Search ga_ls7e_ard_3 across October 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
2018-10-08 10:14:17	ga_ls7e_ard_3-0-0_100071_2018-10-08_final
2018-10-08 10:14:41	ga_ls7e_ard_3-0-0_100072_2018-10-08_final
2018-10-08 10:15:05	ga_ls7e_ard_3-0-0_100073_2018-10-08_final
2018-10-08 10:15:29	ga_ls7e_ard_3-0-0_100074_2018-10-08_final
2018-10-08 10:15:53	ga_ls7e_ard_3-0-0_100075_2018-10-08_final
2018-10-08 10:16:17	ga_ls7e_ard_3-0-0_100076_2018-10-08_final
2018-10-08 10:17:52	ga_ls7e_ard_3-0-0_100080_2018-10-08_final
2018-10-09 09:20:38	ga_ls7e_ard_3-0-0_091076_2018-10-08_final
2018-10-09 09:21:02	ga_ls7e_ard_3-0-0_091077_2018-10-08_final
2018-10-09 09:21:50	ga_ls7e_ard_3-0-0_091079_2018-10-08_final
2018-10-09 09:22:14	ga_ls7e_ard_3-0-0_091080_2018-10-08_final
2018-10-09 09:22:38	ga_ls7e_ard_3-0-0_091081_2018-10-08_final
2018-10-09 09:23:01	ga_ls7e_ard_3-0-0_091082_2018-10-08_final
2018-10-09 09:23:25	ga_ls7e_ard_3-0-0_091083_2018-10-08_final
2018-10-09 09:23:49	ga_ls7e_ard_3-0-0_091084_2018-10-08_final
2018-10-09 09:24:13	ga_ls7e_ard_3-0-0_091085_2018-10-08_final
2018-10-09 09:24:37	ga_ls7e_ard_3-1-0_091086_2018-10-08_final
2018-10-09 09:25:25	ga_ls7e_ard_3-0-0_091088_2018-10-08_final
2018-10-09 10:57:07	ga_ls7e_ard_3-0-0_107070_2018-10-09_final
2018-10-09 10:57:31	ga_ls7e_ard_3-0-0_107071_2018-10-09_final
2018-10-09 10:57:55	ga_ls7e_ard_3-0-0_107072_2018-10-09_final
2018-10-09 10:58:19	ga_ls7e_ard_3-0-0_107073_2018-10-09_final
2018-10-09 10:58:43	ga_ls7e_ard_3-0-0_107074_2018-10-09_final
2018-10-09 10:59:07	ga_ls7e_ard_3-0-0_107075_2018-10-09_final
2018-10-09 10:59:31	ga_ls7e_ard_3-0-0_107076_2018-10-09_final
2018-10-09 10:59:55	ga_ls7e_ard_3-0-0_107077_2018-10-09_final
2018-10-09 11:00:19	ga_ls7e_ard_3-0-0_107078_2018-10-09_final
2018-10-09 11:00:43	ga_ls7e_ard_3-0-0_107079_2018-10-09_final
2018-10-09 11:01:06	ga_ls7e_ard_3-0-0_107080_2018-10-09_final
2018-10-09 11:01:30	ga_ls7e_ard_3-0-0_107081_2018-10-09_final
2018-10-10 10:00:17	ga_ls7e_ard_3-0-0_098067_2018-10-10_final
2018-10-10 10:00:40	ga_ls7e_ard_3-0-0_098068_2018-10-10_final
2018-10-10 10:01:04	ga_ls7e_ard_3-0-0_098069_2018-10-10_final
2018-10-10 10:01:28	ga_ls7e_ard_3-0-0_098070_2018-10-10_final
2018-10-10 10:01:52	ga_ls7e_ard_3-0-0_098071_2018-10-10_final
2018-10-10 10:02:16	ga_ls7e_ard_3-0-0_098072_2018-10-10_final
2018-10-10 10:02:40	ga_ls7e_ard_3-0-0_098073_2018-10-10_final
2018-10-10 10:03:04	ga_ls7e_ard_3-0-0_098074_2018-10-10_final
2018-10-10 10:03:28	ga_ls7e_ard_3-0-0_098075_2018-10-10_final
2018-10-10 10:03:52	ga_ls7e_ard_3-0-0_098076_2018-10-10_final
2018-10-10 10:04:16	ga_ls7e_ard_3-0-0_098077_2018-10-10_final
2018-10-10 10:04:40	ga_ls7e_ard_3-0-0_098078_2018-10-10_final
2018-10-10 10:05:04	ga_ls7e_ard_3-0-0_098079_2018-10-10_final
2018-10-10 10:05:28	ga_ls7e_ard_3-1-0_098080_2018-10-10_final
2018-10-10 10:05:52	ga_ls7e_ard_3-0-0_098081_2018-10-10_final
2018-10-10 10:06:16	ga_ls7e_ard_3-0-0_098082_2018-10-10_final
2018-10-10 10:06:40	ga_ls7e_ard_3-0-0_098083_2018-10-10_final
2018-10-10 10:07:04	ga_ls7e_ard_3-0-0_098084_2018-10-10_final
2018-10-10 10:07:27	ga_ls7e_ard_3-0-0_098085_2018-10-10_final
2018-10-10 11:39:09	ga_ls7e_ard_3-0-0_114067_2018-10-10_final
2018-10-10 11:41:57	ga_ls7e_ard_3-0-0_114074_2018-10-10_final
2018-10-10 11:42:21	ga_ls7e_ard_3-0-0_114075_2018-10-10_final
2018-10-10 11:42:45	ga_ls7e_ard_3-0-0_114076_2018-10-10_final
2018-10-10 11:43:09	ga_ls7e_ard_3-0-0_114077_2018-10-10_final
2018-10-10 11:43:33	ga_ls7e_ard_3-0-0_114078_2018-10-10_final
2018-10-10 11:43:57	ga_ls7e_ard_3-0-0_114079_2018-10-10_final
2018-10-10 11:44:21	ga_ls7e_ard_3-0-0_114080_2018-10-10_final
2018-10-14 09:35:27	ga_ls7e_ard_3-1-0_094067_2018-10-14_final
2018-10-14 09:37:51	ga_ls7e_ard_3-0-0_094073_2018-10-14_final
2018-10-14 09:38:15	ga_ls7e_ard_3-0-0_094074_2018-10-14_final
2018-10-14 09:38:39	ga_ls7e_ard_3-0-0_094075_2018-10-14_final
2018-10-14 09:39:03	ga_ls7e_ard_3-0-0_094076_2018-10-14_final
2018-10-14 09:39:26	ga_ls7e_ard_3-0-0_094077_2018-10-14_final
2018-10-14 09:39:50	ga_ls7e_ard_3-0-0_094078_2018-10-14_final
2018-10-14 09:40:14	ga_ls7e_ard_3-0-0_094079_2018-10-14_final
2018-10-14 09:40:38	ga_ls7e_ard_3-0-0_094080_2018-10-14_final
2018-10-14 09:41:02	ga_ls7e_ard_3-0-0_094081_2018-10-14_final
2018-10-14 09:41:26	ga_ls7e_ard_3-0-0_094082_2018-10-14_final
2018-10-14 09:41:50	ga_ls7e_ard_3-1-0_094083_2018-10-14_final
2018-10-14 09:42:14	ga_ls7e_ard_3-0-0_094084_2018-10-14_final
2018-10-14 09:42:38	ga_ls7e_ard_3-0-0_094085_2018-10-14_final
2018-10-14 09:43:02	ga_ls7e_ard_3-0-0_094086_2018-10-14_final
2018-10-14 11:14:20	ga_ls7e_ard_3-0-0_110067_2018-10-14_final
2018-10-14 11:15:32	ga_ls7e_ard_3-0-0_110070_2018-10-14_final
2018-10-14 11:15:56	ga_ls7e_ard_3-0-0_110071_2018-10-14_final
2018-10-14 11:16:20	ga_ls7e_ard_3-1-0_110072_2018-10-14_final
2018-10-14 11:16:44	ga_ls7e_ard_3-0-0_110073_2018-10-14_final
2018-10-14 11:17:08	ga_ls7e_ard_3-0-0_110074_2018-10-14_final
2018-10-14 11:17:32	ga_ls7e_ard_3-1-0_110075_2018-10-14_final
2018-10-14 11:17:55	ga_ls7e_ard_3-0-0_110076_2018-10-14_final
2018-10-14 11:18:19	ga_ls7e_ard_3-0-0_110077_2018-10-14_final
2018-10-15 10:20:17	ga_ls7e_ard_3-0-0_101071_2018-10-15_final
2018-10-15 10:20:41	ga_ls7e_ard_3-0-0_101072_2018-10-15_final
2018-10-15 10:21:05	ga_ls7e_ard_3-0-0_101073_2018-10-15_final
2018-10-15 10:21:53	ga_ls7e_ard_3-1-0_101075_2018-10-15_final
2018-10-15 10:22:17	ga_ls7e_ard_3-0-0_101076_2018-10-15_final
2018-10-15 10:22:41	ga_ls7e_ard_3-0-0_101077_2018-10-15_final
2018-10-15 10:23:04	ga_ls7e_ard_3-0-0_101078_2018-10-15_final
2018-10-15 10:23:28	ga_ls7e_ard_3-0-0_101079_2018-10-15_final
2018-10-15 10:23:52	ga_ls7e_ard_3-0-0_101080_2018-10-15_final
2018-10-15 10:24:16	ga_ls7e_ard_3-0-0_101081_2018-10-15_final
2018-10-15 10:25:04	ga_ls7e_ard_3-0-0_101083_2018-10-15_final
2018-10-16 09:26:14	ga_ls7e_ard_3-0-0_092075_2018-10-15_final
2018-10-16 09:26:38	ga_ls7e_ard_3-0-0_092076_2018-10-15_final
2018-10-16 09:27:02	ga_ls7e_ard_3-0-0_092077_2018-10-15_final
2018-10-16 09:27:26	ga_ls7e_ard_3-0-0_092078_2018-10-15_final
2018-10-16 09:27:50	ga_ls7e_ard_3-0-0_092079_2018-10-15_final
2018-10-16 09:28:14	ga_ls7e_ard_3-0-0_092080_2018-10-15_final
2018-10-16 09:28:37	ga_ls7e_ard_3-0-0_092081_2018-10-15_final
2018-10-16 09:29:01	ga_ls7e_ard_3-0-0_092082_2018-10-15_final

Return to ga_ls7e_ard_3 across October 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

October

All days