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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

55 results

Time (local)	Label
2021-12-16 09:47:05	ga_ls9c_ard_3-2-1_095073_2021-12-16_final.stac-item
2021-12-20 09:23:10	ga_ls9c_ard_3-2-1_091075_2021-12-19_final.stac-item
2022-01-15 09:57:00	ga_ls9c_ard_3-2-1_097067_2022-01-15_final.stac-item
2022-03-24 11:12:30	ga_ls9c_ard_3-2-1_109071_2022-03-24_final.stac-item
2022-04-05 11:41:10	ga_ls9c_ard_3-2-1_113081_2022-04-05_final.stac-item
2022-04-09 11:17:37	ga_ls9c_ard_3-2-1_109084_2022-04-09_final.stac-item
2022-05-04 09:28:47	ga_ls9c_ard_3-2-1_092075_2022-05-03_final.stac-item
2022-05-14 11:46:14	ga_ls9c_ard_3-2-1_114079_2022-05-14_final.stac-item
2022-07-12 09:52:12	ga_ls9c_ard_3-2-1_095087_2022-07-12_final.stac-item
2022-07-19 09:58:03	ga_ls9c_ard_3-2-1_096086_2022-07-19_final.stac-item
2022-08-04 09:57:28	ga_ls9c_ard_3-2-1_096084_2022-08-04_final.stac-item
2022-08-17 09:22:17	ga_ls9c_ard_3-2-1_091073_2022-08-16_final.stac-item
2022-09-04 10:46:50	ga_ls9c_ard_3-2-1_105068_2022-09-04_final.stac-item
2022-09-30 09:52:21	ga_ls9c_ard_3-2-1_095086_2022-09-30_final.stac-item
2022-10-04 09:25:14	ga_ls9c_ard_3-2-1_091080_2022-10-03_final.stac-item
2022-10-11 11:10:20	ga_ls9c_ard_3-2-1_108080_2022-10-11_final.stac-item
2022-10-18 09:37:17	ga_ls9c_ard_3-2-1_093079_2022-10-18_final.stac-item
2022-10-25 09:40:41	ga_ls9c_ard_3-2-1_094072_2022-10-25_final.stac-item
2022-10-29 10:54:26	ga_ls9c_ard_3-2-1_106071_2022-10-29_final.stac-item
2022-11-05 09:29:17	ga_ls9c_ard_3-2-1_091090_2022-11-04_final.stac-item
2022-11-05 11:01:47	ga_ls9c_ard_3-2-1_107074_2022-11-05_final.stac-item
2022-11-17 09:46:53	ga_ls9c_ard_3-2-1_095072_2022-11-17_final.stac-item
2022-11-25 10:38:41	ga_ls9c_ard_3-2-1_103078_2022-11-25_final.stac-item
2022-12-15 11:52:03	ga_ls9c_ard_3-2-1_115076_2022-12-15_final.stac-item
2022-12-16 09:21:08	ga_ls9c_ard_3-2-1_090085_2022-12-15_final.stac-item
2022-12-30 09:33:05	ga_ls9c_ard_3-2-1_092084_2022-12-30_final.stac-item
2023-01-14 10:28:16	ga_ls9c_ard_3-2-1_101083_2023-01-14_final.stac-item
2023-02-03 11:42:31	ga_ls9c_ard_3-2-1_113083_2023-02-03_final.stac-item
2023-02-12 09:57:52	ga_ls9c_ard_3-2-1_096084_2023-02-12_final.stac-item
2023-02-21 09:46:20	ga_ls9c_ard_3-2-1_095071_2023-02-21_final.stac-item
2023-02-25 09:23:57	ga_ls9c_ard_3-2-1_091077_2023-02-24_final.stac-item
2023-03-04 09:33:17	ga_ls9c_ard_3-2-1_092085_2023-03-04_final.stac-item
2023-03-16 09:57:31	ga_ls9c_ard_3-2-1_096084_2023-03-16_final.stac-item
2023-04-28 09:39:00	ga_ls9c_ard_3-2-1_093085_2023-04-28_final.stac-item
2023-05-14 11:12:57	ga_ls9c_ard_3-2-1_109073_2023-05-14_final.stac-item
2023-05-17 10:04:08	ga_ls9c_ard_3-2-1_098071_2023-05-17_final.stac-item
2023-05-18 09:11:42	ga_ls9c_ard_3-2-1_089079_2023-05-17_final.stac-item
2023-05-18 10:46:35	ga_ls9c_ard_3-2-1_105069_2023-05-18_final.stac-item
2023-05-19 09:56:56	ga_ls9c_ard_3-2-1_096084_2023-05-19_final.stac-item
2023-07-15 09:46:24	ga_ls9c_ard_3-2-1_095073_2023-07-15_final.stac-item
2023-07-21 10:49:24	ga_ls9c_ard_3-2-1_105076_2023-07-21_final.stac-item
2023-08-18 11:17:29	ga_ls9c_ard_3-2-1_109084_2023-08-18_final.stac-item
2023-09-19 09:40:07	ga_ls9c_ard_3-2-1_093087_2023-09-19_final.stac-item
2023-09-22 10:04:36	ga_ls9c_ard_3-2-1_098071_2023-09-22_final.stac-item
2023-11-23 10:22:23	ga_ls9c_ard_3-2-1_100084_2023-11-23_final.stac-item
2023-12-12 09:12:47	ga_ls9c_ard_3-2-1_089080_2023-12-11_final.stac-item
2023-12-17 11:06:16	ga_ls9c_ard_3-2-1_108070_2023-12-17_final.stac-item
2023-12-25 11:58:56	ga_ls9c_ard_3-2-1_116078_2023-12-25_final.stac-item
2024-01-11 09:25:33	ga_ls9c_ard_3-2-1_091081_2024-01-10_final.stac-item
2024-01-30 09:57:39	ga_ls9c_ard_3-2-1_096084_2024-01-30_final.stac-item
2024-02-06 11:38:46	ga_ls9c_ard_3-2-1_113074_2024-02-06_final.stac-item
2024-02-14 09:16:46	ga_ls9c_ard_3-2-1_089090_2024-02-13_final.stac-item
2024-03-18 09:51:56	ga_ls9c_ard_3-2-1_096070_2024-03-18_final.stac-item
2024-05-11 09:17:30	ga_ls9c_ard_3-2-1_090078_2024-05-10_nrt.stac-item
2024-05-12 10:03:32	ga_ls9c_ard_3-2-1_097085_2024-05-12_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