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
2015-10-21 12:09:42	ga_s2am_ard_3-2-1_49KGQ_2015-10-21_final.stac-item
2015-10-21 12:09:42	ga_s2am_ard_3-2-1_49KHP_2015-10-21_final.stac-item
2015-12-17 12:07:19	ga_s2am_ard_3-2-1_50JLN_2015-12-17_final.stac-item
2016-01-03 11:57:46	ga_s2am_ard_3-2-1_50HLK_2016-01-03_final.stac-item
2016-01-06 12:05:24	ga_s2am_ard_3-2-1_50JKQ_2016-01-06_final.stac-item
2016-03-03 11:49:03	ga_s2am_ard_3-2-1_50HLK_2016-03-03_final.stac-item
2016-04-15 11:55:08	ga_s2am_ard_3-2-1_50JLQ_2016-04-15_final.stac-item
2016-11-14 12:13:24	ga_s2am_ard_3-2-1_49KGR_2016-11-14_final.stac-item
2016-11-14 12:13:24	ga_s2am_ard_3-2-1_49KHP_2016-11-14_final.stac-item
2016-12-01 12:03:46	ga_s2am_ard_3-2-1_49KHQ_2016-12-01_final.stac-item
2016-12-14 12:13:17	ga_s2am_ard_3-2-1_49KGQ_2016-12-14_final.stac-item
2016-12-21 12:04:53	ga_s2am_ard_3-2-1_50JLM_2016-12-21_final.stac-item
2017-01-24 11:44:18	ga_s2am_ard_3-2-1_50HQH_2017-01-24_final.stac-item
2017-01-30 12:02:43	ga_s2am_ard_3-2-1_49KHP_2017-01-30_final.stac-item
2017-02-26 11:52:43	ga_s2am_ard_3-2-1_50JLL_2017-02-26_final.stac-item
2017-03-08 11:56:00	ga_s2am_ard_3-2-1_50HLK_2017-03-08_final.stac-item
2017-08-01 12:13:37	ga_s2am_ard_3-2-1_49KGP_2017-08-01_final.stac-item
2017-08-18 12:02:50	ga_s2am_ard_3-2-1_49KGQ_2017-08-18_final.stac-item
2017-11-19 12:13:18	ga_s2am_ard_3-2-1_49JGN_2017-11-19_final.stac-item
2017-11-19 12:13:18	ga_s2am_ard_3-2-1_49KGQ_2017-11-19_final.stac-item
2017-12-10 11:42:24	ga_s2am_ard_3-2-1_50HQH_2017-12-10_final.stac-item
2019-01-13 12:14:17	ga_s2am_ard_3-2-1_49KHP_2019-01-13_final.stac-item
2019-01-27 11:55:24	ga_s2am_ard_3-2-1_50JLQ_2019-01-27_final.stac-item
2019-01-27 11:56:08	ga_s2am_ard_3-2-1_50JLM_2019-01-27_final.stac-item
2019-02-09 12:04:25	ga_s2am_ard_3-2-1_49KHP_2019-02-09_final.stac-item
2019-02-19 12:04:10	ga_s2am_ard_3-2-1_49KHQ_2019-02-19_final.stac-item
2019-02-19 12:04:14	ga_s2am_ard_3-2-1_49KGQ_2019-02-19_final.stac-item
2019-02-19 12:04:24	ga_s2am_ard_3-2-1_49KHP_2019-02-19_final.stac-item
2019-02-19 12:04:27	ga_s2am_ard_3-2-1_49KGP_2019-02-19_final.stac-item
2019-02-22 12:14:23	ga_s2am_ard_3-2-1_49KGP_2019-02-22_final.stac-item
2019-08-01 12:14:03	ga_s2am_ard_3-2-1_49KGR_2019-08-01_final.stac-item
2019-11-26 12:04:18	ga_s2am_ard_3-2-1_49KGQ_2019-11-26_final.stac-item
2019-12-09 12:14:05	ga_s2am_ard_3-2-1_49KHQ_2019-12-09_final.stac-item
2020-07-20 11:57:00	ga_s2am_ard_3-2-1_50HLJ_2020-07-20_final.stac-item
2020-09-22 11:36:57	ga_s2am_ard_3-2-1_50HPJ_2020-09-22_final.stac-item
2020-09-22 11:37:12	ga_s2am_ard_3-2-1_50HPH_2020-09-22_final.stac-item
2020-11-13 12:14:25	ga_s2am_ard_3-2-1_49KHP_2020-11-13_final.stac-item
2020-12-10 12:04:10	ga_s2am_ard_3-2-1_49KHQ_2020-12-10_final.stac-item
2021-08-24 11:55:29	ga_s2am_ard_3-2-1_50JLQ_2021-08-24_final.stac-item
2021-08-24 11:56:28	ga_s2am_ard_3-2-1_50JLL_2021-08-24_final.stac-item
2021-10-10 11:46:58	ga_s2am_ard_3-2-1_50HQH_2021-10-10_final.stac-item
2021-11-22 11:55:27	ga_s2am_ard_3-2-1_50JLQ_2021-11-22_final.stac-item
2021-11-25 12:05:23	ga_s2am_ard_3-2-1_50JLQ_2021-11-25_final.stac-item
2021-11-25 12:06:10	ga_s2am_ard_3-2-1_50JKM_2021-11-25_final.stac-item
2021-12-02 11:55:27	ga_s2am_ard_3-2-1_50JLQ_2021-12-02_final.stac-item
2021-12-02 11:56:40	ga_s2am_ard_3-2-1_50HLK_2021-12-02_final.stac-item
2021-12-05 12:06:03	ga_s2am_ard_3-2-1_50JLM_2021-12-05_final.stac-item
2021-12-06 11:36:51	ga_s2am_ard_3-2-1_50HPJ_2021-12-06_final.stac-item
2021-12-12 11:55:26	ga_s2am_ard_3-2-1_50JLQ_2021-12-12_final.stac-item
2021-12-25 12:05:24	ga_s2am_ard_3-2-1_50JLQ_2021-12-25_final.stac-item
2022-02-10 11:56:10	ga_s2am_ard_3-2-1_50JLM_2022-02-10_final.stac-item
2022-03-05 12:05:27	ga_s2am_ard_3-2-1_50JLQ_2022-03-05_final.stac-item
2022-04-14 12:05:07	ga_s2am_ard_3-2-1_50JKR_2022-04-14_final.stac-item
2022-04-14 12:05:12	ga_s2am_ard_3-2-1_50JLR_2022-04-14_final.stac-item
2022-04-21 11:55:32	ga_s2am_ard_3-2-1_50JLQ_2022-04-21_final.stac-item
2022-05-04 12:04:23	ga_s2am_ard_3-2-1_49KGQ_2022-05-04_final.stac-item
2022-05-04 12:05:14	ga_s2am_ard_3-2-1_50JLR_2022-05-04_final.stac-item
2022-06-30 11:57:19	ga_s2am_ard_3-2-1_50HMH_2022-06-30_final.stac-item
2022-07-20 11:55:39	ga_s2am_ard_3-2-1_50JLQ_2022-07-20_final.stac-item
2022-08-12 12:05:35	ga_s2am_ard_3-2-1_50JLQ_2022-08-12_final.stac-item
2022-08-22 12:05:35	ga_s2am_ard_3-2-1_50JLQ_2022-08-22_final.stac-item
2022-10-18 11:55:30	ga_s2am_ard_3-2-1_50JLQ_2022-10-18_final.stac-item
2022-10-28 11:55:16	ga_s2am_ard_3-2-1_50JLR_2022-10-28_final.stac-item
2022-10-28 11:56:14	ga_s2am_ard_3-2-1_50JLM_2022-10-28_final.stac-item
2022-11-20 12:05:24	ga_s2am_ard_3-2-1_50JLQ_2022-11-20_final.stac-item
2022-12-21 11:36:36	ga_s2am_ard_3-2-1_50HPK_2022-12-21_final.stac-item
2022-12-30 12:06:03	ga_s2am_ard_3-2-1_50JLM_2022-12-30_final.stac-item
2023-02-22 11:46:51	ga_s2am_ard_3-2-1_50HQH_2023-02-22_nrt.stac-item
2023-02-22 11:46:51	ga_s2am_ard_3-2-1_50HQH_2023-02-22_final
2023-03-17 11:55:25	ga_s2am_ard_3-2-1_50JLQ_2023-03-17_final
2023-04-19 12:05:22	ga_s2am_ard_3-2-1_50JLQ_2023-04-19_final
2023-04-19 12:06:03	ga_s2am_ard_3-2-1_50JLM_2023-04-19_final
2023-05-16 11:57:10	ga_s2am_ard_3-2-1_50HLH_2023-05-16_final
2023-05-16 11:57:23	ga_s2am_ard_3-2-1_50HLG_2023-05-16_final
2023-05-26 11:56:58	ga_s2am_ard_3-2-1_50HLJ_2023-05-26_final
2023-08-04 11:55:32	ga_s2am_ard_3-2-1_50JLQ_2023-08-04_final
2023-08-24 11:56:32	ga_s2am_ard_3-2-1_50JLL_2023-08-24_final
2023-09-06 12:05:14	ga_s2am_ard_3-2-1_49JFM_2023-09-06_final
2023-10-20 11:46:53	ga_s2am_ard_3-2-1_50HQH_2023-10-20_final
2023-11-02 11:56:13	ga_s2am_ard_3-2-1_50JKM_2023-11-02_final
2023-11-28 12:14:01	ga_s2am_ard_3-2-1_49KHQ_2023-11-28_final
2023-12-02 11:56:17	ga_s2am_ard_3-2-1_50JLL_2023-12-02_final
2023-12-05 12:05:16	ga_s2am_ard_3-2-1_50JLQ_2023-12-05_final
2023-12-06 11:36:45	ga_s2am_ard_3-2-1_50HPJ_2023-12-06_final
2023-12-22 11:56:33	ga_s2am_ard_3-2-1_50HMK_2023-12-22_final
2024-01-01 11:56:22	ga_s2am_ard_3-2-1_50JLL_2024-01-01_final
2024-01-11 11:56:35	ga_s2am_ard_3-2-1_50HLK_2024-01-11_final
2024-01-24 12:05:16	ga_s2am_ard_3-2-1_50JLQ_2024-01-24_final
2024-01-28 11:46:25	ga_s2am_ard_3-2-1_50HQK_2024-01-28_final
2024-01-31 11:56:08	ga_s2am_ard_3-2-1_50JLM_2024-01-31_final
2024-02-13 12:05:20	ga_s2am_ard_3-2-1_50JLQ_2024-02-13_final
2024-02-17 11:46:51	ga_s2am_ard_3-2-1_50HQH_2024-02-17_final
2024-03-01 11:56:11	ga_s2am_ard_3-2-1_50JLM_2024-03-01_final
2024-03-01 11:56:35	ga_s2am_ard_3-2-1_50HMK_2024-03-01_final
2024-03-11 11:56:37	ga_s2am_ard_3-2-1_50HLK_2024-03-11_final
2024-03-21 11:55:28	ga_s2am_ard_3-2-1_50JLQ_2024-03-21_final
2024-03-21 11:57:23	ga_s2am_ard_3-2-1_50HLG_2024-03-21_final
2024-03-27 12:14:27	ga_s2am_ard_3-2-1_49KGP_2024-03-27_final
2024-04-03 12:05:20	ga_s2am_ard_3-2-1_50JLQ_2024-04-03_final
2024-04-20 11:57:25	ga_s2am_ard_3-2-1_50HLG_2024-04-20_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

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