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
2023-06-11 12:15:14	ga_s2am_ard_3-2-1_49JGL_2023-06-11_final
2023-06-11 12:15:18	ga_s2am_ard_3-2-1_49JFL_2023-06-11_nrt
2023-06-11 12:15:23	ga_s2am_ard_3-2-1_49JGK_2023-06-11_final
2023-06-11 12:15:24	ga_s2am_ard_3-2-1_49JFK_2023-06-11_nrt
2023-06-12 08:24:27	ga_s2am_ard_3-2-1_59JKH_2023-06-11_final
2023-06-12 08:24:28	ga_s2am_ard_3-2-1_58JGN_2023-06-11_final
2023-06-12 08:24:31	ga_s2am_ard_3-2-1_59JKH_2023-06-11_final
2023-06-12 08:24:31	ga_s2am_ard_3-2-1_58JGN_2023-06-11_final
2023-06-12 09:59:42	ga_s2am_ard_3-2-1_55LHJ_2023-06-12_nrt
2023-06-12 09:59:48	ga_s2am_ard_3-2-1_55LGJ_2023-06-12_nrt
2023-06-12 09:59:51	ga_s2am_ard_3-2-1_55LFJ_2023-06-12_nrt
2023-06-12 09:59:52	ga_s2am_ard_3-2-1_55LHH_2023-06-12_nrt
2023-06-12 09:59:54	ga_s2am_ard_3-2-1_55LEJ_2023-06-12_nrt
2023-06-12 10:00:02	ga_s2am_ard_3-2-1_55LGH_2023-06-12_nrt
2023-06-12 10:00:06	ga_s2am_ard_3-2-1_55LFH_2023-06-12_nrt
2023-06-12 10:00:09	ga_s2am_ard_3-2-1_55LEH_2023-06-12_nrt
2023-06-12 10:00:13	ga_s2am_ard_3-2-1_55LDH_2023-06-12_nrt
2023-06-12 10:00:17	ga_s2am_ard_3-2-1_55LGG_2023-06-12_nrt
2023-06-12 10:00:20	ga_s2am_ard_3-2-1_55LFG_2023-06-12_nrt
2023-06-12 10:00:23	ga_s2am_ard_3-2-1_55LEG_2023-06-12_nrt
2023-06-12 10:00:26	ga_s2am_ard_3-2-1_55LDG_2023-06-12_nrt
2023-06-12 10:00:31	ga_s2am_ard_3-2-1_55LGF_2023-06-12_nrt
2023-06-12 10:00:35	ga_s2am_ard_3-2-1_55LFF_2023-06-12_nrt
2023-06-12 10:00:38	ga_s2am_ard_3-2-1_55LEF_2023-06-12_nrt
2023-06-12 10:00:41	ga_s2am_ard_3-2-1_55LDF_2023-06-12_nrt
2023-06-12 10:00:44	ga_s2am_ard_3-2-1_55LGE_2023-06-12_nrt
2023-06-12 10:00:49	ga_s2am_ard_3-2-1_55LFE_2023-06-12_nrt
2023-06-12 10:00:52	ga_s2am_ard_3-2-1_55LEE_2023-06-12_nrt
2023-06-12 10:00:55	ga_s2am_ard_3-2-1_55LGD_2023-06-12_nrt
2023-06-12 10:00:56	ga_s2am_ard_3-2-1_55LDE_2023-06-12_nrt
2023-06-12 10:01:03	ga_s2am_ard_3-2-1_55LFD_2023-06-12_nrt
2023-06-12 10:01:07	ga_s2am_ard_3-2-1_55LED_2023-06-12_nrt
2023-06-12 10:01:10	ga_s2am_ard_3-2-1_55LDD_2023-06-12_nrt
2023-06-12 10:01:15	ga_s2am_ard_3-2-1_55LCD_2023-06-12_final
2023-06-12 10:01:18	ga_s2am_ard_3-2-1_55LFC_2023-06-12_final
2023-06-12 10:01:22	ga_s2am_ard_3-2-1_55LEC_2023-06-12_final
2023-06-12 10:01:25	ga_s2am_ard_3-2-1_55LDC_2023-06-12_nrt
2023-06-12 10:01:28	ga_s2am_ard_3-2-1_55LCC_2023-06-12_final
2023-06-12 10:01:33	ga_s2am_ard_3-2-1_55KFB_2023-06-12_final
2023-06-12 10:01:36	ga_s2am_ard_3-2-1_55KEB_2023-06-12_final
2023-06-12 10:01:39	ga_s2am_ard_3-2-1_55KDB_2023-06-12_final
2023-06-12 10:01:42	ga_s2am_ard_3-2-1_55KCB_2023-06-12_final
2023-06-12 10:01:46	ga_s2am_ard_3-2-1_55KFA_2023-06-12_final
2023-06-12 10:01:48	ga_s2am_ard_3-2-1_55KEA_2023-06-12_nrt
2023-06-12 10:01:50	ga_s2am_ard_3-2-1_55KDA_2023-06-12_final
2023-06-12 10:01:51	ga_s2am_ard_3-2-1_55KCA_2023-06-12_final
2023-06-12 10:01:52	ga_s2am_ard_3-2-1_55KFA_2023-06-12_final
2023-06-12 10:01:55	ga_s2am_ard_3-2-1_55KEA_2023-06-12_nrt
2023-06-12 10:01:56	ga_s2am_ard_3-2-1_55KDA_2023-06-12_final
2023-06-12 10:07:48	ga_s2am_ard_3-2-1_54HVC_2023-06-12_final
2023-06-12 10:07:51	ga_s2am_ard_3-2-1_54HUC_2023-06-12_nrt
2023-06-12 10:07:53	ga_s2am_ard_3-2-1_54HXB_2023-06-12_nrt
2023-06-12 10:07:54	ga_s2am_ard_3-2-1_54HWB_2023-06-12_nrt
2023-06-12 10:07:56	ga_s2am_ard_3-2-1_54HVB_2023-06-12_nrt
2023-06-12 10:07:57	ga_s2am_ard_3-2-1_54HUB_2023-06-12_nrt
2023-06-12 11:40:29	ga_s2am_ard_3-2-1_51LXJ_2023-06-12_nrt
2023-06-12 11:40:32	ga_s2am_ard_3-2-1_51LWJ_2023-06-12_nrt
2023-06-12 11:40:35	ga_s2am_ard_3-2-1_51LVJ_2023-06-12_nrt
2023-06-12 11:40:39	ga_s2am_ard_3-2-1_51LUJ_2023-06-12_nrt
2023-06-12 11:40:43	ga_s2am_ard_3-2-1_51LXH_2023-06-12_nrt
2023-06-12 11:40:47	ga_s2am_ard_3-2-1_51LWH_2023-06-12_nrt
2023-06-12 11:40:50	ga_s2am_ard_3-2-1_51LVH_2023-06-12_nrt
2023-06-12 11:40:53	ga_s2am_ard_3-2-1_51LUH_2023-06-12_nrt
2023-06-12 11:40:58	ga_s2am_ard_3-2-1_51LXG_2023-06-12_nrt
2023-06-12 11:41:01	ga_s2am_ard_3-2-1_51LWG_2023-06-12_final
2023-06-12 11:41:05	ga_s2am_ard_3-2-1_51LVG_2023-06-12_final
2023-06-12 11:41:08	ga_s2am_ard_3-2-1_51LUG_2023-06-12_nrt
2023-06-12 11:41:11	ga_s2am_ard_3-2-1_51LXF_2023-06-12_nrt
2023-06-12 11:41:16	ga_s2am_ard_3-2-1_51LWF_2023-06-12_nrt
2023-06-12 11:41:19	ga_s2am_ard_3-2-1_51LVF_2023-06-12_final
2023-06-12 11:41:21	ga_s2am_ard_3-2-1_51LXE_2023-06-12_nrt
2023-06-12 11:41:22	ga_s2am_ard_3-2-1_51LUF_2023-06-12_final
2023-06-12 11:41:30	ga_s2am_ard_3-2-1_51LWE_2023-06-12_final
2023-06-12 11:41:34	ga_s2am_ard_3-2-1_51LVE_2023-06-12_final
2023-06-12 11:41:37	ga_s2am_ard_3-2-1_51LUE_2023-06-12_final
2023-06-12 11:41:41	ga_s2am_ard_3-2-1_51LTE_2023-06-12_nrt
2023-06-12 11:41:45	ga_s2am_ard_3-2-1_51LWD_2023-06-12_nrt
2023-06-12 11:41:48	ga_s2am_ard_3-2-1_51LVD_2023-06-12_nrt
2023-06-12 11:41:51	ga_s2am_ard_3-2-1_51LUD_2023-06-12_final
2023-06-12 11:41:54	ga_s2am_ard_3-2-1_51LTD_2023-06-12_nrt
2023-06-12 11:41:59	ga_s2am_ard_3-2-1_51LWC_2023-06-12_final
2023-06-12 11:42:03	ga_s2am_ard_3-2-1_51LVC_2023-06-12_final
2023-06-12 11:42:06	ga_s2am_ard_3-2-1_51LUC_2023-06-12_final
2023-06-12 11:42:09	ga_s2am_ard_3-2-1_51LTC_2023-06-12_nrt
2023-06-12 11:42:11	ga_s2am_ard_3-2-1_50LRH_2023-06-12_nrt
2023-06-12 11:42:13	ga_s2am_ard_3-2-1_51KWB_2023-06-12_final
2023-06-12 11:42:17	ga_s2am_ard_3-2-1_51KVB_2023-06-12_final
2023-06-12 11:42:20	ga_s2am_ard_3-2-1_51KUB_2023-06-12_final
2023-06-12 11:42:24	ga_s2am_ard_3-2-1_51KTB_2023-06-12_nrt
2023-06-12 11:42:24	ga_s2am_ard_3-2-1_50KRG_2023-06-12_nrt
2023-06-12 11:42:25	ga_s2am_ard_3-2-1_51KWA_2023-06-12_final
2023-06-12 11:42:31	ga_s2am_ard_3-2-1_51KVA_2023-06-12_final
2023-06-12 11:42:35	ga_s2am_ard_3-2-1_51KUA_2023-06-12_final
2023-06-12 11:42:38	ga_s2am_ard_3-2-1_51KTA_2023-06-12_nrt
2023-06-12 11:44:51	ga_s2am_ard_3-2-1_50JPS_2023-06-12_final
2023-06-12 11:44:54	ga_s2am_ard_3-2-1_51JTM_2023-06-12_final
2023-06-12 11:44:58	ga_s2am_ard_3-2-1_50JPS_2023-06-12_final
2023-06-12 11:45:07	ga_s2am_ard_3-2-1_50JQR_2023-06-12_final
2023-06-12 11:45:22	ga_s2am_ard_3-2-1_50JQQ_2023-06-12_final
2023-06-12 11:45:36	ga_s2am_ard_3-2-1_50JQP_2023-06-12_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