Search ga_s2bm_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

65 results

Time (local)	Label
2023-12-03 10:31:47	ga_s2bm_ard_3-2-1_53KRB_2023-12-03_final
2023-12-03 10:33:46	ga_s2bm_ard_3-2-1_53KQP_2023-12-03_final
2023-12-03 12:14:38	ga_s2bm_ard_3-2-1_49JGN_2023-12-03_final
2023-12-03 12:14:42	ga_s2bm_ard_3-2-1_49JFN_2023-12-03_final
2023-12-04 10:01:21	ga_s2bm_ard_3-2-1_55LCC_2023-12-04_final
2023-12-04 10:03:35	ga_s2bm_ard_3-2-1_55KBP_2023-12-04_final
2023-12-04 10:05:32	ga_s2bm_ard_3-2-1_55JBF_2023-12-04_final
2023-12-04 11:41:15	ga_s2bm_ard_3-2-1_51LUF_2023-12-04_final
2023-12-04 11:44:17	ga_s2bm_ard_3-2-1_50KQU_2023-12-04_final
2023-12-14 11:40:41	ga_s2bm_ard_3-2-1_51LWH_2023-12-14_nrt
2023-12-14 11:40:44	ga_s2bm_ard_3-2-1_51LVH_2023-12-14_nrt
2023-12-14 11:41:10	ga_s2bm_ard_3-2-1_51LWF_2023-12-14_nrt
2023-12-14 11:41:42	ga_s2bm_ard_3-2-1_51LVD_2023-12-14_nrt
2023-12-14 11:41:49	ga_s2bm_ard_3-2-1_51LTD_2023-12-14_nrt
2023-12-14 11:42:19	ga_s2bm_ard_3-2-1_50KRG_2023-12-14_nrt
2023-12-14 11:42:33	ga_s2bm_ard_3-2-1_50KRF_2023-12-14_nrt
2023-12-15 09:29:39	ga_s2bm_ard_3-2-1_56LPP_2023-12-14_nrt
2023-12-15 09:29:53	ga_s2bm_ard_3-2-1_56LPN_2023-12-14_nrt
2023-12-15 09:30:03	ga_s2bm_ard_3-2-1_56LPM_2023-12-15_nrt
2023-12-15 09:31:25	ga_s2bm_ard_3-2-1_56KPG_2023-12-15_nrt
2023-12-15 09:31:26	ga_s2bm_ard_3-2-1_56KNG_2023-12-15_nrt
2023-12-15 09:31:29	ga_s2bm_ard_3-2-1_56KPF_2023-12-15_nrt
2023-12-15 09:31:30	ga_s2bm_ard_3-2-1_56KNF_2023-12-15_nrt
2023-12-15 09:32:27	ga_s2bm_ard_3-2-1_56KNC_2023-12-15_nrt
2023-12-15 09:32:29	ga_s2bm_ard_3-2-1_56KMC_2023-12-15_nrt
2023-12-15 09:32:47	ga_s2bm_ard_3-2-1_56KPA_2023-12-15_nrt
2023-12-15 09:33:02	ga_s2bm_ard_3-2-1_56KPV_2023-12-15_nrt
2023-12-15 09:33:05	ga_s2bm_ard_3-2-1_56KNV_2023-12-15_nrt
2023-12-15 09:33:09	ga_s2bm_ard_3-2-1_56KMV_2023-12-15_nrt
2023-12-15 09:33:16	ga_s2bm_ard_3-2-1_56KPU_2023-12-15_nrt
2023-12-15 09:33:20	ga_s2bm_ard_3-2-1_56KNU_2023-12-15_nrt
2023-12-15 09:33:29	ga_s2bm_ard_3-2-1_56JPT_2023-12-15_nrt
2023-12-15 09:33:39	ga_s2bm_ard_3-2-1_56JPS_2023-12-15_nrt
2023-12-15 09:35:57	ga_s2bm_ard_3-2-1_56HMH_2023-12-15_nrt
2023-12-15 09:36:36	ga_s2bm_ard_3-2-1_56HLF_2023-12-15_nrt
2023-12-15 09:36:50	ga_s2bm_ard_3-2-1_56HLE_2023-12-15_nrt
2023-12-15 09:36:54	ga_s2bm_ard_3-2-1_56HKE_2023-12-15_nrt
2023-12-15 09:37:02	ga_s2bm_ard_3-2-1_56HLD_2023-12-15_nrt
2023-12-15 09:37:08	ga_s2bm_ard_3-2-1_56HKD_2023-12-15_nrt
2023-12-15 09:37:12	ga_s2bm_ard_3-2-1_56HLC_2023-12-15_nrt
2023-12-15 09:37:23	ga_s2bm_ard_3-2-1_56HKC_2023-12-15_nrt
2023-12-15 09:37:23	ga_s2bm_ard_3-2-1_55HGT_2023-12-15_nrt
2023-12-15 09:37:37	ga_s2bm_ard_3-2-1_56HKB_2023-12-15_nrt
2023-12-15 09:37:38	ga_s2bm_ard_3-2-1_55HGS_2023-12-15_nrt
2023-12-15 09:37:52	ga_s2bm_ard_3-2-1_56GKA_2023-12-15_nrt
2023-12-15 09:37:53	ga_s2bm_ard_3-2-1_55GGR_2023-12-15_nrt
2023-12-15 09:38:06	ga_s2bm_ard_3-2-1_56GKV_2023-12-15_nrt
2023-12-15 09:38:06	ga_s2bm_ard_3-2-1_55GGQ_2023-12-15_nrt
2023-12-15 09:38:18	ga_s2bm_ard_3-2-1_55GGP_2023-12-15_nrt
2023-12-15 09:38:19	ga_s2bm_ard_3-2-1_56GKU_2023-12-15_nrt
2023-12-15 09:38:54	ga_s2bm_ard_3-2-1_55GFM_2023-12-15_nrt
2023-12-15 11:10:13	ga_s2bm_ard_3-2-1_52LGP_2023-12-15_nrt
2023-12-15 11:10:16	ga_s2bm_ard_3-2-1_52LFP_2023-12-15_nrt
2023-12-15 11:10:19	ga_s2bm_ard_3-2-1_52LEP_2023-12-15_nrt
2023-12-15 11:10:34	ga_s2bm_ard_3-2-1_52LEN_2023-12-15_nrt
2023-12-15 11:10:49	ga_s2bm_ard_3-2-1_52LEM_2023-12-15_nrt
2023-12-15 11:10:56	ga_s2bm_ard_3-2-1_52LDM_2023-12-15_nrt
2023-12-15 11:11:07	ga_s2bm_ard_3-2-1_52LDL_2023-12-15_nrt
2023-12-15 11:11:21	ga_s2bm_ard_3-2-1_52LDK_2023-12-15_nrt
2023-12-15 11:16:26	ga_s2bm_ard_3-2-1_52HCJ_2023-12-15_nrt
2023-12-15 11:16:33	ga_s2bm_ard_3-2-1_52HBJ_2023-12-15_nrt
2023-12-15 11:16:48	ga_s2bm_ard_3-2-1_52HBH_2023-12-15_nrt
2023-12-15 11:16:50	ga_s2bm_ard_3-2-1_51HYC_2023-12-15_nrt
2023-12-15 11:17:00	ga_s2bm_ard_3-2-1_52HBG_2023-12-15_nrt
2023-12-15 11:17:01	ga_s2bm_ard_3-2-1_51HYB_2023-12-15_nrt

Return to ga_s2bm_ard_3 whole collection overview

ga_s2bm_ard_3: Geoscience Australia Sentinel 2B MSI Analysis Ready Data Collection 3

Baseline satellite data

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

All years