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
2018-01-02 10:11:17	ga_ls7e_ard_3-0-0_099067_2018-01-02_final
2018-01-02 10:11:41	ga_ls7e_ard_3-0-0_099068_2018-01-02_final
2018-01-02 10:12:05	ga_ls7e_ard_3-0-0_099069_2018-01-02_final
2018-01-02 10:12:29	ga_ls7e_ard_3-0-0_099070_2018-01-02_final
2018-01-03 09:19:38	ga_ls7e_ard_3-0-0_090077_2018-01-02_final
2018-01-03 09:20:02	ga_ls7e_ard_3-0-0_090078_2018-01-02_final
2018-01-03 09:20:26	ga_ls7e_ard_3-0-0_090079_2018-01-02_final
2018-01-03 09:20:50	ga_ls7e_ard_3-0-0_090080_2018-01-02_final
2018-01-03 09:21:14	ga_ls7e_ard_3-0-0_090081_2018-01-02_final
2018-01-03 09:21:38	ga_ls7e_ard_3-1-0_090082_2018-01-02_final
2018-01-03 09:22:02	ga_ls7e_ard_3-0-0_090083_2018-01-02_final
2018-01-03 09:22:26	ga_ls7e_ard_3-0-0_090084_2018-01-02_final
2018-01-03 09:22:50	ga_ls7e_ard_3-0-0_090085_2018-01-02_final
2018-01-03 09:23:14	ga_ls7e_ard_3-0-0_090086_2018-01-02_final
2018-01-03 09:24:02	ga_ls7e_ard_3-0-0_090088_2018-01-02_final
2018-01-03 09:24:26	ga_ls7e_ard_3-0-0_090089_2018-01-02_final
2018-01-03 09:24:50	ga_ls7e_ard_3-0-0_090090_2018-01-02_final
2018-01-27 10:05:55	ga_ls7e_ard_3-0-0_098070_2018-01-27_final
2018-01-27 10:06:43	ga_ls7e_ard_3-0-0_098072_2018-01-27_final
2018-01-27 10:07:07	ga_ls7e_ard_3-0-0_098073_2018-01-27_final
2018-01-27 10:07:31	ga_ls7e_ard_3-0-0_098074_2018-01-27_final
2018-01-27 10:07:55	ga_ls7e_ard_3-0-0_098075_2018-01-27_final
2018-01-27 10:08:19	ga_ls7e_ard_3-0-0_098076_2018-01-27_final
2018-01-27 10:08:43	ga_ls7e_ard_3-0-0_098077_2018-01-27_final
2018-01-27 10:09:07	ga_ls7e_ard_3-0-0_098078_2018-01-27_final
2018-01-27 10:09:31	ga_ls7e_ard_3-0-0_098079_2018-01-27_final
2018-01-27 10:09:55	ga_ls7e_ard_3-0-0_098080_2018-01-27_final
2018-01-27 10:10:19	ga_ls7e_ard_3-0-0_098081_2018-01-27_final
2018-01-27 10:10:43	ga_ls7e_ard_3-0-0_098082_2018-01-27_final
2018-01-27 10:11:07	ga_ls7e_ard_3-0-0_098083_2018-01-27_final
2018-01-27 10:11:31	ga_ls7e_ard_3-0-0_098084_2018-01-27_final
2018-01-27 10:11:55	ga_ls7e_ard_3-0-0_098085_2018-01-27_final
2018-01-27 11:46:24	ga_ls7e_ard_3-0-0_114074_2018-01-27_final
2018-01-27 11:46:48	ga_ls7e_ard_3-0-0_114075_2018-01-27_final
2018-01-27 11:47:12	ga_ls7e_ard_3-0-0_114076_2018-01-27_final
2018-01-27 11:47:36	ga_ls7e_ard_3-0-0_114077_2018-01-27_final
2018-01-27 11:48:00	ga_ls7e_ard_3-0-0_114078_2018-01-27_final
2018-01-27 11:48:24	ga_ls7e_ard_3-0-0_114079_2018-01-27_final
2018-01-27 11:48:48	ga_ls7e_ard_3-0-0_114080_2018-01-27_final
2018-01-28 09:13:29	ga_ls7e_ard_3-0-0_089078_2018-01-27_final
2018-01-28 09:13:53	ga_ls7e_ard_3-0-0_089079_2018-01-27_final
2018-01-28 09:14:17	ga_ls7e_ard_3-0-0_089080_2018-01-27_final
2018-01-28 09:15:04	ga_ls7e_ard_3-0-0_089082_2018-01-27_final
2018-01-28 09:15:28	ga_ls7e_ard_3-0-0_089083_2018-01-27_final
2018-01-28 09:15:52	ga_ls7e_ard_3-0-0_089084_2018-01-27_final
2018-01-28 09:16:40	ga_ls7e_ard_3-0-0_089086_2018-01-27_final
2018-01-28 10:51:10	ga_ls7e_ard_3-0-0_105075_2018-01-28_final
2018-01-28 10:51:34	ga_ls7e_ard_3-0-0_105076_2018-01-28_final
2018-01-28 10:51:58	ga_ls7e_ard_3-0-0_105077_2018-01-28_final
2018-01-28 10:52:22	ga_ls7e_ard_3-0-0_105078_2018-01-28_final
2018-01-28 10:52:46	ga_ls7e_ard_3-0-0_105079_2018-01-28_final
2018-01-28 10:53:09	ga_ls7e_ard_3-1-0_105080_2018-01-28_final
2018-01-29 09:53:32	ga_ls7e_ard_3-0-0_096070_2018-01-29_final
2018-01-29 09:53:56	ga_ls7e_ard_3-0-0_096071_2018-01-29_final
2018-01-29 09:54:20	ga_ls7e_ard_3-0-0_096072_2018-01-29_final
2018-01-29 09:54:44	ga_ls7e_ard_3-0-0_096073_2018-01-29_final
2018-01-29 09:55:08	ga_ls7e_ard_3-0-0_096074_2018-01-29_final
2018-01-29 09:55:31	ga_ls7e_ard_3-0-0_096075_2018-01-29_final
2018-01-29 09:55:55	ga_ls7e_ard_3-0-0_096076_2018-01-29_final
2018-01-29 09:56:19	ga_ls7e_ard_3-0-0_096077_2018-01-29_final
2018-01-29 09:56:43	ga_ls7e_ard_3-0-0_096078_2018-01-29_final
2018-01-29 09:57:07	ga_ls7e_ard_3-0-0_096079_2018-01-29_final
2018-01-29 09:57:31	ga_ls7e_ard_3-0-0_096080_2018-01-29_final
2018-01-29 09:57:55	ga_ls7e_ard_3-1-0_096081_2018-01-29_final
2018-01-29 09:58:19	ga_ls7e_ard_3-0-0_096082_2018-01-29_final
2018-01-29 09:59:07	ga_ls7e_ard_3-0-0_096084_2018-01-29_final
2018-01-29 09:59:31	ga_ls7e_ard_3-0-0_096085_2018-01-29_final
2018-01-29 11:31:13	ga_ls7e_ard_3-0-0_112067_2018-01-29_final
2018-01-29 11:34:48	ga_ls7e_ard_3-0-0_112076_2018-01-29_final
2018-01-29 11:35:12	ga_ls7e_ard_3-0-0_112077_2018-01-29_final
2018-01-29 11:35:36	ga_ls7e_ard_3-0-0_112078_2018-01-29_final
2018-01-29 11:36:00	ga_ls7e_ard_3-0-0_112079_2018-01-29_final
2018-01-29 11:36:24	ga_ls7e_ard_3-0-0_112080_2018-01-29_final
2018-01-29 11:36:48	ga_ls7e_ard_3-0-0_112081_2018-01-29_final
2018-01-29 11:37:12	ga_ls7e_ard_3-0-0_112082_2018-01-29_final
2018-01-29 11:37:36	ga_ls7e_ard_3-0-0_112083_2018-01-29_final
2018-01-29 11:38:00	ga_ls7e_ard_3-1-0_112084_2018-01-29_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

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

2018

January

All days