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
2018-02-19 09:28:39	ga_ls8c_ard_3-0-0_091090_2018-02-18_final
2018-04-01 09:21:44	ga_ls8c_ard_3-0-0_090089_2018-03-31_final
2018-04-01 09:22:08	ga_ls8c_ard_3-0-0_090090_2018-03-31_final
2018-04-10 09:15:28	ga_ls8c_ard_3-0-0_089089_2018-04-09_final
2018-04-10 09:15:52	ga_ls8c_ard_3-0-0_089090_2018-04-09_final
2018-04-17 09:21:35	ga_ls8c_ard_3-0-0_090089_2018-04-16_final
2018-04-17 09:21:59	ga_ls8c_ard_3-0-0_090090_2018-04-16_final
2018-05-03 09:21:26	ga_ls8c_ard_3-0-0_090089_2018-05-02_final
2018-05-03 09:21:50	ga_ls8c_ard_3-0-0_090090_2018-05-02_final
2018-06-04 09:21:04	ga_ls8c_ard_3-0-0_090089_2018-06-03_final
2018-06-04 09:21:28	ga_ls8c_ard_3-0-0_090090_2018-06-03_final
2018-06-13 09:14:58	ga_ls8c_ard_3-0-0_089089_2018-06-12_final
2018-06-13 09:15:22	ga_ls8c_ard_3-0-0_089090_2018-06-12_final
2018-06-29 09:15:09	ga_ls8c_ard_3-0-0_089089_2018-06-28_final
2018-06-29 09:15:33	ga_ls8c_ard_3-0-0_089090_2018-06-28_final
2018-07-06 09:21:23	ga_ls8c_ard_3-0-0_090089_2018-07-05_final
2018-07-06 09:21:47	ga_ls8c_ard_3-0-0_090090_2018-07-05_final
2018-07-29 09:27:44	ga_ls8c_ard_3-0-0_091089_2018-07-28_final
2018-07-29 09:28:08	ga_ls8c_ard_3-0-0_091090_2018-07-28_final
2018-08-05 09:33:59	ga_ls8c_ard_3-0-0_092089_2018-08-05_final
2018-08-05 09:34:23	ga_ls8c_ard_3-0-0_092090_2018-08-05_final
2018-08-30 09:28:01	ga_ls8c_ard_3-0-0_091089_2018-08-29_final
2018-08-30 09:28:24	ga_ls8c_ard_3-0-0_091090_2018-08-29_final
2018-09-01 09:15:40	ga_ls8c_ard_3-0-0_089089_2018-08-31_final
2018-09-01 09:16:04	ga_ls8c_ard_3-0-0_089090_2018-08-31_final
2018-09-08 09:21:53	ga_ls8c_ard_3-0-0_090089_2018-09-07_final
2018-09-08 09:22:17	ga_ls8c_ard_3-0-0_090090_2018-09-07_final
2018-09-15 09:28:05	ga_ls8c_ard_3-0-0_091089_2018-09-14_final
2018-09-15 09:28:29	ga_ls8c_ard_3-0-0_091090_2018-09-14_final
2018-09-17 09:15:44	ga_ls8c_ard_3-0-0_089089_2018-09-16_final
2018-09-24 09:21:58	ga_ls8c_ard_3-0-0_090089_2018-09-23_final
2018-09-24 09:22:22	ga_ls8c_ard_3-0-0_090090_2018-09-23_final
2018-10-17 09:28:19	ga_ls8c_ard_3-0-0_091089_2018-10-16_final
2018-10-17 09:28:43	ga_ls8c_ard_3-0-0_091090_2018-10-16_final
2018-10-19 09:15:58	ga_ls8c_ard_3-0-0_089089_2018-10-18_final
2018-10-19 09:16:22	ga_ls8c_ard_3-0-0_089090_2018-10-18_final
2018-10-26 09:22:11	ga_ls8c_ard_3-0-0_090089_2018-10-25_final
2018-10-26 09:22:35	ga_ls8c_ard_3-0-0_090090_2018-10-25_final
2018-11-02 09:28:23	ga_ls8c_ard_3-0-0_091089_2018-11-01_final
2018-11-02 09:28:47	ga_ls8c_ard_3-0-0_091090_2018-11-01_final
2018-11-04 09:16:02	ga_ls8c_ard_3-0-0_089089_2018-11-03_final
2018-11-04 09:16:26	ga_ls8c_ard_3-0-0_089090_2018-11-03_final
2018-11-09 09:34:35	ga_ls8c_ard_3-0-0_092089_2018-11-09_final
2018-11-09 09:34:59	ga_ls8c_ard_3-0-0_092090_2018-11-09_final
2018-11-11 09:22:13	ga_ls8c_ard_3-0-0_090089_2018-11-10_final
2018-11-11 09:22:37	ga_ls8c_ard_3-0-0_090090_2018-11-10_final
2018-11-18 09:28:24	ga_ls8c_ard_3-0-0_091089_2018-11-17_final
2018-11-18 09:28:48	ga_ls8c_ard_3-0-0_091090_2018-11-17_final
2018-11-20 09:16:03	ga_ls8c_ard_3-0-0_089089_2018-11-19_final
2018-11-20 09:16:27	ga_ls8c_ard_3-0-0_089090_2018-11-19_final
2018-11-25 09:34:35	ga_ls8c_ard_3-0-0_092089_2018-11-25_final
2018-11-25 09:34:59	ga_ls8c_ard_3-0-0_092090_2018-11-25_final
2018-11-27 09:22:13	ga_ls8c_ard_3-0-0_090089_2018-11-26_final
2018-11-27 09:22:37	ga_ls8c_ard_3-0-0_090090_2018-11-26_final
2018-12-04 09:28:22	ga_ls8c_ard_3-0-0_091089_2018-12-03_final
2018-12-04 09:28:46	ga_ls8c_ard_3-0-0_091090_2018-12-03_final
2018-12-06 09:16:00	ga_ls8c_ard_3-0-0_089089_2018-12-05_final
2018-12-06 09:16:24	ga_ls8c_ard_3-0-0_089090_2018-12-05_final
2018-12-11 09:34:32	ga_ls8c_ard_3-0-0_092089_2018-12-11_final
2018-12-11 09:34:56	ga_ls8c_ard_3-0-0_092090_2018-12-11_final
2018-12-13 09:22:09	ga_ls8c_ard_3-0-0_090089_2018-12-12_final
2018-12-13 09:22:33	ga_ls8c_ard_3-0-0_090090_2018-12-12_final
2018-12-22 09:15:59	ga_ls8c_ard_3-0-0_089089_2018-12-21_final
2018-12-22 09:16:23	ga_ls8c_ard_3-0-0_089090_2018-12-21_final
2018-12-27 09:34:31	ga_ls8c_ard_3-0-0_092089_2018-12-27_final
2018-12-27 09:34:55	ga_ls8c_ard_3-0-0_092090_2018-12-27_final
2018-12-29 09:22:10	ga_ls8c_ard_3-0-0_090089_2018-12-28_final
2018-12-29 09:22:34	ga_ls8c_ard_3-0-0_090090_2018-12-28_final
2019-01-05 09:28:20	ga_ls8c_ard_3-0-0_091089_2019-01-04_final
2019-01-05 09:28:44	ga_ls8c_ard_3-0-0_091090_2019-01-04_final
2019-01-07 09:16:22	ga_ls8c_ard_3-0-0_089090_2019-01-06_final
2019-01-12 09:34:30	ga_ls8c_ard_3-0-0_092089_2019-01-12_final
2019-01-12 09:34:54	ga_ls8c_ard_3-0-0_092090_2019-01-12_final
2019-01-14 09:22:08	ga_ls8c_ard_3-0-0_090089_2019-01-13_final
2019-01-14 09:22:32	ga_ls8c_ard_3-0-0_090090_2019-01-13_final
2019-01-21 09:28:41	ga_ls8c_ard_3-0-0_091090_2019-01-20_final
2019-01-28 09:34:26	ga_ls8c_ard_3-0-0_092089_2019-01-28_final
2019-01-28 09:34:50	ga_ls8c_ard_3-0-0_092090_2019-01-28_final
2022-10-05 09:22:54	ga_ls8c_ard_3-2-1_090089_2022-10-04_final
2022-10-05 09:23:18	ga_ls8c_ard_3-2-1_090090_2022-10-04_final
2022-10-12 09:28:59	ga_ls8c_ard_3-2-1_091089_2022-10-11_final
2022-10-12 09:29:23	ga_ls8c_ard_3-2-1_091090_2022-10-11_final
2022-10-14 09:16:36	ga_ls8c_ard_3-2-1_089089_2022-10-13_final
2022-10-14 09:17:00	ga_ls8c_ard_3-2-1_089090_2022-10-13_final
2022-10-19 09:35:12	ga_ls8c_ard_3-2-1_092089_2022-10-19_final
2022-10-19 09:35:36	ga_ls8c_ard_3-2-1_092090_2022-10-19_final
2022-10-21 09:22:52	ga_ls8c_ard_3-2-1_090089_2022-10-20_final
2022-10-21 09:23:16	ga_ls8c_ard_3-2-1_090090_2022-10-20_final
2022-10-28 09:29:06	ga_ls8c_ard_3-2-1_091089_2022-10-27_final
2022-10-28 09:29:30	ga_ls8c_ard_3-2-1_091090_2022-10-27_final
2022-10-30 09:16:45	ga_ls8c_ard_3-2-1_089089_2022-10-29_final
2022-10-30 09:17:09	ga_ls8c_ard_3-2-1_089090_2022-10-29_final
2023-04-29 09:34:32	ga_ls8c_ard_3-2-1_092090_2023-04-29_final
2023-05-01 09:22:10	ga_ls8c_ard_3-2-1_090090_2023-04-30_final
2023-05-08 09:28:15	ga_ls8c_ard_3-2-1_091090_2023-05-07_final
2023-05-10 09:15:51	ga_ls8c_ard_3-2-1_089090_2023-05-09_final
2023-05-15 09:34:19	ga_ls8c_ard_3-2-1_092090_2023-05-15_final
2023-05-17 09:21:33	ga_ls8c_ard_3-2-1_090089_2023-05-16_final
2023-05-17 09:21:57	ga_ls8c_ard_3-2-1_090090_2023-05-16_final
2023-05-24 09:27:46	ga_ls8c_ard_3-2-1_091089_2023-05-23_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

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

All years