Search ga_ls7e_ard_3 across June 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
2018-06-14 10:40:09	ga_ls7e_ard_3-0-0_104068_2018-06-14_final
2018-06-14 10:40:33	ga_ls7e_ard_3-0-0_104069_2018-06-14_final
2018-06-14 10:40:57	ga_ls7e_ard_3-1-0_104070_2018-06-14_final
2018-06-14 10:41:21	ga_ls7e_ard_3-0-0_104071_2018-06-14_final
2018-06-14 10:41:45	ga_ls7e_ard_3-0-0_104072_2018-06-14_final
2018-06-14 10:42:09	ga_ls7e_ard_3-0-0_104073_2018-06-14_final
2018-06-14 10:42:33	ga_ls7e_ard_3-0-0_104074_2018-06-14_final
2018-06-14 10:42:57	ga_ls7e_ard_3-0-0_104075_2018-06-14_final
2018-06-14 10:43:21	ga_ls7e_ard_3-0-0_104076_2018-06-14_final
2018-06-14 10:43:45	ga_ls7e_ard_3-0-0_104077_2018-06-14_final
2018-06-14 10:44:09	ga_ls7e_ard_3-0-0_104078_2018-06-14_final
2018-06-14 10:44:33	ga_ls7e_ard_3-1-0_104079_2018-06-14_final
2018-06-14 10:44:57	ga_ls7e_ard_3-0-0_104080_2018-06-14_final
2018-06-14 10:45:21	ga_ls7e_ard_3-0-0_104081_2018-06-14_final
2018-06-14 10:45:45	ga_ls7e_ard_3-0-0_104082_2018-06-14_final
2018-06-15 09:44:07	ga_ls7e_ard_3-0-0_095067_2018-06-15_final
2018-06-15 09:46:07	ga_ls7e_ard_3-0-0_095072_2018-06-15_final
2018-06-15 09:46:31	ga_ls7e_ard_3-1-0_095073_2018-06-15_final
2018-06-15 09:46:55	ga_ls7e_ard_3-0-0_095074_2018-06-15_final
2018-06-15 09:47:19	ga_ls7e_ard_3-0-0_095075_2018-06-15_final
2018-06-15 09:47:43	ga_ls7e_ard_3-0-0_095076_2018-06-15_final
2018-06-15 09:48:06	ga_ls7e_ard_3-0-0_095077_2018-06-15_final
2018-06-15 09:48:30	ga_ls7e_ard_3-0-0_095078_2018-06-15_final
2018-06-15 09:48:54	ga_ls7e_ard_3-1-0_095079_2018-06-15_final
2018-06-15 09:49:18	ga_ls7e_ard_3-0-0_095080_2018-06-15_final
2018-06-15 09:49:42	ga_ls7e_ard_3-0-0_095081_2018-06-15_final
2018-06-15 09:50:06	ga_ls7e_ard_3-0-0_095082_2018-06-15_final
2018-06-15 09:50:30	ga_ls7e_ard_3-0-0_095083_2018-06-15_final
2018-06-15 09:50:54	ga_ls7e_ard_3-0-0_095084_2018-06-15_final
2018-06-15 09:51:18	ga_ls7e_ard_3-0-0_095085_2018-06-15_final
2018-06-15 09:51:42	ga_ls7e_ard_3-0-0_095086_2018-06-15_final
2018-06-15 11:23:00	ga_ls7e_ard_3-0-0_111067_2018-06-15_final
2018-06-15 11:23:24	ga_ls7e_ard_3-0-0_111068_2018-06-15_final
2018-06-15 11:24:36	ga_ls7e_ard_3-0-0_111071_2018-06-15_final
2018-06-15 11:25:00	ga_ls7e_ard_3-0-0_111072_2018-06-15_final
2018-06-15 11:25:24	ga_ls7e_ard_3-0-0_111073_2018-06-15_final
2018-06-15 11:25:48	ga_ls7e_ard_3-0-0_111074_2018-06-15_final
2018-06-15 11:26:12	ga_ls7e_ard_3-0-0_111075_2018-06-15_final
2018-06-15 11:26:36	ga_ls7e_ard_3-0-0_111076_2018-06-15_final
2018-06-15 11:26:59	ga_ls7e_ard_3-0-0_111077_2018-06-15_final
2018-06-15 11:27:23	ga_ls7e_ard_3-0-0_111078_2018-06-15_final
2018-06-15 11:27:47	ga_ls7e_ard_3-0-0_111079_2018-06-15_final
2018-06-15 11:28:11	ga_ls7e_ard_3-0-0_111080_2018-06-15_final
2018-06-15 11:28:35	ga_ls7e_ard_3-0-0_111081_2018-06-15_final
2018-06-15 11:28:59	ga_ls7e_ard_3-1-0_111082_2018-06-15_final
2018-06-15 11:29:23	ga_ls7e_ard_3-0-0_111083_2018-06-15_final
2018-06-15 11:29:47	ga_ls7e_ard_3-0-0_111084_2018-06-15_final
2018-06-16 10:27:46	ga_ls7e_ard_3-0-0_102068_2018-06-16_final
2018-06-16 10:28:10	ga_ls7e_ard_3-0-0_102069_2018-06-16_final
2018-06-16 10:28:33	ga_ls7e_ard_3-0-0_102070_2018-06-16_final
2018-06-16 10:28:57	ga_ls7e_ard_3-0-0_102071_2018-06-16_final
2018-06-16 10:29:21	ga_ls7e_ard_3-0-0_102072_2018-06-16_final
2018-06-16 10:29:45	ga_ls7e_ard_3-1-0_102073_2018-06-16_final
2018-06-16 10:30:09	ga_ls7e_ard_3-0-0_102074_2018-06-16_final
2018-06-16 10:30:33	ga_ls7e_ard_3-0-0_102075_2018-06-16_final
2018-06-16 10:30:57	ga_ls7e_ard_3-0-0_102076_2018-06-16_final
2018-06-16 10:31:21	ga_ls7e_ard_3-0-0_102077_2018-06-16_final
2018-06-16 10:31:45	ga_ls7e_ard_3-0-0_102078_2018-06-16_final
2018-06-16 10:32:09	ga_ls7e_ard_3-0-0_102079_2018-06-16_final
2018-06-16 10:32:33	ga_ls7e_ard_3-0-0_102080_2018-06-16_final
2018-06-16 10:32:57	ga_ls7e_ard_3-0-0_102081_2018-06-16_final
2018-06-16 10:33:21	ga_ls7e_ard_3-0-0_102082_2018-06-16_final
2018-06-17 09:34:31	ga_ls7e_ard_3-0-0_093074_2018-06-17_final
2018-06-17 09:34:55	ga_ls7e_ard_3-0-0_093075_2018-06-17_final
2018-06-17 09:35:19	ga_ls7e_ard_3-0-0_093076_2018-06-17_final
2018-06-17 09:35:43	ga_ls7e_ard_3-0-0_093077_2018-06-17_final
2018-06-17 09:36:07	ga_ls7e_ard_3-0-0_093078_2018-06-17_final
2018-06-17 09:36:31	ga_ls7e_ard_3-0-0_093079_2018-06-17_final
2018-06-17 09:36:54	ga_ls7e_ard_3-0-0_093080_2018-06-17_final
2018-06-17 09:37:18	ga_ls7e_ard_3-0-0_093081_2018-06-17_final
2018-06-17 09:37:42	ga_ls7e_ard_3-0-0_093082_2018-06-17_final
2018-06-17 09:38:06	ga_ls7e_ard_3-0-0_093083_2018-06-17_final
2018-06-17 09:38:30	ga_ls7e_ard_3-0-0_093084_2018-06-17_final
2018-06-17 09:38:54	ga_ls7e_ard_3-0-0_093085_2018-06-17_final
2018-06-17 09:39:42	ga_ls7e_ard_3-1-0_093087_2018-06-17_final
2018-06-17 09:40:06	ga_ls7e_ard_3-0-0_093088_2018-06-17_final
2018-06-17 11:11:48	ga_ls7e_ard_3-0-0_109070_2018-06-17_final
2018-06-17 11:12:12	ga_ls7e_ard_3-0-0_109071_2018-06-17_final
2018-06-17 11:12:36	ga_ls7e_ard_3-0-0_109072_2018-06-17_final
2018-06-17 11:13:00	ga_ls7e_ard_3-0-0_109073_2018-06-17_final
2018-06-17 11:13:24	ga_ls7e_ard_3-0-0_109074_2018-06-17_final
2018-06-17 11:13:48	ga_ls7e_ard_3-0-0_109075_2018-06-17_final
2018-06-17 11:14:12	ga_ls7e_ard_3-0-0_109076_2018-06-17_final
2018-06-17 11:14:36	ga_ls7e_ard_3-0-0_109077_2018-06-17_final
2018-06-17 11:15:00	ga_ls7e_ard_3-0-0_109078_2018-06-17_final
2018-06-17 11:15:23	ga_ls7e_ard_3-0-0_109079_2018-06-17_final
2018-06-17 11:15:47	ga_ls7e_ard_3-0-0_109080_2018-06-17_final
2018-06-17 11:16:11	ga_ls7e_ard_3-1-0_109081_2018-06-17_final
2018-06-17 11:16:35	ga_ls7e_ard_3-0-0_109082_2018-06-17_final
2018-06-17 11:16:59	ga_ls7e_ard_3-0-0_109083_2018-06-17_final
2018-06-17 11:17:23	ga_ls7e_ard_3-0-0_109084_2018-06-17_final
2018-06-19 09:22:55	ga_ls7e_ard_3-0-0_091076_2018-06-18_final
2018-06-19 09:23:19	ga_ls7e_ard_3-0-0_091077_2018-06-18_final
2018-06-19 09:23:43	ga_ls7e_ard_3-0-0_091078_2018-06-18_final
2018-06-19 09:24:07	ga_ls7e_ard_3-0-0_091079_2018-06-18_final
2018-06-19 09:24:31	ga_ls7e_ard_3-0-0_091080_2018-06-18_final
2018-06-21 09:11:19	ga_ls7e_ard_3-0-0_089078_2018-06-20_final
2018-06-21 09:11:43	ga_ls7e_ard_3-0-0_089079_2018-06-20_final
2018-06-21 09:12:07	ga_ls7e_ard_3-0-0_089080_2018-06-20_final
2018-06-21 09:12:31	ga_ls7e_ard_3-1-0_089081_2018-06-20_final

Return to ga_ls7e_ard_3 across June 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

June

All days