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
2020-10-18 11:56:13	ga_s2am_ard_3-2-1_50JMM_2020-10-18_final.stac-item
2020-10-19 09:43:43	ga_s2am_ard_3-2-1_56JLT_2020-10-19_final.stac-item
2020-10-19 09:43:47	ga_s2am_ard_3-2-1_56JKT_2020-10-19_final.stac-item
2020-10-19 09:43:58	ga_s2am_ard_3-2-1_56JLS_2020-10-19_final.stac-item
2020-10-19 09:44:01	ga_s2am_ard_3-2-1_56JKS_2020-10-19_final.stac-item
2020-10-19 09:44:08	ga_s2am_ard_3-2-1_55JFM_2020-10-19_final.stac-item
2020-10-19 09:44:11	ga_s2am_ard_3-2-1_56JLR_2020-10-19_final.stac-item
2020-10-19 09:44:16	ga_s2am_ard_3-2-1_56JKR_2020-10-19_final.stac-item
2020-10-19 09:44:21	ga_s2am_ard_3-2-1_56JLQ_2020-10-19_final.stac-item
2020-10-19 09:44:23	ga_s2am_ard_3-2-1_55JFL_2020-10-19_final.stac-item
2020-10-19 09:44:30	ga_s2am_ard_3-2-1_56JKQ_2020-10-19_final.stac-item
2020-10-19 09:44:30	ga_s2am_ard_3-2-1_55JEL_2020-10-19_final.stac-item
2020-10-19 09:44:37	ga_s2am_ard_3-2-1_55JFK_2020-10-19_final.stac-item
2020-10-19 09:44:41	ga_s2am_ard_3-2-1_55JEK_2020-10-19_final.stac-item
2020-10-19 09:44:45	ga_s2am_ard_3-2-1_56JKP_2020-10-19_final.stac-item
2020-10-19 09:44:52	ga_s2am_ard_3-2-1_55JFJ_2020-10-19_final.stac-item
2020-10-19 09:44:55	ga_s2am_ard_3-2-1_55JEJ_2020-10-19_final.stac-item
2020-10-19 09:45:00	ga_s2am_ard_3-2-1_56JKN_2020-10-19_final.stac-item
2020-10-19 09:45:06	ga_s2am_ard_3-2-1_55JFH_2020-10-19_final.stac-item
2020-10-19 09:45:10	ga_s2am_ard_3-2-1_55JEH_2020-10-19_final.stac-item
2020-10-19 09:45:14	ga_s2am_ard_3-2-1_56JKM_2020-10-19_final.stac-item
2020-10-19 09:45:21	ga_s2am_ard_3-2-1_55JFG_2020-10-19_final.stac-item
2020-10-19 09:45:24	ga_s2am_ard_3-2-1_55JEG_2020-10-19_final.stac-item
2020-10-19 09:45:27	ga_s2am_ard_3-2-1_56JKL_2020-10-19_final.stac-item
2020-10-19 09:45:32	ga_s2am_ard_3-2-1_55JDG_2020-10-19_final.stac-item
2020-10-19 09:45:35	ga_s2am_ard_3-2-1_55JFF_2020-10-19_final.stac-item
2020-10-19 09:45:38	ga_s2am_ard_3-2-1_56HKK_2020-10-19_final.stac-item
2020-10-19 09:45:39	ga_s2am_ard_3-2-1_55JEF_2020-10-19_final.stac-item
2020-10-19 09:45:43	ga_s2am_ard_3-2-1_55JDF_2020-10-19_final.stac-item
2020-10-19 09:45:46	ga_s2am_ard_3-2-1_55HGE_2020-10-19_final.stac-item
2020-10-19 09:45:50	ga_s2am_ard_3-2-1_55HFE_2020-10-19_final.stac-item
2020-10-19 09:45:53	ga_s2am_ard_3-2-1_55HEE_2020-10-19_final.stac-item
2020-10-19 09:45:56	ga_s2am_ard_3-2-1_55HDE_2020-10-19_final.stac-item
2020-10-19 09:46:00	ga_s2am_ard_3-2-1_55HGD_2020-10-19_final.stac-item
2020-10-19 09:46:04	ga_s2am_ard_3-2-1_55HFD_2020-10-19_final.stac-item
2020-10-19 09:46:08	ga_s2am_ard_3-2-1_55HED_2020-10-19_final.stac-item
2020-10-19 09:46:11	ga_s2am_ard_3-2-1_55HDD_2020-10-19_final.stac-item
2020-10-19 09:46:13	ga_s2am_ard_3-2-1_55HGC_2020-10-19_final.stac-item
2020-10-19 11:22:32	ga_s2am_ard_3-2-1_51KYA_2020-10-19_final.stac-item
2020-10-19 11:22:39	ga_s2am_ard_3-2-1_52KCE_2020-10-19_final.stac-item
2020-10-19 11:22:43	ga_s2am_ard_3-2-1_52KBE_2020-10-19_final.stac-item
2020-10-19 11:22:43	ga_s2am_ard_3-2-1_51KZV_2020-10-19_final.stac-item
2020-10-19 11:22:47	ga_s2am_ard_3-2-1_51KYV_2020-10-19_final.stac-item
2020-10-19 11:22:54	ga_s2am_ard_3-2-1_52KCD_2020-10-19_final.stac-item
2020-10-19 11:22:57	ga_s2am_ard_3-2-1_52KBD_2020-10-19_final.stac-item
2020-10-19 11:22:58	ga_s2am_ard_3-2-1_51KZU_2020-10-19_final.stac-item
2020-10-19 11:23:01	ga_s2am_ard_3-2-1_51KYU_2020-10-19_final.stac-item
2020-10-19 11:23:06	ga_s2am_ard_3-2-1_51KXU_2020-10-19_final.stac-item
2020-10-19 11:23:08	ga_s2am_ard_3-2-1_52KCC_2020-10-19_final.stac-item
2020-10-19 11:23:12	ga_s2am_ard_3-2-1_52KBC_2020-10-19_final.stac-item
2020-10-19 11:23:12	ga_s2am_ard_3-2-1_51KZT_2020-10-19_final.stac-item
2020-10-19 11:23:16	ga_s2am_ard_3-2-1_51KYT_2020-10-19_final.stac-item
2020-10-19 11:23:19	ga_s2am_ard_3-2-1_51KXT_2020-10-19_final.stac-item
2020-10-19 11:23:22	ga_s2am_ard_3-2-1_52KCB_2020-10-19_final.stac-item
2020-10-19 11:23:26	ga_s2am_ard_3-2-1_52KBB_2020-10-19_final.stac-item
2020-10-19 11:23:27	ga_s2am_ard_3-2-1_51KZS_2020-10-19_final.stac-item
2020-10-19 11:23:30	ga_s2am_ard_3-2-1_51KYS_2020-10-19_final.stac-item
2020-10-19 11:23:33	ga_s2am_ard_3-2-1_52KCA_2020-10-19_final.stac-item
2020-10-19 11:23:34	ga_s2am_ard_3-2-1_51KXS_2020-10-19_final.stac-item
2020-10-19 11:23:40	ga_s2am_ard_3-2-1_52KBA_2020-10-19_final.stac-item
2020-10-19 11:23:41	ga_s2am_ard_3-2-1_51KZR_2020-10-19_final.stac-item
2020-10-19 11:23:45	ga_s2am_ard_3-2-1_51KYR_2020-10-19_final.stac-item
2020-10-19 11:23:48	ga_s2am_ard_3-2-1_51KXR_2020-10-19_final.stac-item
2020-10-19 11:23:55	ga_s2am_ard_3-2-1_52KBV_2020-10-19_final.stac-item
2020-10-19 11:23:55	ga_s2am_ard_3-2-1_51KZQ_2020-10-19_final.stac-item
2020-10-19 11:23:59	ga_s2am_ard_3-2-1_51KYQ_2020-10-19_final.stac-item
2020-10-19 11:24:03	ga_s2am_ard_3-2-1_51KXQ_2020-10-19_final.stac-item
2020-10-19 11:24:07	ga_s2am_ard_3-2-1_51KWQ_2020-10-19_final.stac-item
2020-10-19 11:24:10	ga_s2am_ard_3-2-1_51KZP_2020-10-19_final.stac-item
2020-10-19 11:24:10	ga_s2am_ard_3-2-1_52KBU_2020-10-19_final.stac-item
2020-10-19 11:24:14	ga_s2am_ard_3-2-1_51KYP_2020-10-19_final.stac-item
2020-10-19 11:24:17	ga_s2am_ard_3-2-1_51KXP_2020-10-19_final.stac-item
2020-10-19 11:24:20	ga_s2am_ard_3-2-1_51KWP_2020-10-19_final.stac-item
2020-10-19 11:24:24	ga_s2am_ard_3-2-1_52JBT_2020-10-19_final.stac-item
2020-10-19 11:24:24	ga_s2am_ard_3-2-1_51JZN_2020-10-19_final.stac-item
2020-10-19 11:24:28	ga_s2am_ard_3-2-1_51JYN_2020-10-19_final.stac-item
2020-10-19 11:24:32	ga_s2am_ard_3-2-1_51JXN_2020-10-19_final.stac-item
2020-10-19 11:24:35	ga_s2am_ard_3-2-1_51JWN_2020-10-19_final.stac-item
2020-10-19 11:24:38	ga_s2am_ard_3-2-1_51JZM_2020-10-19_final.stac-item
2020-10-19 11:24:38	ga_s2am_ard_3-2-1_52JBS_2020-10-19_final.stac-item
2020-10-19 11:24:42	ga_s2am_ard_3-2-1_51JYM_2020-10-19_final.stac-item
2020-10-19 11:24:46	ga_s2am_ard_3-2-1_51JXM_2020-10-19_final.stac-item
2020-10-19 11:24:49	ga_s2am_ard_3-2-1_52JBR_2020-10-19_final.stac-item
2020-10-19 11:24:50	ga_s2am_ard_3-2-1_51JWM_2020-10-19_final.stac-item
2020-10-19 11:24:57	ga_s2am_ard_3-2-1_51JYL_2020-10-19_final.stac-item
2020-10-19 11:25:01	ga_s2am_ard_3-2-1_51JXL_2020-10-19_final.stac-item
2020-10-19 11:25:04	ga_s2am_ard_3-2-1_51JWL_2020-10-19_final.stac-item
2020-10-19 11:25:09	ga_s2am_ard_3-2-1_51JVL_2020-10-19_final.stac-item
2020-10-19 11:25:11	ga_s2am_ard_3-2-1_51JYK_2020-10-19_final.stac-item
2020-10-19 11:25:15	ga_s2am_ard_3-2-1_51JXK_2020-10-19_final.stac-item
2020-10-19 11:25:19	ga_s2am_ard_3-2-1_51JWK_2020-10-19_final.stac-item
2020-10-19 11:25:22	ga_s2am_ard_3-2-1_51JVK_2020-10-19_final.stac-item
2020-10-19 11:25:26	ga_s2am_ard_3-2-1_51JYJ_2020-10-19_final.stac-item
2020-10-19 11:25:30	ga_s2am_ard_3-2-1_51JXJ_2020-10-19_final.stac-item
2020-10-19 11:25:33	ga_s2am_ard_3-2-1_51JWJ_2020-10-19_final.stac-item
2020-10-19 11:25:36	ga_s2am_ard_3-2-1_51JVJ_2020-10-19_final.stac-item
2020-10-19 11:25:39	ga_s2am_ard_3-2-1_51JYH_2020-10-19_final.stac-item
2020-10-19 11:25:44	ga_s2am_ard_3-2-1_51JXH_2020-10-19_final.stac-item
2020-10-19 11:25:48	ga_s2am_ard_3-2-1_51JWH_2020-10-19_final.stac-item
2020-10-19 11:25:50	ga_s2am_ard_3-2-1_51JYG_2020-10-19_final.stac-item

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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