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
2023-06-01 12:13:32	ga_s2am_ard_3-2-1_49KHT_2023-06-01_nrt
2023-06-01 12:13:34	ga_s2am_ard_3-2-1_49KGT_2023-06-01_nrt
2023-06-01 12:13:44	ga_s2am_ard_3-2-1_49KHS_2023-06-01_final
2023-06-01 12:13:48	ga_s2am_ard_3-2-1_49KGS_2023-06-01_nrt
2023-06-01 12:13:58	ga_s2am_ard_3-2-1_49KHR_2023-06-01_final
2023-06-01 12:14:02	ga_s2am_ard_3-2-1_49KGR_2023-06-01_final
2023-06-01 12:14:12	ga_s2am_ard_3-2-1_49KHQ_2023-06-01_final
2023-06-01 12:14:16	ga_s2am_ard_3-2-1_49KGQ_2023-06-01_final
2023-06-01 12:14:31	ga_s2am_ard_3-2-1_49KGP_2023-06-01_final
2023-06-01 12:14:45	ga_s2am_ard_3-2-1_49JGN_2023-06-01_final
2023-06-05 11:56:47	ga_s2am_ard_3-2-1_50HKK_2023-06-05_nrt
2023-06-05 11:56:49	ga_s2am_ard_3-2-1_49HGE_2023-06-05_nrt
2023-06-05 11:57:01	ga_s2am_ard_3-2-1_50HKJ_2023-06-05_nrt
2023-06-05 11:57:03	ga_s2am_ard_3-2-1_49HGD_2023-06-05_nrt
2023-06-05 11:57:18	ga_s2am_ard_3-2-1_49HGC_2023-06-05_nrt
2023-06-05 11:57:28	ga_s2am_ard_3-2-1_50HKG_2023-06-05_nrt
2023-06-05 11:57:29	ga_s2am_ard_3-2-1_49HGB_2023-06-05_nrt
2023-06-15 11:56:48	ga_s2am_ard_3-2-1_50HKK_2023-06-15_nrt
2023-06-15 11:56:49	ga_s2am_ard_3-2-1_49HGE_2023-06-15_nrt
2023-06-15 11:57:02	ga_s2am_ard_3-2-1_50HKJ_2023-06-15_nrt
2023-06-15 11:57:04	ga_s2am_ard_3-2-1_49HGD_2023-06-15_nrt
2023-06-15 11:57:11	ga_s2am_ard_3-2-1_50HKH_2023-06-15_nrt
2023-06-15 11:57:12	ga_s2am_ard_3-2-1_49HGC_2023-06-15_nrt
2023-06-15 11:57:18	ga_s2am_ard_3-2-1_50HKH_2023-06-15_nrt
2023-06-15 11:57:20	ga_s2am_ard_3-2-1_49HGC_2023-06-15_nrt
2023-06-15 11:57:28	ga_s2am_ard_3-2-1_50HKG_2023-06-15_nrt
2023-06-15 11:57:29	ga_s2am_ard_3-2-1_49HGB_2023-06-15_nrt
2023-06-18 12:04:03	ga_s2am_ard_3-2-1_49KHR_2023-06-18_final
2023-06-18 12:04:18	ga_s2am_ard_3-2-1_49KHQ_2023-06-18_final
2023-06-18 12:04:22	ga_s2am_ard_3-2-1_49KGQ_2023-06-18_final
2023-06-18 12:04:35	ga_s2am_ard_3-2-1_49KGP_2023-06-18_final
2023-06-18 12:04:50	ga_s2am_ard_3-2-1_49JGN_2023-06-18_final
2023-06-18 12:05:05	ga_s2am_ard_3-2-1_49JGM_2023-06-18_final
2023-06-18 12:05:19	ga_s2am_ard_3-2-1_49JGL_2023-06-18_final
2023-06-18 12:05:33	ga_s2am_ard_3-2-1_49JGK_2023-06-18_final
2023-06-18 12:05:45	ga_s2am_ard_3-2-1_50JKP_2023-06-18_final
2023-06-18 12:05:48	ga_s2am_ard_3-2-1_49JGJ_2023-06-18_final
2023-06-18 12:05:59	ga_s2am_ard_3-2-1_50JKN_2023-06-18_final
2023-06-18 12:06:00	ga_s2am_ard_3-2-1_49JGH_2023-06-18_final
2023-06-18 12:06:06	ga_s2am_ard_3-2-1_50JKN_2023-06-18_final
2023-06-18 12:06:07	ga_s2am_ard_3-2-1_49JGH_2023-06-18_final
2023-06-18 12:06:14	ga_s2am_ard_3-2-1_50JKM_2023-06-18_final
2023-06-18 12:06:17	ga_s2am_ard_3-2-1_49JGG_2023-06-18_nrt
2023-06-18 12:06:26	ga_s2am_ard_3-2-1_50JKL_2023-06-18_final
2023-06-18 12:06:27	ga_s2am_ard_3-2-1_49JGF_2023-06-18_nrt
2023-06-28 12:04:48	ga_s2am_ard_3-2-1_49JGN_2023-06-28_final
2023-06-28 12:04:54	ga_s2am_ard_3-2-1_49JGN_2023-06-28_final
2023-06-28 12:05:04	ga_s2am_ard_3-2-1_49JGM_2023-06-28_final
2023-06-28 12:05:19	ga_s2am_ard_3-2-1_49JGL_2023-06-28_final
2023-06-28 12:05:33	ga_s2am_ard_3-2-1_49JGK_2023-06-28_final
2023-06-28 12:05:45	ga_s2am_ard_3-2-1_50JKP_2023-06-28_final
2023-06-28 12:05:48	ga_s2am_ard_3-2-1_49JGJ_2023-06-28_final
2023-06-28 12:05:59	ga_s2am_ard_3-2-1_50JKN_2023-06-28_final
2023-06-28 12:06:02	ga_s2am_ard_3-2-1_49JGH_2023-06-28_final
2023-06-28 12:06:14	ga_s2am_ard_3-2-1_50JKM_2023-06-28_final
2023-06-28 12:06:17	ga_s2am_ard_3-2-1_49JGG_2023-06-28_nrt
2023-06-28 12:06:29	ga_s2am_ard_3-2-1_49JGF_2023-06-28_nrt
2023-07-18 12:04:04	ga_s2am_ard_3-2-1_49KHR_2023-07-18_final
2023-07-18 12:04:18	ga_s2am_ard_3-2-1_49KHQ_2023-07-18_final
2023-07-18 12:04:22	ga_s2am_ard_3-2-1_49KGQ_2023-07-18_final
2023-07-18 12:04:36	ga_s2am_ard_3-2-1_49KGP_2023-07-18_final
2023-07-18 12:04:50	ga_s2am_ard_3-2-1_49JGN_2023-07-18_final
2023-07-18 12:05:05	ga_s2am_ard_3-2-1_49JGM_2023-07-18_final
2023-07-18 12:05:19	ga_s2am_ard_3-2-1_49JGL_2023-07-18_final
2023-07-18 12:05:34	ga_s2am_ard_3-2-1_49JGK_2023-07-18_final
2023-07-18 12:05:45	ga_s2am_ard_3-2-1_50JKP_2023-07-18_final
2023-07-18 12:05:48	ga_s2am_ard_3-2-1_49JGJ_2023-07-18_final
2023-07-28 12:04:04	ga_s2am_ard_3-2-1_49KHR_2023-07-28_final
2023-07-28 12:04:19	ga_s2am_ard_3-2-1_49KHQ_2023-07-28_final
2023-07-28 12:04:23	ga_s2am_ard_3-2-1_49KGQ_2023-07-28_final
2023-07-28 12:04:36	ga_s2am_ard_3-2-1_49KGP_2023-07-28_final
2023-07-28 12:04:51	ga_s2am_ard_3-2-1_49JGN_2023-07-28_final
2023-07-28 12:05:06	ga_s2am_ard_3-2-1_49JGM_2023-07-28_final
2023-07-28 12:05:20	ga_s2am_ard_3-2-1_49JGL_2023-07-28_final
2023-07-28 12:05:34	ga_s2am_ard_3-2-1_49JGK_2023-07-28_final
2023-07-28 12:05:46	ga_s2am_ard_3-2-1_50JKP_2023-07-28_final
2023-07-28 12:05:49	ga_s2am_ard_3-2-1_49JGJ_2023-07-28_final
2023-07-28 12:06:00	ga_s2am_ard_3-2-1_50JKN_2023-07-28_final
2023-07-28 12:06:03	ga_s2am_ard_3-2-1_49JGH_2023-07-28_final
2023-07-28 12:06:15	ga_s2am_ard_3-2-1_50JKM_2023-07-28_final
2023-07-28 12:06:18	ga_s2am_ard_3-2-1_49JGG_2023-07-28_nrt
2023-07-28 12:06:27	ga_s2am_ard_3-2-1_50JKL_2023-07-28_final
2023-07-28 12:06:28	ga_s2am_ard_3-2-1_49JGF_2023-07-28_nrt
2023-07-31 12:13:34	ga_s2am_ard_3-2-1_49KHT_2023-07-31_nrt
2023-07-31 12:13:36	ga_s2am_ard_3-2-1_49KGT_2023-07-31_nrt
2023-07-31 12:13:45	ga_s2am_ard_3-2-1_49KHS_2023-07-31_final
2023-07-31 12:13:49	ga_s2am_ard_3-2-1_49KGS_2023-07-31_nrt
2023-07-31 12:14:00	ga_s2am_ard_3-2-1_49KHR_2023-07-31_final
2023-07-31 12:14:04	ga_s2am_ard_3-2-1_49KGR_2023-07-31_final
2023-07-31 12:14:14	ga_s2am_ard_3-2-1_49KHQ_2023-07-31_final
2023-07-31 12:14:18	ga_s2am_ard_3-2-1_49KGQ_2023-07-31_final
2023-07-31 12:14:32	ga_s2am_ard_3-2-1_49KGP_2023-07-31_final
2023-07-31 12:14:47	ga_s2am_ard_3-2-1_49JGN_2023-07-31_final
2023-11-18 12:13:38	ga_s2am_ard_3-2-1_49KHS_2023-11-18_final
2023-11-18 12:13:42	ga_s2am_ard_3-2-1_49KGS_2023-11-18_nrt
2023-11-18 12:13:52	ga_s2am_ard_3-2-1_49KHR_2023-11-18_final
2023-11-18 12:13:56	ga_s2am_ard_3-2-1_49KGR_2023-11-18_final
2023-11-18 12:14:06	ga_s2am_ard_3-2-1_49KHQ_2023-11-18_final
2023-11-18 12:14:10	ga_s2am_ard_3-2-1_49KGQ_2023-11-18_final
2023-11-18 12:14:25	ga_s2am_ard_3-2-1_49KGP_2023-11-18_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