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
2018-11-25 10:05:04	ga_s2am_ard_3-2-1_54JXN_2018-11-25_final.stac-item
2018-12-02 09:54:51	ga_s2am_ard_3-2-1_55JEH_2018-12-02_final.stac-item
2018-12-02 09:54:55	ga_s2am_ard_3-2-1_55JDH_2018-12-02_final.stac-item
2018-12-02 09:54:58	ga_s2am_ard_3-2-1_55JCH_2018-12-02_final.stac-item
2018-12-02 09:55:02	ga_s2am_ard_3-2-1_55JBH_2018-12-02_final.stac-item
2019-06-07 09:44:56	ga_s2am_ard_3-2-1_56JKN_2019-06-07_final.stac-item
2019-06-07 09:44:59	ga_s2am_ard_3-2-1_55JGH_2019-06-07_final.stac-item
2019-06-07 09:45:03	ga_s2am_ard_3-2-1_55JFH_2019-06-07_final.stac-item
2019-06-07 09:45:06	ga_s2am_ard_3-2-1_55JEH_2019-06-07_final.stac-item
2019-06-07 09:45:11	ga_s2am_ard_3-2-1_56JKM_2019-06-07_final.stac-item
2019-06-07 09:45:13	ga_s2am_ard_3-2-1_55JGG_2019-06-07_final.stac-item
2019-06-17 09:44:58	ga_s2am_ard_3-2-1_56JKN_2019-06-17_final.stac-item
2019-06-17 09:45:00	ga_s2am_ard_3-2-1_55JGH_2019-06-17_final.stac-item
2019-06-17 09:45:04	ga_s2am_ard_3-2-1_55JFH_2019-06-17_final.stac-item
2019-06-17 09:45:08	ga_s2am_ard_3-2-1_55JEH_2019-06-17_final.stac-item
2019-06-17 09:45:12	ga_s2am_ard_3-2-1_56JKM_2019-06-17_final.stac-item
2019-06-17 09:45:15	ga_s2am_ard_3-2-1_55JGG_2019-06-17_final.stac-item
2019-06-18 10:55:28	ga_s2am_ard_3-2-1_53JKH_2019-06-18_final.stac-item
2019-06-18 10:55:31	ga_s2am_ard_3-2-1_52JGN_2019-06-18_final.stac-item
2019-06-19 12:05:55	ga_s2am_ard_3-2-1_50JLN_2019-06-19_final.stac-item
2019-06-19 12:05:59	ga_s2am_ard_3-2-1_50JKN_2019-06-19_final.stac-item
2019-06-19 12:06:13	ga_s2am_ard_3-2-1_50JKM_2019-06-19_final.stac-item
2019-07-03 11:45:48	ga_s2am_ard_3-2-1_51JTH_2019-07-03_final.stac-item
2019-07-03 11:45:51	ga_s2am_ard_3-2-1_50JQN_2019-07-03_final.stac-item
2019-07-03 11:45:55	ga_s2am_ard_3-2-1_50JPN_2019-07-03_final.stac-item
2019-07-03 11:45:59	ga_s2am_ard_3-2-1_50JNN_2019-07-03_final.stac-item
2019-07-03 11:46:00	ga_s2am_ard_3-2-1_51JTG_2019-07-03_final.stac-item
2019-07-03 11:46:03	ga_s2am_ard_3-2-1_50JQM_2019-07-03_final.stac-item
2019-07-03 11:46:06	ga_s2am_ard_3-2-1_50JNN_2019-07-03_final.stac-item
2019-07-13 11:45:49	ga_s2am_ard_3-2-1_51JTH_2019-07-13_final.stac-item
2019-07-13 11:45:52	ga_s2am_ard_3-2-1_50JQN_2019-07-13_final.stac-item
2019-07-13 11:45:55	ga_s2am_ard_3-2-1_50JPN_2019-07-13_final.stac-item
2019-07-13 11:45:59	ga_s2am_ard_3-2-1_50JNN_2019-07-13_final.stac-item
2019-07-13 11:46:02	ga_s2am_ard_3-2-1_51JTG_2019-07-13_final.stac-item
2019-07-13 11:46:06	ga_s2am_ard_3-2-1_50JQM_2019-07-13_final.stac-item
2023-12-03 11:25:26	ga_s2am_ard_3-2-1_51JYH_2023-12-03_final
2023-12-03 11:25:31	ga_s2am_ard_3-2-1_51JXH_2023-12-03_final
2023-12-03 11:25:35	ga_s2am_ard_3-2-1_51JWH_2023-12-03_final
2023-12-03 11:25:38	ga_s2am_ard_3-2-1_51JVH_2023-12-03_final
2023-12-04 10:55:17	ga_s2am_ard_3-2-1_53JKH_2023-12-04_final
2023-12-06 09:54:53	ga_s2am_ard_3-2-1_55JEH_2023-12-06_final
2023-12-06 09:54:57	ga_s2am_ard_3-2-1_55JDH_2023-12-06_final
2023-12-06 09:55:00	ga_s2am_ard_3-2-1_55JCH_2023-12-06_final
2023-12-06 09:55:05	ga_s2am_ard_3-2-1_55JBH_2023-12-06_final
2023-12-06 11:35:38	ga_s2am_ard_3-2-1_51JVH_2023-12-06_final
2023-12-06 11:35:39	ga_s2am_ard_3-2-1_51JUH_2023-12-06_final
2023-12-06 11:35:41	ga_s2am_ard_3-2-1_51JTH_2023-12-06_final
2023-12-06 11:35:44	ga_s2am_ard_3-2-1_50JQN_2023-12-06_final
2023-12-15 10:25:16	ga_s2am_ard_3-2-1_53JQH_2023-12-15_final
2023-12-15 10:25:28	ga_s2am_ard_3-2-1_54JTM_2023-12-15_final
2023-12-15 10:25:30	ga_s2am_ard_3-2-1_53JQG_2023-12-15_final
2023-12-19 10:04:59	ga_s2am_ard_3-2-1_55JCH_2023-12-19_final
2023-12-19 10:05:01	ga_s2am_ard_3-2-1_55JBH_2023-12-19_final
2023-12-19 10:05:06	ga_s2am_ard_3-2-1_55JCH_2023-12-19_final
2023-12-19 10:05:07	ga_s2am_ard_3-2-1_55JBH_2023-12-19_final
2023-12-19 10:05:09	ga_s2am_ard_3-2-1_54JYN_2023-12-19_final
2023-12-19 10:05:11	ga_s2am_ard_3-2-1_54JXN_2023-12-19_final
2023-12-19 10:05:16	ga_s2am_ard_3-2-1_55JBG_2023-12-19_final
2023-12-19 10:05:19	ga_s2am_ard_3-2-1_54JYM_2023-12-19_final
2023-12-20 09:34:48	ga_s2am_ard_3-2-1_56JMN_2023-12-20_final
2023-12-20 09:34:51	ga_s2am_ard_3-2-1_56JLN_2023-12-20_final
2023-12-20 09:34:54	ga_s2am_ard_3-2-1_56JKN_2023-12-20_final
2023-12-20 09:34:57	ga_s2am_ard_3-2-1_55JGH_2023-12-20_final
2023-12-20 09:35:09	ga_s2am_ard_3-2-1_56JKM_2023-12-20_final
2023-12-20 09:35:11	ga_s2am_ard_3-2-1_55JGG_2023-12-20_final
2023-12-20 11:15:29	ga_s2am_ard_3-2-1_52JCN_2023-12-20_final
2023-12-20 11:15:33	ga_s2am_ard_3-2-1_52JBN_2023-12-20_final
2023-12-20 11:15:35	ga_s2am_ard_3-2-1_51JYH_2023-12-20_final
2023-12-20 11:15:39	ga_s2am_ard_3-2-1_51JXH_2023-12-20_final
2023-12-20 11:15:47	ga_s2am_ard_3-2-1_52JBM_2023-12-20_final
2023-12-20 11:15:50	ga_s2am_ard_3-2-1_51JYG_2023-12-20_final
2023-12-21 10:45:15	ga_s2am_ard_3-2-1_53JNH_2023-12-21_final
2023-12-21 10:45:18	ga_s2am_ard_3-2-1_53JMH_2023-12-21_final
2023-12-21 10:45:22	ga_s2am_ard_3-2-1_53JLH_2023-12-21_final
2023-12-21 10:45:25	ga_s2am_ard_3-2-1_53JKH_2023-12-21_final
2023-12-21 10:45:40	ga_s2am_ard_3-2-1_53JKG_2023-12-21_final
2023-12-21 10:45:43	ga_s2am_ard_3-2-1_52JGM_2023-12-21_final
2023-12-22 11:55:45	ga_s2am_ard_3-2-1_50JNN_2023-12-22_final
2023-12-22 11:55:49	ga_s2am_ard_3-2-1_50JMN_2023-12-22_final
2023-12-22 11:55:52	ga_s2am_ard_3-2-1_50JLN_2023-12-22_final
2023-12-22 11:55:55	ga_s2am_ard_3-2-1_50JKN_2023-12-22_final
2023-12-25 10:25:07	ga_s2am_ard_3-2-1_54JVN_2023-12-25_final
2023-12-25 10:25:11	ga_s2am_ard_3-2-1_54JUN_2023-12-25_final
2023-12-25 10:25:14	ga_s2am_ard_3-2-1_54JTN_2023-12-25_final
2023-12-25 10:25:17	ga_s2am_ard_3-2-1_53JQH_2023-12-25_final
2023-12-25 12:06:06	ga_s2am_ard_3-2-1_50JKM_2023-12-25_final
2023-12-25 12:06:09	ga_s2am_ard_3-2-1_49JGG_2023-12-25_nrt
2023-12-26 09:54:57	ga_s2am_ard_3-2-1_55JEH_2023-12-26_final
2023-12-26 09:55:00	ga_s2am_ard_3-2-1_55JDH_2023-12-26_final
2023-12-26 09:55:04	ga_s2am_ard_3-2-1_55JCH_2023-12-26_final
2023-12-26 09:55:08	ga_s2am_ard_3-2-1_55JBH_2023-12-26_final
2024-03-11 11:55:50	ga_s2am_ard_3-2-1_50JNN_2024-03-11_final
2024-03-11 11:55:53	ga_s2am_ard_3-2-1_50JMN_2024-03-11_final
2024-03-11 11:55:56	ga_s2am_ard_3-2-1_50JLN_2024-03-11_final
2024-03-11 11:56:00	ga_s2am_ard_3-2-1_50JKN_2024-03-11_final
2024-03-14 12:05:51	ga_s2am_ard_3-2-1_50JLN_2024-03-14_final
2024-03-14 12:05:55	ga_s2am_ard_3-2-1_50JKN_2024-03-14_final
2024-03-14 12:05:58	ga_s2am_ard_3-2-1_49JGH_2024-03-14_final
2024-03-14 12:06:02	ga_s2am_ard_3-2-1_49JFH_2024-03-14_nrt
2024-03-14 12:06:09	ga_s2am_ard_3-2-1_50JKM_2024-03-14_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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