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
2022-10-09 11:24:25	ga_s2am_ard_3-2-1_52JBT_2022-10-09_final.stac-item
2022-10-09 11:24:25	ga_s2am_ard_3-2-1_51JZN_2022-10-09_final.stac-item
2022-10-09 11:24:29	ga_s2am_ard_3-2-1_51JYN_2022-10-09_final.stac-item
2022-10-09 11:24:32	ga_s2am_ard_3-2-1_51JXN_2022-10-09_final.stac-item
2022-10-09 11:24:36	ga_s2am_ard_3-2-1_51JWN_2022-10-09_final.stac-item
2022-10-11 10:24:00	ga_s2am_ard_3-2-1_54JWT_2022-10-11_final.stac-item
2022-10-11 10:24:04	ga_s2am_ard_3-2-1_54JVT_2022-10-11_final.stac-item
2022-10-11 10:24:07	ga_s2am_ard_3-2-1_54JUT_2022-10-11_final.stac-item
2022-10-11 10:24:10	ga_s2am_ard_3-2-1_53JRN_2022-10-11_final.stac-item
2022-10-11 10:24:10	ga_s2am_ard_3-2-1_54JTT_2022-10-11_final.stac-item
2022-10-11 10:24:22	ga_s2am_ard_3-2-1_53JRM_2022-10-11_final.stac-item
2022-10-13 09:23:45	ga_s2am_ard_3-2-1_56JNT_2022-10-12_final.stac-item
2022-10-18 10:13:48	ga_s2am_ard_3-2-1_54JZT_2022-10-18_final.stac-item
2022-10-18 10:13:54	ga_s2am_ard_3-2-1_54JYT_2022-10-18_final.stac-item
2022-10-18 10:13:58	ga_s2am_ard_3-2-1_54JXT_2022-10-18_final.stac-item
2022-10-18 10:14:02	ga_s2am_ard_3-2-1_54JWT_2022-10-18_final.stac-item
2022-10-18 10:14:09	ga_s2am_ard_3-2-1_54JVT_2022-10-18_final.stac-item
2022-10-19 11:24:21	ga_s2am_ard_3-2-1_51JZN_2022-10-19_final.stac-item
2022-10-19 11:24:21	ga_s2am_ard_3-2-1_52JBT_2022-10-19_final.stac-item
2022-10-19 11:24:25	ga_s2am_ard_3-2-1_51JYN_2022-10-19_final.stac-item
2022-10-19 11:24:28	ga_s2am_ard_3-2-1_51JXN_2022-10-19_final.stac-item
2022-10-19 11:24:32	ga_s2am_ard_3-2-1_51JWN_2022-10-19_final.stac-item
2022-10-25 10:03:49	ga_s2am_ard_3-2-1_55JDN_2022-10-25_final.stac-item
2022-10-25 10:03:52	ga_s2am_ard_3-2-1_55JCN_2022-10-25_final.stac-item
2022-10-25 10:03:55	ga_s2am_ard_3-2-1_54JZT_2022-10-25_final.stac-item
2022-10-25 10:03:55	ga_s2am_ard_3-2-1_55JBN_2022-10-25_final.stac-item
2022-10-25 10:03:59	ga_s2am_ard_3-2-1_54JYT_2022-10-25_final.stac-item
2022-10-25 10:04:10	ga_s2am_ard_3-2-1_54JZS_2022-10-25_final.stac-item
2022-10-29 11:24:22	ga_s2am_ard_3-2-1_51JZN_2022-10-29_final.stac-item
2022-10-29 11:24:22	ga_s2am_ard_3-2-1_52JBT_2022-10-29_final.stac-item
2022-10-29 11:24:26	ga_s2am_ard_3-2-1_51JYN_2022-10-29_final.stac-item
2022-10-29 11:24:30	ga_s2am_ard_3-2-1_51JXN_2022-10-29_final.stac-item
2022-10-29 11:24:33	ga_s2am_ard_3-2-1_51JWN_2022-10-29_final.stac-item
2022-11-01 09:53:42	ga_s2am_ard_3-2-1_55JGN_2022-11-01_final.stac-item
2022-11-01 09:53:48	ga_s2am_ard_3-2-1_55JFN_2022-11-01_final.stac-item
2022-11-01 09:53:51	ga_s2am_ard_3-2-1_55JEN_2022-11-01_final.stac-item
2022-11-01 09:53:55	ga_s2am_ard_3-2-1_55JDN_2022-11-01_final.stac-item
2022-11-01 09:54:02	ga_s2am_ard_3-2-1_55JCN_2022-11-01_final.stac-item
2022-11-12 11:04:14	ga_s2am_ard_3-2-1_52JGT_2022-11-12_final.stac-item
2022-11-12 11:04:18	ga_s2am_ard_3-2-1_52JFT_2022-11-12_final.stac-item
2022-11-12 11:04:21	ga_s2am_ard_3-2-1_52JET_2022-11-12_final.stac-item
2022-11-12 11:04:24	ga_s2am_ard_3-2-1_52JDT_2022-11-12_final.stac-item
2022-11-16 10:44:12	ga_s2am_ard_3-2-1_53JMN_2022-11-16_final.stac-item
2022-11-16 10:44:15	ga_s2am_ard_3-2-1_53JLN_2022-11-16_final.stac-item
2023-02-10 11:04:09	ga_s2am_ard_3-2-1_52JGT_2023-02-10_final.stac-item
2023-02-10 11:04:13	ga_s2am_ard_3-2-1_52JFT_2023-02-10_final.stac-item
2023-02-10 11:04:17	ga_s2am_ard_3-2-1_52JET_2023-02-10_final.stac-item
2023-02-10 11:04:20	ga_s2am_ard_3-2-1_52JDT_2023-02-10_final.stac-item
2023-02-12 10:03:44	ga_s2am_ard_3-2-1_55JDN_2023-02-12_final
2023-02-12 10:03:48	ga_s2am_ard_3-2-1_55JCN_2023-02-12_final
2023-02-12 10:03:51	ga_s2am_ard_3-2-1_54JZT_2023-02-12_final
2023-02-12 10:03:51	ga_s2am_ard_3-2-1_55JBN_2023-02-12_final
2023-02-12 10:03:54	ga_s2am_ard_3-2-1_54JYT_2023-02-12_final
2023-02-12 10:04:05	ga_s2am_ard_3-2-1_54JZS_2023-02-12_final
2023-02-14 10:44:00	ga_s2am_ard_3-2-1_53JPN_2023-02-14_nrt.stac-item
2023-02-14 10:44:04	ga_s2am_ard_3-2-1_53JNN_2023-02-14_final
2023-02-14 10:44:10	ga_s2am_ard_3-2-1_53JLN_2023-02-14_final
2023-02-15 10:13:49	ga_s2am_ard_3-2-1_54JYT_2023-02-15_nrt.stac-item
2023-02-15 10:13:49	ga_s2am_ard_3-2-1_54JYT_2023-02-15_final
2023-02-15 10:13:53	ga_s2am_ard_3-2-1_54JXT_2023-02-15_final
2023-02-17 10:54:06	ga_s2am_ard_3-2-1_53JLN_2023-02-17_final
2023-02-17 10:54:08	ga_s2am_ard_3-2-1_52JHT_2023-02-17_final
2023-02-17 10:54:08	ga_s2am_ard_3-2-1_53JKN_2023-02-17_final
2023-02-17 10:54:12	ga_s2am_ard_3-2-1_52JGT_2023-02-17_final
2023-02-17 10:54:18	ga_s2am_ard_3-2-1_52JFT_2023-02-17_final
2023-02-17 10:54:22	ga_s2am_ard_3-2-1_52JHS_2023-02-17_final
2023-02-18 10:23:51	ga_s2am_ard_3-2-1_54JWT_2023-02-18_nrt.stac-item
2023-02-18 10:23:51	ga_s2am_ard_3-2-1_54JWT_2023-02-18_final
2023-02-18 10:23:55	ga_s2am_ard_3-2-1_54JVT_2023-02-18_final
2023-02-18 10:23:58	ga_s2am_ard_3-2-1_54JUT_2023-02-18_final
2023-02-18 10:24:01	ga_s2am_ard_3-2-1_53JRN_2023-02-18_final
2023-02-18 10:24:01	ga_s2am_ard_3-2-1_54JTT_2023-02-18_final
2023-02-18 10:24:13	ga_s2am_ard_3-2-1_53JRM_2023-02-18_final
2023-02-21 10:33:57	ga_s2am_ard_3-2-1_53JRN_2023-02-21_final
2023-02-21 10:33:57	ga_s2am_ard_3-2-1_54JTT_2023-02-21_final
2023-02-21 10:34:01	ga_s2am_ard_3-2-1_53JQN_2023-02-21_final
2023-02-21 10:34:05	ga_s2am_ard_3-2-1_53JPN_2023-02-21_final
2023-02-21 10:34:11	ga_s2am_ard_3-2-1_53JNN_2023-02-21_final
2023-02-21 12:14:39	ga_s2am_ard_3-2-1_49JGN_2023-02-21_nrt.stac-item
2023-02-21 12:14:39	ga_s2am_ard_3-2-1_49JGN_2023-02-21_final
2023-02-21 12:14:43	ga_s2am_ard_3-2-1_49JFN_2023-02-21_nrt.stac-item
2023-02-21 12:14:43	ga_s2am_ard_3-2-1_49JFN_2023-02-21_final
2023-02-21 12:14:50	ga_s2am_ard_3-2-1_49JFN_2023-02-21_nrt.stac-item
2023-02-21 12:14:50	ga_s2am_ard_3-2-1_49JFN_2023-02-21_final
2023-07-08 10:24:00	ga_s2am_ard_3-2-1_54JWT_2023-07-08_final
2023-07-08 10:24:03	ga_s2am_ard_3-2-1_54JVT_2023-07-08_final
2023-07-08 10:24:07	ga_s2am_ard_3-2-1_54JUT_2023-07-08_final
2023-07-08 10:24:09	ga_s2am_ard_3-2-1_53JRN_2023-07-08_final
2023-07-08 10:24:09	ga_s2am_ard_3-2-1_54JTT_2023-07-08_final
2023-07-08 10:24:24	ga_s2am_ard_3-2-1_53JRM_2023-07-08_final
2023-07-09 09:53:43	ga_s2am_ard_3-2-1_55JGN_2023-07-09_final
2023-07-09 09:53:49	ga_s2am_ard_3-2-1_55JFN_2023-07-09_final
2023-07-09 09:53:53	ga_s2am_ard_3-2-1_55JEN_2023-07-09_final
2023-07-09 09:53:56	ga_s2am_ard_3-2-1_55JDN_2023-07-09_final
2023-07-09 09:54:03	ga_s2am_ard_3-2-1_55JCN_2023-07-09_final
2023-07-09 11:34:30	ga_s2am_ard_3-2-1_51JWN_2023-07-09_final
2023-07-09 11:34:34	ga_s2am_ard_3-2-1_51JVN_2023-07-09_final
2023-07-09 11:34:37	ga_s2am_ard_3-2-1_51JUN_2023-07-09_final
2023-07-09 11:34:41	ga_s2am_ard_3-2-1_51JTN_2023-07-09_final
2023-07-09 11:34:42	ga_s2am_ard_3-2-1_50JRT_2023-07-09_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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