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The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels. For these ARD products, this value comes from the Fmask algorithm. from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels. For these ARD products, this value comes from the Fmask algorithm. 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 (e.g. ‘epsg:32654’) :

One of - final|interim|nrt (near real time):

ID of a dataset type:

Ground sampling distance of the sensor’s best resolution band in metres; represents the size (or spatial resolution) of one pixel. from:

Ground sampling distance of the sensor’s best resolution band in metres; represents the size (or spatial resolution) of one pixel. to (exclusive):

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north from:

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north to (exclusive):

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon. from:

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon. 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 from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm 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 from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm 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 from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm 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 from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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. For Sentinel it is MGRS code. :

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless to (exclusive):

Unique identifier for a datastrip relative to a given Datatake. (e.g. ‘S2A_OPER_MSI_L1C_DS_SGS__20161214T040601_S20161214T005840_N02.04’) :

ESA product URI, with the '.SAFE' ending removed. (e.g. 'S2A_MSIL1C_20220303T000731_N0400_R073_T56LNM_20220303T012845') :

Granule ID according to the ESA naming convention (e.g. ‘S2A_OPER_MSI_L1C_TL_SGS__20161214T040601_A007721_T53KRB_N02.04’) :

Acquisition time range From

Acquisition time range to (exclusive)

Dataset URI:

Add field

First 100 results (query limit)

Time (local)	Label
2019-10-17 12:04:52	ga_s2am_ard_3-2-1_50JMS_2019-10-17_final
2019-10-17 12:05:00	ga_s2am_ard_3-2-1_49JHM_2019-10-17_final
2019-10-17 12:05:02	ga_s2am_ard_3-2-1_50JMR_2019-10-17_final
2019-10-17 12:05:11	ga_s2am_ard_3-2-1_50JLR_2019-10-17_final
2019-10-17 12:05:26	ga_s2am_ard_3-2-1_50JLQ_2019-10-17_final
2019-10-17 12:05:32	ga_s2am_ard_3-2-1_49JGK_2019-10-17_final
2019-10-17 12:06:07	ga_s2am_ard_3-2-1_50JLM_2019-10-17_final
2019-10-17 12:06:13	ga_s2am_ard_3-2-1_50JKM_2019-10-17_final
2019-10-17 12:06:17	ga_s2am_ard_3-2-1_50JLL_2019-10-17_final
2019-10-18 09:51:19	ga_s2am_ard_3-2-1_55LGC_2019-10-18_final
2019-10-18 09:51:22	ga_s2am_ard_3-2-1_55LFC_2019-10-18_final
2019-10-18 09:51:43	ga_s2am_ard_3-2-1_56KKF_2019-10-18_final
2019-10-18 09:52:09	ga_s2am_ard_3-2-1_55KEV_2019-10-18_final
2019-10-18 09:52:17	ga_s2am_ard_3-2-1_55KGU_2019-10-18_final
2019-10-18 09:52:20	ga_s2am_ard_3-2-1_55KFU_2019-10-18_final
2019-10-18 09:52:38	ga_s2am_ard_3-2-1_55KHS_2019-10-18_final
2019-10-18 09:52:58	ga_s2am_ard_3-2-1_55KDS_2019-10-18_final
2019-10-18 09:53:04	ga_s2am_ard_3-2-1_55KFR_2019-10-18_final
2019-10-18 09:53:36	ga_s2am_ard_3-2-1_55KEP_2019-10-18_final
2019-10-18 09:53:39	ga_s2am_ard_3-2-1_55KDP_2019-10-18_final
2019-10-18 09:53:47	ga_s2am_ard_3-2-1_55JFN_2019-10-18_final
2019-10-18 09:54:01	ga_s2am_ard_3-2-1_55JCN_2019-10-18_final
2019-10-18 09:54:05	ga_s2am_ard_3-2-1_55JEM_2019-10-18_final
2019-10-18 09:54:08	ga_s2am_ard_3-2-1_55JDM_2019-10-18_final
2019-10-18 09:54:16	ga_s2am_ard_3-2-1_55JFL_2019-10-18_final
2019-10-18 09:54:23	ga_s2am_ard_3-2-1_55JDL_2019-10-18_final
2019-10-18 09:54:26	ga_s2am_ard_3-2-1_55JCL_2019-10-18_final
2019-10-18 09:54:38	ga_s2am_ard_3-2-1_55JDK_2019-10-18_final
2019-10-18 09:54:41	ga_s2am_ard_3-2-1_55JCK_2019-10-18_final
2019-10-18 09:55:24	ga_s2am_ard_3-2-1_55JCG_2019-10-18_final
2019-10-18 09:55:27	ga_s2am_ard_3-2-1_55JBG_2019-10-18_final
2019-10-18 09:55:32	ga_s2am_ard_3-2-1_55JEF_2019-10-18_final
2019-10-18 09:55:36	ga_s2am_ard_3-2-1_55JDF_2019-10-18_final
2019-10-18 09:55:50	ga_s2am_ard_3-2-1_55HDE_2019-10-18_final
2019-10-18 09:55:53	ga_s2am_ard_3-2-1_55HCE_2019-10-18_final
2019-10-18 09:55:57	ga_s2am_ard_3-2-1_55HED_2019-10-18_final
2019-10-18 09:56:00	ga_s2am_ard_3-2-1_54HYK_2019-10-18_final
2019-10-18 09:56:04	ga_s2am_ard_3-2-1_55HDD_2019-10-18_final
2019-10-18 09:56:11	ga_s2am_ard_3-2-1_55HBD_2019-10-18_final
2019-10-18 09:56:13	ga_s2am_ard_3-2-1_54HYJ_2019-10-18_final
2019-10-18 09:56:19	ga_s2am_ard_3-2-1_55HDC_2019-10-18_final
2019-10-18 09:56:34	ga_s2am_ard_3-2-1_55HDB_2019-10-18_final
2019-10-18 09:56:56	ga_s2am_ard_3-2-1_54HYF_2019-10-18_final
2019-10-18 09:57:01	ga_s2am_ard_3-2-1_55HDV_2019-10-18_final
2019-10-18 09:57:06	ga_s2am_ard_3-2-1_55HCV_2019-10-18_final
2019-10-18 09:57:10	ga_s2am_ard_3-2-1_55HBV_2019-10-18_final
2019-10-18 09:57:11	ga_s2am_ard_3-2-1_54HYE_2019-10-18_final
2019-10-18 09:57:11	ga_s2am_ard_3-2-1_55HDU_2019-10-18_final
2019-10-18 09:57:21	ga_s2am_ard_3-2-1_55HCU_2019-10-18_final
2019-10-18 09:57:24	ga_s2am_ard_3-2-1_55HBU_2019-10-18_final
2019-10-18 09:57:25	ga_s2am_ard_3-2-1_54HYD_2019-10-18_final
2019-10-18 09:57:29	ga_s2am_ard_3-2-1_54HXD_2019-10-18_final
2019-10-18 09:57:50	ga_s2am_ard_3-2-1_55HCS_2019-10-18_final
2019-10-18 09:57:53	ga_s2am_ard_3-2-1_55HBS_2019-10-18_final
2019-10-18 09:57:54	ga_s2am_ard_3-2-1_54HYB_2019-10-18_final
2019-10-18 09:58:04	ga_s2am_ard_3-2-1_55GCR_2019-10-18_final
2019-10-18 09:58:08	ga_s2am_ard_3-2-1_55GBR_2019-10-18_final
2019-10-18 09:58:08	ga_s2am_ard_3-2-1_54GYA_2019-10-18_final
2019-10-18 11:31:21	ga_s2am_ard_3-2-1_52LBK_2019-10-18_final
2019-10-18 11:31:24	ga_s2am_ard_3-2-1_51LZE_2019-10-18_final
2019-10-18 11:31:27	ga_s2am_ard_3-2-1_51LYE_2019-10-18_final
2019-10-18 11:31:32	ga_s2am_ard_3-2-1_52LBJ_2019-10-18_final
2019-10-18 11:31:34	ga_s2am_ard_3-2-1_51LWE_2019-10-18_final
2019-10-18 11:31:38	ga_s2am_ard_3-2-1_51LZD_2019-10-18_final
2019-10-18 11:31:42	ga_s2am_ard_3-2-1_51LYD_2019-10-18_final
2019-10-18 11:31:52	ga_s2am_ard_3-2-1_51LZC_2019-10-18_final
2019-10-18 11:32:00	ga_s2am_ard_3-2-1_51LXC_2019-10-18_final
2019-10-18 11:32:18	ga_s2am_ard_3-2-1_51KWB_2019-10-18_final
2019-10-18 11:32:22	ga_s2am_ard_3-2-1_51KVB_2019-10-18_final
2019-10-18 11:32:25	ga_s2am_ard_3-2-1_51KYA_2019-10-18_final
2019-10-18 11:32:32	ga_s2am_ard_3-2-1_51KWA_2019-10-18_final
2019-10-18 11:32:35	ga_s2am_ard_3-2-1_51KVA_2019-10-18_final
2019-10-18 11:32:43	ga_s2am_ard_3-2-1_51KXV_2019-10-18_final
2019-10-18 11:32:47	ga_s2am_ard_3-2-1_51KWV_2019-10-18_final
2019-10-18 11:32:50	ga_s2am_ard_3-2-1_51KVV_2019-10-18_final
2019-10-18 11:32:58	ga_s2am_ard_3-2-1_51KXU_2019-10-18_final
2019-10-18 11:33:05	ga_s2am_ard_3-2-1_51KYT_2019-10-18_final
2019-10-18 11:33:34	ga_s2am_ard_3-2-1_51KVS_2019-10-18_final
2019-10-18 11:34:14	ga_s2am_ard_3-2-1_51KWP_2019-10-18_final
2019-10-18 11:34:17	ga_s2am_ard_3-2-1_51KVP_2019-10-18_final
2019-10-18 11:34:18	ga_s2am_ard_3-2-1_51JXN_2019-10-18_final
2019-10-18 11:34:35	ga_s2am_ard_3-2-1_51JUN_2019-10-18_final
2019-10-18 11:34:52	ga_s2am_ard_3-2-1_50JRS_2019-10-18_final
2019-10-18 11:34:57	ga_s2am_ard_3-2-1_51JWL_2019-10-18_final
2019-10-18 11:35:01	ga_s2am_ard_3-2-1_51JVL_2019-10-18_final
2019-10-18 11:35:03	ga_s2am_ard_3-2-1_51JUL_2019-10-18_final
2019-10-18 11:35:08	ga_s2am_ard_3-2-1_51JVL_2019-10-18_final
2019-10-18 11:35:10	ga_s2am_ard_3-2-1_51JUL_2019-10-18_final
2019-10-18 11:35:10	ga_s2am_ard_3-2-1_51JUK_2019-10-18_final
2019-10-18 11:35:19	ga_s2am_ard_3-2-1_51JUK_2019-10-18_final
2019-10-18 11:35:53	ga_s2am_ard_3-2-1_50JQN_2019-10-18_final
2019-10-18 11:36:02	ga_s2am_ard_3-2-1_51JUG_2019-10-18_final
2019-10-18 11:36:06	ga_s2am_ard_3-2-1_51JTG_2019-10-18_final
2019-10-18 11:36:08	ga_s2am_ard_3-2-1_50JQM_2019-10-18_final
2019-10-18 11:36:13	ga_s2am_ard_3-2-1_51JVF_2019-10-18_final
2019-10-18 11:36:17	ga_s2am_ard_3-2-1_51JUF_2019-10-18_final
2019-10-18 11:36:29	ga_s2am_ard_3-2-1_50JPL_2019-10-18_final
2019-10-18 11:36:31	ga_s2am_ard_3-2-1_51HUE_2019-10-18_final
2019-10-18 11:36:50	ga_s2am_ard_3-2-1_51HTD_2019-10-18_final
2019-10-18 11:36:51	ga_s2am_ard_3-2-1_50HQJ_2019-10-18_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

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

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