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
2023-08-17 12:05:03	ga_s2am_ard_3-2-1_49JHM_2023-08-17_final
2023-08-17 12:05:03	ga_s2am_ard_3-2-1_50JKS_2023-08-17_final
2023-08-17 12:05:04	ga_s2am_ard_3-2-1_50JMR_2023-08-17_final
2023-08-17 12:05:06	ga_s2am_ard_3-2-1_49JGM_2023-08-17_final
2023-08-17 12:05:14	ga_s2am_ard_3-2-1_50JLR_2023-08-17_final
2023-08-17 12:05:14	ga_s2am_ard_3-2-1_49JFM_2023-08-17_final
2023-08-17 12:05:18	ga_s2am_ard_3-2-1_50JKR_2023-08-17_final
2023-08-17 12:05:21	ga_s2am_ard_3-2-1_49JGL_2023-08-17_final
2023-08-17 12:05:25	ga_s2am_ard_3-2-1_49JFL_2023-08-17_final
2023-08-17 12:05:29	ga_s2am_ard_3-2-1_50JLQ_2023-08-17_final
2023-08-17 12:05:32	ga_s2am_ard_3-2-1_50JKQ_2023-08-17_final
2023-08-17 12:05:35	ga_s2am_ard_3-2-1_49JGK_2023-08-17_final
2023-08-17 12:05:43	ga_s2am_ard_3-2-1_50JLP_2023-08-17_final
2023-08-17 12:05:47	ga_s2am_ard_3-2-1_50JKP_2023-08-17_final
2023-08-17 12:05:50	ga_s2am_ard_3-2-1_49JGJ_2023-08-17_final
2023-08-17 12:05:53	ga_s2am_ard_3-2-1_49JFJ_2023-08-17_nrt
2023-08-17 12:05:57	ga_s2am_ard_3-2-1_50JLN_2023-08-17_final
2023-08-17 12:06:01	ga_s2am_ard_3-2-1_50JKN_2023-08-17_final
2023-08-17 12:06:04	ga_s2am_ard_3-2-1_49JGH_2023-08-17_final
2023-08-17 12:06:08	ga_s2am_ard_3-2-1_49JFH_2023-08-17_nrt
2023-08-17 12:06:10	ga_s2am_ard_3-2-1_50JLM_2023-08-17_final
2023-08-17 12:06:16	ga_s2am_ard_3-2-1_50JKM_2023-08-17_final
2023-08-17 12:06:18	ga_s2am_ard_3-2-1_49JGG_2023-08-17_nrt
2023-08-17 12:06:20	ga_s2am_ard_3-2-1_50JLL_2023-08-17_final
2023-08-17 12:06:22	ga_s2am_ard_3-2-1_49JFG_2023-08-17_nrt
2023-08-17 12:06:29	ga_s2am_ard_3-2-1_50JKL_2023-08-17_final
2023-08-17 12:06:30	ga_s2am_ard_3-2-1_49JGF_2023-08-17_nrt
2023-08-17 12:06:32	ga_s2am_ard_3-2-1_49JFF_2023-08-17_nrt
2023-08-17 12:06:35	ga_s2am_ard_3-2-1_50HKK_2023-08-17_nrt
2023-08-18 09:49:42	ga_s2am_ard_3-2-1_56LMP_2023-08-18_nrt
2023-08-18 09:49:46	ga_s2am_ard_3-2-1_56LLP_2023-08-18_nrt
2023-08-18 09:49:49	ga_s2am_ard_3-2-1_56LKP_2023-08-18_nrt
2023-08-18 09:49:51	ga_s2am_ard_3-2-1_55LHJ_2023-08-18_nrt
2023-08-18 09:49:55	ga_s2am_ard_3-2-1_56LMN_2023-08-18_nrt
2023-08-18 09:49:55	ga_s2am_ard_3-2-1_55LGJ_2023-08-18_nrt
2023-08-18 09:50:00	ga_s2am_ard_3-2-1_56LLN_2023-08-18_nrt
2023-08-18 09:50:04	ga_s2am_ard_3-2-1_56LKN_2023-08-18_nrt
2023-08-18 09:50:04	ga_s2am_ard_3-2-1_55LHH_2023-08-18_nrt
2023-08-18 09:50:05	ga_s2am_ard_3-2-1_56LMM_2023-08-18_nrt
2023-08-18 09:50:06	ga_s2am_ard_3-2-1_55LGH_2023-08-18_nrt
2023-08-18 09:50:09	ga_s2am_ard_3-2-1_56LLM_2023-08-18_nrt
2023-08-18 09:50:11	ga_s2am_ard_3-2-1_56LKM_2023-08-18_nrt
2023-08-18 09:50:11	ga_s2am_ard_3-2-1_55LHG_2023-08-18_nrt
2023-08-18 09:51:05	ga_s2am_ard_3-2-1_56LLJ_2023-08-18_nrt
2023-08-18 09:51:07	ga_s2am_ard_3-2-1_56LKJ_2023-08-18_nrt
2023-08-18 09:51:08	ga_s2am_ard_3-2-1_55LHD_2023-08-18_nrt
2023-08-18 09:51:09	ga_s2am_ard_3-2-1_55LGD_2023-08-18_nrt
2023-08-18 09:51:09	ga_s2am_ard_3-2-1_56LLH_2023-08-18_nrt
2023-08-18 09:51:11	ga_s2am_ard_3-2-1_55LFD_2023-08-18_nrt
2023-08-18 09:51:16	ga_s2am_ard_3-2-1_56LKH_2023-08-18_final
2023-08-18 09:51:18	ga_s2am_ard_3-2-1_55LHC_2023-08-18_final
2023-08-18 09:51:21	ga_s2am_ard_3-2-1_55LGC_2023-08-18_final
2023-08-18 09:51:25	ga_s2am_ard_3-2-1_55LFC_2023-08-18_final
2023-08-18 09:51:31	ga_s2am_ard_3-2-1_56KKG_2023-08-18_final
2023-08-18 09:51:32	ga_s2am_ard_3-2-1_55KHB_2023-08-18_final
2023-08-18 09:51:36	ga_s2am_ard_3-2-1_55KGB_2023-08-18_final
2023-08-18 09:51:39	ga_s2am_ard_3-2-1_55KFB_2023-08-18_final
2023-08-18 09:51:46	ga_s2am_ard_3-2-1_56KKF_2023-08-18_final
2023-08-18 09:51:46	ga_s2am_ard_3-2-1_55KHA_2023-08-18_final
2023-08-18 09:51:50	ga_s2am_ard_3-2-1_55KGA_2023-08-18_final
2023-08-18 09:51:54	ga_s2am_ard_3-2-1_55KFA_2023-08-18_final
2023-08-18 09:51:59	ga_s2am_ard_3-2-1_55KEA_2023-08-18_final
2023-08-18 09:52:00	ga_s2am_ard_3-2-1_56KKE_2023-08-18_nrt
2023-08-18 09:52:01	ga_s2am_ard_3-2-1_55KHV_2023-08-18_nrt
2023-08-18 09:52:05	ga_s2am_ard_3-2-1_55KGV_2023-08-18_nrt
2023-08-18 09:52:08	ga_s2am_ard_3-2-1_55KFV_2023-08-18_final
2023-08-18 09:52:12	ga_s2am_ard_3-2-1_55KEV_2023-08-18_final
2023-08-18 09:52:14	ga_s2am_ard_3-2-1_56KKD_2023-08-18_nrt
2023-08-18 09:52:16	ga_s2am_ard_3-2-1_55KHU_2023-08-18_final
2023-08-18 09:52:19	ga_s2am_ard_3-2-1_55KGU_2023-08-18_final
2023-08-18 09:52:23	ga_s2am_ard_3-2-1_55KFU_2023-08-18_final
2023-08-18 09:52:25	ga_s2am_ard_3-2-1_56KKC_2023-08-18_final
2023-08-18 09:52:26	ga_s2am_ard_3-2-1_55KEU_2023-08-18_final
2023-08-18 09:52:29	ga_s2am_ard_3-2-1_55KHT_2023-08-18_final
2023-08-18 09:52:34	ga_s2am_ard_3-2-1_55KGT_2023-08-18_final
2023-08-18 09:52:37	ga_s2am_ard_3-2-1_55KFT_2023-08-18_final
2023-08-18 09:52:41	ga_s2am_ard_3-2-1_55KET_2023-08-18_final
2023-08-18 09:52:41	ga_s2am_ard_3-2-1_55KHS_2023-08-18_final
2023-08-18 09:52:48	ga_s2am_ard_3-2-1_55KGS_2023-08-18_final
2023-08-18 09:52:52	ga_s2am_ard_3-2-1_55KFS_2023-08-18_final
2023-08-18 09:52:55	ga_s2am_ard_3-2-1_55KES_2023-08-18_final
2023-08-18 09:53:01	ga_s2am_ard_3-2-1_55KDS_2023-08-18_final
2023-08-18 09:53:02	ga_s2am_ard_3-2-1_55KGR_2023-08-18_final
2023-08-18 09:53:06	ga_s2am_ard_3-2-1_55KFR_2023-08-18_final
2023-08-18 09:53:10	ga_s2am_ard_3-2-1_55KER_2023-08-18_final
2023-08-18 09:53:13	ga_s2am_ard_3-2-1_55KDR_2023-08-18_final
2023-08-18 09:53:17	ga_s2am_ard_3-2-1_55KGQ_2023-08-18_final
2023-08-18 09:53:21	ga_s2am_ard_3-2-1_55KFQ_2023-08-18_final
2023-08-18 09:53:24	ga_s2am_ard_3-2-1_55KEQ_2023-08-18_final
2023-08-18 09:53:27	ga_s2am_ard_3-2-1_55KDQ_2023-08-18_final
2023-08-18 09:53:31	ga_s2am_ard_3-2-1_55KGP_2023-08-18_final
2023-08-18 11:37:18	ga_s2am_ard_3-2-1_51HUB_2023-08-18_final
2023-08-18 11:37:21	ga_s2am_ard_3-2-1_51HTB_2023-08-18_final
2023-08-18 11:37:23	ga_s2am_ard_3-2-1_50HQG_2023-08-18_final
2023-08-18 11:37:27	ga_s2am_ard_3-2-1_50HPG_2023-08-18_final
2023-08-18 11:37:31	ga_s2am_ard_3-2-1_51HUA_2023-08-18_nrt
2023-08-18 11:37:32	ga_s2am_ard_3-2-1_50HNG_2023-08-18_final
2023-08-18 11:37:34	ga_s2am_ard_3-2-1_51HTA_2023-08-18_nrt
2023-08-18 11:37:34	ga_s2am_ard_3-2-1_50HQF_2023-08-18_nrt
2023-08-18 11:37:36	ga_s2am_ard_3-2-1_50HPF_2023-08-18_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