Search ga_ls8c_ard_3 whole collection

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
2018-01-30 09:51:30	ga_ls8c_ard_3-0-0_095085_2018-01-30_final
2018-01-30 11:30:23	ga_ls8c_ard_3-0-0_111085_2018-01-30_final
2018-02-01 09:39:07	ga_ls8c_ard_3-0-0_093085_2018-02-01_final
2018-02-01 11:18:00	ga_ls8c_ard_3-0-0_109085_2018-02-01_final
2018-02-02 10:22:22	ga_ls8c_ard_3-0-0_100085_2018-02-02_final
2018-02-19 09:26:39	ga_ls8c_ard_3-0-0_091085_2018-02-18_final
2018-02-19 11:05:32	ga_ls8c_ard_3-0-0_107085_2018-02-19_final
2018-02-26 09:32:47	ga_ls8c_ard_3-0-0_092085_2018-02-26_final
2018-02-26 11:11:40	ga_ls8c_ard_3-0-0_108085_2018-02-26_final
2018-02-27 10:16:02	ga_ls8c_ard_3-0-0_099085_2018-02-27_final
2018-02-28 09:20:24	ga_ls8c_ard_3-0-0_090085_2018-02-27_final
2018-03-01 10:03:39	ga_ls8c_ard_3-0-0_097085_2018-03-01_final
2018-03-05 11:17:47	ga_ls8c_ard_3-0-0_109085_2018-03-05_final
2018-03-06 10:22:09	ga_ls8c_ard_3-0-0_100085_2018-03-06_final
2018-03-07 09:26:31	ga_ls8c_ard_3-0-0_091085_2018-03-06_final
2018-03-07 11:05:24	ga_ls8c_ard_3-0-0_107085_2018-03-07_final
2018-03-21 11:17:39	ga_ls8c_ard_3-0-0_109085_2018-03-21_final
2018-03-22 10:22:01	ga_ls8c_ard_3-0-0_100085_2018-03-22_final
2018-03-23 09:26:23	ga_ls8c_ard_3-0-0_091085_2018-03-22_final
2018-03-23 11:05:16	ga_ls8c_ard_3-0-0_107085_2018-03-23_final
2018-03-30 09:32:31	ga_ls8c_ard_3-0-0_092085_2018-03-30_final
2018-03-30 11:11:24	ga_ls8c_ard_3-0-0_108085_2018-03-30_final
2018-03-31 10:15:46	ga_ls8c_ard_3-0-0_099085_2018-03-31_final
2018-04-01 09:20:08	ga_ls8c_ard_3-0-0_090085_2018-03-31_final
2018-04-02 10:03:23	ga_ls8c_ard_3-0-0_097085_2018-04-02_final
2018-04-08 09:26:15	ga_ls8c_ard_3-0-0_091085_2018-04-07_final
2018-04-08 11:05:08	ga_ls8c_ard_3-0-0_107085_2018-04-08_final
2018-04-11 09:57:08	ga_ls8c_ard_3-0-0_096085_2018-04-11_final
2018-04-11 11:36:01	ga_ls8c_ard_3-0-0_112085_2018-04-11_final
2018-04-13 09:44:45	ga_ls8c_ard_3-0-0_094085_2018-04-13_final
2018-04-13 11:23:38	ga_ls8c_ard_3-0-0_110085_2018-04-13_final
2018-04-22 09:38:29	ga_ls8c_ard_3-0-0_093085_2018-04-22_final
2018-04-22 11:17:22	ga_ls8c_ard_3-0-0_109085_2018-04-22_final
2018-04-23 10:21:44	ga_ls8c_ard_3-0-0_100085_2018-04-23_final
2018-04-24 09:26:06	ga_ls8c_ard_3-0-0_091085_2018-04-23_final
2018-04-24 11:04:59	ga_ls8c_ard_3-0-0_107085_2018-04-24_final
2018-04-25 10:09:21	ga_ls8c_ard_3-0-0_098085_2018-04-25_final
2018-04-26 09:13:43	ga_ls8c_ard_3-0-0_089085_2018-04-25_final
2018-04-27 09:56:58	ga_ls8c_ard_3-0-0_096085_2018-04-27_final
2018-04-27 11:35:52	ga_ls8c_ard_3-0-0_112085_2018-04-27_final
2018-05-02 10:15:28	ga_ls8c_ard_3-0-0_099085_2018-05-02_final
2018-05-03 09:19:50	ga_ls8c_ard_3-0-0_090085_2018-05-02_final
2018-05-04 10:03:05	ga_ls8c_ard_3-0-0_097085_2018-05-04_final
2018-05-18 10:15:17	ga_ls8c_ard_3-0-0_099085_2018-05-18_final
2018-05-19 09:19:39	ga_ls8c_ard_3-0-0_090085_2018-05-18_final
2018-05-20 10:02:54	ga_ls8c_ard_3-0-0_097085_2018-05-20_final
2018-09-16 10:09:45	ga_ls8c_ard_3-0-0_098085_2018-09-16_final
2018-09-17 09:14:08	ga_ls8c_ard_3-0-0_089085_2018-09-16_final
2019-01-06 10:10:00	ga_ls8c_ard_3-0-0_098085_2019-01-06_final
2019-01-07 09:14:22	ga_ls8c_ard_3-0-0_089085_2019-01-06_final
2022-06-18 09:51:47	ga_ls8c_ard_3-2-1_095085_2022-06-18_final
2022-07-15 11:11:45	ga_ls8c_ard_3-2-1_108084_2022-07-15_final
2022-07-16 10:16:32	ga_ls8c_ard_3-2-1_099085_2022-07-16_final
2022-07-17 09:20:56	ga_ls8c_ard_3-2-1_090085_2022-07-16_final
2022-07-18 10:04:12	ga_ls8c_ard_3-2-1_097085_2022-07-18_final
2022-07-20 09:51:52	ga_ls8c_ard_3-2-1_095085_2022-07-20_final
2022-07-22 09:39:32	ga_ls8c_ard_3-2-1_093085_2022-07-22_final
2022-07-24 09:27:12	ga_ls8c_ard_3-2-1_091085_2022-07-23_final
2022-07-25 10:10:29	ga_ls8c_ard_3-2-1_098085_2022-07-25_final
2022-07-26 09:14:52	ga_ls8c_ard_3-2-1_089085_2022-07-25_final
2022-07-27 09:58:09	ga_ls8c_ard_3-2-1_096085_2022-07-27_final
2022-07-29 09:45:48	ga_ls8c_ard_3-2-1_094085_2022-07-29_final
2022-07-29 11:24:41	ga_ls8c_ard_3-2-1_110085_2022-07-29_final
2022-07-31 09:33:28	ga_ls8c_ard_3-2-1_092085_2022-07-31_final
2022-07-31 11:11:57	ga_ls8c_ard_3-2-1_108084_2022-07-31_final
2022-08-01 10:16:44	ga_ls8c_ard_3-2-1_099085_2022-08-01_final
2022-08-02 09:21:07	ga_ls8c_ard_3-2-1_090085_2022-08-01_final
2022-08-03 10:04:00	ga_ls8c_ard_3-2-1_097084_2022-08-03_final
2022-08-03 10:04:24	ga_ls8c_ard_3-2-1_097085_2022-08-03_final
2022-08-05 09:52:03	ga_ls8c_ard_3-2-1_095085_2022-08-05_final
2022-08-19 10:04:29	ga_ls8c_ard_3-2-1_097085_2022-08-19_final
2022-08-21 09:52:08	ga_ls8c_ard_3-2-1_095085_2022-08-21_final
2022-10-19 09:33:37	ga_ls8c_ard_3-2-1_092085_2022-10-19_final
2022-10-20 10:16:53	ga_ls8c_ard_3-2-1_099085_2022-10-20_final
2022-10-21 09:21:16	ga_ls8c_ard_3-2-1_090085_2022-10-20_final
2022-10-22 10:04:32	ga_ls8c_ard_3-2-1_097085_2022-10-22_final
2022-10-24 09:52:12	ga_ls8c_ard_3-2-1_095085_2022-10-24_final
2022-10-26 09:39:51	ga_ls8c_ard_3-2-1_093085_2022-10-26_final
2022-10-28 09:27:30	ga_ls8c_ard_3-2-1_091085_2022-10-27_final
2022-10-29 10:10:46	ga_ls8c_ard_3-2-1_098085_2022-10-29_final
2022-10-30 09:15:09	ga_ls8c_ard_3-2-1_089085_2022-10-29_final
2022-10-31 09:58:25	ga_ls8c_ard_3-2-1_096085_2022-10-31_final
2022-11-02 09:46:03	ga_ls8c_ard_3-2-1_094085_2022-11-02_final
2022-11-02 11:24:56	ga_ls8c_ard_3-2-1_110085_2022-11-02_final
2022-11-04 09:33:42	ga_ls8c_ard_3-2-1_092085_2022-11-04_final
2022-11-05 10:16:57	ga_ls8c_ard_3-2-1_099085_2022-11-05_final
2022-11-06 09:21:20	ga_ls8c_ard_3-2-1_090085_2022-11-05_final
2022-11-07 10:04:35	ga_ls8c_ard_3-2-1_097085_2022-11-07_final
2022-11-11 09:39:50	ga_ls8c_ard_3-2-1_093085_2022-11-11_final
2022-11-13 09:27:27	ga_ls8c_ard_3-2-1_091085_2022-11-12_final
2022-11-14 10:10:42	ga_ls8c_ard_3-2-1_098085_2022-11-14_final
2023-01-07 09:33:27	ga_ls8c_ard_3-2-1_092085_2023-01-07_final
2023-01-08 10:16:43	ga_ls8c_ard_3-2-1_099085_2023-01-08_final
2023-01-26 10:04:19	ga_ls8c_ard_3-2-1_097085_2023-01-26_final
2023-07-07 11:29:45	ga_ls8c_ard_3-2-1_111084_2023-07-07_final
2023-07-09 09:38:55	ga_ls8c_ard_3-2-1_093085_2023-07-09_final
2023-07-14 09:57:05	ga_ls8c_ard_3-2-1_096084_2023-07-14_final
2023-07-14 09:57:29	ga_ls8c_ard_3-2-1_096085_2023-07-14_final
2023-07-16 09:44:43	ga_ls8c_ard_3-2-1_094084_2023-07-16_final
2023-07-16 09:45:07	ga_ls8c_ard_3-2-1_094085_2023-07-16_final

Return to ga_ls8c_ard_3 whole collection overview

ga_ls8c_ard_3: Geoscience Australia Landsat 8 Operational Land Imager and Thermal Infra-Red Scanner Analysis Ready Data Collection 3

Baseline satellite data

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

All years