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 11:14:40	ga_s2am_ard_3-2-1_51JZM_2019-04-15_final.stac-item
2019-04-15 11:14:40	ga_s2am_ard_3-2-1_52JBS_2019-04-15_final.stac-item
2019-04-15 11:14:43	ga_s2am_ard_3-2-1_51JYM_2019-04-15_final.stac-item
2019-04-15 11:14:55	ga_s2am_ard_3-2-1_52JBR_2019-04-15_final.stac-item
2019-04-15 11:14:58	ga_s2am_ard_3-2-1_51JYL_2019-04-15_final.stac-item
2019-04-18 09:43:55	ga_s2am_ard_3-2-1_56JLS_2019-04-18_final.stac-item
2019-04-18 09:43:58	ga_s2am_ard_3-2-1_55JHM_2019-04-18_final.stac-item
2019-04-18 09:43:58	ga_s2am_ard_3-2-1_56JKS_2019-04-18_final.stac-item
2019-04-18 09:44:02	ga_s2am_ard_3-2-1_55JGM_2019-04-18_final.stac-item
2019-04-18 09:44:05	ga_s2am_ard_3-2-1_55JFM_2019-04-18_final.stac-item
2019-04-18 09:44:13	ga_s2am_ard_3-2-1_56JKR_2019-04-18_final.stac-item
2019-04-18 09:44:16	ga_s2am_ard_3-2-1_55JGL_2019-04-18_final.stac-item
2019-04-24 11:44:52	ga_s2am_ard_3-2-1_50JQS_2019-04-24_final.stac-item
2019-04-24 11:44:56	ga_s2am_ard_3-2-1_50JPS_2019-04-24_final.stac-item
2019-04-24 11:45:03	ga_s2am_ard_3-2-1_51JTL_2019-04-24_final.stac-item
2019-04-24 11:45:07	ga_s2am_ard_3-2-1_50JQR_2019-04-24_final.stac-item
2019-04-30 12:05:15	ga_s2am_ard_3-2-1_50JKR_2019-04-30_final.stac-item
2019-04-30 12:05:18	ga_s2am_ard_3-2-1_49JGL_2019-04-30_final.stac-item
2019-05-01 09:54:02	ga_s2am_ard_3-2-1_55JFM_2019-05-01_final.stac-item
2019-05-01 09:54:06	ga_s2am_ard_3-2-1_55JEM_2019-05-01_final.stac-item
2019-05-01 09:54:09	ga_s2am_ard_3-2-1_55JDM_2019-05-01_final.stac-item
2019-05-01 09:54:13	ga_s2am_ard_3-2-1_55JCM_2019-05-01_final.stac-item
2019-05-04 11:44:49	ga_s2am_ard_3-2-1_50JRS_2019-05-04_final.stac-item
2019-05-04 11:44:49	ga_s2am_ard_3-2-1_51JTM_2019-05-04_final.stac-item
2019-05-04 11:44:52	ga_s2am_ard_3-2-1_50JQS_2019-05-04_final.stac-item
2019-05-04 11:44:56	ga_s2am_ard_3-2-1_50JPS_2019-05-04_final.stac-item
2019-05-04 11:45:03	ga_s2am_ard_3-2-1_51JTL_2019-05-04_final.stac-item
2019-05-04 11:45:07	ga_s2am_ard_3-2-1_50JQR_2019-05-04_final.stac-item
2019-05-05 09:33:54	ga_s2am_ard_3-2-1_56JNS_2019-05-05_final.stac-item
2019-05-05 09:33:57	ga_s2am_ard_3-2-1_56JMS_2019-05-05_final.stac-item
2019-05-05 09:34:01	ga_s2am_ard_3-2-1_56JLS_2019-05-05_final.stac-item
2019-05-05 09:34:04	ga_s2am_ard_3-2-1_55JHM_2019-05-05_final.stac-item
2019-05-05 09:34:05	ga_s2am_ard_3-2-1_56JKS_2019-05-05_final.stac-item
2023-07-26 11:24:37	ga_s2am_ard_3-2-1_51JZM_2023-07-26_final
2023-07-26 11:24:37	ga_s2am_ard_3-2-1_52JBS_2023-07-26_final
2023-07-26 11:24:42	ga_s2am_ard_3-2-1_51JYM_2023-07-26_final
2023-07-26 11:24:46	ga_s2am_ard_3-2-1_51JXM_2023-07-26_final
2023-07-26 11:24:48	ga_s2am_ard_3-2-1_52JBR_2023-07-26_final
2023-07-26 11:24:49	ga_s2am_ard_3-2-1_51JWM_2023-07-26_final
2023-07-26 11:24:56	ga_s2am_ard_3-2-1_51JYL_2023-07-26_final
2023-07-28 10:24:14	ga_s2am_ard_3-2-1_54JWS_2023-07-28_final
2023-07-28 10:24:18	ga_s2am_ard_3-2-1_54JVS_2023-07-28_final
2023-07-28 10:24:21	ga_s2am_ard_3-2-1_54JUS_2023-07-28_final
2023-07-28 10:24:24	ga_s2am_ard_3-2-1_53JRM_2023-07-28_final
2023-07-28 10:24:24	ga_s2am_ard_3-2-1_54JTS_2023-07-28_final
2023-07-28 10:24:33	ga_s2am_ard_3-2-1_54JTR_2023-07-28_final
2023-07-29 11:34:45	ga_s2am_ard_3-2-1_51JWM_2023-07-29_final
2023-07-29 11:34:48	ga_s2am_ard_3-2-1_51JVM_2023-07-29_final
2023-07-29 11:34:52	ga_s2am_ard_3-2-1_51JUM_2023-07-29_final
2023-07-29 11:34:54	ga_s2am_ard_3-2-1_50JRS_2023-07-29_final
2023-07-29 11:34:55	ga_s2am_ard_3-2-1_51JTM_2023-07-29_final
2023-07-31 12:15:01	ga_s2am_ard_3-2-1_49JGM_2023-07-31_final
2023-07-31 12:15:05	ga_s2am_ard_3-2-1_49JFM_2023-07-31_final
2023-08-01 10:04:06	ga_s2am_ard_3-2-1_55JDM_2023-08-01_final
2023-08-01 10:04:09	ga_s2am_ard_3-2-1_55JCM_2023-08-01_final
2023-08-01 10:04:12	ga_s2am_ard_3-2-1_54JZS_2023-08-01_final
2023-08-01 10:04:13	ga_s2am_ard_3-2-1_55JBM_2023-08-01_final
2023-08-01 10:04:16	ga_s2am_ard_3-2-1_54JYS_2023-08-01_final
2023-08-01 10:04:27	ga_s2am_ard_3-2-1_55JBL_2023-08-01_final
2023-08-01 10:04:31	ga_s2am_ard_3-2-1_54JYR_2023-08-01_final
2023-08-01 11:45:05	ga_s2am_ard_3-2-1_51JTL_2023-08-01_final
2023-08-01 11:45:09	ga_s2am_ard_3-2-1_50JQR_2023-08-01_final
2023-08-02 09:33:55	ga_s2am_ard_3-2-1_56JNS_2023-08-02_final
2023-08-02 09:33:59	ga_s2am_ard_3-2-1_56JMS_2023-08-02_final
2023-08-02 09:34:02	ga_s2am_ard_3-2-1_56JLS_2023-08-02_final
2023-08-02 09:34:06	ga_s2am_ard_3-2-1_55JHM_2023-08-02_final
2023-08-02 09:34:06	ga_s2am_ard_3-2-1_56JKS_2023-08-02_final
2023-09-09 10:34:19	ga_s2am_ard_3-2-1_53JRM_2023-09-09_final
2023-09-09 10:34:19	ga_s2am_ard_3-2-1_54JTS_2023-09-09_final
2023-09-09 10:34:23	ga_s2am_ard_3-2-1_53JQM_2023-09-09_final
2023-09-09 10:34:27	ga_s2am_ard_3-2-1_53JPM_2023-09-09_final
2023-09-09 10:34:30	ga_s2am_ard_3-2-1_53JNM_2023-09-09_final
2023-09-09 10:34:34	ga_s2am_ard_3-2-1_54JTR_2023-09-09_final
2023-09-09 10:34:38	ga_s2am_ard_3-2-1_53JQL_2023-09-09_final
2023-09-09 12:15:01	ga_s2am_ard_3-2-1_49JGM_2023-09-09_final
2023-09-09 12:15:05	ga_s2am_ard_3-2-1_49JFM_2023-09-09_final
2023-09-10 10:04:05	ga_s2am_ard_3-2-1_55JDM_2023-09-10_final
2023-09-10 10:04:09	ga_s2am_ard_3-2-1_55JCM_2023-09-10_final
2023-09-10 10:04:12	ga_s2am_ard_3-2-1_54JZS_2023-09-10_final
2023-09-10 10:04:12	ga_s2am_ard_3-2-1_55JBM_2023-09-10_final
2023-09-10 10:04:16	ga_s2am_ard_3-2-1_54JYS_2023-09-10_final
2023-09-10 10:04:27	ga_s2am_ard_3-2-1_55JBL_2023-09-10_final
2023-09-10 10:04:30	ga_s2am_ard_3-2-1_54JYR_2023-09-10_final
2023-11-14 10:54:22	ga_s2am_ard_3-2-1_53JLM_2023-11-14_final
2023-11-14 10:54:25	ga_s2am_ard_3-2-1_52JHS_2023-11-14_final
2023-11-14 10:54:25	ga_s2am_ard_3-2-1_53JKM_2023-11-14_final
2023-11-14 10:54:29	ga_s2am_ard_3-2-1_52JGS_2023-11-14_final
2023-11-14 10:54:32	ga_s2am_ard_3-2-1_52JFS_2023-11-14_final
2023-11-14 10:54:40	ga_s2am_ard_3-2-1_53JKL_2023-11-14_final
2023-11-14 10:54:43	ga_s2am_ard_3-2-1_52JGR_2023-11-14_final
2023-11-15 10:24:07	ga_s2am_ard_3-2-1_54JWS_2023-11-15_final
2023-11-15 10:24:11	ga_s2am_ard_3-2-1_54JVS_2023-11-15_final
2023-11-15 10:24:15	ga_s2am_ard_3-2-1_54JUS_2023-11-15_final
2023-11-15 10:24:17	ga_s2am_ard_3-2-1_53JRM_2023-11-15_final
2023-11-15 10:24:18	ga_s2am_ard_3-2-1_54JTS_2023-11-15_final
2023-11-15 10:24:32	ga_s2am_ard_3-2-1_54JTR_2023-11-15_final
2023-11-16 11:34:38	ga_s2am_ard_3-2-1_51JWM_2023-11-16_final
2023-11-16 11:34:42	ga_s2am_ard_3-2-1_51JVM_2023-11-16_final
2023-11-16 11:34:45	ga_s2am_ard_3-2-1_51JUM_2023-11-16_final
2023-11-16 11:34:47	ga_s2am_ard_3-2-1_50JRS_2023-11-16_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