Search ga_ls7e_ard_3 across September 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
2019-09-11 09:49:01	ga_ls7e_ard_3-1-0_098067_2019-09-11_final
2019-09-11 09:49:25	ga_ls7e_ard_3-1-0_098068_2019-09-11_final
2019-09-11 09:49:49	ga_ls7e_ard_3-1-0_098069_2019-09-11_final
2019-09-11 09:50:12	ga_ls7e_ard_3-1-0_098070_2019-09-11_final
2019-09-11 09:50:36	ga_ls7e_ard_3-1-0_098071_2019-09-11_final
2019-09-12 08:57:20	ga_ls7e_ard_3-1-0_089077_2019-09-11_final
2019-09-12 08:57:44	ga_ls7e_ard_3-1-0_089078_2019-09-11_final
2019-09-12 08:58:08	ga_ls7e_ard_3-1-0_089079_2019-09-11_final
2019-09-12 08:58:32	ga_ls7e_ard_3-1-0_089080_2019-09-11_final
2019-09-12 08:58:56	ga_ls7e_ard_3-1-0_089081_2019-09-11_final
2019-09-12 08:59:20	ga_ls7e_ard_3-1-0_089082_2019-09-11_final
2019-09-12 08:59:44	ga_ls7e_ard_3-1-0_089083_2019-09-11_final
2019-09-12 09:00:08	ga_ls7e_ard_3-1-0_089084_2019-09-11_final
2019-09-12 09:00:32	ga_ls7e_ard_3-1-0_089085_2019-09-11_final
2019-09-12 09:00:56	ga_ls7e_ard_3-1-0_089086_2019-09-11_final
2019-09-13 09:36:34	ga_ls7e_ard_3-1-0_096067_2019-09-13_final
2019-09-13 09:38:09	ga_ls7e_ard_3-1-0_096071_2019-09-13_final
2019-09-13 09:38:33	ga_ls7e_ard_3-1-0_096072_2019-09-13_final
2019-09-13 09:38:57	ga_ls7e_ard_3-1-0_096073_2019-09-13_final
2019-09-13 09:39:21	ga_ls7e_ard_3-1-0_096074_2019-09-13_final
2019-09-13 09:39:45	ga_ls7e_ard_3-1-0_096075_2019-09-13_final
2019-09-13 09:40:09	ga_ls7e_ard_3-1-0_096076_2019-09-13_final
2019-09-13 09:40:33	ga_ls7e_ard_3-1-0_096077_2019-09-13_final
2019-09-13 09:40:57	ga_ls7e_ard_3-1-0_096078_2019-09-13_final
2019-09-13 09:41:21	ga_ls7e_ard_3-1-0_096079_2019-09-13_final
2019-09-13 09:41:45	ga_ls7e_ard_3-1-0_096080_2019-09-13_final
2019-09-13 09:42:09	ga_ls7e_ard_3-1-0_096081_2019-09-13_final
2019-09-13 09:42:33	ga_ls7e_ard_3-1-0_096082_2019-09-13_final
2019-09-13 09:42:57	ga_ls7e_ard_3-1-0_096083_2019-09-13_final
2019-09-13 09:43:21	ga_ls7e_ard_3-1-0_096084_2019-09-13_final
2019-09-25 10:02:20	ga_ls7e_ard_3-1-0_100071_2019-09-25_final
2019-09-25 10:02:44	ga_ls7e_ard_3-1-0_100072_2019-09-25_final
2019-09-25 10:03:08	ga_ls7e_ard_3-1-0_100073_2019-09-25_final
2019-09-25 10:03:32	ga_ls7e_ard_3-1-0_100074_2019-09-25_final
2019-09-25 10:03:56	ga_ls7e_ard_3-1-0_100075_2019-09-25_final
2019-09-25 10:04:20	ga_ls7e_ard_3-1-0_100076_2019-09-25_final
2019-09-25 10:04:44	ga_ls7e_ard_3-1-0_100077_2019-09-25_final
2019-09-25 10:05:08	ga_ls7e_ard_3-1-0_100078_2019-09-25_final
2019-09-25 10:05:32	ga_ls7e_ard_3-1-0_100079_2019-09-25_final
2019-09-25 10:05:56	ga_ls7e_ard_3-1-0_100080_2019-09-25_final
2019-09-25 10:06:20	ga_ls7e_ard_3-1-0_100081_2019-09-25_final
2019-09-25 10:06:44	ga_ls7e_ard_3-1-0_100082_2019-09-25_final
2019-09-25 10:07:08	ga_ls7e_ard_3-1-0_100083_2019-09-25_final
2019-09-25 10:07:32	ga_ls7e_ard_3-1-0_100084_2019-09-25_final
2019-09-26 09:08:40	ga_ls7e_ard_3-1-0_091076_2019-09-25_final
2019-09-26 09:09:04	ga_ls7e_ard_3-1-0_091077_2019-09-25_final
2019-09-26 09:09:52	ga_ls7e_ard_3-1-0_091079_2019-09-25_final
2019-09-26 09:10:16	ga_ls7e_ard_3-1-0_091080_2019-09-25_final
2019-09-26 09:10:40	ga_ls7e_ard_3-1-0_091081_2019-09-25_final
2019-09-26 09:11:04	ga_ls7e_ard_3-1-0_091082_2019-09-25_final
2019-09-26 09:11:28	ga_ls7e_ard_3-1-0_091083_2019-09-25_final
2019-09-26 09:11:52	ga_ls7e_ard_3-1-0_091084_2019-09-25_final
2019-09-26 09:12:16	ga_ls7e_ard_3-1-0_091085_2019-09-25_final
2019-09-26 09:12:40	ga_ls7e_ard_3-1-0_091086_2019-09-25_final
2019-09-26 09:13:04	ga_ls7e_ard_3-1-0_091087_2019-09-25_final
2019-09-26 09:13:28	ga_ls7e_ard_3-1-0_091088_2019-09-25_final
2019-09-26 09:13:52	ga_ls7e_ard_3-1-0_091089_2019-09-25_final
2019-09-26 09:14:16	ga_ls7e_ard_3-1-0_091090_2019-09-25_final
2019-09-26 10:45:10	ga_ls7e_ard_3-1-0_107070_2019-09-26_final
2019-09-26 10:45:34	ga_ls7e_ard_3-1-0_107071_2019-09-26_final
2019-09-26 10:45:58	ga_ls7e_ard_3-1-0_107072_2019-09-26_final
2019-09-26 10:46:22	ga_ls7e_ard_3-1-0_107073_2019-09-26_final
2019-09-26 10:46:45	ga_ls7e_ard_3-1-0_107074_2019-09-26_final
2019-09-26 10:47:09	ga_ls7e_ard_3-1-0_107075_2019-09-26_final
2019-09-26 10:47:33	ga_ls7e_ard_3-1-0_107076_2019-09-26_final
2019-09-26 10:47:57	ga_ls7e_ard_3-1-0_107077_2019-09-26_final
2019-09-26 10:48:21	ga_ls7e_ard_3-1-0_107078_2019-09-26_final
2019-09-26 10:48:45	ga_ls7e_ard_3-1-0_107079_2019-09-26_final
2019-09-26 10:49:09	ga_ls7e_ard_3-1-0_107080_2019-09-26_final
2019-09-26 10:49:33	ga_ls7e_ard_3-1-0_107081_2019-09-26_final
2019-09-26 10:49:57	ga_ls7e_ard_3-1-0_107082_2019-09-26_final
2019-09-26 10:50:21	ga_ls7e_ard_3-1-0_107083_2019-09-26_final
2019-09-26 10:50:45	ga_ls7e_ard_3-1-0_107084_2019-09-26_final
2019-09-27 09:48:18	ga_ls7e_ard_3-1-0_098067_2019-09-27_final
2019-09-27 09:48:42	ga_ls7e_ard_3-1-0_098068_2019-09-27_final
2019-09-27 09:49:06	ga_ls7e_ard_3-1-0_098069_2019-09-27_final
2019-09-27 09:49:30	ga_ls7e_ard_3-1-0_098070_2019-09-27_final
2019-09-27 09:49:54	ga_ls7e_ard_3-1-0_098071_2019-09-27_final
2019-09-27 09:50:18	ga_ls7e_ard_3-1-0_098072_2019-09-27_final
2019-09-27 09:50:42	ga_ls7e_ard_3-1-0_098073_2019-09-27_final
2019-09-27 09:51:06	ga_ls7e_ard_3-1-0_098074_2019-09-27_final
2019-09-27 09:51:30	ga_ls7e_ard_3-1-0_098075_2019-09-27_final
2019-09-27 09:51:54	ga_ls7e_ard_3-1-0_098076_2019-09-27_final
2019-09-27 09:52:18	ga_ls7e_ard_3-1-0_098077_2019-09-27_final
2019-09-27 09:52:42	ga_ls7e_ard_3-1-0_098078_2019-09-27_final
2019-09-27 09:53:06	ga_ls7e_ard_3-1-0_098079_2019-09-27_final
2019-09-27 09:53:29	ga_ls7e_ard_3-1-0_098080_2019-09-27_final
2019-09-27 09:53:53	ga_ls7e_ard_3-1-0_098081_2019-09-27_final
2019-09-27 09:54:17	ga_ls7e_ard_3-1-0_098082_2019-09-27_final
2019-09-27 09:54:41	ga_ls7e_ard_3-1-0_098083_2019-09-27_final
2019-09-27 09:55:05	ga_ls7e_ard_3-1-0_098084_2019-09-27_final
2019-09-27 09:55:29	ga_ls7e_ard_3-1-0_098085_2019-09-27_final
2019-09-27 11:27:11	ga_ls7e_ard_3-1-0_114067_2019-09-27_final
2019-09-27 11:29:59	ga_ls7e_ard_3-1-0_114074_2019-09-27_final
2019-09-27 11:30:23	ga_ls7e_ard_3-1-0_114075_2019-09-27_final
2019-09-27 11:30:47	ga_ls7e_ard_3-1-0_114076_2019-09-27_final
2019-09-27 11:31:11	ga_ls7e_ard_3-1-0_114077_2019-09-27_final
2019-09-27 11:31:35	ga_ls7e_ard_3-1-0_114078_2019-09-27_final
2019-09-27 11:31:58	ga_ls7e_ard_3-1-0_114079_2019-09-27_final
2019-09-27 11:32:22	ga_ls7e_ard_3-1-0_114080_2019-09-27_final

Return to ga_ls7e_ard_3 across September 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

September

All days