05-11_final
2019-05-22 09:53:25	ga_ls7e_ard_3-0-0_098067_2019-05-22_final
2019-05-22 09:53:49	ga_ls7e_ard_3-0-0_098068_2019-05-22_final
2019-05-22 09:54:13	ga_ls7e_ard_3-0-0_098069_2019-05-22_final
2019-05-22 09:54:37	ga_ls7e_ard_3-0-0_098070_2019-05-22_final
2019-05-22 09:55:01	ga_ls7e_ard_3-0-0_098071_2019-05-22_final
2019-05-22 09:55:25	ga_ls7e_ard_3-0-0_098072_2019-05-22_final
2019-05-22 09:55:49	ga_ls7e_ard_3-0-0_098073_2019-05-22_final
2019-05-22 09:56:13	ga_ls7e_ard_3-0-0_098074_2019-05-22_final
2019-05-22 09:56:36	ga_ls7e_ard_3-0-0_098075_2019-05-22_final
2019-05-22 09:57:00	ga_ls7e_ard_3-0-0_098076_2019-05-22_final
2019-05-22 09:57:24	ga_ls7e_ard_3-0-0_098077_2019-05-22_final
2019-05-22 09:57:48	ga_ls7e_ard_3-0-0_098078_2019-05-22_final
2019-05-22 09:58:12	ga_ls7e_ard_3-0-0_098079_2019-05-22_final
2019-05-22 09:58:36	ga_ls7e_ard_3-1-0_098080_2019-05-22_final
2019-05-22 09:59:00	ga_ls7e_ard_3-0-0_098081_2019-05-22_final
2019-05-22 09:59:24	ga_ls7e_ard_3-0-0_098082_2019-05-22_final
2019-05-22 09:59:48	ga_ls7e_ard_3-0-0_098083_2019-05-22_final
2019-05-22 10:00:12	ga_ls7e_ard_3-0-0_098084_2019-05-22_final
2019-05-22 10:00:36	ga_ls7e_ard_3-0-0_098085_2019-05-22_final
2019-05-22 11:32:18	ga_ls7e_ard_3-0-0_114067_2019-05-22_final
2019-05-22 11:35:05	ga_ls7e_ard_3-0-0_114074_2019-05-22_final
2019-05-22 11:35:29	ga_ls7e_ard_3-0-0_114075_2019-05-22_final
2019-05-22 11:35:53	ga_ls7e_ard_3-0-0_114076_2019-05-22_final
2019-05-22 11:36:17	ga_ls7e_ard_3-0-0_114077_2019-05-22_final
2019-05-22 11:36:41	ga_ls7e_ard_3-0-0_114078_2019-05-22_final
2019-05-22 11:37:05	ga_ls7e_ard_3-0-0_114079_2019-05-22_final
2019-05-22 11:37:29	ga_ls7e_ard_3-0-0_114080_2019-05-22_final
2019-05-22 11:37:53	ga_ls7e_ard_3-0-0_114081_2019-05-22_final
2019-05-23 09:02:09	ga_ls7e_ard_3-0-0_089078_2019-05-22_final
2019-05-23 09:02:33	ga_ls7e_ard_3-0-0_089079_2019-05-22_final
2019-05-23 09:02:57	ga_ls7e_ard_3-0-0_089080_2019-05-22_final
2019-05-23 09:03:21	ga_ls7e_ard_3-1-0_089081_2019-05-22_final
2019-05-23 09:03:45	ga_ls7e_ard_3-0-0_089082_2019-05-22_final
2019-05-23 09:04:09	ga_ls7e_ard_3-0-0_089083_2019-05-22_final
2019-05-23 09:04:33	ga_ls7e_ard_3-0-0_089084_2019-05-22_final
2019-05-23 09:04:57	ga_ls7e_ard_3-0-0_089085_2019-05-22_final
2019-05-23 09:05:21	ga_ls7e_ard_3-0-0_089086_2019-05-22_final
2019-05-23 10:37:03	ga_ls7e_ard_3-0-0_105068_2019-05-23_final
2019-05-23 10:37:26	ga_ls7e_ard_3-0-0_105069_2019-05-23_final
2019-05-23 10:37:50	ga_ls7e_ard_3-0-0_105070_2019-05-23_final
2019-05-23 10:38:14	ga_ls7e_ard_3-0-0_105071_2019-05-23_final
2019-05-23 10:38:38	ga_ls7e_ard_3-0-0_105072_2019-05-23_final
2019-05-23 10:39:02	ga_ls7e_ard_3-0-0_105073_2019-05-23_final
2019-05-23 10:39:26	ga_ls7e_ard_3-0-0_105074_2019-05-23_final
2019-05-23 10:39:50	ga_ls7e_ard_3-0-0_105075_2019-05-23_final
2019-05-23 10:40:14	ga_ls7e_ard_3-0-0_105076_2019-05-23_final
2019-05-23 10:40:38	ga_ls7e_ard_3-0-0_105077_2019-05-23_final
2019-05-23 10:41:02	ga_ls7e_ard_3-0-0_105078_2019-05-23_final
2019-05-23 10:41:26	ga_ls7e_ard_3-0-0_105079_2019-05-23_final
2019-05-23 10:41:50	ga_ls7e_ard_3-1-0_105080_2019-05-23_final
2019-05-23 10:42:14	ga_ls7e_ard_3-0-0_105081_2019-05-23_final
2019-05-23 10:42:38	ga_ls7e_ard_3-0-0_105082_2019-05-23_final
2019-05-24 09:42:11	ga_ls7e_ard_3-0-0_096070_2019-05-24_final
2019-05-24 09:42:35	ga_ls7e_ard_3-0-0_096071_2019-05-24_final
2019-05-24 09:42:59	ga_ls7e_ard_3-0-0_096072_2019-05-24_final
2019-05-24 09:43:23	ga_ls7e_ard_3-0-0_096073_2019-05-24_final
2019-05-24 09:43:47	ga_ls7e_ard_3-0-0_096074_2019-05-24_final
2019-05-24 09:44:11	ga_ls7e_ard_3-0-0_096075_2019-05-24_final
2019-05-24 09:44:35	ga_ls7e_ard_3-0-0_096076_2019-05-24_final
2019-05-24 09:44:59	ga_ls7e_ard_3-0-0_096077_2019-05-24_final
2019-05-24 09:45:23	ga_ls7e_ard_3-0-0_096078_2019-05-24_final
2019-05-24 09:45:47	ga_ls7e_ard_3-0-0_096079_2019-05-24_final
2019-05-24 09:46:11	ga_ls7e_ard_3-0-0_096080_2019-05-24_final
2019-05-24 09:46:34	ga_ls7e_ard_3-0-0_096081_2019-05-24_final
2019-05-24 09:46:58	ga_ls7e_ard_3-0-0_096082_2019-05-24_final
2019-05-24 09:47:22	ga_ls7e_ard_3-0-0_096083_2019-05-24_final
2019-05-24 09:47:46	ga_ls7e_ard_3-0-0_096084_2019-05-24_final
2019-05-24 09:48:10	ga_ls7e_ard_3-0-0_096085_2019-05-24_final
2019-05-24 09:48:34	ga_ls7e_ard_3-1-0_096086_2019-05-24_final
2019-05-24 11:19:52	ga_ls7e_ard_3-0-0_112067_2019-05-24_final
2019-05-24 11:22:40	ga_ls7e_ard_3-0-0_112074_2019-05-24_final
2019-05-24 11:23:04	ga_ls7e_ard_3-0-0_112075_2019-05-24_final
2019-05-24 11:23:28	ga_ls7e_ard_3-0-0_112076_2019-05-24_final
2019-05-24 11:23:52	ga_ls7e_ard_3-0-0_112077_2019-05-24_final
2019-05-24 11:24:16	ga_ls7e_ard_3-0-0_112078_2019-05-24_final
2019-05-24 11:24:39	ga_ls7e_ard_3-0-0_112079_2019-05-24_final
2019-05-24 11:25:03	ga_ls7e_ard_3-0-0_112080_2019-05-24_final
2019-05-24 11:25:27	ga_ls7e_ard_3-0-0_112081_2019-05-24_final
2019-05-24 11:25:51	ga_ls7e_ard_3-0-0_112082_2019-05-24_final
2019-05-24 11:26:15	ga_ls7e_ard_3-0-0_112083_2019-05-24_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

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

2019

May

All days