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_49KHR_2015-10-28_final.stac-item
2015-12-04 11:50:12	ga_s2am_ard_3-2-1_50JLL_2015-12-04_final.stac-item
2016-01-06 12:05:24	ga_s2am_ard_3-2-1_49JGK_2016-01-06_final.stac-item
2016-01-09 12:16:04	ga_s2am_ard_3-2-1_49JFM_2016-01-09_final.stac-item
2016-01-09 12:16:04	ga_s2am_ard_3-2-1_49JGN_2016-01-09_final.stac-item
2016-01-09 12:16:04	ga_s2am_ard_3-2-1_49KHR_2016-01-09_final.stac-item
2016-02-08 12:10:34	ga_s2am_ard_3-2-1_49JGM_2016-02-08_final.stac-item
2016-02-18 12:13:39	ga_s2am_ard_3-2-1_49KGR_2016-02-18_final.stac-item
2016-03-06 12:04:37	ga_s2am_ard_3-2-1_50JKR_2016-03-06_final.stac-item
2016-04-02 11:49:05	ga_s2am_ard_3-2-1_50HNK_2016-04-02_final.stac-item
2016-04-25 12:03:51	ga_s2am_ard_3-2-1_49KHQ_2016-04-25_final.stac-item
2016-05-28 12:13:49	ga_s2am_ard_3-2-1_49KGR_2016-05-28_final.stac-item
2016-11-14 12:13:24	ga_s2am_ard_3-2-1_49KHQ_2016-11-14_final.stac-item
2016-12-14 12:13:17	ga_s2am_ard_3-2-1_49KHQ_2016-12-14_final.stac-item
2017-01-27 11:56:19	ga_s2am_ard_3-2-1_50HNK_2017-01-27_final.stac-item
2017-01-30 12:02:43	ga_s2am_ard_3-2-1_49KHP_2017-01-30_final.stac-item
2017-02-22 12:13:15	ga_s2am_ard_3-2-1_49JFM_2017-02-22_final.stac-item
2017-02-22 12:13:15	ga_s2am_ard_3-2-1_49KHP_2017-02-22_final.stac-item
2017-03-05 11:44:15	ga_s2am_ard_3-2-1_50HPH_2017-03-05_final.stac-item
2017-08-01 12:13:37	ga_s2am_ard_3-2-1_49KHR_2017-08-01_final.stac-item
2017-09-17 12:03:44	ga_s2am_ard_3-2-1_49KHR_2017-09-17_final.stac-item
2017-10-20 12:13:21	ga_s2am_ard_3-2-1_49KGR_2017-10-20_final.stac-item
2017-10-20 12:13:21	ga_s2am_ard_3-2-1_49KHR_2017-10-20_final.stac-item
2017-11-09 12:13:20	ga_s2am_ard_3-2-1_49KGQ_2017-11-09_final.stac-item
2018-05-09 11:44:46	ga_s2am_ard_3-2-1_50HPH_2018-05-09_final.stac-item
2018-12-14 12:13:46	ga_s2am_ard_3-2-1_49KHR_2018-12-14_final.stac-item
2019-01-23 12:14:53	ga_s2am_ard_3-2-1_49JGM_2019-01-23_final.stac-item
2019-01-27 11:56:37	ga_s2am_ard_3-2-1_50HLK_2019-01-27_final.stac-item
2019-05-10 12:05:48	ga_s2am_ard_3-2-1_49JGJ_2019-05-10_final.stac-item
2019-06-12 12:13:57	ga_s2am_ard_3-2-1_49KHR_2019-06-12_final.stac-item
2019-06-12 12:14:16	ga_s2am_ard_3-2-1_49KGQ_2019-06-12_final.stac-item
2020-01-15 12:04:12	ga_s2am_ard_3-2-1_49KGQ_2020-01-15_final.stac-item
2020-07-10 11:57:13	ga_s2am_ard_3-2-1_50HLH_2020-07-10_final.stac-item
2020-09-01 12:04:04	ga_s2am_ard_3-2-1_49KHR_2020-09-01_final.stac-item
2020-09-18 11:56:16	ga_s2am_ard_3-2-1_50JLM_2020-09-18_final.stac-item
2020-09-18 11:56:45	ga_s2am_ard_3-2-1_50HLK_2020-09-18_final.stac-item
2020-09-18 11:57:10	ga_s2am_ard_3-2-1_50HMH_2020-09-18_final.stac-item
2020-12-10 12:04:15	ga_s2am_ard_3-2-1_49KGQ_2020-12-10_final.stac-item
2020-12-13 12:14:10	ga_s2am_ard_3-2-1_49KGQ_2020-12-13_final.stac-item
2021-01-29 12:04:18	ga_s2am_ard_3-2-1_49KGQ_2021-01-29_final.stac-item
2021-02-18 12:05:43	ga_s2am_ard_3-2-1_49JGJ_2021-02-18_final.stac-item
2021-08-24 11:56:13	ga_s2am_ard_3-2-1_50JLM_2021-08-24_final.stac-item
2021-10-10 11:47:07	ga_s2am_ard_3-2-1_50HPH_2021-10-10_final.stac-item
2021-11-22 11:56:40	ga_s2am_ard_3-2-1_50HLK_2021-11-22_final.stac-item
2021-11-25 12:05:29	ga_s2am_ard_3-2-1_49JGK_2021-11-25_final.stac-item
2022-01-11 11:57:10	ga_s2am_ard_3-2-1_50HLH_2022-01-11_final.stac-item
2022-01-15 11:36:53	ga_s2am_ard_3-2-1_50HPJ_2022-01-15_final.stac-item
2022-01-15 11:37:03	ga_s2am_ard_3-2-1_50HQH_2022-01-15_final.stac-item
2022-02-17 11:46:54	ga_s2am_ard_3-2-1_50HQH_2022-02-17_final.stac-item
2022-03-09 11:46:47	ga_s2am_ard_3-2-1_50HQJ_2022-03-09_final.stac-item
2022-03-16 11:37:07	ga_s2am_ard_3-2-1_50HQH_2022-03-16_final.stac-item
2022-03-22 11:56:43	ga_s2am_ard_3-2-1_50HLK_2022-03-22_final.stac-item
2022-04-11 11:56:57	ga_s2am_ard_3-2-1_50HLJ_2022-04-11_final.stac-item
2022-06-30 11:56:53	ga_s2am_ard_3-2-1_50HLK_2022-06-30_final.stac-item
2022-06-30 11:57:04	ga_s2am_ard_3-2-1_50HMJ_2022-06-30_final.stac-item
2022-08-12 12:04:30	ga_s2am_ard_3-2-1_49KGQ_2022-08-12_final.stac-item
2022-08-26 11:47:06	ga_s2am_ard_3-2-1_50HQH_2022-08-26_final
2022-10-18 11:56:28	ga_s2am_ard_3-2-1_50JLL_2022-10-18_final.stac-item
2022-11-17 11:56:57	ga_s2am_ard_3-2-1_50HLJ_2022-11-17_final.stac-item
2022-11-20 12:05:30	ga_s2am_ard_3-2-1_49JGK_2022-11-20_final.stac-item
2022-12-04 11:46:48	ga_s2am_ard_3-2-1_50HPJ_2022-12-04_final.stac-item
2023-01-26 11:56:23	ga_s2am_ard_3-2-1_50JLL_2023-01-26_final.stac-item
2023-02-02 11:47:01	ga_s2am_ard_3-2-1_50HPH_2023-02-02_final
2023-02-12 11:47:00	ga_s2am_ard_3-2-1_50HPH_2023-02-12_nrt.stac-item
2023-02-12 11:47:00	ga_s2am_ard_3-2-1_50HPH_2023-02-12_final
2023-03-17 11:56:38	ga_s2am_ard_3-2-1_50HLK_2023-03-17_final
2023-04-23 11:47:01	ga_s2am_ard_3-2-1_50HPH_2023-04-23_final
2023-04-26 11:56:25	ga_s2am_ard_3-2-1_50JLL_2023-04-26_final
2023-04-26 11:56:54	ga_s2am_ard_3-2-1_50HLJ_2023-04-26_final
2023-05-06 11:57:09	ga_s2am_ard_3-2-1_50HLH_2023-05-06_final
2023-05-26 11:56:58	ga_s2am_ard_3-2-1_50HLJ_2023-05-26_final
2023-06-02 11:47:06	ga_s2am_ard_3-2-1_50HPH_2023-06-02_final
2023-06-12 11:47:28	ga_s2am_ard_3-2-1_50HMG_2023-06-12_final
2023-06-15 11:56:30	ga_s2am_ard_3-2-1_50JLL_2023-06-15_final
2023-06-28 12:05:48	ga_s2am_ard_3-2-1_49JGJ_2023-06-28_final
2023-07-05 11:57:14	ga_s2am_ard_3-2-1_50HLH_2023-07-05_final
2023-07-22 11:46:56	ga_s2am_ard_3-2-1_50HNJ_2023-07-22_final
2023-07-22 11:47:11	ga_s2am_ard_3-2-1_50HNH_2023-07-22_final
2023-08-04 11:56:42	ga_s2am_ard_3-2-1_50HMK_2023-08-04_final
2023-08-04 11:57:11	ga_s2am_ard_3-2-1_50HMH_2023-08-04_final
2023-08-14 11:56:31	ga_s2am_ard_3-2-1_50JLL_2023-08-14_final
2023-08-14 11:56:45	ga_s2am_ard_3-2-1_50HLK_2023-08-14_final
2023-08-28 11:36:58	ga_s2am_ard_3-2-1_50HPJ_2023-08-28_final
2023-09-27 11:37:09	ga_s2am_ard_3-2-1_50HPH_2023-09-27_final
2023-11-28 12:14:34	ga_s2am_ard_3-2-1_49JGN_2023-11-28_final
2023-12-02 11:56:22	ga_s2am_ard_3-2-1_50HNK_2023-12-02_final
2023-12-02 11:56:32	ga_s2am_ard_3-2-1_50HLK_2023-12-02_final
2023-12-05 12:04:11	ga_s2am_ard_3-2-1_49KGQ_2023-12-05_final
2023-12-06 11:37:15	ga_s2am_ard_3-2-1_50HPG_2023-12-06_final
2023-12-22 11:57:18	ga_s2am_ard_3-2-1_50HLG_2023-12-22_final
2023-12-26 11:36:49	ga_s2am_ard_3-2-1_50HPJ_2023-12-26_final
2024-01-11 11:57:17	ga_s2am_ard_3-2-1_50HLG_2024-01-11_final
2024-01-18 11:46:39	ga_s2am_ard_3-2-1_50HPJ_2024-01-18_final
2024-01-28 11:46:38	ga_s2am_ard_3-2-1_50HQJ_2024-01-28_final
2024-02-07 11:46:33	ga_s2am_ard_3-2-1_50HQK_2024-02-07_final
2024-02-07 11:46:39	ga_s2am_ard_3-2-1_50HQJ_2024-02-07_final
2024-02-20 11:56:23	ga_s2am_ard_3-2-1_50JLL_2024-02-20_final
2024-03-08 11:46:43	ga_s2am_ard_3-2-1_50HQJ_2024-03-08_final
2024-04-10 11:56:31	ga_s2am_ard_3-2-1_50JLL_2024-04-10_final
2024-04-14 11:37:09	ga_s2am_ard_3-2-1_50HPH_2024-04-14_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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