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
2019-10-22 09:38:34	ga_s2am_ard_3-2-1_55GEP_2019-10-22_final.stac-item
2019-10-22 09:38:38	ga_s2am_ard_3-2-1_55GDP_2019-10-22_final.stac-item
2019-10-22 09:38:43	ga_s2am_ard_3-2-1_55GCP_2019-10-22_final.stac-item
2019-10-22 09:38:44	ga_s2am_ard_3-2-1_55GFN_2019-10-22_final.stac-item
2019-10-22 09:38:48	ga_s2am_ard_3-2-1_55GEN_2019-10-22_final.stac-item
2019-10-22 09:38:52	ga_s2am_ard_3-2-1_55GDN_2019-10-22_final.stac-item
2019-10-22 09:38:56	ga_s2am_ard_3-2-1_55GCN_2019-10-22_final.stac-item
2019-10-22 11:10:28	ga_s2am_ard_3-2-1_52LHN_2019-10-22_final.stac-item
2019-10-22 11:10:32	ga_s2am_ard_3-2-1_52LGN_2019-10-22_final.stac-item
2019-10-22 11:10:40	ga_s2am_ard_3-2-1_52LHM_2019-10-22_final.stac-item
2019-10-22 11:10:46	ga_s2am_ard_3-2-1_52LGM_2019-10-22_final.stac-item
2019-10-22 11:11:01	ga_s2am_ard_3-2-1_52LGL_2019-10-22_final.stac-item
2019-10-22 11:11:05	ga_s2am_ard_3-2-1_52LFL_2019-10-22_final.stac-item
2019-10-22 11:11:16	ga_s2am_ard_3-2-1_52LGK_2019-10-22_final.stac-item
2019-10-22 11:11:19	ga_s2am_ard_3-2-1_52LFK_2019-10-22_final.stac-item
2019-10-22 11:11:30	ga_s2am_ard_3-2-1_52LGJ_2019-10-22_final.stac-item
2019-10-22 11:11:34	ga_s2am_ard_3-2-1_52LFJ_2019-10-22_final.stac-item
2019-10-22 11:11:37	ga_s2am_ard_3-2-1_52LEJ_2019-10-22_final.stac-item
2019-10-22 11:11:40	ga_s2am_ard_3-2-1_52LDJ_2019-10-22_final.stac-item
2019-10-22 11:11:43	ga_s2am_ard_3-2-1_52LGH_2019-10-22_final.stac-item
2019-10-22 11:11:48	ga_s2am_ard_3-2-1_52LFH_2019-10-22_final.stac-item
2019-10-22 11:11:51	ga_s2am_ard_3-2-1_52LEH_2019-10-22_final.stac-item
2019-10-22 11:11:53	ga_s2am_ard_3-2-1_52KGG_2019-10-22_final.stac-item
2019-10-22 11:11:55	ga_s2am_ard_3-2-1_52LDH_2019-10-22_final.stac-item
2019-10-22 11:12:02	ga_s2am_ard_3-2-1_52KFG_2019-10-22_final.stac-item
2019-10-22 11:12:06	ga_s2am_ard_3-2-1_52KEG_2019-10-22_final.stac-item
2019-10-22 11:12:09	ga_s2am_ard_3-2-1_52KDG_2019-10-22_final.stac-item
2019-10-22 11:12:14	ga_s2am_ard_3-2-1_52KCG_2019-10-22_final.stac-item
2019-10-22 11:12:17	ga_s2am_ard_3-2-1_52KFF_2019-10-22_final.stac-item
2019-10-22 11:12:20	ga_s2am_ard_3-2-1_52KEF_2019-10-22_final.stac-item
2019-10-22 11:12:24	ga_s2am_ard_3-2-1_52KDF_2019-10-22_final.stac-item
2019-10-22 11:12:27	ga_s2am_ard_3-2-1_52KCF_2019-10-22_final.stac-item
2019-10-22 11:12:32	ga_s2am_ard_3-2-1_52KFE_2019-10-22_final.stac-item
2019-10-22 11:12:35	ga_s2am_ard_3-2-1_52KEE_2019-10-22_final.stac-item
2019-10-22 11:12:38	ga_s2am_ard_3-2-1_52KDE_2019-10-22_final.stac-item
2019-10-22 11:12:41	ga_s2am_ard_3-2-1_52KCE_2019-10-22_final.stac-item
2019-10-22 11:12:45	ga_s2am_ard_3-2-1_52KFD_2019-10-22_final.stac-item
2019-10-22 11:12:49	ga_s2am_ard_3-2-1_52KED_2019-10-22_final.stac-item
2019-10-22 11:12:53	ga_s2am_ard_3-2-1_52KDD_2019-10-22_final.stac-item
2019-10-22 11:12:56	ga_s2am_ard_3-2-1_52KCD_2019-10-22_final.stac-item
2019-10-22 11:12:57	ga_s2am_ard_3-2-1_52KFC_2019-10-22_final.stac-item
2019-10-22 11:13:04	ga_s2am_ard_3-2-1_52KEC_2019-10-22_final.stac-item
2019-10-22 11:13:07	ga_s2am_ard_3-2-1_52KDC_2019-10-22_final.stac-item
2019-10-22 11:13:11	ga_s2am_ard_3-2-1_52KCC_2019-10-22_final.stac-item
2019-10-22 11:13:17	ga_s2am_ard_3-2-1_52KBC_2019-10-22_final.stac-item
2019-10-22 11:13:18	ga_s2am_ard_3-2-1_52KEB_2019-10-22_final.stac-item
2019-10-22 11:13:22	ga_s2am_ard_3-2-1_52KDB_2019-10-22_final.stac-item
2019-10-22 11:13:25	ga_s2am_ard_3-2-1_52KCB_2019-10-22_final.stac-item
2019-10-22 11:13:28	ga_s2am_ard_3-2-1_52KBB_2019-10-22_final.stac-item
2019-10-22 11:13:30	ga_s2am_ard_3-2-1_52KEA_2019-10-22_final.stac-item
2019-10-22 11:13:31	ga_s2am_ard_3-2-1_52KDA_2019-10-22_final.stac-item
2019-10-22 11:13:32	ga_s2am_ard_3-2-1_51KZS_2019-10-22_final.stac-item
2019-10-22 11:13:32	ga_s2am_ard_3-2-1_52KCB_2019-10-22_final.stac-item
2019-10-22 11:13:33	ga_s2am_ard_3-2-1_52KCA_2019-10-22_final.stac-item
2019-10-22 11:13:34	ga_s2am_ard_3-2-1_52KBB_2019-10-22_final.stac-item
2019-10-22 11:13:34	ga_s2am_ard_3-2-1_52KBA_2019-10-22_final.stac-item
2019-10-22 11:13:34	ga_s2am_ard_3-2-1_51KZS_2019-10-22_final.stac-item
2019-10-22 11:13:35	ga_s2am_ard_3-2-1_51KZR_2019-10-22_final.stac-item
2019-10-22 11:13:36	ga_s2am_ard_3-2-1_52KEA_2019-10-22_final.stac-item
2019-10-22 11:13:38	ga_s2am_ard_3-2-1_52KDA_2019-10-22_final.stac-item
2019-10-22 11:13:40	ga_s2am_ard_3-2-1_52KCA_2019-10-22_final.stac-item
2019-10-22 11:13:43	ga_s2am_ard_3-2-1_52KBA_2019-10-22_final.stac-item
2019-10-22 11:13:43	ga_s2am_ard_3-2-1_51KZR_2019-10-22_final.stac-item
2019-10-22 11:13:47	ga_s2am_ard_3-2-1_52KEV_2019-10-22_final.stac-item
2019-10-22 11:13:51	ga_s2am_ard_3-2-1_52KDV_2019-10-22_final.stac-item
2019-10-22 11:13:54	ga_s2am_ard_3-2-1_52KCV_2019-10-22_final.stac-item
2019-10-22 11:13:57	ga_s2am_ard_3-2-1_51KZQ_2019-10-22_final.stac-item
2019-10-22 11:13:57	ga_s2am_ard_3-2-1_52KBV_2019-10-22_final.stac-item
2019-10-22 11:14:00	ga_s2am_ard_3-2-1_52KEU_2019-10-22_final.stac-item
2019-10-22 11:14:05	ga_s2am_ard_3-2-1_52KDU_2019-10-22_final.stac-item
2019-10-22 11:14:09	ga_s2am_ard_3-2-1_52KCU_2019-10-22_final.stac-item
2019-10-22 11:14:11	ga_s2am_ard_3-2-1_52JET_2019-10-22_final.stac-item
2019-10-22 11:14:12	ga_s2am_ard_3-2-1_51KZP_2019-10-22_final.stac-item
2019-10-22 11:14:12	ga_s2am_ard_3-2-1_52KBU_2019-10-22_final.stac-item
2019-10-22 11:14:27	ga_s2am_ard_3-2-1_51JZN_2019-10-22_final.stac-item
2019-10-22 11:14:32	ga_s2am_ard_3-2-1_51JYN_2019-10-22_final.stac-item
2019-10-22 11:14:41	ga_s2am_ard_3-2-1_51JZM_2019-10-22_final.stac-item
2019-10-22 11:14:44	ga_s2am_ard_3-2-1_51JYM_2019-10-22_final.stac-item
2019-10-22 11:14:59	ga_s2am_ard_3-2-1_51JYL_2019-10-22_final.stac-item
2019-10-22 11:15:10	ga_s2am_ard_3-2-1_52JBQ_2019-10-22_final.stac-item
2019-10-22 11:15:14	ga_s2am_ard_3-2-1_51JYK_2019-10-22_final.stac-item
2019-10-22 11:15:25	ga_s2am_ard_3-2-1_52JBP_2019-10-22_final.stac-item
2019-10-22 11:15:28	ga_s2am_ard_3-2-1_51JYJ_2019-10-22_final.stac-item
2019-10-22 11:15:33	ga_s2am_ard_3-2-1_51JXJ_2019-10-22_final.stac-item
2019-10-22 11:15:40	ga_s2am_ard_3-2-1_52JBN_2019-10-22_final.stac-item
2019-10-22 11:15:42	ga_s2am_ard_3-2-1_51JYH_2019-10-22_final.stac-item
2019-10-22 11:15:46	ga_s2am_ard_3-2-1_51JXH_2019-10-22_final.stac-item
2019-10-22 11:15:54	ga_s2am_ard_3-2-1_52JBM_2019-10-22_final.stac-item
2019-10-22 11:15:57	ga_s2am_ard_3-2-1_51JYG_2019-10-22_final.stac-item
2019-10-22 11:16:00	ga_s2am_ard_3-2-1_51JXG_2019-10-22_final.stac-item
2019-10-22 11:16:09	ga_s2am_ard_3-2-1_52JBL_2019-10-22_final.stac-item
2019-10-22 11:16:11	ga_s2am_ard_3-2-1_51JYF_2019-10-22_final.stac-item
2019-10-22 11:16:15	ga_s2am_ard_3-2-1_51JXF_2019-10-22_final.stac-item
2019-10-22 11:16:23	ga_s2am_ard_3-2-1_51JWF_2019-10-22_final.stac-item
2019-10-22 11:16:23	ga_s2am_ard_3-2-1_52HBK_2019-10-22_final.stac-item
2019-10-22 11:16:26	ga_s2am_ard_3-2-1_51HYE_2019-10-22_final.stac-item
2019-10-22 11:16:29	ga_s2am_ard_3-2-1_51HXE_2019-10-22_final.stac-item
2019-10-22 11:16:34	ga_s2am_ard_3-2-1_51HWE_2019-10-22_final.stac-item
2019-10-22 11:16:40	ga_s2am_ard_3-2-1_51HYD_2019-10-22_final.stac-item
2019-10-22 11:16:44	ga_s2am_ard_3-2-1_51HXD_2019-10-22_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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