Search ga_ls9c_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

59 results

Time (local)	Label
2021-11-06 10:19:22	ga_ls9c_ard_3-2-1_100083_2021-11-06_final.stac-item
2021-11-11 10:21:50	ga_ls9c_ard_3-2-1_100083_2021-11-11_final.stac-item
2021-11-16 10:24:18	ga_ls9c_ard_3-2-1_100083_2021-11-16_final.stac-item
2021-12-19 10:21:58	ga_ls9c_ard_3-2-1_100083_2021-12-19_final.stac-item
2022-01-04 10:21:58	ga_ls9c_ard_3-2-1_100083_2022-01-04_final.stac-item
2022-01-20 10:21:57	ga_ls9c_ard_3-2-1_100083_2022-01-20_final.stac-item
2022-02-05 10:21:58	ga_ls9c_ard_3-2-1_100083_2022-02-05_final.stac-item
2022-02-21 10:21:50	ga_ls9c_ard_3-2-1_100083_2022-02-21_final.stac-item
2022-03-09 10:21:43	ga_ls9c_ard_3-2-1_100083_2022-03-09_final.stac-item
2022-03-25 10:21:39	ga_ls9c_ard_3-2-1_100083_2022-03-25_final.stac-item
2022-04-10 10:21:36	ga_ls9c_ard_3-2-1_100083_2022-04-10_final.stac-item
2022-04-26 10:21:27	ga_ls9c_ard_3-2-1_100083_2022-04-26_final.stac-item
2022-05-12 10:21:20	ga_ls9c_ard_3-2-1_100083_2022-05-12_final.stac-item
2022-05-28 10:21:12	ga_ls9c_ard_3-2-1_100083_2022-05-28_final.stac-item
2022-06-13 10:21:17	ga_ls9c_ard_3-2-1_100083_2022-06-13_final.stac-item
2022-06-29 10:21:34	ga_ls9c_ard_3-2-1_100083_2022-06-29_final.stac-item
2022-07-15 10:21:29	ga_ls9c_ard_3-2-1_100083_2022-07-15_final.stac-item
2022-07-31 10:21:45	ga_ls9c_ard_3-2-1_100083_2022-07-31_final.stac-item
2022-08-16 10:21:54	ga_ls9c_ard_3-2-1_100083_2022-08-16_final.stac-item
2022-09-01 10:21:56	ga_ls9c_ard_3-2-1_100083_2022-09-01_final.stac-item
2022-09-17 10:22:00	ga_ls9c_ard_3-2-1_100083_2022-09-17_final.stac-item
2022-10-03 10:22:03	ga_ls9c_ard_3-2-1_100083_2022-10-03_final.stac-item
2022-10-19 10:22:09	ga_ls9c_ard_3-2-1_100083_2022-10-19_final.stac-item
2022-11-04 10:22:06	ga_ls9c_ard_3-2-1_100083_2022-11-04_final.stac-item
2022-11-20 10:22:10	ga_ls9c_ard_3-2-1_100083_2022-11-20_final.stac-item
2022-12-06 10:22:08	ga_ls9c_ard_3-2-1_100083_2022-12-06_final.stac-item
2022-12-22 10:22:06	ga_ls9c_ard_3-2-1_100083_2022-12-22_final.stac-item
2023-01-07 10:22:09	ga_ls9c_ard_3-2-1_100083_2023-01-07_final.stac-item
2023-01-23 10:22:02	ga_ls9c_ard_3-2-1_100083_2023-01-23_final.stac-item
2023-02-08 10:22:12	ga_ls9c_ard_3-2-1_100083_2023-02-08_final.stac-item
2023-02-24 10:21:57	ga_ls9c_ard_3-2-1_100083_2023-02-24_final.stac-item
2023-03-12 10:21:50	ga_ls9c_ard_3-2-1_100083_2023-03-12_final.stac-item
2023-03-28 10:21:41	ga_ls9c_ard_3-2-1_100083_2023-03-28_final.stac-item
2023-04-13 10:21:34	ga_ls9c_ard_3-2-1_100083_2023-04-13_final.stac-item
2023-04-29 10:21:27	ga_ls9c_ard_3-2-1_100083_2023-04-29_final.stac-item
2023-05-15 10:21:18	ga_ls9c_ard_3-2-1_100083_2023-05-15_final.stac-item
2023-05-31 10:21:10	ga_ls9c_ard_3-2-1_100083_2023-05-31_final.stac-item
2023-06-16 10:21:01	ga_ls9c_ard_3-2-1_100083_2023-06-16_final.stac-item
2023-07-02 10:21:11	ga_ls9c_ard_3-2-1_100083_2023-07-02_final.stac-item
2023-07-18 10:21:18	ga_ls9c_ard_3-2-1_100083_2023-07-18_final.stac-item
2023-08-03 10:21:25	ga_ls9c_ard_3-2-1_100083_2023-08-03_final.stac-item
2023-08-19 10:21:29	ga_ls9c_ard_3-2-1_100083_2023-08-19_final.stac-item
2023-09-04 10:21:40	ga_ls9c_ard_3-2-1_100083_2023-09-04_final.stac-item
2023-09-20 10:21:46	ga_ls9c_ard_3-2-1_100083_2023-09-20_final.stac-item
2023-10-06 10:21:49	ga_ls9c_ard_3-2-1_100083_2023-10-06_final.stac-item
2023-10-22 10:21:56	ga_ls9c_ard_3-2-1_100083_2023-10-22_final.stac-item
2023-11-07 10:21:52	ga_ls9c_ard_3-2-1_100083_2023-11-07_final.stac-item
2023-11-23 10:21:59	ga_ls9c_ard_3-2-1_100083_2023-11-23_final.stac-item
2023-12-09 10:21:56	ga_ls9c_ard_3-2-1_100083_2023-12-09_final.stac-item
2023-12-25 10:22:02	ga_ls9c_ard_3-2-1_100083_2023-12-25_final.stac-item
2024-01-10 10:21:57	ga_ls9c_ard_3-2-1_100083_2024-01-10_final.stac-item
2024-01-26 10:21:56	ga_ls9c_ard_3-2-1_100083_2024-01-26_final.stac-item
2024-02-11 10:21:59	ga_ls9c_ard_3-2-1_100083_2024-02-11_final.stac-item
2024-02-27 10:21:49	ga_ls9c_ard_3-2-1_100083_2024-02-27_final.stac-item
2024-03-14 10:21:51	ga_ls9c_ard_3-2-1_100083_2024-03-14_final.stac-item
2024-03-30 10:21:42	ga_ls9c_ard_3-2-1_100083_2024-03-30_final.stac-item
2024-04-15 10:21:35	ga_ls9c_ard_3-2-1_100083_2024-04-15_final.stac-item
2024-05-01 10:21:18	ga_ls9c_ard_3-2-1_100083_2024-05-01_final.stac-item
2024-05-17 10:21:10	ga_ls9c_ard_3-2-1_100083_2024-05-17_nrt.stac-item

Return to ga_ls9c_ard_3 whole collection overview

ga_ls9c_ard_3: Geoscience Australia Landsat 9 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