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
2023-04-21 11:04:54	ga_s2am_ard_3-2-1_52JCR_2023-04-21_final
2023-04-21 11:04:56	ga_s2am_ard_3-2-1_52JFQ_2023-04-21_final
2023-04-21 11:05:03	ga_s2am_ard_3-2-1_52JDQ_2023-04-21_final
2023-04-22 10:31:48	ga_s2am_ard_3-2-1_54KTG_2023-04-22_final
2023-04-22 10:35:04	ga_s2am_ard_3-2-1_53JPJ_2023-04-22_final
2023-04-22 12:13:53	ga_s2am_ard_3-2-1_49KHR_2023-04-22_final
2023-04-22 12:14:44	ga_s2am_ard_3-2-1_49JFN_2023-04-22_final
2023-04-22 12:15:09	ga_s2am_ard_3-2-1_49JGL_2023-04-22_final
2023-04-23 10:01:34	ga_s2am_ard_3-2-1_55KDB_2023-04-23_final
2023-04-23 10:02:25	ga_s2am_ard_3-2-1_55KBU_2023-04-23_final
2023-04-23 10:02:33	ga_s2am_ard_3-2-1_55KDT_2023-04-23_final
2023-04-23 10:02:39	ga_s2am_ard_3-2-1_55KBT_2023-04-23_final
2023-04-23 10:02:47	ga_s2am_ard_3-2-1_55KDS_2023-04-23_final
2023-04-23 10:02:51	ga_s2am_ard_3-2-1_55KCS_2023-04-23_final
2023-04-23 10:02:54	ga_s2am_ard_3-2-1_54KZB_2023-04-23_final
2023-04-23 10:03:05	ga_s2am_ard_3-2-1_55KCR_2023-04-23_final
2023-04-23 10:03:19	ga_s2am_ard_3-2-1_55KCQ_2023-04-23_final
2023-04-23 10:03:25	ga_s2am_ard_3-2-1_55KDP_2023-04-23_final
2023-04-23 10:03:26	ga_s2am_ard_3-2-1_55KCQ_2023-04-23_final
2023-04-23 10:03:31	ga_s2am_ard_3-2-1_55KDP_2023-04-23_final
2023-04-23 10:03:37	ga_s2am_ard_3-2-1_54KZU_2023-04-23_final
2023-04-23 10:03:49	ga_s2am_ard_3-2-1_55JCN_2023-04-23_final
2023-04-23 10:03:52	ga_s2am_ard_3-2-1_54JZT_2023-04-23_final
2023-04-23 10:03:52	ga_s2am_ard_3-2-1_55JBN_2023-04-23_final
2023-04-23 10:04:22	ga_s2am_ard_3-2-1_55JDK_2023-04-23_final
2023-04-23 10:04:24	ga_s2am_ard_3-2-1_54JYR_2023-04-23_final
2023-04-23 10:04:32	ga_s2am_ard_3-2-1_55JCK_2023-04-23_final
2023-04-23 10:04:57	ga_s2am_ard_3-2-1_54JXP_2023-04-23_final
2023-04-23 10:05:22	ga_s2am_ard_3-2-1_54JYM_2023-04-23_final
2023-04-23 10:05:28	ga_s2am_ard_3-2-1_55JCF_2023-04-23_final
2023-04-23 10:05:36	ga_s2am_ard_3-2-1_54JYL_2023-04-23_final
2023-04-23 10:05:40	ga_s2am_ard_3-2-1_54JWL_2023-04-23_final
2023-04-23 10:05:43	ga_s2am_ard_3-2-1_54JYL_2023-04-23_final
2023-04-23 10:05:46	ga_s2am_ard_3-2-1_54JWL_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 10:06:24	ga_s2am_ard_3-2-1_54HXH_2023-04-23_final
2023-04-23 10:06:32	ga_s2am_ard_3-2-1_55HBB_2023-04-23_final
2023-04-23 10:06:38	ga_s2am_ard_3-2-1_54HXG_2023-04-23_final
2023-04-23 10:06:42	ga_s2am_ard_3-2-1_54HWG_2023-04-23_final
2023-04-23 10:06:49	ga_s2am_ard_3-2-1_54HYF_2023-04-23_final
2023-04-23 10:07:11	ga_s2am_ard_3-2-1_54HWE_2023-04-23_final
2023-04-23 10:07:14	ga_s2am_ard_3-2-1_54HVE_2023-04-23_final
2023-04-23 10:07:29	ga_s2am_ard_3-2-1_54HVD_2023-04-23_final
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 11:41:14	ga_s2am_ard_3-2-1_51LVF_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 11:42:12	ga_s2am_ard_3-2-1_51KVB_2023-04-23_final
2023-04-23 11:42:20	ga_s2am_ard_3-2-1_51KWA_2023-04-23_final
2023-04-23 11:43:02	ga_s2am_ard_3-2-1_51KTU_2023-04-23_final
2023-04-23 11:43:08	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
2023-04-23 11:43:17	ga_s2am_ard_3-2-1_51KTT_2023-04-23_final
2023-04-23 11:43:17	ga_s2am_ard_3-2-1_50KRC_2023-04-23_final
2023-04-23 11:43:21	ga_s2am_ard_3-2-1_50KQC_2023-04-23_final
2023-04-23 11:43:31	ga_s2am_ard_3-2-1_51KTS_2023-04-23_final
2023-04-23 11:43:43	ga_s2am_ard_3-2-1_51KUR_2023-04-23_final
2023-04-23 11:43:46	ga_s2am_ard_3-2-1_51KTR_2023-04-23_final
2023-04-23 11:44:01	ga_s2am_ard_3-2-1_51KTQ_2023-04-23_final
2023-04-23 11:44:19	ga_s2am_ard_3-2-1_50KQU_2023-04-23_final
2023-04-23 11:44:30	ga_s2am_ard_3-2-1_51JTN_2023-04-23_final
2023-04-23 11:44:33	ga_s2am_ard_3-2-1_50JQT_2023-04-23_final
2023-04-23 11:44:40	ga_s2am_ard_3-2-1_51JUM_2023-04-23_final
2023-04-23 11:44:44	ga_s2am_ard_3-2-1_50JRS_2023-04-23_final
2023-04-23 11:44:52	ga_s2am_ard_3-2-1_50JPS_2023-04-23_final
2023-04-23 11:45:10	ga_s2am_ard_3-2-1_50JNR_2023-04-23_final
2023-04-23 11:45:24	ga_s2am_ard_3-2-1_50JNQ_2023-04-23_final
2023-04-23 11:45:28	ga_s2am_ard_3-2-1_51JTJ_2023-04-23_final
2023-04-23 11:45:35	ga_s2am_ard_3-2-1_50JPP_2023-04-23_final
2023-04-23 11:45:49	ga_s2am_ard_3-2-1_50JPN_2023-04-23_final
2023-04-23 11:45:53	ga_s2am_ard_3-2-1_50JNN_2023-04-23_final
2023-04-23 11:46:00	ga_s2am_ard_3-2-1_50JQM_2023-04-23_final
2023-04-23 11:46:04	ga_s2am_ard_3-2-1_50JPM_2023-04-23_final
2023-04-23 11:46:06	ga_s2am_ard_3-2-1_51JTF_2023-04-23_final
2023-04-23 11:46:12	ga_s2am_ard_3-2-1_50JMM_2023-04-23_final
2023-04-23 11:46:22	ga_s2am_ard_3-2-1_50JNL_2023-04-23_final
2023-04-23 11:46:25	ga_s2am_ard_3-2-1_50JML_2023-04-23_final
2023-04-23 11:46:40	ga_s2am_ard_3-2-1_50HMK_2023-04-23_final
2023-04-23 11:46:42	ga_s2am_ard_3-2-1_50HQJ_2023-04-23_final
2023-04-23 11:46:47	ga_s2am_ard_3-2-1_50HPJ_2023-04-23_final
2023-04-23 11:46:52	ga_s2am_ard_3-2-1_50HQH_2023-04-23_final
2023-04-23 11:46:55	ga_s2am_ard_3-2-1_50HMJ_2023-04-23_final
2023-04-23 11:47:01	ga_s2am_ard_3-2-1_50HPH_2023-04-23_final
2023-04-23 11:47:09	ga_s2am_ard_3-2-1_50HMH_2023-04-23_final
2023-04-23 11:47:13	ga_s2am_ard_3-2-1_50HLH_2023-04-23_final
2023-04-23 11:47:16	ga_s2am_ard_3-2-1_50HPG_2023-04-23_final
2023-04-23 11:47:20	ga_s2am_ard_3-2-1_50HNG_2023-04-23_final
2023-04-23 11:47:26	ga_s2am_ard_3-2-1_50HLG_2023-04-23_final
2023-05-03 10:07:48	ga_s2am_ard_3-2-1_54HXB_2023-05-03_nrt
2023-05-03 11:41:12	ga_s2am_ard_3-2-1_51LWF_2023-05-03_nrt
2023-05-03 11:41:37	ga_s2am_ard_3-2-1_51LTE_2023-05-03_nrt
2023-05-03 11:41:51	ga_s2am_ard_3-2-1_51LTD_2023-05-03_nrt
2023-05-03 11:47:29	ga_s2am_ard_3-2-1_50HPF_2023-05-03_nrt
2023-05-03 11:47:33	ga_s2am_ard_3-2-1_50HLF_2023-05-03_nrt
2023-05-04 09:37:25	ga_s2am_ard_3-2-1_55HGT_2023-05-04_nrt
2023-05-04 09:37:39	ga_s2am_ard_3-2-1_55HGS_2023-05-04_nrt
2023-05-04 09:38:56	ga_s2am_ard_3-2-1_55GFM_2023-05-04_nrt

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