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
2023-12-12 11:57:01	ga_s2am_ard_3-2-1_50HMH_2023-12-12_final
2023-12-12 11:57:04	ga_s2am_ard_3-2-1_50HLH_2023-12-12_final
2023-12-12 11:57:08	ga_s2am_ard_3-2-1_50HKH_2023-12-12_final
2023-12-12 11:57:10	ga_s2am_ard_3-2-1_49HGC_2023-12-12_nrt
2023-12-12 11:57:15	ga_s2am_ard_3-2-1_50HMG_2023-12-12_final
2023-12-12 11:57:17	ga_s2am_ard_3-2-1_50HLG_2023-12-12_final
2023-12-12 11:57:18	ga_s2am_ard_3-2-1_49HFC_2023-12-12_nrt
2023-12-12 11:57:19	ga_s2am_ard_3-2-1_50HKG_2023-12-12_final
2023-12-12 11:57:20	ga_s2am_ard_3-2-1_49HGB_2023-12-12_nrt
2023-12-12 11:57:22	ga_s2am_ard_3-2-1_49HFB_2023-12-12_nrt
2023-12-12 11:57:22	ga_s2am_ard_3-2-1_50HMF_2023-12-12_final
2023-12-13 09:39:30	ga_s2am_ard_3-2-1_56LPP_2023-12-13_nrt
2023-12-13 09:39:34	ga_s2am_ard_3-2-1_56LNP_2023-12-13_nrt
2023-12-13 09:39:37	ga_s2am_ard_3-2-1_56LMP_2023-12-13_nrt
2023-12-13 09:39:44	ga_s2am_ard_3-2-1_56LLP_2023-12-13_nrt
2023-12-13 09:39:45	ga_s2am_ard_3-2-1_56LPN_2023-12-13_nrt
2023-12-13 09:39:49	ga_s2am_ard_3-2-1_56LNN_2023-12-13_nrt
2023-12-13 09:39:52	ga_s2am_ard_3-2-1_56LMN_2023-12-13_nrt
2023-12-13 09:39:55	ga_s2am_ard_3-2-1_56LLN_2023-12-13_nrt
2023-12-13 09:39:58	ga_s2am_ard_3-2-1_56LPM_2023-12-13_nrt
2023-12-13 09:40:00	ga_s2am_ard_3-2-1_56LNM_2023-12-13_nrt
2023-12-13 09:40:02	ga_s2am_ard_3-2-1_56LMM_2023-12-13_nrt
2023-12-13 09:40:03	ga_s2am_ard_3-2-1_56LLM_2023-12-13_nrt
2023-12-13 09:41:10	ga_s2am_ard_3-2-1_56LNH_2023-12-13_nrt
2023-12-13 09:41:11	ga_s2am_ard_3-2-1_56LMH_2023-12-13_nrt
2023-12-13 09:41:12	ga_s2am_ard_3-2-1_56LLH_2023-12-13_nrt
2023-12-13 09:41:14	ga_s2am_ard_3-2-1_56LKH_2023-12-13_final
2023-12-13 09:41:16	ga_s2am_ard_3-2-1_56KNG_2023-12-13_nrt
2023-12-13 09:41:19	ga_s2am_ard_3-2-1_56KMG_2023-12-13_final
2023-12-13 09:41:22	ga_s2am_ard_3-2-1_56KLG_2023-12-13_final
2023-12-13 09:41:25	ga_s2am_ard_3-2-1_56KKG_2023-12-13_final
2023-12-13 09:41:28	ga_s2am_ard_3-2-1_55KHB_2023-12-13_final
2023-12-13 09:41:30	ga_s2am_ard_3-2-1_56KNF_2023-12-13_nrt
2023-12-13 09:41:34	ga_s2am_ard_3-2-1_56KMF_2023-12-13_final
2023-12-13 09:41:37	ga_s2am_ard_3-2-1_56KLF_2023-12-13_final
2023-12-13 09:41:40	ga_s2am_ard_3-2-1_56KKF_2023-12-13_final
2023-12-13 09:41:41	ga_s2am_ard_3-2-1_55KHA_2023-12-13_final
2023-12-13 09:41:42	ga_s2am_ard_3-2-1_56KNE_2023-12-13_nrt
2023-12-13 09:41:48	ga_s2am_ard_3-2-1_56KME_2023-12-13_final
2023-12-13 09:41:51	ga_s2am_ard_3-2-1_56KLE_2023-12-13_final
2023-12-13 09:41:55	ga_s2am_ard_3-2-1_56KKE_2023-12-13_nrt
2023-12-13 09:41:55	ga_s2am_ard_3-2-1_55KHV_2023-12-13_nrt
2023-12-13 09:42:02	ga_s2am_ard_3-2-1_56KMD_2023-12-13_final
2023-12-13 09:42:06	ga_s2am_ard_3-2-1_56KLD_2023-12-13_final
2023-12-13 09:42:09	ga_s2am_ard_3-2-1_56KKD_2023-12-13_nrt
2023-12-13 09:42:10	ga_s2am_ard_3-2-1_55KHU_2023-12-13_final
2023-12-13 09:42:17	ga_s2am_ard_3-2-1_56KMC_2023-12-13_nrt
2023-12-13 09:42:21	ga_s2am_ard_3-2-1_56KLC_2023-12-13_final
2023-12-13 09:42:24	ga_s2am_ard_3-2-1_56KKC_2023-12-13_final
2023-12-13 09:42:24	ga_s2am_ard_3-2-1_55KHT_2023-12-13_final
2023-12-13 09:42:29	ga_s2am_ard_3-2-1_55KGT_2023-12-13_final
2023-12-13 09:42:32	ga_s2am_ard_3-2-1_56KMB_2023-12-13_final
2023-12-13 09:42:35	ga_s2am_ard_3-2-1_56KLB_2023-12-13_final
2023-12-13 09:42:38	ga_s2am_ard_3-2-1_56KKB_2023-12-13_final
2023-12-13 09:42:39	ga_s2am_ard_3-2-1_55KHS_2023-12-13_final
2023-12-13 09:42:42	ga_s2am_ard_3-2-1_55KGS_2023-12-13_final
2023-12-13 09:42:45	ga_s2am_ard_3-2-1_56KMA_2023-12-13_final
2023-12-13 09:42:49	ga_s2am_ard_3-2-1_56KLA_2023-12-13_final
2023-12-13 09:42:53	ga_s2am_ard_3-2-1_56KKA_2023-12-13_final
2023-12-13 09:42:53	ga_s2am_ard_3-2-1_55KHR_2023-12-13_final
2023-12-13 09:42:56	ga_s2am_ard_3-2-1_56KMV_2023-12-13_final
2023-12-13 09:42:57	ga_s2am_ard_3-2-1_55KGR_2023-12-13_final
2023-12-13 09:43:04	ga_s2am_ard_3-2-1_56KLV_2023-12-13_final
2023-12-13 09:43:07	ga_s2am_ard_3-2-1_56KKV_2023-12-13_final
2023-12-13 09:43:08	ga_s2am_ard_3-2-1_55KHQ_2023-12-13_final
2023-12-13 09:43:11	ga_s2am_ard_3-2-1_55KGQ_2023-12-13_final
2023-12-13 09:43:18	ga_s2am_ard_3-2-1_56KLU_2023-12-13_final
2023-12-13 09:43:22	ga_s2am_ard_3-2-1_56KKU_2023-12-13_final
2023-12-13 09:43:22	ga_s2am_ard_3-2-1_55KHP_2023-12-13_final
2023-12-13 09:43:26	ga_s2am_ard_3-2-1_55KGP_2023-12-13_final
2023-12-13 09:43:30	ga_s2am_ard_3-2-1_55KFP_2023-12-13_final
2023-12-13 09:43:33	ga_s2am_ard_3-2-1_56JLT_2023-12-13_final
2023-12-13 09:43:36	ga_s2am_ard_3-2-1_55JHN_2023-12-13_final
2023-12-13 09:43:37	ga_s2am_ard_3-2-1_56JKT_2023-12-13_final
2023-12-13 09:43:40	ga_s2am_ard_3-2-1_55JGN_2023-12-13_final
2023-12-13 09:43:43	ga_s2am_ard_3-2-1_55JFN_2023-12-13_final
2023-12-13 09:43:48	ga_s2am_ard_3-2-1_56JLS_2023-12-13_final
2023-12-13 09:43:51	ga_s2am_ard_3-2-1_55JHM_2023-12-13_final
2023-12-13 09:43:51	ga_s2am_ard_3-2-1_56JKS_2023-12-13_final
2023-12-13 09:43:55	ga_s2am_ard_3-2-1_55JGM_2023-12-13_final
2023-12-13 11:25:40	ga_s2am_ard_3-2-1_51JYG_2023-12-13_final
2023-12-13 11:25:41	ga_s2am_ard_3-2-1_51JVH_2023-12-13_final
2023-12-13 11:25:48	ga_s2am_ard_3-2-1_51JXG_2023-12-13_final
2023-12-13 11:25:52	ga_s2am_ard_3-2-1_51JWG_2023-12-13_final
2023-12-13 11:25:56	ga_s2am_ard_3-2-1_51JVG_2023-12-13_final
2023-12-13 11:26:03	ga_s2am_ard_3-2-1_51JXF_2023-12-13_final
2023-12-13 11:26:07	ga_s2am_ard_3-2-1_51JWF_2023-12-13_final
2023-12-13 11:26:13	ga_s2am_ard_3-2-1_51JUF_2023-12-13_final
2023-12-13 11:26:18	ga_s2am_ard_3-2-1_51HXE_2023-12-13_final
2023-12-13 11:26:25	ga_s2am_ard_3-2-1_51HVE_2023-12-13_final
2023-12-13 11:26:28	ga_s2am_ard_3-2-1_51HUE_2023-12-13_final
2023-12-13 11:26:31	ga_s2am_ard_3-2-1_51HXD_2023-12-13_final
2023-12-13 11:26:39	ga_s2am_ard_3-2-1_51HVD_2023-12-13_final
2023-12-13 11:26:43	ga_s2am_ard_3-2-1_51HUD_2023-12-13_final
2023-12-13 11:26:54	ga_s2am_ard_3-2-1_51HVC_2023-12-13_final
2023-12-13 11:26:57	ga_s2am_ard_3-2-1_51HUC_2023-12-13_final
2023-12-13 11:27:03	ga_s2am_ard_3-2-1_51HWB_2023-12-13_final
2023-12-13 11:27:03	ga_s2am_ard_3-2-1_51HTC_2023-12-13_final
2023-12-13 11:27:05	ga_s2am_ard_3-2-1_51HVB_2023-12-13_final
2023-12-13 11:27:07	ga_s2am_ard_3-2-1_51HUB_2023-12-13_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