Search ga_ls7e_ard_3 across January 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
2018-01-08 09:41:42	ga_ls7e_ard_3-0-0_093086_2018-01-08_final
2018-01-08 09:42:06	ga_ls7e_ard_3-1-0_093087_2018-01-08_final
2018-01-08 09:42:30	ga_ls7e_ard_3-0-0_093088_2018-01-08_final
2018-01-08 11:14:36	ga_ls7e_ard_3-0-0_109071_2018-01-08_final
2018-01-08 11:15:00	ga_ls7e_ard_3-0-0_109072_2018-01-08_final
2018-01-08 11:15:24	ga_ls7e_ard_3-0-0_109073_2018-01-08_final
2018-01-08 11:15:48	ga_ls7e_ard_3-0-0_109074_2018-01-08_final
2018-01-08 11:16:12	ga_ls7e_ard_3-0-0_109075_2018-01-08_final
2018-01-08 11:16:36	ga_ls7e_ard_3-0-0_109076_2018-01-08_final
2018-01-08 11:16:59	ga_ls7e_ard_3-0-0_109077_2018-01-08_final
2018-01-08 11:17:23	ga_ls7e_ard_3-0-0_109078_2018-01-08_final
2018-01-08 11:17:47	ga_ls7e_ard_3-0-0_109079_2018-01-08_final
2018-01-08 11:18:11	ga_ls7e_ard_3-0-0_109080_2018-01-08_final
2018-01-08 11:18:35	ga_ls7e_ard_3-1-0_109081_2018-01-08_final
2018-01-08 11:18:59	ga_ls7e_ard_3-0-0_109082_2018-01-08_final
2018-01-08 11:19:23	ga_ls7e_ard_3-0-0_109083_2018-01-08_final
2018-01-08 11:19:47	ga_ls7e_ard_3-0-0_109084_2018-01-08_final
2018-01-09 10:18:58	ga_ls7e_ard_3-0-0_100071_2018-01-09_final
2018-01-09 10:19:22	ga_ls7e_ard_3-0-0_100072_2018-01-09_final
2018-01-09 10:19:46	ga_ls7e_ard_3-0-0_100073_2018-01-09_final
2018-01-09 10:20:09	ga_ls7e_ard_3-0-0_100074_2018-01-09_final
2018-01-09 10:20:33	ga_ls7e_ard_3-0-0_100075_2018-01-09_final
2018-01-09 10:20:57	ga_ls7e_ard_3-0-0_100076_2018-01-09_final
2018-01-09 10:21:21	ga_ls7e_ard_3-0-0_100077_2018-01-09_final
2018-01-09 10:21:45	ga_ls7e_ard_3-0-0_100078_2018-01-09_final
2018-01-09 10:22:09	ga_ls7e_ard_3-0-0_100079_2018-01-09_final
2018-01-09 10:22:33	ga_ls7e_ard_3-0-0_100080_2018-01-09_final
2018-01-09 10:22:57	ga_ls7e_ard_3-0-0_100081_2018-01-09_final
2018-01-09 10:23:21	ga_ls7e_ard_3-0-0_100082_2018-01-09_final
2018-01-09 10:23:45	ga_ls7e_ard_3-0-0_100083_2018-01-09_final
2018-01-09 10:24:09	ga_ls7e_ard_3-0-0_100084_2018-01-09_final
2018-01-10 09:25:19	ga_ls7e_ard_3-0-0_091076_2018-01-09_final
2018-01-10 09:25:43	ga_ls7e_ard_3-0-0_091077_2018-01-09_final
2018-01-10 09:26:07	ga_ls7e_ard_3-0-0_091078_2018-01-09_final
2018-01-10 09:26:31	ga_ls7e_ard_3-0-0_091079_2018-01-09_final
2018-01-10 09:26:55	ga_ls7e_ard_3-0-0_091080_2018-01-09_final
2018-01-10 09:27:19	ga_ls7e_ard_3-0-0_091081_2018-01-09_final
2018-01-10 09:27:43	ga_ls7e_ard_3-0-0_091082_2018-01-09_final
2018-01-10 09:28:07	ga_ls7e_ard_3-0-0_091083_2018-01-09_final
2018-01-10 09:28:31	ga_ls7e_ard_3-0-0_091084_2018-01-09_final
2018-01-10 09:28:55	ga_ls7e_ard_3-0-0_091085_2018-01-09_final
2018-01-10 09:29:19	ga_ls7e_ard_3-1-0_091086_2018-01-09_final
2018-01-10 09:29:43	ga_ls7e_ard_3-0-0_091087_2018-01-09_final
2018-01-10 09:30:31	ga_ls7e_ard_3-0-0_091089_2018-01-10_final
2018-01-10 09:30:55	ga_ls7e_ard_3-0-0_091090_2018-01-10_final
2018-01-10 11:04:36	ga_ls7e_ard_3-0-0_107077_2018-01-10_final
2018-01-10 11:05:00	ga_ls7e_ard_3-0-0_107078_2018-01-10_final
2018-01-10 11:05:24	ga_ls7e_ard_3-0-0_107079_2018-01-10_final
2018-01-10 11:05:48	ga_ls7e_ard_3-0-0_107080_2018-01-10_final
2018-01-10 11:06:12	ga_ls7e_ard_3-0-0_107081_2018-01-10_final
2018-01-10 11:07:00	ga_ls7e_ard_3-0-0_107083_2018-01-10_final
2018-01-10 11:07:24	ga_ls7e_ard_3-0-0_107084_2018-01-10_final
2018-01-11 10:04:59	ga_ls7e_ard_3-0-0_098067_2018-01-11_final
2018-01-11 10:05:23	ga_ls7e_ard_3-0-0_098068_2018-01-11_final
2018-01-11 10:05:46	ga_ls7e_ard_3-0-0_098069_2018-01-11_final
2018-01-11 10:06:10	ga_ls7e_ard_3-0-0_098070_2018-01-11_final
2018-01-11 10:06:34	ga_ls7e_ard_3-0-0_098071_2018-01-11_final
2018-01-11 10:06:58	ga_ls7e_ard_3-0-0_098072_2018-01-11_final
2018-01-11 10:07:22	ga_ls7e_ard_3-0-0_098073_2018-01-11_final
2018-01-11 10:07:46	ga_ls7e_ard_3-0-0_098074_2018-01-11_final
2018-01-11 10:08:10	ga_ls7e_ard_3-0-0_098075_2018-01-11_final
2018-01-11 10:08:34	ga_ls7e_ard_3-0-0_098076_2018-01-11_final
2018-01-11 10:08:58	ga_ls7e_ard_3-0-0_098077_2018-01-11_final
2018-01-11 10:09:22	ga_ls7e_ard_3-0-0_098078_2018-01-11_final
2018-01-11 10:09:46	ga_ls7e_ard_3-0-0_098079_2018-01-11_final
2018-01-11 10:10:10	ga_ls7e_ard_3-0-0_098080_2018-01-11_final
2018-01-11 10:10:34	ga_ls7e_ard_3-0-0_098081_2018-01-11_final
2018-01-11 10:10:58	ga_ls7e_ard_3-0-0_098082_2018-01-11_final
2018-01-11 10:11:22	ga_ls7e_ard_3-0-0_098083_2018-01-11_final
2018-01-11 10:11:46	ga_ls7e_ard_3-0-0_098084_2018-01-11_final
2018-01-11 10:12:10	ga_ls7e_ard_3-0-0_098085_2018-01-11_final
2018-01-11 11:46:39	ga_ls7e_ard_3-0-0_114074_2018-01-11_final
2018-01-11 11:47:03	ga_ls7e_ard_3-0-0_114075_2018-01-11_final
2018-01-11 11:47:27	ga_ls7e_ard_3-0-0_114076_2018-01-11_final
2018-01-11 11:47:51	ga_ls7e_ard_3-0-0_114077_2018-01-11_final
2018-01-11 11:48:15	ga_ls7e_ard_3-0-0_114078_2018-01-11_final
2018-01-11 11:48:39	ga_ls7e_ard_3-0-0_114079_2018-01-11_final
2018-01-30 10:38:22	ga_ls7e_ard_3-0-0_103074_2018-01-30_final
2018-01-30 10:38:46	ga_ls7e_ard_3-0-0_103075_2018-01-30_final
2018-01-30 10:39:10	ga_ls7e_ard_3-0-0_103076_2018-01-30_final
2018-01-30 10:39:34	ga_ls7e_ard_3-0-0_103077_2018-01-30_final
2018-01-30 10:39:58	ga_ls7e_ard_3-0-0_103078_2018-01-30_final
2018-01-31 09:39:56	ga_ls7e_ard_3-1-0_094067_2018-01-31_final
2018-01-31 09:42:20	ga_ls7e_ard_3-0-0_094073_2018-01-31_final
2018-01-31 09:42:44	ga_ls7e_ard_3-0-0_094074_2018-01-31_final
2018-01-31 09:43:08	ga_ls7e_ard_3-0-0_094075_2018-01-31_final
2018-01-31 09:43:32	ga_ls7e_ard_3-0-0_094076_2018-01-31_final
2018-01-31 09:43:56	ga_ls7e_ard_3-0-0_094077_2018-01-31_final
2018-01-31 09:44:20	ga_ls7e_ard_3-0-0_094078_2018-01-31_final
2018-01-31 09:44:44	ga_ls7e_ard_3-0-0_094079_2018-01-31_final
2018-01-31 09:45:08	ga_ls7e_ard_3-0-0_094080_2018-01-31_final
2018-01-31 09:45:55	ga_ls7e_ard_3-0-0_094082_2018-01-31_final
2018-01-31 09:46:19	ga_ls7e_ard_3-1-0_094083_2018-01-31_final
2018-01-31 09:46:43	ga_ls7e_ard_3-0-0_094084_2018-01-31_final
2018-01-31 09:47:07	ga_ls7e_ard_3-0-0_094085_2018-01-31_final
2018-01-31 09:47:31	ga_ls7e_ard_3-0-0_094086_2018-01-31_final
2018-01-31 09:47:55	ga_ls7e_ard_3-0-0_094087_2018-01-31_final
2018-01-31 11:24:24	ga_ls7e_ard_3-0-0_110081_2018-01-31_final
2018-01-31 11:24:48	ga_ls7e_ard_3-0-0_110082_2018-01-31_final
2018-01-31 11:25:12	ga_ls7e_ard_3-0-0_110083_2018-01-31_final

Return to ga_ls7e_ard_3 across January 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

January

All days