Search ga_ls9c_ard_3 across May 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
2022-05-01 10:34:23	ga_ls9c_ard_3-2-1_103069_2022-05-01_final.stac-item
2022-05-01 10:37:11	ga_ls9c_ard_3-2-1_103076_2022-05-01_final.stac-item
2022-05-02 09:43:57	ga_ls9c_ard_3-2-1_094082_2022-05-02_final.stac-item
2022-05-02 11:22:02	ga_ls9c_ard_3-2-1_110080_2022-05-02_final.stac-item
2022-05-02 11:22:26	ga_ls9c_ard_3-2-1_110081_2022-05-02_final.stac-item
2022-05-03 08:48:19	ga_ls9c_ard_3-2-1_085082_2022-05-02_final.stac-item
2022-05-03 10:26:24	ga_ls9c_ard_3-2-1_101080_2022-05-03_final.stac-item
2022-05-03 10:27:12	ga_ls9c_ard_3-2-1_101082_2022-05-03_final.stac-item
2022-05-04 09:31:34	ga_ls9c_ard_3-2-1_092082_2022-05-04_final.stac-item
2022-05-04 11:09:15	ga_ls9c_ard_3-2-1_108079_2022-05-04_final.stac-item
2022-05-04 11:09:39	ga_ls9c_ard_3-2-1_108080_2022-05-04_final.stac-item
2022-05-04 11:10:03	ga_ls9c_ard_3-2-1_108081_2022-05-04_final.stac-item
2022-05-05 10:08:50	ga_ls9c_ard_3-2-1_099067_2022-05-05_final.stac-item
2022-05-05 10:10:02	ga_ls9c_ard_3-2-1_099070_2022-05-05_final.stac-item
2022-05-05 10:13:38	ga_ls9c_ard_3-2-1_099079_2022-05-05_final.stac-item
2022-05-05 10:14:02	ga_ls9c_ard_3-2-1_099080_2022-05-05_final.stac-item
2022-05-06 09:19:12	ga_ls9c_ard_3-2-1_090082_2022-05-05_final.stac-item
2022-05-06 10:53:17	ga_ls9c_ard_3-2-1_106070_2022-05-06_final.stac-item
2022-05-06 10:56:53	ga_ls9c_ard_3-2-1_106079_2022-05-06_final.stac-item
2022-05-06 10:57:17	ga_ls9c_ard_3-2-1_106080_2022-05-06_final.stac-item
2022-05-06 10:57:41	ga_ls9c_ard_3-2-1_106081_2022-05-06_final.stac-item
2022-05-07 10:00:03	ga_ls9c_ard_3-2-1_097076_2022-05-07_final.stac-item
2022-05-07 10:01:15	ga_ls9c_ard_3-2-1_097079_2022-05-07_final.stac-item
2022-05-07 10:01:39	ga_ls9c_ard_3-2-1_097080_2022-05-07_final.stac-item
2022-05-07 10:02:27	ga_ls9c_ard_3-2-1_097082_2022-05-07_final.stac-item
2022-05-07 11:38:32	ga_ls9c_ard_3-2-1_113075_2022-05-07_final.stac-item
2022-05-07 11:41:20	ga_ls9c_ard_3-2-1_113082_2022-05-07_final.stac-item
2022-05-08 09:06:49	ga_ls9c_ard_3-2-1_088082_2022-05-07_final.stac-item
2022-05-08 10:40:55	ga_ls9c_ard_3-2-1_104070_2022-05-08_final.stac-item
2022-05-09 09:49:16	ga_ls9c_ard_3-2-1_095080_2022-05-09_final.stac-item
2022-05-09 09:50:04	ga_ls9c_ard_3-2-1_095082_2022-05-09_final.stac-item
2022-05-09 11:27:45	ga_ls9c_ard_3-2-1_111079_2022-05-09_final.stac-item
2022-05-09 11:28:09	ga_ls9c_ard_3-2-1_111080_2022-05-09_final.stac-item
2022-05-10 10:30:55	ga_ls9c_ard_3-2-1_102076_2022-05-10_final.stac-item
2022-05-10 10:32:31	ga_ls9c_ard_3-2-1_102080_2022-05-10_final.stac-item
2022-05-10 10:33:19	ga_ls9c_ard_3-2-1_102082_2022-05-10_final.stac-item
2022-05-11 09:37:41	ga_ls9c_ard_3-2-1_093082_2022-05-11_final.stac-item
2022-05-11 11:15:22	ga_ls9c_ard_3-2-1_109079_2022-05-11_final.stac-item
2022-05-11 11:16:10	ga_ls9c_ard_3-2-1_109081_2022-05-11_final.stac-item
2022-05-12 08:42:03	ga_ls9c_ard_3-2-1_084082_2022-05-11_final.stac-item
2022-05-12 10:19:44	ga_ls9c_ard_3-2-1_100079_2022-05-12_final.stac-item
2022-05-12 10:20:08	ga_ls9c_ard_3-2-1_100080_2022-05-12_final.stac-item
2022-05-13 09:24:06	ga_ls9c_ard_3-2-1_091079_2022-05-12_final.stac-item
2022-05-13 09:25:18	ga_ls9c_ard_3-2-1_091082_2022-05-12_final.stac-item
2022-05-13 10:59:23	ga_ls9c_ard_3-2-1_107070_2022-05-13_final.stac-item
2022-05-13 11:03:23	ga_ls9c_ard_3-2-1_107080_2022-05-13_final.stac-item
2022-05-14 10:07:21	ga_ls9c_ard_3-2-1_098079_2022-05-14_final.stac-item
2022-05-14 11:44:38	ga_ls9c_ard_3-2-1_114075_2022-05-14_final.stac-item
2022-05-14 11:46:38	ga_ls9c_ard_3-2-1_114080_2022-05-14_final.stac-item
2022-05-15 09:12:54	ga_ls9c_ard_3-2-1_089082_2022-05-14_final.stac-item
2022-05-15 10:47:00	ga_ls9c_ard_3-2-1_105070_2022-05-15_final.stac-item
2022-05-15 10:50:59	ga_ls9c_ard_3-2-1_105080_2022-05-15_final.stac-item
2022-05-15 10:51:47	ga_ls9c_ard_3-2-1_105082_2022-05-15_final.stac-item
2022-05-16 09:56:09	ga_ls9c_ard_3-2-1_096082_2022-05-16_final.stac-item
2022-05-17 10:34:13	ga_ls9c_ard_3-2-1_103069_2022-05-17_final.stac-item
2022-05-18 09:43:45	ga_ls9c_ard_3-2-1_094082_2022-05-18_final.stac-item
2022-05-18 11:21:50	ga_ls9c_ard_3-2-1_110080_2022-05-18_final.stac-item
2022-05-18 11:22:14	ga_ls9c_ard_3-2-1_110081_2022-05-18_final.stac-item
2022-05-19 08:48:07	ga_ls9c_ard_3-2-1_085082_2022-05-18_final.stac-item
2022-05-19 10:25:48	ga_ls9c_ard_3-2-1_101079_2022-05-19_final.stac-item
2022-05-19 10:26:12	ga_ls9c_ard_3-2-1_101080_2022-05-19_final.stac-item
2022-05-19 10:27:00	ga_ls9c_ard_3-2-1_101082_2022-05-19_final.stac-item
2022-05-20 09:31:22	ga_ls9c_ard_3-2-1_092082_2022-05-20_final.stac-item
2022-05-20 11:07:27	ga_ls9c_ard_3-2-1_108075_2022-05-20_final.stac-item
2022-05-20 11:09:03	ga_ls9c_ard_3-2-1_108079_2022-05-20_final.stac-item
2022-05-20 11:09:27	ga_ls9c_ard_3-2-1_108080_2022-05-20_final.stac-item
2022-05-20 11:09:51	ga_ls9c_ard_3-2-1_108081_2022-05-20_final.stac-item
2022-05-21 10:08:38	ga_ls9c_ard_3-2-1_099067_2022-05-21_final.stac-item
2022-05-21 10:11:50	ga_ls9c_ard_3-2-1_099075_2022-05-21_final.stac-item
2022-05-21 10:13:26	ga_ls9c_ard_3-2-1_099079_2022-05-21_final.stac-item
2022-05-21 10:13:50	ga_ls9c_ard_3-2-1_099080_2022-05-21_final.stac-item
2022-05-22 09:19:00	ga_ls9c_ard_3-2-1_090082_2022-05-21_final.stac-item
2022-05-22 10:56:41	ga_ls9c_ard_3-2-1_106079_2022-05-22_final.stac-item
2022-05-22 10:57:05	ga_ls9c_ard_3-2-1_106080_2022-05-22_final.stac-item
2022-05-23 09:59:52	ga_ls9c_ard_3-2-1_097076_2022-05-23_final.stac-item
2022-05-23 10:01:04	ga_ls9c_ard_3-2-1_097079_2022-05-23_final.stac-item
2022-05-23 10:01:28	ga_ls9c_ard_3-2-1_097080_2022-05-23_final.stac-item
2022-05-23 10:02:16	ga_ls9c_ard_3-2-1_097082_2022-05-23_final.stac-item
2022-05-23 11:41:09	ga_ls9c_ard_3-2-1_113082_2022-05-23_final.stac-item
2022-05-24 09:06:39	ga_ls9c_ard_3-2-1_088082_2022-05-23_final.stac-item
2022-05-24 10:40:44	ga_ls9c_ard_3-2-1_104070_2022-05-24_final.stac-item
2022-05-25 09:49:06	ga_ls9c_ard_3-2-1_095080_2022-05-25_final.stac-item
2022-05-25 09:49:54	ga_ls9c_ard_3-2-1_095082_2022-05-25_final.stac-item
2022-05-25 11:23:36	ga_ls9c_ard_3-2-1_111069_2022-05-25_final.stac-item
2022-05-25 11:27:35	ga_ls9c_ard_3-2-1_111079_2022-05-25_final.stac-item
2022-05-25 11:27:59	ga_ls9c_ard_3-2-1_111080_2022-05-25_final.stac-item
2022-05-26 10:33:10	ga_ls9c_ard_3-2-1_102082_2022-05-26_final.stac-item
2022-05-27 09:36:44	ga_ls9c_ard_3-2-1_093080_2022-05-27_final.stac-item
2022-05-27 11:15:13	ga_ls9c_ard_3-2-1_109079_2022-05-27_final.stac-item
2022-05-27 11:15:37	ga_ls9c_ard_3-2-1_109080_2022-05-27_final.stac-item
2022-05-27 11:16:01	ga_ls9c_ard_3-2-1_109081_2022-05-27_final.stac-item
2022-05-28 08:41:55	ga_ls9c_ard_3-2-1_084082_2022-05-27_final.stac-item
2022-05-28 10:19:36	ga_ls9c_ard_3-2-1_100079_2022-05-28_final.stac-item
2022-05-28 10:20:00	ga_ls9c_ard_3-2-1_100080_2022-05-28_final.stac-item
2022-05-29 10:59:17	ga_ls9c_ard_3-2-1_107070_2022-05-29_final.stac-item
2022-05-30 10:07:39	ga_ls9c_ard_3-2-1_098080_2022-05-30_final.stac-item
2022-05-30 10:08:27	ga_ls9c_ard_3-2-1_098082_2022-05-30_final.stac-item
2022-05-30 11:44:33	ga_ls9c_ard_3-2-1_114075_2022-05-30_final.stac-item
2022-05-31 09:12:50	ga_ls9c_ard_3-2-1_089082_2022-05-30_final.stac-item
2022-05-31 10:50:55	ga_ls9c_ard_3-2-1_105080_2022-05-31_final.stac-item

Return to ga_ls9c_ard_3 across May 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

May

All days