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
2015-10-08 12:03:08	ga_s2am_ard_3-2-1_50JKS_2015-10-08_final.stac-item
2015-10-08 12:03:08	ga_s2am_ard_3-2-1_50JKT_2015-10-08_final.stac-item
2015-10-08 12:03:08	ga_s2am_ard_3-2-1_50JLT_2015-10-08_final.stac-item
2015-10-08 12:03:08	ga_s2am_ard_3-2-1_50JMT_2015-10-08_final.stac-item
2015-10-08 12:03:08	ga_s2am_ard_3-2-1_49JGM_2015-10-08_final.stac-item
2015-10-08 12:03:08	ga_s2am_ard_3-2-1_49JGN_2015-10-08_final.stac-item
2015-10-08 12:03:08	ga_s2am_ard_3-2-1_49JHM_2015-10-08_final.stac-item
2015-10-08 12:03:08	ga_s2am_ard_3-2-1_49JHN_2015-10-08_final.stac-item
2015-11-21 11:41:49	ga_s2am_ard_3-2-1_50JPT_2015-11-21_final.stac-item
2015-11-21 11:41:49	ga_s2am_ard_3-2-1_50JQT_2015-11-21_final.stac-item
2015-11-21 11:41:49	ga_s2am_ard_3-2-1_50JRS_2015-11-21_final.stac-item
2015-11-21 11:41:49	ga_s2am_ard_3-2-1_50JRT_2015-11-21_final.stac-item
2015-11-21 11:41:49	ga_s2am_ard_3-2-1_51JTN_2015-11-21_final.stac-item
2015-11-21 11:41:49	ga_s2am_ard_3-2-1_51JUN_2015-11-21_final.stac-item
2019-04-19 10:54:11	ga_s2am_ard_3-2-1_53JLN_2019-04-19_final.stac-item
2019-04-19 10:54:14	ga_s2am_ard_3-2-1_52JHT_2019-04-19_final.stac-item
2019-04-19 10:54:15	ga_s2am_ard_3-2-1_53JKN_2019-04-19_final.stac-item
2019-04-19 10:54:18	ga_s2am_ard_3-2-1_52JGT_2019-04-19_final.stac-item
2019-04-19 10:54:24	ga_s2am_ard_3-2-1_52JFT_2019-04-19_final.stac-item
2019-04-19 10:54:29	ga_s2am_ard_3-2-1_52JHS_2019-04-19_final.stac-item
2019-04-19 10:54:29	ga_s2am_ard_3-2-1_53JKM_2019-04-19_final.stac-item
2019-04-19 10:54:32	ga_s2am_ard_3-2-1_52JGS_2019-04-19_final.stac-item
2019-04-29 10:54:12	ga_s2am_ard_3-2-1_53JLN_2019-04-29_final.stac-item
2019-04-29 10:54:15	ga_s2am_ard_3-2-1_52JHT_2019-04-29_final.stac-item
2019-04-29 10:54:15	ga_s2am_ard_3-2-1_53JKN_2019-04-29_final.stac-item
2019-04-29 10:54:19	ga_s2am_ard_3-2-1_52JGT_2019-04-29_final.stac-item
2019-04-29 10:54:25	ga_s2am_ard_3-2-1_52JFT_2019-04-29_final.stac-item
2019-04-29 10:54:30	ga_s2am_ard_3-2-1_52JHS_2019-04-29_final.stac-item
2019-04-29 10:54:30	ga_s2am_ard_3-2-1_53JKM_2019-04-29_final.stac-item
2019-04-29 10:54:33	ga_s2am_ard_3-2-1_52JGS_2019-04-29_final.stac-item
2023-09-24 11:24:21	ga_s2am_ard_3-2-1_52JBT_2023-09-24_final
2023-09-24 11:24:21	ga_s2am_ard_3-2-1_51JZN_2023-09-24_final
2023-09-24 11:24:25	ga_s2am_ard_3-2-1_51JYN_2023-09-24_final
2023-09-24 11:24:28	ga_s2am_ard_3-2-1_51JXN_2023-09-24_final
2023-09-24 11:24:31	ga_s2am_ard_3-2-1_51JWN_2023-09-24_final
2023-09-24 11:24:34	ga_s2am_ard_3-2-1_51JZM_2023-09-24_final
2023-09-24 11:24:34	ga_s2am_ard_3-2-1_52JBS_2023-09-24_final
2023-09-24 11:24:39	ga_s2am_ard_3-2-1_51JYM_2023-09-24_final
2023-09-25 10:54:10	ga_s2am_ard_3-2-1_53JLN_2023-09-25_final
2023-09-25 10:54:14	ga_s2am_ard_3-2-1_52JHT_2023-09-25_final
2023-09-25 10:54:14	ga_s2am_ard_3-2-1_53JKN_2023-09-25_final
2023-09-26 12:04:38	ga_s2am_ard_3-2-1_50JMT_2023-09-26_final
2023-09-26 12:04:41	ga_s2am_ard_3-2-1_50JLT_2023-09-26_final
2023-09-26 12:04:45	ga_s2am_ard_3-2-1_49JHN_2023-09-26_final
2023-09-26 12:04:45	ga_s2am_ard_3-2-1_50JKT_2023-09-26_final
2023-09-26 12:04:48	ga_s2am_ard_3-2-1_49JGN_2023-09-26_final
2023-09-28 09:23:39	ga_s2am_ard_3-2-1_56JPT_2023-09-27_nrt
2023-09-28 09:23:42	ga_s2am_ard_3-2-1_56JNT_2023-09-27_final
2023-09-29 10:34:02	ga_s2am_ard_3-2-1_53JRN_2023-09-29_final
2023-09-29 10:34:02	ga_s2am_ard_3-2-1_54JTT_2023-09-29_final
2023-09-29 10:34:06	ga_s2am_ard_3-2-1_53JQN_2023-09-29_final
2023-09-29 10:34:09	ga_s2am_ard_3-2-1_53JPN_2023-09-29_final
2023-09-29 10:34:16	ga_s2am_ard_3-2-1_53JNN_2023-09-29_final
2023-09-29 10:34:16	ga_s2am_ard_3-2-1_53JRM_2023-09-29_final
2023-09-29 10:34:17	ga_s2am_ard_3-2-1_54JTS_2023-09-29_final
2023-09-29 10:34:20	ga_s2am_ard_3-2-1_53JQM_2023-09-29_final
2023-09-29 12:14:44	ga_s2am_ard_3-2-1_49JGN_2023-09-29_final
2023-09-29 12:14:48	ga_s2am_ard_3-2-1_49JFN_2023-09-29_final
2023-10-01 09:33:32	ga_s2am_ard_3-2-1_56JPT_2023-10-01_nrt
2023-10-01 09:33:38	ga_s2am_ard_3-2-1_56JNT_2023-10-01_final
2023-10-01 09:33:41	ga_s2am_ard_3-2-1_56JMT_2023-10-01_final
2023-10-01 09:33:44	ga_s2am_ard_3-2-1_56JLT_2023-10-01_final
2023-10-01 09:33:52	ga_s2am_ard_3-2-1_56JKT_2023-10-01_final
2023-10-01 11:14:19	ga_s2am_ard_3-2-1_52JDT_2023-10-01_final
2023-10-01 11:14:22	ga_s2am_ard_3-2-1_52JCT_2023-10-01_final
2023-10-01 11:14:25	ga_s2am_ard_3-2-1_51JZN_2023-10-01_final
2023-10-01 11:14:25	ga_s2am_ard_3-2-1_52JBT_2023-10-01_final
2023-10-01 11:14:31	ga_s2am_ard_3-2-1_51JYN_2023-10-01_final
2023-10-01 11:14:40	ga_s2am_ard_3-2-1_51JZM_2023-10-01_final
2023-10-01 11:14:40	ga_s2am_ard_3-2-1_52JBS_2023-10-01_final
2023-10-01 11:14:43	ga_s2am_ard_3-2-1_51JYM_2023-10-01_final
2023-10-02 10:44:05	ga_s2am_ard_3-2-1_53JPN_2023-10-02_final
2023-10-02 10:44:08	ga_s2am_ard_3-2-1_53JNN_2023-10-02_final
2023-10-02 10:44:12	ga_s2am_ard_3-2-1_53JMN_2023-10-02_final
2023-10-02 10:44:15	ga_s2am_ard_3-2-1_53JLN_2023-10-02_final
2023-10-03 11:54:35	ga_s2am_ard_3-2-1_50JPT_2023-10-03_final
2023-10-03 11:54:39	ga_s2am_ard_3-2-1_50JNT_2023-10-03_final
2023-10-03 11:54:42	ga_s2am_ard_3-2-1_50JMT_2023-10-03_final
2023-10-03 11:54:46	ga_s2am_ard_3-2-1_50JLT_2023-10-03_final
2023-10-04 11:24:20	ga_s2am_ard_3-2-1_52JBT_2023-10-04_final
2023-10-04 11:24:20	ga_s2am_ard_3-2-1_51JZN_2023-10-04_final
2023-10-04 11:24:24	ga_s2am_ard_3-2-1_51JYN_2023-10-04_final
2023-10-04 11:24:28	ga_s2am_ard_3-2-1_51JXN_2023-10-04_final
2023-10-04 11:24:31	ga_s2am_ard_3-2-1_51JWN_2023-10-04_final
2023-10-04 11:24:34	ga_s2am_ard_3-2-1_51JZM_2023-10-04_final
2023-10-04 11:24:34	ga_s2am_ard_3-2-1_52JBS_2023-10-04_final
2023-10-04 11:24:38	ga_s2am_ard_3-2-1_51JYM_2023-10-04_final
2023-10-06 12:04:36	ga_s2am_ard_3-2-1_50JMT_2023-10-06_final
2023-10-06 12:04:40	ga_s2am_ard_3-2-1_50JLT_2023-10-06_final
2023-10-06 12:04:43	ga_s2am_ard_3-2-1_49JHN_2023-10-06_final
2023-10-06 12:04:43	ga_s2am_ard_3-2-1_50JKT_2023-10-06_final
2023-10-06 12:04:47	ga_s2am_ard_3-2-1_49JGN_2023-10-06_final
2023-10-06 12:04:58	ga_s2am_ard_3-2-1_49JHM_2023-10-06_final
2023-10-06 12:04:58	ga_s2am_ard_3-2-1_50JKS_2023-10-06_final
2023-10-06 12:05:01	ga_s2am_ard_3-2-1_49JGM_2023-10-06_final
2023-10-07 11:34:25	ga_s2am_ard_3-2-1_51JWN_2023-10-07_final
2023-10-07 11:34:29	ga_s2am_ard_3-2-1_51JVN_2023-10-07_final
2023-10-07 11:34:32	ga_s2am_ard_3-2-1_51JUN_2023-10-07_final
2023-10-07 11:34:36	ga_s2am_ard_3-2-1_51JTN_2023-10-07_final
2023-10-07 11:34:37	ga_s2am_ard_3-2-1_50JRT_2023-10-07_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

All years