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

69 results

Time (local)	Label
2021-12-05 10:03:21	ga_ls9c_ard_3-2-1_098067_2021-12-05_final.stac-item
2021-12-31 09:07:49	ga_ls9c_ard_3-2-1_088083_2021-12-30_final.stac-item
2022-01-29 11:53:29	ga_ls9c_ard_3-2-1_115080_2022-01-29_final.stac-item
2022-02-01 10:42:18	ga_ls9c_ard_3-2-1_104072_2022-02-01_final.stac-item
2022-03-08 09:33:40	ga_ls9c_ard_3-2-1_093071_2022-03-08_final.stac-item
2022-03-11 10:02:59	ga_ls9c_ard_3-2-1_098067_2022-03-11_final.stac-item
2022-04-05 09:57:06	ga_ls9c_ard_3-2-1_097068_2022-04-05_final.stac-item
2022-04-11 09:21:58	ga_ls9c_ard_3-2-1_091073_2022-04-10_final.stac-item
2022-05-06 10:58:29	ga_ls9c_ard_3-2-1_106083_2022-05-06_final.stac-item
2022-05-21 10:09:50	ga_ls9c_ard_3-2-1_099070_2022-05-21_final.stac-item
2022-05-25 09:45:31	ga_ls9c_ard_3-2-1_095071_2022-05-25_final.stac-item
2022-06-06 11:52:44	ga_ls9c_ard_3-2-1_115080_2022-06-06_final.stac-item
2022-06-14 09:27:40	ga_ls9c_ard_3-2-1_091088_2022-06-13_final.stac-item
2022-07-01 10:02:49	ga_ls9c_ard_3-2-1_098067_2022-07-01_final.stac-item
2022-07-15 11:57:35	ga_ls9c_ard_3-2-1_116076_2022-07-15_final.stac-item
2022-07-19 11:31:21	ga_ls9c_ard_3-2-1_112072_2022-07-19_final.stac-item
2022-07-20 10:39:20	ga_ls9c_ard_3-2-1_103081_2022-07-20_final.stac-item
2022-08-02 10:03:02	ga_ls9c_ard_3-2-1_098067_2022-08-02_final.stac-item
2022-08-25 10:09:46	ga_ls9c_ard_3-2-1_099068_2022-08-25_final.stac-item
2022-09-17 10:19:36	ga_ls9c_ard_3-2-1_100077_2022-09-17_final.stac-item
2022-10-04 09:22:26	ga_ls9c_ard_3-2-1_091073_2022-10-03_final.stac-item
2022-10-13 09:18:43	ga_ls9c_ard_3-2-1_090079_2022-10-12_final.stac-item
2022-10-18 09:34:53	ga_ls9c_ard_3-2-1_093073_2022-10-18_final.stac-item
2022-10-27 09:32:42	ga_ls9c_ard_3-2-1_092083_2022-10-27_final.stac-item
2022-11-05 09:26:53	ga_ls9c_ard_3-2-1_091084_2022-11-04_final.stac-item
2022-11-21 09:22:33	ga_ls9c_ard_3-2-1_091073_2022-11-20_final.stac-item
2022-11-29 10:11:09	ga_ls9c_ard_3-2-1_099071_2022-11-29_final.stac-item
2022-12-12 11:21:33	ga_ls9c_ard_3-2-1_110077_2022-12-12_final.stac-item
2023-01-09 10:08:59	ga_ls9c_ard_3-2-1_098081_2023-01-09_final.stac-item
2023-01-11 09:52:37	ga_ls9c_ard_3-2-1_096071_2023-01-11_final.stac-item
2023-02-09 09:29:23	ga_ls9c_ard_3-2-1_091090_2023-02-08_final.stac-item
2023-03-05 11:53:25	ga_ls9c_ard_3-2-1_115080_2023-03-05_final.stac-item
2023-03-11 09:34:35	ga_ls9c_ard_3-2-1_093073_2023-03-11_final.stac-item
2023-03-12 11:57:32	ga_ls9c_ard_3-2-1_116075_2023-03-12_final.stac-item
2023-03-12 11:57:56	ga_ls9c_ard_3-2-1_116076_2023-03-12_final.stac-item
2023-03-27 09:34:27	ga_ls9c_ard_3-2-1_093073_2023-03-27_final.stac-item
2023-03-29 09:21:39	ga_ls9c_ard_3-2-1_091072_2023-03-28_final.stac-item
2023-04-04 10:22:39	ga_ls9c_ard_3-2-1_101070_2023-04-04_final.stac-item
2023-04-21 09:33:42	ga_ls9c_ard_3-2-1_092087_2023-04-21_final.stac-item
2023-05-07 09:34:18	ga_ls9c_ard_3-2-1_092089_2023-05-07_final.stac-item
2023-05-15 10:18:55	ga_ls9c_ard_3-2-1_100077_2023-05-15_final.stac-item
2023-05-16 12:37:04	ga_ls9c_ard_3-2-1_123067_2023-05-16_final.stac-item
2023-05-21 09:39:46	ga_ls9c_ard_3-2-1_094072_2023-05-21_final.stac-item
2023-06-13 11:23:27	ga_ls9c_ard_3-2-1_111069_2023-06-13_final.stac-item
2023-06-15 09:38:33	ga_ls9c_ard_3-2-1_093085_2023-06-15_final.stac-item
2023-06-17 09:22:12	ga_ls9c_ard_3-2-1_091075_2023-06-16_final.stac-item
2023-06-22 09:39:09	ga_ls9c_ard_3-2-1_094071_2023-06-22_final.stac-item
2023-07-08 09:39:23	ga_ls9c_ard_3-2-1_094071_2023-07-08_final.stac-item
2023-07-10 09:27:26	ga_ls9c_ard_3-2-1_092072_2023-07-09_final.stac-item
2023-07-13 09:57:10	ga_ls9c_ard_3-2-1_097069_2023-07-13_final.stac-item
2023-07-19 09:22:29	ga_ls9c_ard_3-2-1_091075_2023-07-18_final.stac-item
2023-07-28 09:16:20	ga_ls9c_ard_3-2-1_090075_2023-07-27_final.stac-item
2023-08-14 09:56:56	ga_ls9c_ard_3-2-1_097068_2023-08-14_final.stac-item
2023-08-14 11:42:13	ga_ls9c_ard_3-2-1_113084_2023-08-14_final.stac-item
2023-08-31 10:42:22	ga_ls9c_ard_3-2-1_104073_2023-08-31_final.stac-item
2023-09-13 10:09:12	ga_ls9c_ard_3-2-1_099067_2023-09-13_final.stac-item
2023-09-13 10:10:00	ga_ls9c_ard_3-2-1_099069_2023-09-13_final.stac-item
2023-10-06 10:19:25	ga_ls9c_ard_3-2-1_100077_2023-10-06_final.stac-item
2023-10-08 11:47:33	ga_ls9c_ard_3-2-1_114081_2023-10-08_final.stac-item
2023-10-15 10:09:21	ga_ls9c_ard_3-2-1_099067_2023-10-15_final.stac-item
2023-10-21 09:34:18	ga_ls9c_ard_3-2-1_093072_2023-10-21_final.stac-item
2023-10-31 11:53:22	ga_ls9c_ard_3-2-1_115080_2023-10-31_final.stac-item
2023-12-04 09:57:02	ga_ls9c_ard_3-2-1_097067_2023-12-04_final.stac-item
2024-01-08 10:28:19	ga_ls9c_ard_3-2-1_102068_2024-01-08_final.stac-item
2024-01-22 09:06:34	ga_ls9c_ard_3-2-1_088080_2024-01-21_final.stac-item
2024-03-20 09:39:58	ga_ls9c_ard_3-2-1_094071_2024-03-20_final.stac-item
2024-03-25 09:56:52	ga_ls9c_ard_3-2-1_097067_2024-03-25_final.stac-item
2024-04-03 09:52:11	ga_ls9c_ard_3-2-1_096071_2024-04-03_final.stac-item
2024-04-24 10:08:45	ga_ls9c_ard_3-2-1_099067_2024-04-24_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