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

81 results

Time (local)	Label
2023-08-10 10:34:51	ga_s2am_ard_3-2-1_53JQK_2023-08-10_final
2023-08-10 10:35:35	ga_s2am_ard_3-2-1_53JQG_2023-08-10_final
2023-08-10 10:35:42	ga_s2am_ard_3-2-1_53JNG_2023-08-10_final
2023-08-10 10:35:53	ga_s2am_ard_3-2-1_53JPF_2023-08-10_final
2023-08-10 10:36:00	ga_s2am_ard_3-2-1_53JMF_2023-08-10_final
2023-08-10 10:36:07	ga_s2am_ard_3-2-1_53HPE_2023-08-10_final
2023-08-10 10:36:25	ga_s2am_ard_3-2-1_53HND_2023-08-10_final
2023-08-10 10:37:03	ga_s2am_ard_3-2-1_53HPA_2023-08-10_final
2023-08-10 12:13:44	ga_s2am_ard_3-2-1_50KKB_2023-08-10_final
2023-08-10 12:13:58	ga_s2am_ard_3-2-1_50KKA_2023-08-10_final
2023-08-10 12:13:59	ga_s2am_ard_3-2-1_49KHR_2023-08-10_final
2023-08-10 12:14:25	ga_s2am_ard_3-2-1_49KHP_2023-08-10_final
2023-08-10 12:15:00	ga_s2am_ard_3-2-1_49JGM_2023-08-10_final
2023-08-10 12:15:04	ga_s2am_ard_3-2-1_49JFM_2023-08-10_final
2023-09-08 11:00:19	ga_s2am_ard_3-2-1_53LKJ_2023-09-08_final
2023-09-08 11:00:27	ga_s2am_ard_3-2-1_53LMH_2023-09-08_final
2023-09-08 11:00:42	ga_s2am_ard_3-2-1_52LGN_2023-09-08_final
2023-09-08 11:01:07	ga_s2am_ard_3-2-1_52LGL_2023-09-08_final
2023-09-08 11:01:18	ga_s2am_ard_3-2-1_53LKE_2023-09-08_final
2023-09-08 11:01:31	ga_s2am_ard_3-2-1_53LKD_2023-09-08_final
2023-09-08 11:01:37	ga_s2am_ard_3-2-1_53LLC_2023-09-08_final
2023-09-08 11:01:51	ga_s2am_ard_3-2-1_52LGH_2023-09-08_final
2023-09-08 11:02:15	ga_s2am_ard_3-2-1_53KKA_2023-09-08_final
2023-09-08 11:02:20	ga_s2am_ard_3-2-1_52KGF_2023-09-08_final
2023-09-08 11:02:30	ga_s2am_ard_3-2-1_53KKV_2023-09-08_final
2023-09-08 11:02:31	ga_s2am_ard_3-2-1_52KHE_2023-09-08_final
2023-09-08 11:02:35	ga_s2am_ard_3-2-1_52KGE_2023-09-08_final
2023-09-08 11:02:53	ga_s2am_ard_3-2-1_52KFD_2023-09-08_final
2023-09-08 11:03:07	ga_s2am_ard_3-2-1_52KFC_2023-09-08_final
2023-09-08 11:03:14	ga_s2am_ard_3-2-1_52KHB_2023-09-08_final
2023-09-08 11:03:28	ga_s2am_ard_3-2-1_52KHA_2023-09-08_final
2023-09-08 11:03:47	ga_s2am_ard_3-2-1_52KGV_2023-09-08_final
2023-09-08 11:03:51	ga_s2am_ard_3-2-1_52KFV_2023-09-08_final
2023-09-08 11:04:01	ga_s2am_ard_3-2-1_52KGU_2023-09-08_final
2023-09-08 11:04:26	ga_s2am_ard_3-2-1_52JDT_2023-09-08_final
2023-09-08 11:04:29	ga_s2am_ard_3-2-1_52JGS_2023-09-08_final
2023-09-08 11:05:25	ga_s2am_ard_3-2-1_52JDP_2023-09-08_final
2023-09-08 11:05:50	ga_s2am_ard_3-2-1_52JEM_2023-09-08_final
2023-09-08 11:06:12	ga_s2am_ard_3-2-1_52JCL_2023-09-08_final
2023-09-09 10:30:23	ga_s2am_ard_3-2-1_54LXM_2023-09-09_final
2023-09-09 10:30:31	ga_s2am_ard_3-2-1_54LWM_2023-09-09_final
2023-09-09 10:31:55	ga_s2am_ard_3-2-1_53KRB_2023-09-09_final
2023-09-09 10:32:02	ga_s2am_ard_3-2-1_54KVF_2023-09-09_final
2023-09-09 10:32:16	ga_s2am_ard_3-2-1_53KQA_2023-09-09_final
2023-09-09 10:33:11	ga_s2am_ard_3-2-1_53KQS_2023-09-09_final
2023-09-09 10:33:17	ga_s2am_ard_3-2-1_53KPS_2023-09-09_final
2023-09-09 10:33:22	ga_s2am_ard_3-2-1_53KRR_2023-09-09_final
2023-09-09 10:33:36	ga_s2am_ard_3-2-1_53KRQ_2023-09-09_final
2023-09-09 10:33:43	ga_s2am_ard_3-2-1_53KPQ_2023-09-09_final
2023-09-09 10:34:18	ga_s2am_ard_3-2-1_53JNN_2023-09-09_final
2023-09-09 10:34:30	ga_s2am_ard_3-2-1_53JNM_2023-09-09_final
2023-09-09 10:35:06	ga_s2am_ard_3-2-1_53JQJ_2023-09-09_final
2023-09-09 10:35:21	ga_s2am_ard_3-2-1_53JQH_2023-09-09_final
2023-09-09 10:35:28	ga_s2am_ard_3-2-1_53JNH_2023-09-09_final
2023-09-09 10:35:32	ga_s2am_ard_3-2-1_53JMH_2023-09-09_final
2023-09-09 10:36:37	ga_s2am_ard_3-2-1_53HPC_2023-09-09_final
2023-09-09 12:13:59	ga_s2am_ard_3-2-1_50KKA_2023-09-09_final
2023-09-09 12:14:00	ga_s2am_ard_3-2-1_49KHR_2023-09-09_final
2023-09-09 12:14:13	ga_s2am_ard_3-2-1_50KKV_2023-09-09_final
2023-09-09 12:14:14	ga_s2am_ard_3-2-1_49KHQ_2023-09-09_final
2023-09-09 12:14:23	ga_s2am_ard_3-2-1_50KKU_2023-09-09_final
2023-09-09 12:14:26	ga_s2am_ard_3-2-1_49KHP_2023-09-09_final
2023-09-09 12:14:50	ga_s2am_ard_3-2-1_49JFN_2023-09-09_final
2023-09-09 12:15:15	ga_s2am_ard_3-2-1_49JGL_2023-09-09_final
2023-09-18 11:06:33	ga_s2am_ard_3-2-1_52HDJ_2023-09-18_nrt
2023-09-19 10:30:06	ga_s2am_ard_3-2-1_54LUP_2023-09-19_nrt
2023-09-19 10:30:21	ga_s2am_ard_3-2-1_54LUN_2023-09-19_nrt
2023-09-19 10:30:36	ga_s2am_ard_3-2-1_54LUM_2023-09-19_nrt
2023-09-19 10:30:50	ga_s2am_ard_3-2-1_54LUL_2023-09-19_nrt
2023-09-19 10:30:53	ga_s2am_ard_3-2-1_54LTL_2023-09-19_nrt
2023-09-19 10:31:05	ga_s2am_ard_3-2-1_54LUK_2023-09-19_nrt
2023-09-19 10:31:08	ga_s2am_ard_3-2-1_54LTK_2023-09-19_nrt
2023-09-19 10:31:12	ga_s2am_ard_3-2-1_53LRE_2023-09-19_nrt
2023-09-19 10:31:24	ga_s2am_ard_3-2-1_53LRD_2023-09-19_nrt
2023-09-19 10:31:43	ga_s2am_ard_3-2-1_54LTH_2023-09-19_nrt
2023-09-19 10:31:43	ga_s2am_ard_3-2-1_53LRC_2023-09-19_nrt
2023-09-19 10:36:45	ga_s2am_ard_3-2-1_53HLC_2023-09-19_nrt
2023-09-19 10:37:00	ga_s2am_ard_3-2-1_53HLB_2023-09-19_nrt
2023-09-19 10:37:05	ga_s2am_ard_3-2-1_53HNA_2023-09-19_nrt
2023-09-19 10:37:07	ga_s2am_ard_3-2-1_53HMA_2023-09-19_nrt
2023-09-19 10:37:08	ga_s2am_ard_3-2-1_53HLA_2023-09-19_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