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
2015-10-28 11:59:52	ga_s2am_ard_3-2-1_49KGP_2015-10-28_final.stac-item
2015-10-28 11:59:52	ga_s2am_ard_3-2-1_49KHQ_2015-10-28_final.stac-item
2015-12-17 12:07:19	ga_s2am_ard_3-2-1_49JHM_2015-12-17_final.stac-item
2015-12-17 12:07:19	ga_s2am_ard_3-2-1_49JHN_2015-12-17_final.stac-item
2016-06-07 12:13:25	ga_s2am_ard_3-2-1_49JFM_2016-06-07_final.stac-item
2016-11-21 12:02:48	ga_s2am_ard_3-2-1_49KHP_2016-11-21_final.stac-item
2016-12-01 12:03:46	ga_s2am_ard_3-2-1_49KGP_2016-12-01_final.stac-item
2017-01-30 12:02:43	ga_s2am_ard_3-2-1_49JGM_2017-01-30_final.stac-item
2017-02-19 12:02:47	ga_s2am_ard_3-2-1_49KHP_2017-02-19_final.stac-item
2017-03-11 12:02:41	ga_s2am_ard_3-2-1_49KGQ_2017-03-11_final.stac-item
2017-10-20 12:14:08	ga_s2am_ard_3-2-1_49KGP_2017-10-20_final.stac-item
2017-10-30 12:14:41	ga_s2am_ard_3-2-1_49JFM_2017-10-30_final.stac-item
2017-11-19 12:13:18	ga_s2am_ard_3-2-1_49JGM_2017-11-19_final.stac-item
2019-01-23 12:14:38	ga_s2am_ard_3-2-1_49JGN_2019-01-23_final.stac-item
2019-02-12 12:14:05	ga_s2am_ard_3-2-1_49KHQ_2019-02-12_final.stac-item
2019-05-10 12:05:48	ga_s2am_ard_3-2-1_49JGJ_2019-05-10_final.stac-item
2019-07-22 12:14:03	ga_s2am_ard_3-2-1_49KGR_2019-07-22_final.stac-item
2019-09-30 12:14:14	ga_s2am_ard_3-2-1_49KGQ_2019-09-30_final.stac-item
2019-11-06 12:05:47	ga_s2am_ard_3-2-1_49JGJ_2019-11-06_final.stac-item
2019-11-09 12:14:29	ga_s2am_ard_3-2-1_49KGP_2019-11-09_final.stac-item
2020-01-28 12:14:52	ga_s2am_ard_3-2-1_49JFM_2020-01-28_final.stac-item
2020-02-04 12:05:36	ga_s2am_ard_3-2-1_49JGJ_2020-02-04_final.stac-item
2020-02-24 12:04:10	ga_s2am_ard_3-2-1_49KHQ_2020-02-24_final.stac-item
2020-02-27 12:13:53	ga_s2am_ard_3-2-1_49KGR_2020-02-27_final.stac-item
2020-04-17 12:14:21	ga_s2am_ard_3-2-1_49KHP_2020-04-17_final.stac-item
2020-10-01 12:05:49	ga_s2am_ard_3-2-1_49JGJ_2020-10-01_final.stac-item
2020-11-03 12:14:03	ga_s2am_ard_3-2-1_49KGR_2020-11-03_final.stac-item
2020-11-03 12:14:18	ga_s2am_ard_3-2-1_49KGQ_2020-11-03_final.stac-item
2020-12-23 12:14:08	ga_s2am_ard_3-2-1_49KHQ_2020-12-23_final.stac-item
2021-09-16 12:04:33	ga_s2am_ard_3-2-1_49KGP_2021-09-16_final.stac-item
2021-10-06 12:04:23	ga_s2am_ard_3-2-1_49KGQ_2021-10-06_final.stac-item
2021-10-29 12:14:17	ga_s2am_ard_3-2-1_49KGQ_2021-10-29_final.stac-item
2021-11-15 12:04:19	ga_s2am_ard_3-2-1_49KGQ_2021-11-15_final.stac-item
2021-11-25 12:04:18	ga_s2am_ard_3-2-1_49KGQ_2021-11-25_final.stac-item
2021-11-25 12:04:28	ga_s2am_ard_3-2-1_49KHP_2021-11-25_final.stac-item
2021-12-08 12:14:11	ga_s2am_ard_3-2-1_49KGQ_2021-12-08_final.stac-item
2022-01-04 12:05:16	ga_s2am_ard_3-2-1_49JGL_2022-01-04_final.stac-item
2022-01-14 12:04:32	ga_s2am_ard_3-2-1_49KGP_2022-01-14_final.stac-item
2022-02-03 12:04:31	ga_s2am_ard_3-2-1_49KGP_2022-02-03_final.stac-item
2022-02-16 12:14:13	ga_s2am_ard_3-2-1_49KGQ_2022-02-16_final.stac-item
2022-03-05 12:04:22	ga_s2am_ard_3-2-1_49KGQ_2022-03-05_final.stac-item
2022-03-05 12:04:32	ga_s2am_ard_3-2-1_49KHP_2022-03-05_final.stac-item
2022-03-05 12:05:00	ga_s2am_ard_3-2-1_49JHM_2022-03-05_final.stac-item
2022-03-08 12:14:31	ga_s2am_ard_3-2-1_49KGP_2022-03-08_final.stac-item
2022-04-07 12:14:13	ga_s2am_ard_3-2-1_49KGQ_2022-04-07_final.stac-item
2022-04-14 12:04:59	ga_s2am_ard_3-2-1_49JHM_2022-04-14_final.stac-item
2022-05-04 12:04:33	ga_s2am_ard_3-2-1_49KHP_2022-05-04_final.stac-item
2022-06-06 12:14:35	ga_s2am_ard_3-2-1_49KGP_2022-06-06_final.stac-item
2022-06-23 12:04:39	ga_s2am_ard_3-2-1_49KHP_2022-06-23_final.stac-item
2022-07-03 12:04:41	ga_s2am_ard_3-2-1_49KHP_2022-07-03_final.stac-item
2022-07-06 12:14:11	ga_s2am_ard_3-2-1_49KGR_2022-07-06_final.stac-item
2022-07-26 12:14:23	ga_s2am_ard_3-2-1_49KGQ_2022-07-26_final.stac-item
2022-08-12 12:04:40	ga_s2am_ard_3-2-1_49KHP_2022-08-12_final.stac-item
2022-08-12 12:05:27	ga_s2am_ard_3-2-1_49JGL_2022-08-12_final.stac-item
2022-08-22 12:04:44	ga_s2am_ard_3-2-1_49KGP_2022-08-22_final.stac-item
2022-08-25 12:14:25	ga_s2am_ard_3-2-1_49KGQ_2022-08-25_final
2022-10-01 12:04:42	ga_s2am_ard_3-2-1_49KGP_2022-10-01_final.stac-item
2022-10-04 12:14:05	ga_s2am_ard_3-2-1_49KGR_2022-10-04_final.stac-item
2022-11-23 12:13:58	ga_s2am_ard_3-2-1_49KGR_2022-11-23_final.stac-item
2022-11-30 12:04:31	ga_s2am_ard_3-2-1_49KGP_2022-11-30_final.stac-item
2022-12-03 12:15:00	ga_s2am_ard_3-2-1_49JFM_2022-12-03_final.stac-item
2022-12-13 12:14:10	ga_s2am_ard_3-2-1_49KGQ_2022-12-13_final.stac-item
2022-12-20 12:04:30	ga_s2am_ard_3-2-1_49KGP_2022-12-20_final.stac-item
2022-12-30 12:04:17	ga_s2am_ard_3-2-1_49KGQ_2022-12-30_final.stac-item
2023-01-02 12:14:12	ga_s2am_ard_3-2-1_49KGQ_2023-01-02_final.stac-item
2023-01-09 12:04:17	ga_s2am_ard_3-2-1_49KGQ_2023-01-09_final.stac-item
2023-01-12 12:13:56	ga_s2am_ard_3-2-1_49KGR_2023-01-12_final.stac-item
2023-01-12 12:14:10	ga_s2am_ard_3-2-1_49KGQ_2023-01-12_final.stac-item
2023-02-08 12:04:30	ga_s2am_ard_3-2-1_49KGP_2023-02-08_final
2023-02-18 12:04:28	ga_s2am_ard_3-2-1_49KGP_2023-02-18_nrt.stac-item
2023-02-18 12:04:28	ga_s2am_ard_3-2-1_49KGP_2023-02-18_final
2023-02-28 12:04:16	ga_s2am_ard_3-2-1_49KGQ_2023-02-28_final
2023-03-03 12:13:55	ga_s2am_ard_3-2-1_49KGR_2023-03-03_final
2023-03-03 12:14:10	ga_s2am_ard_3-2-1_49KGQ_2023-03-03_final
2023-05-09 12:04:18	ga_s2am_ard_3-2-1_49KGQ_2023-05-09_final
2023-05-09 12:04:30	ga_s2am_ard_3-2-1_49KHP_2023-05-09_final
2023-05-09 12:04:32	ga_s2am_ard_3-2-1_49KGP_2023-05-09_final
2023-06-21 12:14:16	ga_s2am_ard_3-2-1_49KGQ_2023-06-21_final
2023-06-28 12:05:48	ga_s2am_ard_3-2-1_49JGJ_2023-06-28_final
2023-07-31 12:14:18	ga_s2am_ard_3-2-1_49KGQ_2023-07-31_final
2023-08-10 12:14:25	ga_s2am_ard_3-2-1_49KHP_2023-08-10_final
2023-09-09 12:14:03	ga_s2am_ard_3-2-1_49KGR_2023-09-09_final
2023-09-26 12:05:46	ga_s2am_ard_3-2-1_49JGJ_2023-09-26_final
2023-10-06 12:05:45	ga_s2am_ard_3-2-1_49JGJ_2023-10-06_final
2023-10-26 12:04:13	ga_s2am_ard_3-2-1_49KHQ_2023-10-26_final
2023-10-26 12:04:31	ga_s2am_ard_3-2-1_49KGP_2023-10-26_final
2023-11-15 12:04:30	ga_s2am_ard_3-2-1_49KGP_2023-11-15_final
2023-11-18 12:14:25	ga_s2am_ard_3-2-1_49KGP_2023-11-18_final
2023-11-25 12:04:26	ga_s2am_ard_3-2-1_49KGP_2023-11-25_final
2023-11-25 12:05:10	ga_s2am_ard_3-2-1_49JGL_2023-11-25_final
2023-12-15 12:04:27	ga_s2am_ard_3-2-1_49KGP_2023-12-15_final
2023-12-18 12:14:52	ga_s2am_ard_3-2-1_49JGM_2023-12-18_final
2024-01-07 12:14:23	ga_s2am_ard_3-2-1_49KGP_2024-01-07_final
2024-01-14 12:04:12	ga_s2am_ard_3-2-1_49KGQ_2024-01-14_final
2024-02-06 12:14:56	ga_s2am_ard_3-2-1_49JFM_2024-02-06_final
2024-02-26 12:14:55	ga_s2am_ard_3-2-1_49JGM_2024-02-26_final
2024-03-04 12:05:30	ga_s2am_ard_3-2-1_49JGK_2024-03-04_final
2024-03-24 12:04:32	ga_s2am_ard_3-2-1_49KGP_2024-03-24_final
2024-04-16 12:14:01	ga_s2am_ard_3-2-1_49KGR_2024-04-16_final
2024-04-16 12:14:30	ga_s2am_ard_3-2-1_49KGP_2024-04-16_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

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