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

42 results

Time (local)	Label
2023-10-24 10:03:35	ga_ls9c_ard_3-2-1_098068_2023-10-24_final.stac-item
2023-10-24 10:03:59	ga_ls9c_ard_3-2-1_098069_2023-10-24_final.stac-item
2023-10-24 10:04:23	ga_ls9c_ard_3-2-1_098070_2023-10-24_final.stac-item
2023-10-24 10:04:47	ga_ls9c_ard_3-2-1_098071_2023-10-24_final.stac-item
2023-10-24 10:05:11	ga_ls9c_ard_3-2-1_098072_2023-10-24_final.stac-item
2023-10-24 10:05:35	ga_ls9c_ard_3-2-1_098073_2023-10-24_final.stac-item
2023-10-24 10:05:59	ga_ls9c_ard_3-2-1_098074_2023-10-24_final.stac-item
2023-10-24 10:06:23	ga_ls9c_ard_3-2-1_098075_2023-10-24_final.stac-item
2023-10-24 10:06:47	ga_ls9c_ard_3-2-1_098076_2023-10-24_final.stac-item
2023-10-24 10:07:11	ga_ls9c_ard_3-2-1_098077_2023-10-24_final.stac-item
2023-10-24 10:07:35	ga_ls9c_ard_3-2-1_098078_2023-10-24_final.stac-item
2023-10-24 10:07:59	ga_ls9c_ard_3-2-1_098079_2023-10-24_final.stac-item
2023-10-24 10:08:22	ga_ls9c_ard_3-2-1_098080_2023-10-24_final.stac-item
2023-10-24 10:08:46	ga_ls9c_ard_3-2-1_098081_2023-10-24_final.stac-item
2023-10-24 10:09:10	ga_ls9c_ard_3-2-1_098082_2023-10-24_final.stac-item
2023-10-24 10:09:34	ga_ls9c_ard_3-2-1_098083_2023-10-24_final.stac-item
2023-10-24 10:09:58	ga_ls9c_ard_3-2-1_098084_2023-10-24_final.stac-item
2023-10-24 10:10:22	ga_ls9c_ard_3-2-1_098085_2023-10-24_final.stac-item
2023-10-24 11:44:52	ga_ls9c_ard_3-2-1_114074_2023-10-24_final.stac-item
2023-10-24 11:45:16	ga_ls9c_ard_3-2-1_114075_2023-10-24_final.stac-item
2023-10-24 11:45:40	ga_ls9c_ard_3-2-1_114076_2023-10-24_final.stac-item
2023-10-24 11:46:04	ga_ls9c_ard_3-2-1_114077_2023-10-24_final.stac-item
2023-10-24 11:46:28	ga_ls9c_ard_3-2-1_114078_2023-10-24_final.stac-item
2023-10-24 11:46:52	ga_ls9c_ard_3-2-1_114079_2023-10-24_final.stac-item
2023-10-24 11:47:16	ga_ls9c_ard_3-2-1_114080_2023-10-24_final.stac-item
2023-10-24 11:47:39	ga_ls9c_ard_3-2-1_114081_2023-10-24_final.stac-item
2023-10-25 09:10:21	ga_ls9c_ard_3-2-1_089074_2023-10-24_final.stac-item
2023-10-25 09:10:45	ga_ls9c_ard_3-2-1_089075_2023-10-24_final.stac-item
2023-10-25 09:11:09	ga_ls9c_ard_3-2-1_089076_2023-10-24_final.stac-item
2023-10-25 09:11:33	ga_ls9c_ard_3-2-1_089077_2023-10-24_final.stac-item
2023-10-25 09:11:57	ga_ls9c_ard_3-2-1_089078_2023-10-24_final.stac-item
2023-10-25 09:12:21	ga_ls9c_ard_3-2-1_089079_2023-10-24_final.stac-item
2023-10-25 09:12:45	ga_ls9c_ard_3-2-1_089080_2023-10-24_final.stac-item
2023-10-25 09:13:09	ga_ls9c_ard_3-2-1_089081_2023-10-24_final.stac-item
2023-10-25 09:13:33	ga_ls9c_ard_3-2-1_089082_2023-10-24_final.stac-item
2023-10-25 09:13:57	ga_ls9c_ard_3-2-1_089083_2023-10-24_final.stac-item
2023-10-25 09:14:21	ga_ls9c_ard_3-2-1_089084_2023-10-24_final.stac-item
2023-10-25 09:14:45	ga_ls9c_ard_3-2-1_089085_2023-10-24_final.stac-item
2023-10-25 09:15:09	ga_ls9c_ard_3-2-1_089086_2023-10-24_final.stac-item
2023-10-25 09:15:56	ga_ls9c_ard_3-2-1_089088_2023-10-24_final.stac-item
2023-10-25 09:16:20	ga_ls9c_ard_3-2-1_089089_2023-10-24_final.stac-item
2023-10-25 09:16:44	ga_ls9c_ard_3-2-1_089090_2023-10-24_final.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

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

All years