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

88 results

Time (local)	Label
2023-10-11 09:32:33	ga_s2am_ard_3-2-1_56KPB_2023-10-11_final
2023-10-11 09:32:40	ga_s2am_ard_3-2-1_56KMB_2023-10-11_final
2023-10-11 09:32:57	ga_s2am_ard_3-2-1_56KLA_2023-10-11_final
2023-10-11 09:33:59	ga_s2am_ard_3-2-1_56JKS_2023-10-11_final
2023-10-11 09:34:07	ga_s2am_ard_3-2-1_56JMR_2023-10-11_final
2023-10-11 09:34:13	ga_s2am_ard_3-2-1_56JKR_2023-10-11_final
2023-10-11 09:34:36	ga_s2am_ard_3-2-1_56JMP_2023-10-11_final
2023-10-11 09:34:57	ga_s2am_ard_3-2-1_56JKN_2023-10-11_final
2023-10-11 09:35:08	ga_s2am_ard_3-2-1_56JLM_2023-10-11_final
2023-10-11 09:35:14	ga_s2am_ard_3-2-1_55JGG_2023-10-11_final
2023-10-11 09:35:19	ga_s2am_ard_3-2-1_56JML_2023-10-11_final
2023-10-11 09:35:37	ga_s2am_ard_3-2-1_56HLK_2023-10-11_final
2023-10-11 09:35:41	ga_s2am_ard_3-2-1_56HKK_2023-10-11_final
2023-10-11 09:36:30	ga_s2am_ard_3-2-1_55HFB_2023-10-11_final
2023-10-11 09:36:41	ga_s2am_ard_3-2-1_55HGA_2023-10-11_final
2023-10-11 09:36:55	ga_s2am_ard_3-2-1_55HGV_2023-10-11_final
2023-10-11 09:37:17	ga_s2am_ard_3-2-1_55HEU_2023-10-11_final
2023-10-11 09:37:50	ga_s2am_ard_3-2-1_55HDS_2023-10-11_final
2023-10-11 09:38:00	ga_s2am_ard_3-2-1_55GER_2023-10-11_final
2023-10-11 09:38:25	ga_s2am_ard_3-2-1_55GFP_2023-10-11_final
2023-10-11 09:38:33	ga_s2am_ard_3-2-1_55GDP_2023-10-11_final
2023-10-11 09:38:44	ga_s2am_ard_3-2-1_55GEN_2023-10-11_final
2023-10-11 09:39:02	ga_s2am_ard_3-2-1_55GCM_2023-10-11_final
2023-10-11 11:12:05	ga_s2am_ard_3-2-1_52KDG_2023-10-11_final
2023-10-11 11:12:22	ga_s2am_ard_3-2-1_52KCF_2023-10-11_final
2023-10-11 11:14:19	ga_s2am_ard_3-2-1_52JCT_2023-10-11_final
2023-10-11 11:15:02	ga_s2am_ard_3-2-1_52JCQ_2023-10-11_final
2023-10-11 11:15:49	ga_s2am_ard_3-2-1_52JBM_2023-10-11_final
2023-10-11 11:16:25	ga_s2am_ard_3-2-1_51HXE_2023-10-11_final
2023-10-11 11:17:03	ga_s2am_ard_3-2-1_51HXB_2023-10-11_final
2023-10-12 10:40:34	ga_s2am_ard_3-2-1_53LQG_2023-10-12_final
2023-10-12 10:41:18	ga_s2am_ard_3-2-1_53LQD_2023-10-12_final
2023-10-12 10:41:21	ga_s2am_ard_3-2-1_53LPD_2023-10-12_final
2023-10-12 10:41:36	ga_s2am_ard_3-2-1_53LPC_2023-10-12_final
2023-10-12 10:43:03	ga_s2am_ard_3-2-1_53KPS_2023-10-12_final
2023-10-12 10:43:39	ga_s2am_ard_3-2-1_53KMQ_2023-10-12_final
2023-10-12 10:43:53	ga_s2am_ard_3-2-1_53KMP_2023-10-12_final
2023-10-12 10:44:11	ga_s2am_ard_3-2-1_53JLN_2023-10-12_final
2023-10-12 10:44:56	ga_s2am_ard_3-2-1_53JKK_2023-10-12_final
2023-10-12 10:45:24	ga_s2am_ard_3-2-1_53JLH_2023-10-12_final
2023-10-12 10:45:45	ga_s2am_ard_3-2-1_52JGM_2023-10-12_final
2023-10-12 10:46:28	ga_s2am_ard_3-2-1_53HMC_2023-10-12_final
2023-10-22 10:40:14	ga_s2am_ard_3-2-1_54LUN_2023-10-22_nrt
2023-10-22 10:41:00	ga_s2am_ard_3-2-1_54LTK_2023-10-22_nrt
2023-10-22 10:41:16	ga_s2am_ard_3-2-1_53LRD_2023-10-22_nrt
2023-10-22 10:41:29	ga_s2am_ard_3-2-1_54LTH_2023-10-22_nrt
2023-10-22 10:46:27	ga_s2am_ard_3-2-1_52HGJ_2023-10-22_nrt
2023-10-23 10:09:45	ga_s2am_ard_3-2-1_55LEJ_2023-10-23_nrt
2023-10-23 10:09:52	ga_s2am_ard_3-2-1_55LCJ_2023-10-23_nrt
2023-10-23 10:09:58	ga_s2am_ard_3-2-1_55LEH_2023-10-23_nrt
2023-10-23 10:10:03	ga_s2am_ard_3-2-1_55LDH_2023-10-23_nrt
2023-10-23 10:10:07	ga_s2am_ard_3-2-1_55LCH_2023-10-23_nrt
2023-10-23 10:10:09	ga_s2am_ard_3-2-1_55LEG_2023-10-23_nrt
2023-10-23 10:10:10	ga_s2am_ard_3-2-1_55LBH_2023-10-23_nrt
2023-10-23 10:10:18	ga_s2am_ard_3-2-1_55LDG_2023-10-23_nrt
2023-10-23 10:10:24	ga_s2am_ard_3-2-1_55LBG_2023-10-23_nrt
2023-10-23 10:10:32	ga_s2am_ard_3-2-1_55LDF_2023-10-23_nrt
2023-10-23 10:10:36	ga_s2am_ard_3-2-1_55LCF_2023-10-23_nrt
2023-10-23 10:10:39	ga_s2am_ard_3-2-1_55LBF_2023-10-23_nrt
2023-10-23 10:10:47	ga_s2am_ard_3-2-1_55LDE_2023-10-23_nrt
2023-10-23 10:11:01	ga_s2am_ard_3-2-1_55LDD_2023-10-23_nrt
2023-10-23 10:11:13	ga_s2am_ard_3-2-1_55LDC_2023-10-23_nrt
2023-10-23 10:17:32	ga_s2am_ard_3-2-1_54HTD_2023-10-23_nrt
2023-10-23 10:17:33	ga_s2am_ard_3-2-1_53HQU_2023-10-23_nrt
2023-10-23 10:17:38	ga_s2am_ard_3-2-1_54HUC_2023-10-23_nrt
2023-10-23 10:17:39	ga_s2am_ard_3-2-1_53HPU_2023-10-23_nrt
2023-10-23 10:17:40	ga_s2am_ard_3-2-1_54HTC_2023-10-23_nrt
2023-10-23 10:17:40	ga_s2am_ard_3-2-1_53HQT_2023-10-23_nrt
2023-10-23 11:50:24	ga_s2am_ard_3-2-1_51LVJ_2023-10-23_nrt
2023-10-23 11:50:34	ga_s2am_ard_3-2-1_51LVH_2023-10-23_nrt
2023-10-23 11:50:35	ga_s2am_ard_3-2-1_50LRP_2023-10-23_nrt
2023-10-23 11:50:49	ga_s2am_ard_3-2-1_50LRN_2023-10-23_nrt
2023-10-23 11:51:17	ga_s2am_ard_3-2-1_51LTF_2023-10-23_nrt
2023-10-23 11:51:26	ga_s2am_ard_3-2-1_50LPL_2023-10-23_nrt
2023-10-23 11:51:36	ga_s2am_ard_3-2-1_50LQK_2023-10-23_nrt
2023-10-23 11:51:39	ga_s2am_ard_3-2-1_50LPK_2023-10-23_nrt
2023-10-23 11:51:45	ga_s2am_ard_3-2-1_51LTD_2023-10-23_nrt
2023-10-23 11:51:51	ga_s2am_ard_3-2-1_50LQJ_2023-10-23_nrt
2023-10-23 11:52:00	ga_s2am_ard_3-2-1_51LTC_2023-10-23_nrt
2023-10-23 11:52:09	ga_s2am_ard_3-2-1_50LPH_2023-10-23_nrt
2023-10-23 11:52:15	ga_s2am_ard_3-2-1_51KTB_2023-10-23_nrt
2023-10-23 11:52:30	ga_s2am_ard_3-2-1_50KRF_2023-10-23_nrt
2023-10-23 11:52:41	ga_s2am_ard_3-2-1_50KNF_2023-10-23_nrt
2023-10-23 11:52:43	ga_s2am_ard_3-2-1_51KTV_2023-10-23_nrt
2023-10-23 11:52:45	ga_s2am_ard_3-2-1_50KRE_2023-10-23_nrt
2023-10-23 11:56:31	ga_s2am_ard_3-2-1_50JKL_2023-10-23_nrt
2023-10-23 11:57:25	ga_s2am_ard_3-2-1_49HGB_2023-10-23_nrt
2023-10-23 11:57:27	ga_s2am_ard_3-2-1_50HMF_2023-10-23_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