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
2023-04-23 10:05:40	ga_s2am_ard_3-2-1_54JWL_2023-04-23_final
2023-04-23 10:05:42	ga_s2am_ard_3-2-1_55HBE_2023-04-23_final
2023-04-23 10:05:43	ga_s2am_ard_3-2-1_54HYK_2023-04-23_final
2023-04-23 10:05:43	ga_s2am_ard_3-2-1_54JYL_2023-04-23_final
2023-04-23 10:05:45	ga_s2am_ard_3-2-1_54JXL_2023-04-23_final
2023-04-23 10:05:46	ga_s2am_ard_3-2-1_54JWL_2023-04-23_final
2023-04-23 10:05:49	ga_s2am_ard_3-2-1_55HBE_2023-04-23_final
2023-04-23 10:05:51	ga_s2am_ard_3-2-1_54HYK_2023-04-23_final
2023-04-23 10:05:55	ga_s2am_ard_3-2-1_54HXK_2023-04-23_final
2023-04-23 10:05:58	ga_s2am_ard_3-2-1_54HWK_2023-04-23_final
2023-04-23 10:06:03	ga_s2am_ard_3-2-1_55HBD_2023-04-23_final
2023-04-23 10:06:05	ga_s2am_ard_3-2-1_54HYJ_2023-04-23_final
2023-04-23 10:06:09	ga_s2am_ard_3-2-1_54HXJ_2023-04-23_final
2023-04-23 10:06:13	ga_s2am_ard_3-2-1_54HWJ_2023-04-23_final
2023-04-23 10:06:18	ga_s2am_ard_3-2-1_55HBC_2023-04-23_final
2023-04-23 10:06:19	ga_s2am_ard_3-2-1_54HYH_2023-04-23_final
2023-04-23 10:06:24	ga_s2am_ard_3-2-1_54HXH_2023-04-23_final
2023-04-23 10:06:27	ga_s2am_ard_3-2-1_54HWH_2023-04-23_final
2023-04-23 10:06:32	ga_s2am_ard_3-2-1_55HBB_2023-04-23_final
2023-04-23 10:06:33	ga_s2am_ard_3-2-1_54HVH_2023-04-23_final
2023-04-23 10:06:34	ga_s2am_ard_3-2-1_54HYG_2023-04-23_final
2023-04-23 10:06:42	ga_s2am_ard_3-2-1_54HWG_2023-04-23_final
2023-04-23 10:06:44	ga_s2am_ard_3-2-1_55HBA_2023-04-23_final
2023-04-23 10:06:45	ga_s2am_ard_3-2-1_54HVG_2023-04-23_final
2023-04-23 10:06:49	ga_s2am_ard_3-2-1_54HYF_2023-04-23_final
2023-04-23 10:06:52	ga_s2am_ard_3-2-1_54HXF_2023-04-23_final
2023-04-23 10:06:54	ga_s2am_ard_3-2-1_55HBV_2023-04-23_final
2023-04-23 10:06:56	ga_s2am_ard_3-2-1_54HWF_2023-04-23_final
2023-04-23 10:07:00	ga_s2am_ard_3-2-1_54HVF_2023-04-23_final
2023-04-23 10:07:02	ga_s2am_ard_3-2-1_54HYE_2023-04-23_final
2023-04-23 10:07:07	ga_s2am_ard_3-2-1_54HXE_2023-04-23_final
2023-04-23 10:07:11	ga_s2am_ard_3-2-1_54HWE_2023-04-23_final
2023-04-23 10:07:13	ga_s2am_ard_3-2-1_54HYD_2023-04-23_final
2023-04-23 10:07:14	ga_s2am_ard_3-2-1_54HVE_2023-04-23_final
2023-04-23 10:07:21	ga_s2am_ard_3-2-1_54HXD_2023-04-23_final
2023-04-23 10:07:25	ga_s2am_ard_3-2-1_54HWD_2023-04-23_final
2023-04-23 10:07:29	ga_s2am_ard_3-2-1_54HVD_2023-04-23_final
2023-04-23 10:07:36	ga_s2am_ard_3-2-1_54HXC_2023-04-23_final
2023-04-23 10:07:40	ga_s2am_ard_3-2-1_54HWC_2023-04-23_final
2023-04-23 10:07:43	ga_s2am_ard_3-2-1_54HVC_2023-04-23_final
2023-04-23 10:07:45	ga_s2am_ard_3-2-1_54HUC_2023-04-23_nrt
2023-04-23 10:07:46	ga_s2am_ard_3-2-1_54HXB_2023-04-23_nrt
2023-04-23 10:07:48	ga_s2am_ard_3-2-1_54HWB_2023-04-23_nrt
2023-04-23 10:07:49	ga_s2am_ard_3-2-1_54HVB_2023-04-23_nrt
2023-04-23 11:40:24	ga_s2am_ard_3-2-1_51LXJ_2023-04-23_nrt
2023-04-23 11:40:27	ga_s2am_ard_3-2-1_51LWJ_2023-04-23_nrt
2023-04-23 11:40:30	ga_s2am_ard_3-2-1_51LVJ_2023-04-23_nrt
2023-04-23 11:40:34	ga_s2am_ard_3-2-1_51LUJ_2023-04-23_nrt
2023-04-23 11:40:38	ga_s2am_ard_3-2-1_51LXH_2023-04-23_nrt
2023-04-23 11:40:42	ga_s2am_ard_3-2-1_51LWH_2023-04-23_nrt
2023-04-23 11:40:45	ga_s2am_ard_3-2-1_51LVH_2023-04-23_nrt
2023-04-23 11:40:48	ga_s2am_ard_3-2-1_51LUH_2023-04-23_nrt
2023-04-23 11:40:53	ga_s2am_ard_3-2-1_51LXG_2023-04-23_nrt
2023-04-23 11:40:56	ga_s2am_ard_3-2-1_51LWG_2023-04-23_final
2023-04-23 11:41:00	ga_s2am_ard_3-2-1_51LVG_2023-04-23_final
2023-04-23 11:41:03	ga_s2am_ard_3-2-1_51LUG_2023-04-23_nrt
2023-04-23 11:41:06	ga_s2am_ard_3-2-1_51LXF_2023-04-23_nrt
2023-04-23 11:41:11	ga_s2am_ard_3-2-1_51LWF_2023-04-23_nrt
2023-04-23 11:41:14	ga_s2am_ard_3-2-1_51LVF_2023-04-23_final
2023-04-23 11:41:16	ga_s2am_ard_3-2-1_51LXE_2023-04-23_nrt
2023-04-23 11:41:17	ga_s2am_ard_3-2-1_51LUF_2023-04-23_final
2023-04-23 11:41:25	ga_s2am_ard_3-2-1_51LWE_2023-04-23_final
2023-04-23 11:41:29	ga_s2am_ard_3-2-1_51LVE_2023-04-23_final
2023-04-23 11:41:32	ga_s2am_ard_3-2-1_51LUE_2023-04-23_final
2023-04-23 11:41:36	ga_s2am_ard_3-2-1_51LTE_2023-04-23_nrt
2023-04-23 11:41:40	ga_s2am_ard_3-2-1_51LWD_2023-04-23_nrt
2023-04-23 11:41:43	ga_s2am_ard_3-2-1_51LVD_2023-04-23_nrt
2023-04-23 11:41:46	ga_s2am_ard_3-2-1_51LUD_2023-04-23_final
2023-04-23 11:41:49	ga_s2am_ard_3-2-1_51LTD_2023-04-23_nrt
2023-04-23 11:41:54	ga_s2am_ard_3-2-1_51LWC_2023-04-23_final
2023-04-23 11:41:58	ga_s2am_ard_3-2-1_51LVC_2023-04-23_final
2023-04-23 11:42:01	ga_s2am_ard_3-2-1_51LUC_2023-04-23_final
2023-04-23 11:42:04	ga_s2am_ard_3-2-1_51LTC_2023-04-23_nrt
2023-04-23 11:42:07	ga_s2am_ard_3-2-1_50LRH_2023-04-23_nrt
2023-04-23 11:42:08	ga_s2am_ard_3-2-1_51KWB_2023-04-23_final
2023-04-23 11:42:12	ga_s2am_ard_3-2-1_51KVB_2023-04-23_final
2023-04-23 11:42:16	ga_s2am_ard_3-2-1_51KUB_2023-04-23_final
2023-04-23 11:42:19	ga_s2am_ard_3-2-1_51KTB_2023-04-23_nrt
2023-04-23 11:42:19	ga_s2am_ard_3-2-1_50KRG_2023-04-23_nrt
2023-04-23 11:42:20	ga_s2am_ard_3-2-1_51KWA_2023-04-23_final
2023-04-23 11:42:27	ga_s2am_ard_3-2-1_51KVA_2023-04-23_final
2023-04-23 11:42:30	ga_s2am_ard_3-2-1_51KUA_2023-04-23_final
2023-04-23 11:42:33	ga_s2am_ard_3-2-1_51KTA_2023-04-23_nrt
2023-04-23 11:42:34	ga_s2am_ard_3-2-1_50KRF_2023-04-23_nrt
2023-04-23 11:42:38	ga_s2am_ard_3-2-1_51KVV_2023-04-23_final
2023-04-23 11:42:40	ga_s2am_ard_3-2-1_51KUV_2023-04-23_final
2023-04-23 11:42:41	ga_s2am_ard_3-2-1_51KTA_2023-04-23_nrt
2023-04-23 11:42:42	ga_s2am_ard_3-2-1_51KTV_2023-04-23_nrt
2023-04-23 11:42:42	ga_s2am_ard_3-2-1_50KRF_2023-04-23_nrt
2023-04-23 11:42:42	ga_s2am_ard_3-2-1_50KRE_2023-04-23_nrt
2023-04-23 11:42:45	ga_s2am_ard_3-2-1_51KVV_2023-04-23_final
2023-04-23 11:42:47	ga_s2am_ard_3-2-1_51KUV_2023-04-23_final
2023-04-23 11:42:48	ga_s2am_ard_3-2-1_51KTV_2023-04-23_nrt
2023-04-23 11:42:49	ga_s2am_ard_3-2-1_50KRE_2023-04-23_nrt
2023-04-23 11:42:56	ga_s2am_ard_3-2-1_51KVU_2023-04-23_final
2023-04-23 11:42:59	ga_s2am_ard_3-2-1_51KUU_2023-04-23_final
2023-04-23 11:43:02	ga_s2am_ard_3-2-1_51KTU_2023-04-23_final
2023-04-23 11:43:03	ga_s2am_ard_3-2-1_50KRD_2023-04-23_final
2023-04-23 11:43:08	ga_s2am_ard_3-2-1_50KQD_2023-04-23_final
2023-04-23 11:43:10	ga_s2am_ard_3-2-1_51KVT_2023-04-23_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