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
2018-02-17 09:31:51	ga_ls8c_ard_3-0-0_093067_2018-02-17_final
2018-02-17 09:32:15	ga_ls8c_ard_3-0-0_093068_2018-02-17_final
2018-04-10 09:13:05	ga_ls8c_ard_3-0-0_089083_2018-04-09_final
2018-04-10 09:13:29	ga_ls8c_ard_3-0-0_089084_2018-04-09_final
2018-04-10 09:13:53	ga_ls8c_ard_3-0-0_089085_2018-04-09_final
2018-04-10 09:14:17	ga_ls8c_ard_3-0-0_089086_2018-04-09_final
2018-04-10 09:14:41	ga_ls8c_ard_3-0-0_089087_2018-04-09_final
2018-04-10 09:15:05	ga_ls8c_ard_3-0-0_089088_2018-04-09_final
2018-04-17 09:17:35	ga_ls8c_ard_3-0-0_090079_2018-04-16_final
2018-04-17 09:17:59	ga_ls8c_ard_3-0-0_090080_2018-04-16_final
2018-04-17 09:18:23	ga_ls8c_ard_3-0-0_090081_2018-04-16_final
2018-04-17 09:18:47	ga_ls8c_ard_3-0-0_090082_2018-04-16_final
2018-04-17 09:19:11	ga_ls8c_ard_3-0-0_090083_2018-04-16_final
2018-04-17 09:19:35	ga_ls8c_ard_3-0-0_090084_2018-04-16_final
2018-04-22 09:31:18	ga_ls8c_ard_3-0-0_093067_2018-04-22_final
2018-04-22 09:31:42	ga_ls8c_ard_3-0-0_093068_2018-04-22_final
2018-05-28 09:12:33	ga_ls8c_ard_3-0-0_089083_2018-05-27_final
2018-05-28 09:12:57	ga_ls8c_ard_3-0-0_089084_2018-05-27_final
2018-05-28 09:13:21	ga_ls8c_ard_3-0-0_089085_2018-05-27_final
2018-05-28 09:13:45	ga_ls8c_ard_3-0-0_089086_2018-05-27_final
2018-05-28 09:14:09	ga_ls8c_ard_3-0-0_089087_2018-05-27_final
2018-05-28 09:14:33	ga_ls8c_ard_3-0-0_089088_2018-05-27_final
2018-06-04 09:17:04	ga_ls8c_ard_3-0-0_090079_2018-06-03_final
2018-06-04 09:17:28	ga_ls8c_ard_3-0-0_090080_2018-06-03_final
2018-06-04 09:17:52	ga_ls8c_ard_3-0-0_090081_2018-06-03_final
2018-06-04 09:18:16	ga_ls8c_ard_3-0-0_090082_2018-06-03_final
2018-06-04 09:18:40	ga_ls8c_ard_3-0-0_090083_2018-06-03_final
2018-06-04 09:19:04	ga_ls8c_ard_3-0-0_090084_2018-06-03_final
2018-06-09 09:30:52	ga_ls8c_ard_3-0-0_093067_2018-06-09_final
2018-06-09 09:31:16	ga_ls8c_ard_3-0-0_093068_2018-06-09_final
2018-06-29 09:12:45	ga_ls8c_ard_3-0-0_089083_2018-06-28_final
2018-06-29 09:13:09	ga_ls8c_ard_3-0-0_089084_2018-06-28_final
2018-06-29 09:13:33	ga_ls8c_ard_3-0-0_089085_2018-06-28_final
2018-06-29 09:13:57	ga_ls8c_ard_3-0-0_089086_2018-06-28_final
2018-06-29 09:14:21	ga_ls8c_ard_3-0-0_089087_2018-06-28_final
2018-06-29 09:14:45	ga_ls8c_ard_3-0-0_089088_2018-06-28_final
2019-02-04 09:33:25	ga_ls8c_ard_3-0-0_093071_2019-02-04_final
2019-02-22 09:22:12	ga_ls8c_ard_3-0-0_091074_2019-02-21_final
2019-02-22 09:22:36	ga_ls8c_ard_3-0-0_091075_2019-02-21_final
2019-02-22 09:23:00	ga_ls8c_ard_3-0-0_091076_2019-02-21_final
2019-02-22 09:23:24	ga_ls8c_ard_3-0-0_091077_2019-02-21_final
2019-02-22 09:23:48	ga_ls8c_ard_3-0-0_091078_2019-02-21_final
2019-02-22 09:24:11	ga_ls8c_ard_3-0-0_091079_2019-02-21_final
2019-02-22 09:24:35	ga_ls8c_ard_3-0-0_091080_2019-02-21_final
2019-03-26 09:22:03	ga_ls8c_ard_3-1-0_091074_2019-03-25_final
2019-03-26 09:22:27	ga_ls8c_ard_3-1-0_091075_2019-03-25_final
2019-03-26 09:22:51	ga_ls8c_ard_3-1-0_091076_2019-03-25_final
2019-03-26 09:23:15	ga_ls8c_ard_3-1-0_091077_2019-03-25_final
2019-03-26 09:23:38	ga_ls8c_ard_3-1-0_091078_2019-03-25_final
2019-03-26 09:24:02	ga_ls8c_ard_3-1-0_091079_2019-03-25_final
2019-03-26 09:24:26	ga_ls8c_ard_3-1-0_091080_2019-03-25_final
2019-05-13 09:22:02	ga_ls8c_ard_3-0-0_091074_2019-05-12_final
2019-05-13 09:22:26	ga_ls8c_ard_3-0-0_091075_2019-05-12_final
2019-05-13 09:22:50	ga_ls8c_ard_3-0-0_091076_2019-05-12_final
2019-05-13 09:23:14	ga_ls8c_ard_3-0-0_091077_2019-05-12_final
2019-05-13 09:23:37	ga_ls8c_ard_3-0-0_091078_2019-05-12_final
2019-05-13 09:24:01	ga_ls8c_ard_3-0-0_091079_2019-05-12_final
2019-05-13 09:24:25	ga_ls8c_ard_3-0-0_091080_2019-05-12_final
2022-01-29 09:22:37	ga_ls8c_ard_3-2-1_091074_2022-01-28_final
2022-01-29 09:23:01	ga_ls8c_ard_3-2-1_091075_2022-01-28_final
2022-01-29 09:23:25	ga_ls8c_ard_3-2-1_091076_2022-01-28_final
2022-01-29 09:23:49	ga_ls8c_ard_3-2-1_091077_2022-01-28_final
2022-01-29 09:24:13	ga_ls8c_ard_3-2-1_091078_2022-01-28_final
2022-01-29 09:24:37	ga_ls8c_ard_3-2-1_091079_2022-01-28_final
2022-01-29 09:25:01	ga_ls8c_ard_3-2-1_091080_2022-01-28_final
2022-03-09 09:27:27	ga_ls8c_ard_3-2-1_092071_2022-03-08_final
2022-03-09 09:27:51	ga_ls8c_ard_3-2-1_092072_2022-03-08_final
2022-03-09 09:28:15	ga_ls8c_ard_3-2-1_092073_2022-03-08_final
2022-03-09 09:28:39	ga_ls8c_ard_3-2-1_092074_2022-03-08_final
2022-03-09 09:29:03	ga_ls8c_ard_3-2-1_092075_2022-03-08_final
2022-06-15 09:18:04	ga_ls8c_ard_3-2-1_090078_2022-06-14_final
2022-06-15 09:18:28	ga_ls8c_ard_3-2-1_090079_2022-06-14_final
2022-06-15 09:18:52	ga_ls8c_ard_3-2-1_090080_2022-06-14_final
2022-06-15 09:19:16	ga_ls8c_ard_3-2-1_090081_2022-06-14_final
2022-06-15 09:19:40	ga_ls8c_ard_3-2-1_090082_2022-06-14_final
2022-06-15 09:20:04	ga_ls8c_ard_3-2-1_090083_2022-06-14_final
2022-06-15 09:20:28	ga_ls8c_ard_3-2-1_090084_2022-06-14_final
2022-06-29 09:27:42	ga_ls8c_ard_3-2-1_092071_2022-06-28_final
2022-06-29 09:28:06	ga_ls8c_ard_3-2-1_092072_2022-06-28_final
2022-06-29 09:28:30	ga_ls8c_ard_3-2-1_092073_2022-06-28_final
2022-06-29 09:28:54	ga_ls8c_ard_3-2-1_092074_2022-06-28_final
2022-06-29 09:29:18	ga_ls8c_ard_3-2-1_092075_2022-06-28_final
2022-07-01 09:18:08	ga_ls8c_ard_3-2-1_090078_2022-06-30_final
2022-07-01 09:18:32	ga_ls8c_ard_3-2-1_090079_2022-06-30_final
2022-07-01 09:18:56	ga_ls8c_ard_3-2-1_090080_2022-06-30_final
2022-07-01 09:19:20	ga_ls8c_ard_3-2-1_090081_2022-06-30_final
2022-07-01 09:19:44	ga_ls8c_ard_3-2-1_090082_2022-06-30_final
2022-07-01 09:20:08	ga_ls8c_ard_3-2-1_090083_2022-06-30_final
2022-07-01 09:20:32	ga_ls8c_ard_3-2-1_090084_2022-06-30_final
2022-07-15 09:29:16	ga_ls8c_ard_3-2-1_092075_2022-07-14_final
2022-07-17 09:18:32	ga_ls8c_ard_3-2-1_090079_2022-07-16_final
2022-07-17 09:18:56	ga_ls8c_ard_3-2-1_090080_2022-07-16_final
2022-07-17 09:19:20	ga_ls8c_ard_3-2-1_090081_2022-07-16_final
2022-07-17 09:19:44	ga_ls8c_ard_3-2-1_090082_2022-07-16_final
2022-07-17 09:20:08	ga_ls8c_ard_3-2-1_090083_2022-07-16_final
2022-07-17 09:20:32	ga_ls8c_ard_3-2-1_090084_2022-07-16_final
2022-07-26 09:13:40	ga_ls8c_ard_3-2-1_089082_2022-07-25_final
2022-07-26 09:14:04	ga_ls8c_ard_3-2-1_089083_2022-07-25_final
2022-07-26 09:14:28	ga_ls8c_ard_3-2-1_089084_2022-07-25_final
2022-07-26 09:14:52	ga_ls8c_ard_3-2-1_089085_2022-07-25_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