Search ga_s2am_ard_3 whole collection

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels.\n\nFor these ARD products, this value comes from the Fmask algorithm.\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels.\n\nFor these ARD products, this value comes from the Fmask algorithm.\n 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\n\n(e.g. ‘epsg:32654’)\n:

One of - final|interim|nrt (near real time):

ID of a dataset type:

Ground sampling distance of the sensor’s best resolution band\nin metres; represents the size (or spatial resolution) of one pixel.\n from:

Ground sampling distance of the sensor’s best resolution band\nin metres; represents the size (or spatial resolution) of one pixel.\n to (exclusive):

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north\n from:

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north\n to (exclusive):

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon.\n from:

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon.\n 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\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm\n 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\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm\n 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\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm\n 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\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm\n to (exclusive):

File format (GeoTiff, NetCDF):

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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n 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)\n to (exclusive):

Dataset UUID:

User who indexed the dataset:

When dataset was indexed:

Label:

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.\nFor Sentinel it is MGRS code.\n:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless\n to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless\n from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless\n to (exclusive):

Unique identifier for a datastrip relative to a given Datatake.\n\n(e.g. ‘S2A_OPER_MSI_L1C_DS_SGS__20161214T040601_S20161214T005840_N02.04’)\n:

ESA product URI, with the '.SAFE' ending removed.\n\n(e.g. 'S2A_MSIL1C_20220303T000731_N0400_R073_T56LNM_20220303T012845')\n:

Granule ID according to the ESA naming convention\n\n(e.g. ‘S2A_OPER_MSI_L1C_TL_SGS__20161214T040601_A007721_T53KRB_N02.04’)\n:

Acquisition time range From

Acquisition time range to (exclusive)

Dataset URI:

Add field

First 100 results (query limit)

Time (local)	Label
2016-11-20 10:47:23	ga_s2am_ard_3-2-1_53LPD_2016-11-20_final
2016-12-07 10:38:25	ga_s2am_ard_3-2-1_53LPD_2016-12-07_final
2016-12-10 10:47:57	ga_s2am_ard_3-2-1_53LPD_2016-12-10_final
2016-12-17 10:38:38	ga_s2am_ard_3-2-1_53LPD_2016-12-17_final
2016-12-27 10:38:37	ga_s2am_ard_3-2-1_53LPD_2016-12-27_final
2016-12-30 10:48:09	ga_s2am_ard_3-2-1_53LPD_2016-12-30_final
2017-01-09 10:47:28	ga_s2am_ard_3-2-1_53LPD_2017-01-09_final
2017-01-16 10:38:05	ga_s2am_ard_3-2-1_53LPD_2017-01-16_final
2017-01-19 10:47:10	ga_s2am_ard_3-2-1_53LPD_2017-01-19_final
2017-01-26 10:37:20	ga_s2am_ard_3-2-1_53LPD_2017-01-26_final
2017-02-05 10:37:21	ga_s2am_ard_3-2-1_53LPD_2017-02-05_final
2017-02-15 10:37:21	ga_s2am_ard_3-2-1_53LPD_2017-02-15_final
2017-02-18 10:50:41	ga_s2am_ard_3-2-1_53LPD_2017-02-18_final
2017-02-28 10:47:18	ga_s2am_ard_3-2-1_53LPD_2017-02-28_final
2017-03-07 10:37:20	ga_s2am_ard_3-2-1_53LPD_2017-03-07_final
2017-03-20 10:47:17	ga_s2am_ard_3-2-1_53LPD_2017-03-20_final
2017-03-27 10:37:22	ga_s2am_ard_3-2-1_53LPD_2017-03-27_final
2017-04-09 10:47:23	ga_s2am_ard_3-2-1_53LPD_2017-04-09_final
2017-04-16 10:37:27	ga_s2am_ard_3-2-1_53LPD_2017-04-16_final
2017-04-29 10:47:27	ga_s2am_ard_3-2-1_53LPD_2017-04-29_final
2017-05-06 10:37:30	ga_s2am_ard_3-2-1_53LPD_2017-05-06_final
2017-05-26 10:37:30	ga_s2am_ard_3-2-1_53LPD_2017-05-26_final
2017-06-08 10:47:26	ga_s2am_ard_3-2-1_53LPD_2017-06-08_final
2017-06-15 10:37:28	ga_s2am_ard_3-2-1_53LPD_2017-06-15_final
2017-06-28 10:47:23	ga_s2am_ard_3-2-1_53LPD_2017-06-28_final
2017-07-05 10:37:26	ga_s2am_ard_3-2-1_53LPD_2017-07-05_final
2017-07-18 10:47:26	ga_s2am_ard_3-2-1_53LPD_2017-07-18_final
2017-07-25 10:37:28	ga_s2am_ard_3-2-1_53LPD_2017-07-25_final
2017-08-07 10:47:26	ga_s2am_ard_3-2-1_53LPD_2017-08-07_final
2017-08-14 10:37:29	ga_s2am_ard_3-2-1_53LPD_2017-08-14_final
2017-08-27 10:47:25	ga_s2am_ard_3-2-1_53LPD_2017-08-27_final
2017-09-03 10:37:27	ga_s2am_ard_3-2-1_53LPD_2017-09-03_final
2017-09-16 10:47:22	ga_s2am_ard_3-2-1_53LPD_2017-09-16_final
2017-09-23 10:37:23	ga_s2am_ard_3-2-1_53LPD_2017-09-23_final
2017-10-06 10:47:23	ga_s2am_ard_3-2-1_53LPD_2017-10-06_final
2017-10-13 10:37:26	ga_s2am_ard_3-2-1_53LPD_2017-10-13_final
2017-10-26 10:47:24	ga_s2am_ard_3-2-1_53LPD_2017-10-26_final
2017-11-02 10:37:26	ga_s2am_ard_3-2-1_53LPD_2017-11-02_final
2017-11-15 10:47:23	ga_s2am_ard_3-2-1_53LPD_2017-11-15_final
2017-11-22 10:37:23	ga_s2am_ard_3-2-1_53LPD_2017-11-22_final
2017-12-05 10:47:20	ga_s2am_ard_3-2-1_53LPD_2017-12-05_final
2017-12-12 10:37:22	ga_s2am_ard_3-2-1_53LPD_2017-12-12_final
2017-12-25 10:47:19	ga_s2am_ard_3-2-1_53LPD_2017-12-25_final
2018-01-01 10:37:20	ga_s2am_ard_3-2-1_53LPD_2018-01-01_final
2018-01-14 10:47:17	ga_s2am_ard_3-2-1_53LPD_2018-01-14_final
2018-01-21 10:37:21	ga_s2am_ard_3-2-1_53LPD_2018-01-21_final
2018-02-03 10:47:21	ga_s2am_ard_3-2-1_53LPD_2018-02-03_final
2018-02-10 10:37:23	ga_s2am_ard_3-2-1_53LPD_2018-02-10_final
2018-02-13 10:47:21	ga_s2am_ard_3-2-1_53LPD_2018-02-13_final
2018-02-20 10:37:24	ga_s2am_ard_3-2-1_53LPD_2018-02-20_final
2018-02-23 10:47:22	ga_s2am_ard_3-2-1_53LPD_2018-02-23_final
2018-03-02 10:37:24	ga_s2am_ard_3-2-1_53LPD_2018-03-02_final
2018-03-05 10:47:21	ga_s2am_ard_3-2-1_53LPD_2018-03-05_final
2018-03-12 10:37:23	ga_s2am_ard_3-2-1_53LPD_2018-03-12_final
2018-03-15 10:47:20	ga_s2am_ard_3-2-1_53LPD_2018-03-15_final
2018-03-22 10:37:24	ga_s2am_ard_3-2-1_53LPD_2018-03-22_final
2018-03-25 10:47:23	ga_s2am_ard_3-2-1_53LPD_2018-03-25_final
2018-04-01 10:37:26	ga_s2am_ard_3-2-1_53LPD_2018-04-01_final
2018-04-04 10:47:25	ga_s2am_ard_3-2-1_53LPD_2018-04-04_final
2018-04-11 10:37:28	ga_s2am_ard_3-2-1_53LPD_2018-04-11_final
2018-04-14 10:47:26	ga_s2am_ard_3-2-1_53LPD_2018-04-14_final
2018-04-21 10:37:29	ga_s2am_ard_3-2-1_53LPD_2018-04-21_final
2018-04-24 10:47:27	ga_s2am_ard_3-2-1_53LPD_2018-04-24_final
2018-05-01 10:37:29	ga_s2am_ard_3-2-1_53LPD_2018-05-01_final
2018-05-04 10:47:27	ga_s2am_ard_3-2-1_53LPD_2018-05-04_final
2018-05-11 10:37:30	ga_s2am_ard_3-2-1_53LPD_2018-05-11_final
2018-05-14 10:47:27	ga_s2am_ard_3-2-1_53LPD_2018-05-14_final
2018-05-21 10:37:29	ga_s2am_ard_3-2-1_53LPD_2018-05-21_final
2018-05-24 10:47:27	ga_s2am_ard_3-2-1_53LPD_2018-05-24_final
2018-06-03 10:47:26	ga_s2am_ard_3-2-1_53LPD_2018-06-03_final
2018-06-10 10:37:27	ga_s2am_ard_3-2-1_53LPD_2018-06-10_final
2018-06-13 10:47:24	ga_s2am_ard_3-2-1_53LPD_2018-06-13_final
2018-06-20 10:40:49	ga_s2am_ard_3-2-1_53LPD_2018-06-20_final
2018-06-23 10:47:25	ga_s2am_ard_3-2-1_53LPD_2018-06-23_final
2018-06-30 10:37:28	ga_s2am_ard_3-2-1_53LPD_2018-06-30_final
2018-07-10 10:37:28	ga_s2am_ard_3-2-1_53LPD_2018-07-10_final
2018-07-20 10:37:28	ga_s2am_ard_3-2-1_53LPD_2018-07-20_final
2018-07-30 10:37:28	ga_s2am_ard_3-2-1_53LPD_2018-07-30_final
2018-08-02 10:47:25	ga_s2am_ard_3-2-1_53LPD_2018-08-02_final
2018-08-09 10:37:27	ga_s2am_ard_3-2-1_53LPD_2018-08-09_final
2018-08-19 10:37:26	ga_s2am_ard_3-2-1_53LPD_2018-08-19_final
2018-08-29 10:37:26	ga_s2am_ard_3-2-1_53LPD_2018-08-29_final
2018-09-01 10:47:23	ga_s2am_ard_3-2-1_53LPD_2018-09-01_final
2018-09-08 10:37:24	ga_s2am_ard_3-2-1_53LPD_2018-09-08_final
2018-09-18 10:37:23	ga_s2am_ard_3-2-1_53LPD_2018-09-18_final
2018-09-28 10:37:23	ga_s2am_ard_3-2-1_53LPD_2018-09-28_final
2018-10-08 10:37:24	ga_s2am_ard_3-2-1_53LPD_2018-10-08_final
2018-10-11 10:47:22	ga_s2am_ard_3-2-1_53LPD_2018-10-11_final
2018-10-18 10:37:24	ga_s2am_ard_3-2-1_53LPD_2018-10-18_final
2018-11-07 10:41:19	ga_s2am_ard_3-2-1_53LPD_2018-11-07_final
2018-11-10 10:51:13	ga_s2am_ard_3-2-1_53LPD_2018-11-10_final
2018-11-30 10:51:10	ga_s2am_ard_3-2-1_53LPD_2018-11-30_final
2018-12-10 10:51:10	ga_s2am_ard_3-2-1_53LPD_2018-12-10_final
2018-12-27 10:41:16	ga_s2am_ard_3-2-1_53LPD_2018-12-27_final
2019-01-06 10:41:18	ga_s2am_ard_3-2-1_53LPD_2019-01-06_final
2019-01-09 10:51:13	ga_s2am_ard_3-2-1_53LPD_2019-01-09_final
2019-01-16 10:41:19	ga_s2am_ard_3-2-1_53LPD_2019-01-16_final
2019-01-19 10:51:14	ga_s2am_ard_3-2-1_53LPD_2019-01-19_final
2023-02-07 10:51:17	ga_s2am_ard_3-2-1_53LPD_2023-02-07_final
2023-08-03 10:41:29	ga_s2am_ard_3-2-1_53LPD_2023-08-03_final

Return to ga_s2am_ard_3 whole collection overview

ga_s2am_ard_3: Geoscience Australia Sentinel 2A MSI Analysis Ready Data Collection 3

Baseline satellite data

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

All years