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
2023-10-01 09:35:30	ga_s2am_ard_3-2-1_56JKL_2023-10-01_final
2023-10-01 09:35:44	ga_s2am_ard_3-2-1_56HKK_2023-10-01_final
2023-10-01 09:35:59	ga_s2am_ard_3-2-1_56HKJ_2023-10-01_final
2023-10-01 09:36:01	ga_s2am_ard_3-2-1_55HGD_2023-10-01_final
2023-10-01 09:36:13	ga_s2am_ard_3-2-1_56HKH_2023-10-01_final
2023-10-01 09:36:15	ga_s2am_ard_3-2-1_55HGC_2023-10-01_final
2023-10-01 09:36:28	ga_s2am_ard_3-2-1_56HKG_2023-10-01_final
2023-10-01 09:36:30	ga_s2am_ard_3-2-1_55HGB_2023-10-01_final
2023-10-01 09:36:43	ga_s2am_ard_3-2-1_56HKF_2023-10-01_final
2023-10-01 09:36:44	ga_s2am_ard_3-2-1_55HGA_2023-10-01_final
2023-10-01 09:36:57	ga_s2am_ard_3-2-1_56HKE_2023-10-01_final
2023-10-01 09:36:58	ga_s2am_ard_3-2-1_55HGV_2023-10-01_final
2023-10-01 09:37:11	ga_s2am_ard_3-2-1_56HKD_2023-10-01_final
2023-10-01 09:37:13	ga_s2am_ard_3-2-1_55HGU_2023-10-01_final
2023-10-01 09:37:26	ga_s2am_ard_3-2-1_56HKC_2023-10-01_nrt
2023-10-01 09:37:27	ga_s2am_ard_3-2-1_55HGT_2023-10-01_nrt
2023-10-01 09:37:41	ga_s2am_ard_3-2-1_56HKB_2023-10-01_nrt
2023-10-01 09:37:41	ga_s2am_ard_3-2-1_55HGS_2023-10-01_nrt
2023-10-11 09:34:28	ga_s2am_ard_3-2-1_56JKQ_2023-10-11_final
2023-10-11 09:34:42	ga_s2am_ard_3-2-1_56JKP_2023-10-11_final
2023-10-11 09:34:57	ga_s2am_ard_3-2-1_56JKN_2023-10-11_final
2023-10-11 09:35:11	ga_s2am_ard_3-2-1_56JKM_2023-10-11_final
2023-10-11 09:35:26	ga_s2am_ard_3-2-1_56JKL_2023-10-11_final
2023-10-11 09:35:41	ga_s2am_ard_3-2-1_56HKK_2023-10-11_final
2023-10-11 09:35:55	ga_s2am_ard_3-2-1_56HKJ_2023-10-11_final
2023-10-11 09:35:57	ga_s2am_ard_3-2-1_55HGD_2023-10-11_final
2023-10-11 09:36:10	ga_s2am_ard_3-2-1_56HKH_2023-10-11_final
2023-10-11 09:36:12	ga_s2am_ard_3-2-1_55HGC_2023-10-11_final
2023-10-11 09:36:24	ga_s2am_ard_3-2-1_56HKG_2023-10-11_final
2023-10-11 09:36:26	ga_s2am_ard_3-2-1_55HGB_2023-10-11_final
2023-10-14 09:41:43	ga_s2am_ard_3-2-1_56KKF_2023-10-14_final
2023-10-14 09:41:57	ga_s2am_ard_3-2-1_56KKE_2023-10-14_nrt
2023-10-14 09:41:58	ga_s2am_ard_3-2-1_55KHV_2023-10-14_nrt
2023-10-14 09:42:12	ga_s2am_ard_3-2-1_56KKD_2023-10-14_nrt
2023-10-14 09:42:13	ga_s2am_ard_3-2-1_55KHU_2023-10-14_nrt
2023-10-14 09:42:26	ga_s2am_ard_3-2-1_56KKC_2023-10-14_nrt
2023-10-14 09:42:27	ga_s2am_ard_3-2-1_55KHT_2023-10-14_nrt
2023-10-14 09:42:41	ga_s2am_ard_3-2-1_56KKB_2023-10-14_final
2023-10-14 09:42:41	ga_s2am_ard_3-2-1_55KHS_2023-10-14_final
2023-10-14 09:42:55	ga_s2am_ard_3-2-1_56KKA_2023-10-14_final
2023-10-14 09:42:56	ga_s2am_ard_3-2-1_55KHR_2023-10-14_final
2023-10-14 09:43:10	ga_s2am_ard_3-2-1_56KKV_2023-10-14_final
2023-10-14 09:43:10	ga_s2am_ard_3-2-1_55KHQ_2023-10-14_final
2023-10-14 09:43:25	ga_s2am_ard_3-2-1_56KKU_2023-10-14_final
2023-10-14 09:43:25	ga_s2am_ard_3-2-1_55KHP_2023-10-14_final
2023-10-14 09:43:39	ga_s2am_ard_3-2-1_55JHN_2023-10-14_final
2023-10-14 09:43:39	ga_s2am_ard_3-2-1_56JKT_2023-10-14_final
2023-10-14 09:43:54	ga_s2am_ard_3-2-1_55JHM_2023-10-14_final
2023-10-14 09:43:54	ga_s2am_ard_3-2-1_56JKS_2023-10-14_final
2023-10-14 09:44:08	ga_s2am_ard_3-2-1_56JKR_2023-10-14_final
2023-10-14 09:44:23	ga_s2am_ard_3-2-1_56JKQ_2023-10-14_final
2023-10-14 09:44:37	ga_s2am_ard_3-2-1_56JKP_2023-10-14_final
2023-10-14 09:44:52	ga_s2am_ard_3-2-1_56JKN_2023-10-14_final
2023-10-14 09:45:07	ga_s2am_ard_3-2-1_56JKM_2023-10-14_final
2023-10-18 09:26:44	ga_s2am_ard_3-2-1_56HKF_2023-10-17_final
2023-10-18 09:26:58	ga_s2am_ard_3-2-1_56HKE_2023-10-17_final
2023-10-18 09:27:00	ga_s2am_ard_3-2-1_55HGV_2023-10-17_final
2023-10-18 09:27:13	ga_s2am_ard_3-2-1_56HKD_2023-10-17_final
2023-10-18 09:27:14	ga_s2am_ard_3-2-1_55HGU_2023-10-17_final
2023-10-18 09:27:27	ga_s2am_ard_3-2-1_56HKC_2023-10-17_final
2023-10-18 09:27:28	ga_s2am_ard_3-2-1_55HGT_2023-10-17_final
2023-10-18 09:27:42	ga_s2am_ard_3-2-1_56HKB_2023-10-17_nrt
2023-10-18 09:27:43	ga_s2am_ard_3-2-1_55HGS_2023-10-17_nrt
2023-10-18 09:27:57	ga_s2am_ard_3-2-1_56GKA_2023-10-17_nrt
2023-10-18 09:27:57	ga_s2am_ard_3-2-1_55GGR_2023-10-17_nrt
2023-10-18 09:28:11	ga_s2am_ard_3-2-1_56GKV_2023-10-17_nrt
2023-10-18 09:28:11	ga_s2am_ard_3-2-1_55GGQ_2023-10-17_nrt
2023-10-18 09:28:26	ga_s2am_ard_3-2-1_55GGP_2023-10-17_nrt
2023-10-18 09:28:26	ga_s2am_ard_3-2-1_56GKU_2023-10-17_nrt
2023-10-18 09:28:40	ga_s2am_ard_3-2-1_55GGN_2023-10-17_nrt
2023-10-31 09:35:58	ga_s2am_ard_3-2-1_55HGD_2023-10-31_final
2023-10-31 09:36:11	ga_s2am_ard_3-2-1_56HKH_2023-10-31_final
2023-10-31 09:36:12	ga_s2am_ard_3-2-1_55HGC_2023-10-31_final
2023-10-31 09:36:25	ga_s2am_ard_3-2-1_56HKG_2023-10-31_final
2023-10-31 09:36:27	ga_s2am_ard_3-2-1_55HGB_2023-10-31_final
2023-10-31 09:36:40	ga_s2am_ard_3-2-1_56HKF_2023-10-31_final
2023-10-31 09:36:41	ga_s2am_ard_3-2-1_55HGA_2023-10-31_final
2023-10-31 09:36:54	ga_s2am_ard_3-2-1_56HKE_2023-10-31_final
2023-10-31 09:36:56	ga_s2am_ard_3-2-1_55HGV_2023-10-31_final
2023-10-31 09:37:09	ga_s2am_ard_3-2-1_56HKD_2023-10-31_final
2023-10-31 09:37:10	ga_s2am_ard_3-2-1_55HGU_2023-10-31_final
2023-10-31 09:37:23	ga_s2am_ard_3-2-1_56HKC_2023-10-31_nrt
2023-10-31 09:37:24	ga_s2am_ard_3-2-1_55HGT_2023-10-31_nrt
2023-10-31 09:37:38	ga_s2am_ard_3-2-1_56HKB_2023-10-31_nrt
2023-10-31 09:37:38	ga_s2am_ard_3-2-1_55HGS_2023-10-31_nrt
2023-11-13 09:42:42	ga_s2am_ard_3-2-1_56KKB_2023-11-13_final
2023-11-13 09:42:42	ga_s2am_ard_3-2-1_55KHS_2023-11-13_final
2023-11-13 09:42:56	ga_s2am_ard_3-2-1_56KKA_2023-11-13_final
2023-11-13 09:42:57	ga_s2am_ard_3-2-1_55KHR_2023-11-13_final
2023-11-13 09:43:11	ga_s2am_ard_3-2-1_56KKV_2023-11-13_final
2023-11-13 09:43:11	ga_s2am_ard_3-2-1_55KHQ_2023-11-13_final
2023-11-13 09:43:26	ga_s2am_ard_3-2-1_56KKU_2023-11-13_final
2023-11-13 09:43:26	ga_s2am_ard_3-2-1_55KHP_2023-11-13_final
2023-11-13 09:43:40	ga_s2am_ard_3-2-1_55JHN_2023-11-13_final
2023-11-13 09:43:40	ga_s2am_ard_3-2-1_56JKT_2023-11-13_final
2023-11-13 09:43:55	ga_s2am_ard_3-2-1_56JKS_2023-11-13_final
2023-11-13 09:44:09	ga_s2am_ard_3-2-1_56JKR_2023-11-13_final
2023-11-13 09:44:24	ga_s2am_ard_3-2-1_56JKQ_2023-11-13_final
2023-11-13 09:44:38	ga_s2am_ard_3-2-1_56JKP_2023-11-13_final
2023-11-13 09:44:53	ga_s2am_ard_3-2-1_56JKN_2023-11-13_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