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
2019-04-15 11:15:02	ga_s2am_ard_3-2-1_52JDQ_2019-04-15_final.stac-item
2019-04-15 11:15:06	ga_s2am_ard_3-2-1_52JCQ_2019-04-15_final.stac-item
2019-04-15 11:15:09	ga_s2am_ard_3-2-1_52JBQ_2019-04-15_final.stac-item
2019-04-15 11:15:13	ga_s2am_ard_3-2-1_51JYK_2019-04-15_final.stac-item
2019-04-15 11:15:21	ga_s2am_ard_3-2-1_52JCP_2019-04-15_final.stac-item
2019-04-15 11:15:24	ga_s2am_ard_3-2-1_52JBP_2019-04-15_final.stac-item
2019-04-15 11:15:27	ga_s2am_ard_3-2-1_51JYJ_2019-04-15_final.stac-item
2019-04-15 11:15:32	ga_s2am_ard_3-2-1_51JXJ_2019-04-15_final.stac-item
2019-04-17 10:14:38	ga_s2am_ard_3-2-1_54JYQ_2019-04-17_final.stac-item
2019-04-17 10:14:42	ga_s2am_ard_3-2-1_54JXQ_2019-04-17_final.stac-item
2019-04-17 10:14:45	ga_s2am_ard_3-2-1_54JWQ_2019-04-17_final.stac-item
2019-04-17 10:14:48	ga_s2am_ard_3-2-1_54JVQ_2019-04-17_final.stac-item
2019-04-17 10:14:50	ga_s2am_ard_3-2-1_54JYP_2019-04-17_final.stac-item
2019-04-17 10:14:56	ga_s2am_ard_3-2-1_54JXP_2019-04-17_final.stac-item
2019-04-22 11:05:01	ga_s2am_ard_3-2-1_52JFQ_2019-04-22_final.stac-item
2019-04-22 11:05:05	ga_s2am_ard_3-2-1_52JEQ_2019-04-22_final.stac-item
2019-04-22 11:05:08	ga_s2am_ard_3-2-1_52JDQ_2019-04-22_final.stac-item
2019-04-22 11:05:11	ga_s2am_ard_3-2-1_52JCQ_2019-04-22_final.stac-item
2019-04-22 11:05:16	ga_s2am_ard_3-2-1_52JFP_2019-04-22_final.stac-item
2019-04-22 11:05:26	ga_s2am_ard_3-2-1_52JCP_2019-04-22_final.stac-item
2019-04-27 10:14:38	ga_s2am_ard_3-2-1_54JYQ_2019-04-27_final.stac-item
2019-04-27 10:14:42	ga_s2am_ard_3-2-1_54JXQ_2019-04-27_final.stac-item
2019-04-27 10:14:46	ga_s2am_ard_3-2-1_54JWQ_2019-04-27_final.stac-item
2019-04-27 10:14:49	ga_s2am_ard_3-2-1_54JVQ_2019-04-27_final.stac-item
2019-04-27 10:14:51	ga_s2am_ard_3-2-1_54JYP_2019-04-27_final.stac-item
2019-04-27 10:14:57	ga_s2am_ard_3-2-1_54JXP_2019-04-27_final.stac-item
2019-05-02 11:05:01	ga_s2am_ard_3-2-1_52JFQ_2019-05-02_final.stac-item
2019-05-02 11:05:05	ga_s2am_ard_3-2-1_52JEQ_2019-05-02_final.stac-item
2019-05-02 11:05:08	ga_s2am_ard_3-2-1_52JDQ_2019-05-02_final.stac-item
2019-05-02 11:05:12	ga_s2am_ard_3-2-1_52JCQ_2019-05-02_final.stac-item
2019-05-02 11:05:16	ga_s2am_ard_3-2-1_52JFP_2019-05-02_final.stac-item
2019-05-02 11:05:26	ga_s2am_ard_3-2-1_52JCP_2019-05-02_final.stac-item
2019-05-05 09:34:27	ga_s2am_ard_3-2-1_56JMQ_2019-05-05_final.stac-item
2019-05-05 09:34:30	ga_s2am_ard_3-2-1_56JLQ_2019-05-05_final.stac-item
2019-05-05 09:34:33	ga_s2am_ard_3-2-1_56JKQ_2019-05-05_final.stac-item
2019-05-05 09:34:44	ga_s2am_ard_3-2-1_56JLP_2019-05-05_final.stac-item
2019-05-05 09:34:48	ga_s2am_ard_3-2-1_56JKP_2019-05-05_final.stac-item
2023-01-14 09:34:16	ga_s2am_ard_3-2-1_56JNQ_2023-01-14_final
2023-01-14 09:34:20	ga_s2am_ard_3-2-1_56JMQ_2023-01-14_final
2023-01-14 09:34:23	ga_s2am_ard_3-2-1_56JLQ_2023-01-14_final.stac-item
2023-01-14 09:34:26	ga_s2am_ard_3-2-1_56JKQ_2023-01-14_final.stac-item
2023-01-14 09:34:38	ga_s2am_ard_3-2-1_56JLP_2023-01-14_final.stac-item
2023-01-14 09:34:41	ga_s2am_ard_3-2-1_56JKP_2023-01-14_final.stac-item
2023-01-14 11:14:57	ga_s2am_ard_3-2-1_52JDQ_2023-01-14_final.stac-item
2023-01-14 11:15:01	ga_s2am_ard_3-2-1_52JCQ_2023-01-14_final.stac-item
2023-01-14 11:15:04	ga_s2am_ard_3-2-1_52JBQ_2023-01-14_final.stac-item
2023-01-14 11:15:07	ga_s2am_ard_3-2-1_51JYK_2023-01-14_final.stac-item
2023-01-14 11:15:15	ga_s2am_ard_3-2-1_52JCP_2023-01-14_final.stac-item
2023-01-14 11:15:19	ga_s2am_ard_3-2-1_52JBP_2023-01-14_final.stac-item
2023-01-14 11:15:22	ga_s2am_ard_3-2-1_51JYJ_2023-01-14_final.stac-item
2023-01-14 11:15:27	ga_s2am_ard_3-2-1_51JXJ_2023-01-14_final.stac-item
2023-01-15 10:45:06	ga_s2am_ard_3-2-1_53JLJ_2023-01-15_final.stac-item
2023-01-15 10:45:08	ga_s2am_ard_3-2-1_53JKJ_2023-01-15_final.stac-item
2023-01-16 10:14:31	ga_s2am_ard_3-2-1_54JYQ_2023-01-16_final.stac-item
2023-01-16 10:14:35	ga_s2am_ard_3-2-1_54JXQ_2023-01-16_final.stac-item
2023-01-16 10:14:39	ga_s2am_ard_3-2-1_54JWQ_2023-01-16_final.stac-item
2023-01-16 10:14:42	ga_s2am_ard_3-2-1_54JVQ_2023-01-16_final.stac-item
2023-01-16 10:14:44	ga_s2am_ard_3-2-1_54JYP_2023-01-16_final.stac-item
2023-01-16 10:14:50	ga_s2am_ard_3-2-1_54JXP_2023-01-16_final.stac-item
2023-02-06 11:25:05	ga_s2am_ard_3-2-1_51JYK_2023-02-06_final
2023-02-06 11:25:08	ga_s2am_ard_3-2-1_51JXK_2023-02-06_final
2023-02-06 11:25:12	ga_s2am_ard_3-2-1_51JWK_2023-02-06_final
2023-02-06 11:25:15	ga_s2am_ard_3-2-1_51JVK_2023-02-06_final
2023-02-06 11:25:19	ga_s2am_ard_3-2-1_51JYJ_2023-02-06_final
2023-02-06 11:25:23	ga_s2am_ard_3-2-1_51JXJ_2023-02-06_final
2023-02-07 10:54:51	ga_s2am_ard_3-2-1_53JLK_2023-02-07_final
2023-02-07 10:54:54	ga_s2am_ard_3-2-1_53JKK_2023-02-07_final
2023-02-07 10:54:57	ga_s2am_ard_3-2-1_52JGQ_2023-02-07_final
2023-02-07 10:55:01	ga_s2am_ard_3-2-1_53JLJ_2023-02-07_final
2023-02-07 10:55:03	ga_s2am_ard_3-2-1_53JKK_2023-02-07_final
2023-02-07 10:55:03	ga_s2am_ard_3-2-1_53JKJ_2023-02-07_final
2023-02-07 10:55:04	ga_s2am_ard_3-2-1_52JGP_2023-02-07_final
2023-02-07 10:55:04	ga_s2am_ard_3-2-1_52JGQ_2023-02-07_final
2023-02-07 10:55:06	ga_s2am_ard_3-2-1_52JFQ_2023-02-07_final
2023-02-07 10:55:10	ga_s2am_ard_3-2-1_53JKJ_2023-02-07_final
2023-02-07 10:55:12	ga_s2am_ard_3-2-1_52JGP_2023-02-07_final
2023-02-07 10:55:16	ga_s2am_ard_3-2-1_52JFP_2023-02-07_final
2023-02-27 10:54:49	ga_s2am_ard_3-2-1_53JLK_2023-02-27_final
2023-02-27 10:54:53	ga_s2am_ard_3-2-1_53JKK_2023-02-27_final
2023-02-27 10:54:56	ga_s2am_ard_3-2-1_52JGQ_2023-02-27_final
2023-02-27 10:55:00	ga_s2am_ard_3-2-1_52JFQ_2023-02-27_final
2023-02-27 10:55:03	ga_s2am_ard_3-2-1_53JLJ_2023-02-27_final
2023-02-27 10:55:07	ga_s2am_ard_3-2-1_53JKJ_2023-02-27_final
2023-02-27 10:55:11	ga_s2am_ard_3-2-1_52JGP_2023-02-27_final
2023-02-27 10:55:14	ga_s2am_ard_3-2-1_52JFP_2023-02-27_final
2023-03-02 11:05:10	ga_s2am_ard_3-2-1_52JFP_2023-03-02_final
2023-03-03 10:34:41	ga_s2am_ard_3-2-1_54JTQ_2023-03-03_final
2023-03-03 10:34:44	ga_s2am_ard_3-2-1_53JQK_2023-03-03_final
2023-03-03 10:34:48	ga_s2am_ard_3-2-1_53JPK_2023-03-03_final
2023-03-03 10:34:51	ga_s2am_ard_3-2-1_53JNK_2023-03-03_final
2023-03-03 10:34:54	ga_s2am_ard_3-2-1_54JTP_2023-03-03_final
2023-03-03 10:34:58	ga_s2am_ard_3-2-1_53JQJ_2023-03-03_final
2023-03-03 10:35:02	ga_s2am_ard_3-2-1_53JPJ_2023-03-03_final
2023-03-05 09:34:16	ga_s2am_ard_3-2-1_56JNQ_2023-03-05_final
2023-03-05 09:34:19	ga_s2am_ard_3-2-1_56JMQ_2023-03-05_final
2023-03-06 10:44:46	ga_s2am_ard_3-2-1_53JNK_2023-03-06_final
2023-03-06 10:44:50	ga_s2am_ard_3-2-1_53JMK_2023-03-06_final
2023-03-06 10:44:53	ga_s2am_ard_3-2-1_53JLK_2023-03-06_final
2023-03-06 10:44:56	ga_s2am_ard_3-2-1_53JKK_2023-03-06_final
2023-03-06 10:45:08	ga_s2am_ard_3-2-1_53JLJ_2023-03-06_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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