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

First 100 results (query limit)

Time (local)	Label
2021-11-11 10:18:15	ga_ls9c_ard_3-2-1_100074_2021-11-11_final
2021-11-11 10:18:39	ga_ls9c_ard_3-2-1_100075_2021-11-11_final
2021-11-16 10:21:07	ga_ls9c_ard_3-2-1_100075_2021-11-16_final
2021-12-19 10:18:23	ga_ls9c_ard_3-2-1_100074_2021-12-19_final
2021-12-19 10:18:47	ga_ls9c_ard_3-2-1_100075_2021-12-19_final
2021-12-26 10:24:34	ga_ls9c_ard_3-2-1_101074_2021-12-26_final
2022-02-05 10:18:22	ga_ls9c_ard_3-2-1_100074_2022-02-05_final
2022-02-05 10:18:46	ga_ls9c_ard_3-2-1_100075_2022-02-05_final
2022-02-21 10:18:14	ga_ls9c_ard_3-2-1_100074_2022-02-21_final
2022-02-21 10:18:38	ga_ls9c_ard_3-2-1_100075_2022-02-21_final
2022-03-09 10:18:08	ga_ls9c_ard_3-2-1_100074_2022-03-09_final
2022-03-09 10:18:32	ga_ls9c_ard_3-2-1_100075_2022-03-09_final
2022-03-25 10:18:04	ga_ls9c_ard_3-2-1_100074_2022-03-25_final
2022-03-25 10:18:28	ga_ls9c_ard_3-2-1_100075_2022-03-25_final
2022-04-10 10:18:00	ga_ls9c_ard_3-2-1_100074_2022-04-10_final
2022-04-10 10:18:24	ga_ls9c_ard_3-2-1_100075_2022-04-10_final
2022-04-26 10:18:15	ga_ls9c_ard_3-2-1_100075_2022-04-26_final
2022-05-12 10:17:44	ga_ls9c_ard_3-2-1_100074_2022-05-12_final
2022-05-12 10:18:08	ga_ls9c_ard_3-2-1_100075_2022-05-12_final
2022-05-14 10:09:20	ga_ls9c_ard_3-2-1_098084_2022-05-14_final
2022-05-21 10:14:37	ga_ls9c_ard_3-2-1_099082_2022-05-21_final
2022-05-28 10:17:37	ga_ls9c_ard_3-2-1_100074_2022-05-28_final
2022-05-28 10:18:01	ga_ls9c_ard_3-2-1_100075_2022-05-28_final
2022-06-13 10:17:42	ga_ls9c_ard_3-2-1_100074_2022-06-13_final
2022-06-29 10:17:59	ga_ls9c_ard_3-2-1_100074_2022-06-29_final
2022-07-15 10:17:54	ga_ls9c_ard_3-2-1_100074_2022-07-15_final
2022-07-22 10:24:12	ga_ls9c_ard_3-2-1_101074_2022-07-22_final
2022-07-31 10:18:09	ga_ls9c_ard_3-2-1_100074_2022-07-31_final
2022-08-16 10:18:18	ga_ls9c_ard_3-2-1_100074_2022-08-16_final
2022-08-16 10:18:42	ga_ls9c_ard_3-2-1_100075_2022-08-16_final
2022-09-01 10:18:20	ga_ls9c_ard_3-2-1_100074_2022-09-01_final
2022-09-01 10:18:44	ga_ls9c_ard_3-2-1_100075_2022-09-01_final
2022-09-17 10:18:24	ga_ls9c_ard_3-2-1_100074_2022-09-17_final
2022-09-17 10:18:48	ga_ls9c_ard_3-2-1_100075_2022-09-17_final
2022-10-03 10:18:28	ga_ls9c_ard_3-2-1_100074_2022-10-03_final
2022-10-03 10:18:51	ga_ls9c_ard_3-2-1_100075_2022-10-03_final
2022-10-10 10:24:40	ga_ls9c_ard_3-2-1_101074_2022-10-10_final
2022-10-19 10:18:33	ga_ls9c_ard_3-2-1_100074_2022-10-19_final
2022-10-19 10:18:57	ga_ls9c_ard_3-2-1_100075_2022-10-19_final
2022-10-21 10:09:23	ga_ls9c_ard_3-2-1_098082_2022-10-21_final
2022-12-06 10:18:32	ga_ls9c_ard_3-2-1_100074_2022-12-06_final
2022-12-06 10:18:56	ga_ls9c_ard_3-2-1_100075_2022-12-06_final
2022-12-15 10:15:34	ga_ls9c_ard_3-2-1_099082_2022-12-15_final
2022-12-22 10:18:30	ga_ls9c_ard_3-2-1_100074_2022-12-22_final
2022-12-22 10:18:54	ga_ls9c_ard_3-2-1_100075_2022-12-22_final
2022-12-29 10:24:43	ga_ls9c_ard_3-2-1_101074_2022-12-29_final
2022-12-31 10:15:34	ga_ls9c_ard_3-2-1_099082_2022-12-31_final
2023-01-16 10:15:29	ga_ls9c_ard_3-2-1_099082_2023-01-16_final
2023-01-23 10:18:26	ga_ls9c_ard_3-2-1_100074_2023-01-23_final
2023-01-23 10:18:50	ga_ls9c_ard_3-2-1_100075_2023-01-23_final
2023-02-01 10:15:35	ga_ls9c_ard_3-2-1_099082_2023-02-01_final
2023-02-08 10:19:01	ga_ls9c_ard_3-2-1_100075_2023-02-08_final
2023-02-10 10:09:27	ga_ls9c_ard_3-2-1_098082_2023-02-10_final
2023-02-17 10:15:33	ga_ls9c_ard_3-2-1_099082_2023-02-17_final
2023-02-24 10:18:22	ga_ls9c_ard_3-2-1_100074_2023-02-24_final
2023-02-26 10:09:12	ga_ls9c_ard_3-2-1_098082_2023-02-26_final
2023-03-05 10:15:20	ga_ls9c_ard_3-2-1_099082_2023-03-05_final
2023-03-12 10:18:15	ga_ls9c_ard_3-2-1_100074_2023-03-12_final
2023-03-12 10:18:39	ga_ls9c_ard_3-2-1_100075_2023-03-12_final
2023-03-21 10:15:14	ga_ls9c_ard_3-2-1_099082_2023-03-21_final
2023-03-28 10:18:05	ga_ls9c_ard_3-2-1_100074_2023-03-28_final
2023-03-28 10:18:29	ga_ls9c_ard_3-2-1_100075_2023-03-28_final
2023-03-30 10:08:53	ga_ls9c_ard_3-2-1_098082_2023-03-30_final
2023-04-06 10:15:04	ga_ls9c_ard_3-2-1_099082_2023-04-06_final
2023-04-13 10:17:58	ga_ls9c_ard_3-2-1_100074_2023-04-13_final
2023-04-15 10:08:47	ga_ls9c_ard_3-2-1_098082_2023-04-15_final
2023-04-20 10:24:08	ga_ls9c_ard_3-2-1_101074_2023-04-20_final
2023-04-22 10:14:57	ga_ls9c_ard_3-2-1_099082_2023-04-22_final
2023-04-29 10:17:52	ga_ls9c_ard_3-2-1_100074_2023-04-29_final
2023-05-01 10:08:39	ga_ls9c_ard_3-2-1_098082_2023-05-01_final
2023-05-06 10:23:56	ga_ls9c_ard_3-2-1_101074_2023-05-06_final
2023-05-08 10:14:46	ga_ls9c_ard_3-2-1_099082_2023-05-08_final
2023-05-15 10:17:43	ga_ls9c_ard_3-2-1_100074_2023-05-15_final
2023-05-15 10:18:07	ga_ls9c_ard_3-2-1_100075_2023-05-15_final
2023-05-17 10:08:32	ga_ls9c_ard_3-2-1_098082_2023-05-17_final
2023-05-22 10:23:48	ga_ls9c_ard_3-2-1_101074_2023-05-22_final
2023-05-24 10:14:36	ga_ls9c_ard_3-2-1_099082_2023-05-24_final
2023-05-31 10:17:35	ga_ls9c_ard_3-2-1_100074_2023-05-31_final
2023-06-02 10:08:24	ga_ls9c_ard_3-2-1_098082_2023-06-02_final
2023-06-09 10:14:31	ga_ls9c_ard_3-2-1_099082_2023-06-09_final
2023-06-18 10:08:15	ga_ls9c_ard_3-2-1_098082_2023-06-18_final
2023-06-25 10:14:26	ga_ls9c_ard_3-2-1_099082_2023-06-25_final
2023-08-03 10:18:13	ga_ls9c_ard_3-2-1_100075_2023-08-03_final
2023-08-19 10:18:17	ga_ls9c_ard_3-2-1_100075_2023-08-19_final
2023-09-04 10:18:28	ga_ls9c_ard_3-2-1_100075_2023-09-04_final
2023-09-20 10:18:35	ga_ls9c_ard_3-2-1_100075_2023-09-20_final
2023-10-06 10:18:37	ga_ls9c_ard_3-2-1_100075_2023-10-06_final
2023-10-22 10:18:45	ga_ls9c_ard_3-2-1_100075_2023-10-22_final
2023-11-07 10:18:40	ga_ls9c_ard_3-2-1_100075_2023-11-07_final
2023-11-23 10:18:47	ga_ls9c_ard_3-2-1_100075_2023-11-23_final
2023-12-09 10:18:44	ga_ls9c_ard_3-2-1_100075_2023-12-09_final
2023-12-25 10:18:51	ga_ls9c_ard_3-2-1_100075_2023-12-25_final
2024-01-10 10:18:46	ga_ls9c_ard_3-2-1_100075_2024-01-10_final
2024-01-26 10:18:44	ga_ls9c_ard_3-2-1_100075_2024-01-26_final
2024-02-11 10:18:48	ga_ls9c_ard_3-2-1_100075_2024-02-11_final
2024-02-27 10:18:38	ga_ls9c_ard_3-2-1_100075_2024-02-27_final
2024-03-14 10:18:39	ga_ls9c_ard_3-2-1_100075_2024-03-14_final
2024-03-30 10:18:31	ga_ls9c_ard_3-2-1_100075_2024-03-30_final
2024-04-15 10:18:23	ga_ls9c_ard_3-2-1_100075_2024-04-15_final
2024-05-01 10:18:07	ga_ls9c_ard_3-2-1_100075_2024-05-01_final

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

GA Collection 3

USGS Products

Collection 2 NBAR

Collection 2 NBAR Tiled

Pixel Quality

Collection 2 Level1

Tidal

Water Observations

Fractional Cover

Sentinel 2

Ingested Pixel Quality

Geomedians

TMAD

Telemetry data

Summary

Other Products

All years