Search ga_ls7e_ard_3 across 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
2018-08-09 09:50:28	ga_ls7e_ard_3-0-0_096070_2018-08-09_final
2018-08-09 09:50:52	ga_ls7e_ard_3-0-0_096071_2018-08-09_final
2018-08-09 09:51:16	ga_ls7e_ard_3-0-0_096072_2018-08-09_final
2018-08-09 09:51:40	ga_ls7e_ard_3-0-0_096073_2018-08-09_final
2018-08-09 09:52:04	ga_ls7e_ard_3-0-0_096074_2018-08-09_final
2018-08-09 09:52:28	ga_ls7e_ard_3-0-0_096075_2018-08-09_final
2018-08-09 09:52:52	ga_ls7e_ard_3-0-0_096076_2018-08-09_final
2018-08-09 09:53:16	ga_ls7e_ard_3-0-0_096077_2018-08-09_final
2018-08-09 09:53:40	ga_ls7e_ard_3-0-0_096078_2018-08-09_final
2018-08-09 09:54:04	ga_ls7e_ard_3-0-0_096079_2018-08-09_final
2018-08-09 09:54:28	ga_ls7e_ard_3-0-0_096080_2018-08-09_final
2018-08-09 09:54:52	ga_ls7e_ard_3-0-0_096081_2018-08-09_final
2018-08-09 09:55:16	ga_ls7e_ard_3-0-0_096082_2018-08-09_final
2018-08-09 09:55:40	ga_ls7e_ard_3-0-0_096083_2018-08-09_final
2018-08-09 09:56:03	ga_ls7e_ard_3-0-0_096084_2018-08-09_final
2018-08-09 09:56:27	ga_ls7e_ard_3-0-0_096085_2018-08-09_final
2018-08-09 11:28:09	ga_ls7e_ard_3-0-0_112067_2018-08-09_final
2018-08-09 11:30:57	ga_ls7e_ard_3-0-0_112074_2018-08-09_final
2018-08-09 11:31:21	ga_ls7e_ard_3-0-0_112075_2018-08-09_final
2018-08-09 11:31:45	ga_ls7e_ard_3-0-0_112076_2018-08-09_final
2018-08-09 11:32:09	ga_ls7e_ard_3-0-0_112077_2018-08-09_final
2018-08-09 11:32:33	ga_ls7e_ard_3-0-0_112078_2018-08-09_final
2018-08-09 11:32:57	ga_ls7e_ard_3-1-0_112079_2018-08-09_final
2018-08-09 11:33:21	ga_ls7e_ard_3-0-0_112080_2018-08-09_final
2018-08-09 11:33:45	ga_ls7e_ard_3-0-0_112081_2018-08-09_final
2018-08-09 11:34:09	ga_ls7e_ard_3-0-0_112082_2018-08-09_final
2018-08-09 11:34:33	ga_ls7e_ard_3-0-0_112083_2018-08-09_final
2018-08-09 11:34:56	ga_ls7e_ard_3-0-0_112084_2018-08-09_final
2018-08-10 10:32:55	ga_ls7e_ard_3-0-0_103068_2018-08-10_final
2018-08-10 10:33:19	ga_ls7e_ard_3-0-0_103069_2018-08-10_final
2018-08-10 10:33:43	ga_ls7e_ard_3-0-0_103070_2018-08-10_final
2018-08-10 10:34:07	ga_ls7e_ard_3-0-0_103071_2018-08-10_final
2018-08-10 10:34:31	ga_ls7e_ard_3-0-0_103072_2018-08-10_final
2018-08-10 10:34:55	ga_ls7e_ard_3-0-0_103073_2018-08-10_final
2018-08-10 10:35:19	ga_ls7e_ard_3-0-0_103074_2018-08-10_final
2018-08-10 10:35:42	ga_ls7e_ard_3-0-0_103075_2018-08-10_final
2018-08-10 10:36:06	ga_ls7e_ard_3-0-0_103076_2018-08-10_final
2018-08-10 10:36:30	ga_ls7e_ard_3-0-0_103077_2018-08-10_final
2018-08-10 10:36:54	ga_ls7e_ard_3-0-0_103078_2018-08-10_final
2018-08-10 10:37:18	ga_ls7e_ard_3-0-0_103079_2018-08-10_final
2018-08-10 10:37:42	ga_ls7e_ard_3-0-0_103080_2018-08-10_final
2018-08-10 10:38:30	ga_ls7e_ard_3-0-0_103082_2018-08-10_final
2018-08-11 09:36:53	ga_ls7e_ard_3-1-0_094067_2018-08-11_final
2018-08-11 09:39:16	ga_ls7e_ard_3-0-0_094073_2018-08-11_final
2018-08-11 09:39:40	ga_ls7e_ard_3-0-0_094074_2018-08-11_final
2018-08-11 09:40:04	ga_ls7e_ard_3-0-0_094075_2018-08-11_final
2018-08-11 09:40:28	ga_ls7e_ard_3-0-0_094076_2018-08-11_final
2018-08-11 09:40:52	ga_ls7e_ard_3-0-0_094077_2018-08-11_final
2018-08-11 09:41:16	ga_ls7e_ard_3-0-0_094078_2018-08-11_final
2018-08-11 09:41:40	ga_ls7e_ard_3-0-0_094079_2018-08-11_final
2018-08-11 09:42:04	ga_ls7e_ard_3-0-0_094080_2018-08-11_final
2018-08-11 09:42:28	ga_ls7e_ard_3-0-0_094081_2018-08-11_final
2018-08-11 09:42:52	ga_ls7e_ard_3-0-0_094082_2018-08-11_final
2018-08-11 09:43:16	ga_ls7e_ard_3-1-0_094083_2018-08-11_final
2018-08-11 09:43:40	ga_ls7e_ard_3-0-0_094084_2018-08-11_final
2018-08-11 09:44:04	ga_ls7e_ard_3-0-0_094085_2018-08-11_final
2018-08-11 09:44:28	ga_ls7e_ard_3-0-0_094086_2018-08-11_final
2018-08-11 09:44:51	ga_ls7e_ard_3-0-0_094087_2018-08-11_final
2018-08-11 11:15:46	ga_ls7e_ard_3-0-0_110067_2018-08-11_final
2018-08-11 11:16:57	ga_ls7e_ard_3-0-0_110070_2018-08-11_final
2018-08-11 11:17:21	ga_ls7e_ard_3-0-0_110071_2018-08-11_final
2018-08-11 11:17:45	ga_ls7e_ard_3-1-0_110072_2018-08-11_final
2018-08-11 11:18:09	ga_ls7e_ard_3-0-0_110073_2018-08-11_final
2018-08-11 11:18:33	ga_ls7e_ard_3-0-0_110074_2018-08-11_final
2018-08-11 11:18:57	ga_ls7e_ard_3-0-0_110075_2018-08-11_final
2018-08-11 11:19:21	ga_ls7e_ard_3-0-0_110076_2018-08-11_final
2018-08-11 11:19:45	ga_ls7e_ard_3-0-0_110077_2018-08-11_final
2018-08-11 11:20:09	ga_ls7e_ard_3-0-0_110078_2018-08-11_final
2018-08-11 11:20:33	ga_ls7e_ard_3-0-0_110079_2018-08-11_final
2018-08-11 11:20:57	ga_ls7e_ard_3-0-0_110080_2018-08-11_final
2018-08-11 11:21:21	ga_ls7e_ard_3-0-0_110081_2018-08-11_final
2018-08-11 11:21:45	ga_ls7e_ard_3-0-0_110082_2018-08-11_final
2018-08-11 11:22:09	ga_ls7e_ard_3-0-0_110083_2018-08-11_final
2018-08-11 11:22:33	ga_ls7e_ard_3-0-0_110084_2018-08-11_final
2018-08-12 10:21:43	ga_ls7e_ard_3-0-0_101071_2018-08-12_final
2018-08-12 10:22:07	ga_ls7e_ard_3-0-0_101072_2018-08-12_final
2018-08-12 10:22:31	ga_ls7e_ard_3-0-0_101073_2018-08-12_final
2018-08-12 10:22:55	ga_ls7e_ard_3-0-0_101074_2018-08-12_final
2018-08-12 10:23:19	ga_ls7e_ard_3-1-0_101075_2018-08-12_final
2018-08-12 10:23:43	ga_ls7e_ard_3-0-0_101076_2018-08-12_final
2018-08-12 10:24:07	ga_ls7e_ard_3-0-0_101077_2018-08-12_final
2018-08-12 10:24:30	ga_ls7e_ard_3-0-0_101078_2018-08-12_final
2018-08-12 10:24:54	ga_ls7e_ard_3-0-0_101079_2018-08-12_final
2018-08-12 10:25:18	ga_ls7e_ard_3-0-0_101080_2018-08-12_final
2018-08-12 10:25:42	ga_ls7e_ard_3-0-0_101081_2018-08-12_final
2018-08-12 10:26:06	ga_ls7e_ard_3-0-0_101082_2018-08-12_final
2018-08-12 10:26:30	ga_ls7e_ard_3-0-0_101083_2018-08-12_final
2018-08-13 09:27:40	ga_ls7e_ard_3-0-0_092075_2018-08-12_final
2018-08-13 09:28:04	ga_ls7e_ard_3-0-0_092076_2018-08-12_final
2018-08-13 09:28:28	ga_ls7e_ard_3-0-0_092077_2018-08-12_final
2018-08-13 09:28:52	ga_ls7e_ard_3-0-0_092078_2018-08-12_final
2018-08-13 09:29:16	ga_ls7e_ard_3-0-0_092079_2018-08-12_final
2018-08-13 09:29:40	ga_ls7e_ard_3-0-0_092080_2018-08-12_final
2018-08-13 09:30:28	ga_ls7e_ard_3-0-0_092082_2018-08-13_final
2018-08-13 09:30:52	ga_ls7e_ard_3-0-0_092083_2018-08-13_final
2018-09-05 09:32:59	ga_ls7e_ard_3-0-0_093074_2018-09-05_final
2018-09-05 09:33:47	ga_ls7e_ard_3-0-0_093076_2018-09-05_final
2018-09-05 09:34:11	ga_ls7e_ard_3-0-0_093077_2018-09-05_final
2018-09-05 09:34:35	ga_ls7e_ard_3-0-0_093078_2018-09-05_final
2018-09-05 09:34:59	ga_ls7e_ard_3-0-0_093079_2018-09-05_final

Return to ga_ls7e_ard_3 across 2018 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

2018

All months