Search ga_ls9c_ard_3 across 2022

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
2022-01-08 11:35:43	ga_ls9c_ard_3-2-1_112082_2022-01-08_final.stac-item
2022-01-10 09:43:40	ga_ls9c_ard_3-2-1_094080_2022-01-10_final.stac-item
2022-01-13 10:14:09	ga_ls9c_ard_3-2-1_099079_2022-01-13_final.stac-item
2022-01-22 10:08:00	ga_ls9c_ard_3-2-1_098079_2022-01-22_final.stac-item
2022-01-24 11:35:44	ga_ls9c_ard_3-2-1_112082_2022-01-24_final.stac-item
2022-01-26 09:43:41	ga_ls9c_ard_3-2-1_094080_2022-01-26_final.stac-item
2022-01-29 10:14:12	ga_ls9c_ard_3-2-1_099079_2022-01-29_final.stac-item
2022-01-31 10:01:50	ga_ls9c_ard_3-2-1_097079_2022-01-31_final.stac-item
2022-02-07 10:08:00	ga_ls9c_ard_3-2-1_098079_2022-02-07_final.stac-item
2022-02-09 11:35:42	ga_ls9c_ard_3-2-1_112082_2022-02-09_final.stac-item
2022-02-11 09:43:39	ga_ls9c_ard_3-2-1_094080_2022-02-11_final.stac-item
2022-02-14 10:14:08	ga_ls9c_ard_3-2-1_099079_2022-02-14_final.stac-item
2022-02-16 10:01:45	ga_ls9c_ard_3-2-1_097079_2022-02-16_final.stac-item
2022-02-18 09:49:46	ga_ls9c_ard_3-2-1_095080_2022-02-18_final.stac-item
2022-02-23 10:07:51	ga_ls9c_ard_3-2-1_098079_2022-02-23_final.stac-item
2022-02-25 11:35:33	ga_ls9c_ard_3-2-1_112082_2022-02-25_final.stac-item
2022-03-11 10:07:46	ga_ls9c_ard_3-2-1_098079_2022-03-11_final.stac-item
2022-03-27 10:07:41	ga_ls9c_ard_3-2-1_098079_2022-03-27_final.stac-item
2022-03-29 11:35:24	ga_ls9c_ard_3-2-1_112082_2022-03-29_final.stac-item
2022-04-03 10:13:52	ga_ls9c_ard_3-2-1_099079_2022-04-03_final.stac-item
2022-04-05 10:01:30	ga_ls9c_ard_3-2-1_097079_2022-04-05_final.stac-item
2022-04-12 10:07:37	ga_ls9c_ard_3-2-1_098079_2022-04-12_final.stac-item
2022-04-14 11:35:19	ga_ls9c_ard_3-2-1_112082_2022-04-14_final.stac-item
2022-04-28 10:07:29	ga_ls9c_ard_3-2-1_098079_2022-04-28_final.stac-item
2022-04-30 11:35:12	ga_ls9c_ard_3-2-1_112082_2022-04-30_final.stac-item
2022-05-05 10:13:38	ga_ls9c_ard_3-2-1_099079_2022-05-05_final.stac-item
2022-05-07 10:01:15	ga_ls9c_ard_3-2-1_097079_2022-05-07_final.stac-item
2022-05-14 10:07:21	ga_ls9c_ard_3-2-1_098079_2022-05-14_final.stac-item
2022-05-21 10:13:26	ga_ls9c_ard_3-2-1_099079_2022-05-21_final.stac-item
2022-05-23 10:01:04	ga_ls9c_ard_3-2-1_097079_2022-05-23_final.stac-item
2022-05-28 10:19:36	ga_ls9c_ard_3-2-1_100079_2022-05-28_final.stac-item
2022-06-03 09:42:57	ga_ls9c_ard_3-2-1_094080_2022-06-03_final.stac-item
2022-06-13 10:19:41	ga_ls9c_ard_3-2-1_100079_2022-06-13_final.stac-item
2022-06-15 10:07:21	ga_ls9c_ard_3-2-1_098079_2022-06-15_final.stac-item
2022-06-19 09:43:06	ga_ls9c_ard_3-2-1_094080_2022-06-19_final.stac-item
2022-06-22 10:13:43	ga_ls9c_ard_3-2-1_099079_2022-06-22_final.stac-item
2022-07-01 10:07:37	ga_ls9c_ard_3-2-1_098079_2022-07-01_final.stac-item
2022-07-03 11:35:20	ga_ls9c_ard_3-2-1_112082_2022-07-03_final.stac-item
2022-07-05 09:43:16	ga_ls9c_ard_3-2-1_094080_2022-07-05_final.stac-item
2022-07-06 10:26:32	ga_ls9c_ard_3-2-1_101080_2022-07-06_final.stac-item
2022-07-08 10:13:46	ga_ls9c_ard_3-2-1_099079_2022-07-08_final.stac-item
2022-07-15 10:19:54	ga_ls9c_ard_3-2-1_100079_2022-07-15_final.stac-item
2022-07-17 10:07:34	ga_ls9c_ard_3-2-1_098079_2022-07-17_final.stac-item
2022-07-19 11:35:20	ga_ls9c_ard_3-2-1_112082_2022-07-19_final.stac-item
2022-07-21 09:43:19	ga_ls9c_ard_3-2-1_094080_2022-07-21_final.stac-item
2022-07-24 10:13:52	ga_ls9c_ard_3-2-1_099079_2022-07-24_final.stac-item
2022-07-26 10:01:32	ga_ls9c_ard_3-2-1_097079_2022-07-26_final.stac-item
2022-08-02 10:07:49	ga_ls9c_ard_3-2-1_098079_2022-08-02_final.stac-item
2022-08-04 11:35:33	ga_ls9c_ard_3-2-1_112082_2022-08-04_final.stac-item
2022-08-09 10:14:04	ga_ls9c_ard_3-2-1_099079_2022-08-09_final.stac-item
2022-08-11 11:41:48	ga_ls9c_ard_3-2-1_113082_2022-08-11_final.stac-item
2022-08-13 11:29:28	ga_ls9c_ard_3-2-1_111082_2022-08-13_final.stac-item
2022-08-18 10:07:57	ga_ls9c_ard_3-2-1_098079_2022-08-18_final.stac-item
2022-08-22 09:43:38	ga_ls9c_ard_3-2-1_094080_2022-08-22_final.stac-item
2022-08-27 11:41:52	ga_ls9c_ard_3-2-1_113082_2022-08-27_final.stac-item
2022-09-05 11:35:41	ga_ls9c_ard_3-2-1_112082_2022-09-05_final.stac-item
2022-09-10 10:14:09	ga_ls9c_ard_3-2-1_099079_2022-09-10_final.stac-item
2022-09-12 11:41:54	ga_ls9c_ard_3-2-1_113082_2022-09-12_final.stac-item
2022-09-17 10:20:48	ga_ls9c_ard_3-2-1_100080_2022-09-17_final.stac-item
2022-09-19 10:08:03	ga_ls9c_ard_3-2-1_098079_2022-09-19_final.stac-item
2022-09-19 10:08:27	ga_ls9c_ard_3-2-1_098080_2022-09-19_final.stac-item
2022-09-21 11:35:48	ga_ls9c_ard_3-2-1_112082_2022-09-21_final.stac-item
2022-09-26 10:14:16	ga_ls9c_ard_3-2-1_099079_2022-09-26_final.stac-item
2022-09-28 10:01:55	ga_ls9c_ard_3-2-1_097079_2022-09-28_final.stac-item
2022-10-07 11:35:48	ga_ls9c_ard_3-2-1_112082_2022-10-07_final.stac-item
2022-10-09 09:43:47	ga_ls9c_ard_3-2-1_094080_2022-10-09_final.stac-item
2022-10-12 10:14:44	ga_ls9c_ard_3-2-1_099080_2022-10-12_final.stac-item
2022-10-14 10:01:59	ga_ls9c_ard_3-2-1_097079_2022-10-14_final.stac-item
2022-10-14 11:42:04	ga_ls9c_ard_3-2-1_113082_2022-10-14_final.stac-item
2022-10-19 10:20:57	ga_ls9c_ard_3-2-1_100080_2022-10-19_final.stac-item
2022-10-23 11:35:55	ga_ls9c_ard_3-2-1_112082_2022-10-23_final.stac-item
2022-10-25 09:43:52	ga_ls9c_ard_3-2-1_094080_2022-10-25_final.stac-item
2022-10-28 10:14:22	ga_ls9c_ard_3-2-1_099079_2022-10-28_final.stac-item
2022-10-28 10:14:46	ga_ls9c_ard_3-2-1_099080_2022-10-28_final.stac-item
2022-10-30 11:42:04	ga_ls9c_ard_3-2-1_113082_2022-10-30_final.stac-item
2022-11-01 09:50:01	ga_ls9c_ard_3-2-1_095080_2022-11-01_final.stac-item
2022-11-06 10:08:10	ga_ls9c_ard_3-2-1_098079_2022-11-06_final.stac-item
2022-11-06 10:08:34	ga_ls9c_ard_3-2-1_098080_2022-11-06_final.stac-item
2022-11-08 11:35:54	ga_ls9c_ard_3-2-1_112082_2022-11-08_final.stac-item
2022-11-09 10:39:29	ga_ls9c_ard_3-2-1_103080_2022-11-09_final.stac-item
2022-11-10 09:43:52	ga_ls9c_ard_3-2-1_094080_2022-11-10_final.stac-item
2022-11-17 09:50:04	ga_ls9c_ard_3-2-1_095080_2022-11-17_final.stac-item
2022-11-20 10:20:34	ga_ls9c_ard_3-2-1_100079_2022-11-20_final.stac-item
2022-11-22 10:08:12	ga_ls9c_ard_3-2-1_098079_2022-11-22_final.stac-item
2022-11-26 09:43:52	ga_ls9c_ard_3-2-1_094080_2022-11-26_final.stac-item
2022-11-29 10:14:20	ga_ls9c_ard_3-2-1_099079_2022-11-29_final.stac-item
2022-12-01 11:42:03	ga_ls9c_ard_3-2-1_113082_2022-12-01_final.stac-item
2022-12-03 09:50:00	ga_ls9c_ard_3-2-1_095080_2022-12-03_final.stac-item
2022-12-08 10:08:11	ga_ls9c_ard_3-2-1_098079_2022-12-08_final.stac-item
2022-12-08 10:08:35	ga_ls9c_ard_3-2-1_098080_2022-12-08_final.stac-item
2022-12-10 11:35:54	ga_ls9c_ard_3-2-1_112082_2022-12-10_final.stac-item
2022-12-11 10:39:29	ga_ls9c_ard_3-2-1_103080_2022-12-11_final.stac-item
2022-12-12 09:43:52	ga_ls9c_ard_3-2-1_094080_2022-12-12_final.stac-item
2022-12-17 10:02:00	ga_ls9c_ard_3-2-1_097079_2022-12-17_final.stac-item
2022-12-19 09:50:01	ga_ls9c_ard_3-2-1_095080_2022-12-19_final.stac-item
2022-12-20 10:33:17	ga_ls9c_ard_3-2-1_102080_2022-12-20_final.stac-item
2022-12-22 10:20:30	ga_ls9c_ard_3-2-1_100079_2022-12-22_final.stac-item
2022-12-24 10:08:08	ga_ls9c_ard_3-2-1_098079_2022-12-24_final.stac-item
2022-12-26 11:35:52	ga_ls9c_ard_3-2-1_112082_2022-12-26_final.stac-item
2022-12-28 09:43:51	ga_ls9c_ard_3-2-1_094080_2022-12-28_final.stac-item

Return to ga_ls9c_ard_3 across 2022 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

2022

All months