Search ga_ls7e_ard_3 across October 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
2019-10-04 09:54:12	ga_ls7e_ard_3-1-0_099067_2019-10-04_final
2019-10-04 09:54:36	ga_ls7e_ard_3-1-0_099068_2019-10-04_final
2019-10-04 09:55:00	ga_ls7e_ard_3-1-0_099069_2019-10-04_final
2019-10-04 09:55:24	ga_ls7e_ard_3-1-0_099070_2019-10-04_final
2019-10-04 09:55:48	ga_ls7e_ard_3-1-0_099071_2019-10-04_final
2019-10-04 09:56:12	ga_ls7e_ard_3-1-0_099072_2019-10-04_final
2019-10-04 09:56:36	ga_ls7e_ard_3-1-0_099073_2019-10-04_final
2019-10-04 09:57:00	ga_ls7e_ard_3-1-0_099074_2019-10-04_final
2019-10-04 09:57:24	ga_ls7e_ard_3-1-0_099075_2019-10-04_final
2019-10-04 09:57:47	ga_ls7e_ard_3-1-0_099076_2019-10-04_final
2019-10-04 09:58:11	ga_ls7e_ard_3-1-0_099077_2019-10-04_final
2019-10-04 09:58:35	ga_ls7e_ard_3-1-0_099078_2019-10-04_final
2019-10-04 09:58:59	ga_ls7e_ard_3-1-0_099079_2019-10-04_final
2019-10-04 09:59:23	ga_ls7e_ard_3-1-0_099080_2019-10-04_final
2019-10-04 09:59:47	ga_ls7e_ard_3-1-0_099081_2019-10-04_final
2019-10-04 10:00:11	ga_ls7e_ard_3-1-0_099082_2019-10-04_final
2019-10-04 10:00:35	ga_ls7e_ard_3-1-0_099083_2019-10-04_final
2019-10-04 10:00:59	ga_ls7e_ard_3-1-0_099084_2019-10-04_final
2019-10-04 11:35:53	ga_ls7e_ard_3-1-0_115074_2019-10-04_final
2019-10-04 11:36:16	ga_ls7e_ard_3-1-0_115075_2019-10-04_final
2019-10-04 11:36:40	ga_ls7e_ard_3-1-0_115076_2019-10-04_final
2019-10-04 11:37:04	ga_ls7e_ard_3-1-0_115077_2019-10-04_final
2019-10-04 11:37:28	ga_ls7e_ard_3-1-0_115078_2019-10-04_final
2019-10-05 09:02:32	ga_ls7e_ard_3-1-0_090077_2019-10-04_final
2019-10-05 09:02:56	ga_ls7e_ard_3-1-0_090078_2019-10-04_final
2019-10-05 09:03:20	ga_ls7e_ard_3-1-0_090079_2019-10-04_final
2019-10-05 09:03:44	ga_ls7e_ard_3-1-0_090080_2019-10-04_final
2019-10-05 09:04:08	ga_ls7e_ard_3-1-0_090081_2019-10-04_final
2019-10-05 09:05:43	ga_ls7e_ard_3-1-0_090085_2019-10-04_final
2019-10-05 09:06:07	ga_ls7e_ard_3-1-0_090086_2019-10-04_final
2019-10-05 09:06:55	ga_ls7e_ard_3-1-0_090088_2019-10-04_final
2019-10-05 09:07:19	ga_ls7e_ard_3-1-0_090089_2019-10-04_final
2019-10-05 09:07:43	ga_ls7e_ard_3-1-0_090090_2019-10-04_final
2019-10-05 10:37:49	ga_ls7e_ard_3-1-0_106068_2019-10-05_final
2019-10-05 10:38:13	ga_ls7e_ard_3-1-0_106069_2019-10-05_final
2019-10-05 10:38:37	ga_ls7e_ard_3-1-0_106070_2019-10-05_final
2019-10-05 10:39:01	ga_ls7e_ard_3-1-0_106071_2019-10-05_final
2019-10-05 10:39:25	ga_ls7e_ard_3-1-0_106072_2019-10-05_final
2019-10-05 10:39:49	ga_ls7e_ard_3-1-0_106073_2019-10-05_final
2019-10-05 10:40:13	ga_ls7e_ard_3-1-0_106074_2019-10-05_final
2019-10-05 10:40:37	ga_ls7e_ard_3-1-0_106075_2019-10-05_final
2019-10-05 10:41:01	ga_ls7e_ard_3-1-0_106076_2019-10-05_final
2019-10-05 10:41:25	ga_ls7e_ard_3-1-0_106077_2019-10-05_final
2019-10-05 10:41:48	ga_ls7e_ard_3-1-0_106078_2019-10-05_final
2019-10-05 10:42:12	ga_ls7e_ard_3-1-0_106079_2019-10-05_final
2019-10-05 10:42:36	ga_ls7e_ard_3-1-0_106080_2019-10-05_final
2019-10-05 10:43:00	ga_ls7e_ard_3-1-0_106081_2019-10-05_final
2019-10-05 10:43:24	ga_ls7e_ard_3-1-0_106082_2019-10-05_final
2019-10-05 10:43:48	ga_ls7e_ard_3-1-0_106083_2019-10-05_final
2019-10-06 09:42:33	ga_ls7e_ard_3-1-0_097069_2019-10-06_final
2019-10-06 09:42:57	ga_ls7e_ard_3-1-0_097070_2019-10-06_final
2019-10-06 09:43:21	ga_ls7e_ard_3-1-0_097071_2019-10-06_final
2019-10-06 09:43:45	ga_ls7e_ard_3-1-0_097072_2019-10-06_final
2019-10-06 09:44:09	ga_ls7e_ard_3-1-0_097073_2019-10-06_final
2019-10-06 09:44:33	ga_ls7e_ard_3-1-0_097074_2019-10-06_final
2019-10-06 09:44:57	ga_ls7e_ard_3-1-0_097075_2019-10-06_final
2019-10-06 09:45:21	ga_ls7e_ard_3-1-0_097076_2019-10-06_final
2019-10-06 09:45:45	ga_ls7e_ard_3-1-0_097077_2019-10-06_final
2019-10-06 09:46:09	ga_ls7e_ard_3-1-0_097078_2019-10-06_final
2019-10-06 09:46:33	ga_ls7e_ard_3-1-0_097079_2019-10-06_final
2019-10-06 09:46:57	ga_ls7e_ard_3-1-0_097080_2019-10-06_final
2019-10-06 09:47:21	ga_ls7e_ard_3-1-0_097081_2019-10-06_final
2019-10-06 09:47:45	ga_ls7e_ard_3-1-0_097082_2019-10-06_final
2019-10-06 09:48:09	ga_ls7e_ard_3-1-0_097083_2019-10-06_final
2019-10-06 09:48:32	ga_ls7e_ard_3-1-0_097084_2019-10-06_final
2019-10-06 11:20:38	ga_ls7e_ard_3-1-0_113067_2019-10-06_final
2019-10-06 11:23:26	ga_ls7e_ard_3-1-0_113074_2019-10-06_final
2019-10-06 11:23:50	ga_ls7e_ard_3-1-0_113075_2019-10-06_final
2019-10-06 11:24:14	ga_ls7e_ard_3-1-0_113076_2019-10-06_final
2019-10-06 11:24:38	ga_ls7e_ard_3-1-0_113077_2019-10-06_final
2019-10-06 11:25:02	ga_ls7e_ard_3-1-0_113078_2019-10-06_final
2019-10-06 11:25:26	ga_ls7e_ard_3-1-0_113079_2019-10-06_final
2019-10-06 11:25:50	ga_ls7e_ard_3-1-0_113080_2019-10-06_final
2019-10-06 11:26:14	ga_ls7e_ard_3-1-0_113081_2019-10-06_final
2019-10-06 11:26:37	ga_ls7e_ard_3-1-0_113082_2019-10-06_final
2019-10-06 11:27:25	ga_ls7e_ard_3-1-0_113084_2019-10-06_final
2019-10-28 09:07:20	ga_ls7e_ard_3-1-0_091076_2019-10-27_final
2019-10-28 09:07:44	ga_ls7e_ard_3-1-0_091077_2019-10-27_final
2019-10-28 09:08:08	ga_ls7e_ard_3-1-0_091078_2019-10-27_final
2019-10-28 09:08:32	ga_ls7e_ard_3-1-0_091079_2019-10-27_final
2019-10-28 09:08:56	ga_ls7e_ard_3-1-0_091080_2019-10-27_final
2019-10-28 09:09:20	ga_ls7e_ard_3-1-0_091081_2019-10-27_final
2019-10-28 09:09:44	ga_ls7e_ard_3-1-0_091082_2019-10-27_final
2019-10-28 09:10:08	ga_ls7e_ard_3-1-0_091083_2019-10-27_final
2019-10-28 09:10:32	ga_ls7e_ard_3-1-0_091084_2019-10-27_final
2019-10-28 09:10:55	ga_ls7e_ard_3-1-0_091085_2019-10-27_final
2019-10-28 09:11:19	ga_ls7e_ard_3-1-0_091086_2019-10-27_final
2019-10-28 09:11:43	ga_ls7e_ard_3-1-0_091087_2019-10-27_final
2019-10-28 09:12:07	ga_ls7e_ard_3-1-0_091088_2019-10-27_final
2019-10-28 09:12:31	ga_ls7e_ard_3-1-0_091089_2019-10-27_final
2019-10-28 09:12:55	ga_ls7e_ard_3-1-0_091090_2019-10-27_final
2019-10-29 09:48:57	ga_ls7e_ard_3-1-0_098072_2019-10-29_final
2019-10-29 09:49:21	ga_ls7e_ard_3-1-0_098073_2019-10-29_final
2019-10-29 09:51:21	ga_ls7e_ard_3-1-0_098078_2019-10-29_final
2019-10-30 08:56:05	ga_ls7e_ard_3-1-0_089079_2019-10-29_final
2019-10-30 08:56:53	ga_ls7e_ard_3-1-0_089081_2019-10-29_final
2019-10-30 08:57:17	ga_ls7e_ard_3-1-0_089082_2019-10-29_final
2019-10-30 08:57:41	ga_ls7e_ard_3-1-0_089083_2019-10-29_final
2019-10-30 08:58:05	ga_ls7e_ard_3-1-0_089084_2019-10-29_final
2019-10-30 08:58:29	ga_ls7e_ard_3-1-0_089085_2019-10-29_final

Return to ga_ls7e_ard_3 across October 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

October

All days