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
2022-12-10 10:27:09	ga_s2am_ard_3-2-1_53HNA_2022-12-10_final
2022-12-10 12:03:20	ga_s2am_ard_3-2-1_50KNC_2022-12-10_final.stac-item
2022-12-10 12:03:24	ga_s2am_ard_3-2-1_50KMC_2022-12-10_final.stac-item
2022-12-10 12:03:27	ga_s2am_ard_3-2-1_50KLC_2022-12-10_final.stac-item
2022-12-10 12:03:30	ga_s2am_ard_3-2-1_50KKC_2022-12-10_final
2022-12-10 12:03:34	ga_s2am_ard_3-2-1_50KNB_2022-12-10_final.stac-item
2022-12-10 12:03:38	ga_s2am_ard_3-2-1_50KMB_2022-12-10_final.stac-item
2022-12-10 12:03:41	ga_s2am_ard_3-2-1_50KLB_2022-12-10_final.stac-item
2022-12-10 12:03:44	ga_s2am_ard_3-2-1_50KNA_2022-12-10_final.stac-item
2022-12-10 12:03:45	ga_s2am_ard_3-2-1_50KKB_2022-12-10_final.stac-item
2022-12-10 12:03:52	ga_s2am_ard_3-2-1_50KMA_2022-12-10_final.stac-item
2022-12-10 12:03:56	ga_s2am_ard_3-2-1_50KLA_2022-12-10_final.stac-item
2022-12-10 12:03:59	ga_s2am_ard_3-2-1_50KKA_2022-12-10_final.stac-item
2022-12-10 12:04:00	ga_s2am_ard_3-2-1_49KHR_2022-12-10_final.stac-item
2022-12-10 12:04:07	ga_s2am_ard_3-2-1_50KMV_2022-12-10_final.stac-item
2022-12-10 12:04:11	ga_s2am_ard_3-2-1_50KLV_2022-12-10_final.stac-item
2022-12-10 12:04:14	ga_s2am_ard_3-2-1_50KKV_2022-12-10_final.stac-item
2022-12-10 12:04:14	ga_s2am_ard_3-2-1_49KHQ_2022-12-10_final.stac-item
2022-12-10 12:04:18	ga_s2am_ard_3-2-1_49KGQ_2022-12-10_final.stac-item
2022-12-10 12:04:22	ga_s2am_ard_3-2-1_50KMU_2022-12-10_final.stac-item
2022-12-10 12:04:25	ga_s2am_ard_3-2-1_50KLU_2022-12-10_final.stac-item
2022-12-10 12:04:28	ga_s2am_ard_3-2-1_49KHP_2022-12-10_final.stac-item
2022-12-10 12:04:28	ga_s2am_ard_3-2-1_50KKU_2022-12-10_final.stac-item
2022-12-10 12:04:32	ga_s2am_ard_3-2-1_49KGP_2022-12-10_final.stac-item
2022-12-10 12:04:36	ga_s2am_ard_3-2-1_50JMT_2022-12-10_final.stac-item
2022-12-10 12:04:40	ga_s2am_ard_3-2-1_50JLT_2022-12-10_final.stac-item
2022-12-10 12:04:43	ga_s2am_ard_3-2-1_49JHN_2022-12-10_final.stac-item
2022-12-10 12:04:43	ga_s2am_ard_3-2-1_50JKT_2022-12-10_final.stac-item
2022-12-10 12:04:46	ga_s2am_ard_3-2-1_49JGN_2022-12-10_final.stac-item
2022-12-10 12:04:49	ga_s2am_ard_3-2-1_50JMS_2022-12-10_final.stac-item
2022-12-10 12:04:54	ga_s2am_ard_3-2-1_50JLS_2022-12-10_final.stac-item
2022-12-10 12:04:57	ga_s2am_ard_3-2-1_49JHM_2022-12-10_final.stac-item
2022-12-10 12:04:57	ga_s2am_ard_3-2-1_50JKS_2022-12-10_final.stac-item
2022-12-10 12:04:59	ga_s2am_ard_3-2-1_50JMR_2022-12-10_final.stac-item
2022-12-10 12:05:01	ga_s2am_ard_3-2-1_49JGM_2022-12-10_final.stac-item
2022-12-10 12:05:09	ga_s2am_ard_3-2-1_50JLR_2022-12-10_final.stac-item
2022-12-10 12:05:12	ga_s2am_ard_3-2-1_50JKR_2022-12-10_final.stac-item
2022-12-10 12:05:15	ga_s2am_ard_3-2-1_49JGL_2022-12-10_final.stac-item
2022-12-10 12:05:20	ga_s2am_ard_3-2-1_49JFL_2022-12-10_final
2022-12-10 12:05:23	ga_s2am_ard_3-2-1_50JLQ_2022-12-10_final.stac-item
2022-12-10 12:05:27	ga_s2am_ard_3-2-1_50JKQ_2022-12-10_final.stac-item
2022-12-10 12:05:30	ga_s2am_ard_3-2-1_49JGK_2022-12-10_final.stac-item
2022-12-10 12:05:38	ga_s2am_ard_3-2-1_50JLP_2022-12-10_final.stac-item
2022-12-10 12:05:41	ga_s2am_ard_3-2-1_50JKP_2022-12-10_final.stac-item
2022-12-10 12:05:44	ga_s2am_ard_3-2-1_49JGJ_2022-12-10_final.stac-item
2022-12-10 12:05:52	ga_s2am_ard_3-2-1_50JLN_2022-12-10_final.stac-item
2022-12-10 12:05:56	ga_s2am_ard_3-2-1_50JKN_2022-12-10_final.stac-item
2022-12-10 12:06:10	ga_s2am_ard_3-2-1_50JKM_2022-12-10_final.stac-item
2022-12-11 09:55:29	ga_s2am_ard_3-2-1_55JEF_2022-12-11_final.stac-item
2022-12-11 09:55:33	ga_s2am_ard_3-2-1_55JDF_2022-12-11_final.stac-item
2022-12-11 09:55:36	ga_s2am_ard_3-2-1_55JCF_2022-12-11_final.stac-item
2022-12-11 09:55:39	ga_s2am_ard_3-2-1_55JBF_2022-12-11_final.stac-item
2022-12-11 09:55:43	ga_s2am_ard_3-2-1_55HEE_2022-12-11_final.stac-item
2022-12-11 09:55:47	ga_s2am_ard_3-2-1_55HDE_2022-12-11_final.stac-item
2022-12-11 09:55:51	ga_s2am_ard_3-2-1_55HCE_2022-12-11_final.stac-item
2022-12-11 09:55:54	ga_s2am_ard_3-2-1_55HBE_2022-12-11_final.stac-item
2022-12-11 09:55:54	ga_s2am_ard_3-2-1_55HED_2022-12-11_final.stac-item
2022-12-11 09:55:57	ga_s2am_ard_3-2-1_54HYK_2022-12-11_final.stac-item
2022-12-11 09:56:02	ga_s2am_ard_3-2-1_55HDD_2022-12-11_final.stac-item
2022-12-11 09:56:05	ga_s2am_ard_3-2-1_55HCD_2022-12-11_final.stac-item
2022-12-11 09:56:09	ga_s2am_ard_3-2-1_55HBD_2022-12-11_final.stac-item
2022-12-11 09:56:10	ga_s2am_ard_3-2-1_54HYJ_2022-12-11_final.stac-item
2022-12-11 09:56:16	ga_s2am_ard_3-2-1_55HDC_2022-12-11_final.stac-item
2022-12-11 09:56:20	ga_s2am_ard_3-2-1_55HCC_2022-12-11_final.stac-item
2022-12-11 09:56:23	ga_s2am_ard_3-2-1_55HBC_2022-12-11_final.stac-item
2022-12-11 09:56:25	ga_s2am_ard_3-2-1_54HYH_2022-12-11_final.stac-item
2022-12-11 09:56:31	ga_s2am_ard_3-2-1_55HDB_2022-12-11_final.stac-item
2022-12-11 09:56:34	ga_s2am_ard_3-2-1_55HCB_2022-12-11_final.stac-item
2022-12-11 09:56:38	ga_s2am_ard_3-2-1_55HBB_2022-12-11_final.stac-item
2022-12-11 09:56:39	ga_s2am_ard_3-2-1_54HYG_2022-12-11_final.stac-item
2022-12-11 09:56:45	ga_s2am_ard_3-2-1_55HDA_2022-12-11_final.stac-item
2022-12-11 09:56:46	ga_s2am_ard_3-2-1_54HXG_2022-12-11_final.stac-item
2022-12-11 09:56:49	ga_s2am_ard_3-2-1_55HCA_2022-12-11_final.stac-item
2022-12-11 09:56:52	ga_s2am_ard_3-2-1_55HBA_2022-12-11_final.stac-item
2022-12-11 09:56:54	ga_s2am_ard_3-2-1_54HYF_2022-12-11_final.stac-item
2022-12-11 09:56:58	ga_s2am_ard_3-2-1_54HXF_2022-12-11_final.stac-item
2022-12-11 09:56:58	ga_s2am_ard_3-2-1_55HDV_2022-12-11_final.stac-item
2022-12-11 09:57:03	ga_s2am_ard_3-2-1_55HCV_2022-12-11_final.stac-item
2022-12-11 09:57:07	ga_s2am_ard_3-2-1_55HBV_2022-12-11_final.stac-item
2022-12-11 09:57:08	ga_s2am_ard_3-2-1_54HYE_2022-12-11_final.stac-item
2022-12-11 09:57:09	ga_s2am_ard_3-2-1_55HDU_2022-12-11_final.stac-item
2022-12-11 09:57:12	ga_s2am_ard_3-2-1_54HXE_2022-12-11_final.stac-item
2022-12-11 09:57:18	ga_s2am_ard_3-2-1_55HCU_2022-12-11_final.stac-item
2022-12-11 09:57:22	ga_s2am_ard_3-2-1_55HBU_2022-12-11_final.stac-item
2022-12-11 09:57:22	ga_s2am_ard_3-2-1_54HYD_2022-12-11_final.stac-item
2022-12-11 09:57:27	ga_s2am_ard_3-2-1_54HXD_2022-12-11_final.stac-item
2022-12-11 09:57:32	ga_s2am_ard_3-2-1_55HCT_2022-12-11_final.stac-item
2022-12-11 09:57:36	ga_s2am_ard_3-2-1_55HBT_2022-12-11_final.stac-item
2022-12-11 09:57:37	ga_s2am_ard_3-2-1_54HYC_2022-12-11_final.stac-item
2022-12-11 09:57:41	ga_s2am_ard_3-2-1_54HXC_2022-12-11_final.stac-item
2022-12-11 09:57:46	ga_s2am_ard_3-2-1_54HWC_2022-12-11_final.stac-item
2022-12-11 09:57:47	ga_s2am_ard_3-2-1_55HCS_2022-12-11_final.stac-item
2022-12-11 09:57:51	ga_s2am_ard_3-2-1_55HBS_2022-12-11_final.stac-item
2022-12-11 09:57:51	ga_s2am_ard_3-2-1_54HYB_2022-12-11_final.stac-item
2022-12-11 09:57:55	ga_s2am_ard_3-2-1_54HXB_2022-12-11_final
2022-12-11 09:58:01	ga_s2am_ard_3-2-1_55GCR_2022-12-11_final.stac-item
2022-12-11 09:58:05	ga_s2am_ard_3-2-1_55GBR_2022-12-11_final.stac-item
2022-12-11 09:58:06	ga_s2am_ard_3-2-1_54GYA_2022-12-11_final.stac-item
2022-12-11 09:58:14	ga_s2am_ard_3-2-1_55GCQ_2022-12-11_final.stac-item
2022-12-11 09:58:24	ga_s2am_ard_3-2-1_55GCP_2022-12-11_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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