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

First 100 results (query limit)

Time (local)	Label
2023-11-10 10:20:23	ga_s2bm_ard_3-2-1_54LZM_2023-11-10_final
2023-11-10 10:20:37	ga_s2bm_ard_3-2-1_54LZL_2023-11-10_final
2023-11-10 10:20:55	ga_s2bm_ard_3-2-1_54LYK_2023-11-10_final
2023-11-10 10:20:59	ga_s2bm_ard_3-2-1_54LXK_2023-11-10_final
2023-11-10 10:21:17	ga_s2bm_ard_3-2-1_54LWJ_2023-11-10_final
2023-11-10 10:21:22	ga_s2bm_ard_3-2-1_54LVJ_2023-11-10_final
2023-11-10 10:22:50	ga_s2bm_ard_3-2-1_54KUC_2023-11-10_final
2023-11-10 10:23:13	ga_s2bm_ard_3-2-1_54KWA_2023-11-10_final
2023-11-10 10:23:27	ga_s2bm_ard_3-2-1_54KWV_2023-11-10_final
2023-11-10 10:23:31	ga_s2bm_ard_3-2-1_54KVV_2023-11-10_final
2023-11-10 10:23:41	ga_s2bm_ard_3-2-1_53KRQ_2023-11-10_final
2023-11-10 10:23:45	ga_s2bm_ard_3-2-1_54KVU_2023-11-10_final
2023-11-10 10:23:56	ga_s2bm_ard_3-2-1_54JWT_2023-11-10_final
2023-11-10 10:24:14	ga_s2bm_ard_3-2-1_54JVS_2023-11-10_final
2023-11-10 10:25:16	ga_s2bm_ard_3-2-1_54JUN_2023-11-10_final
2023-11-10 10:25:37	ga_s2bm_ard_3-2-1_54JVL_2023-11-10_final
2023-11-10 10:25:42	ga_s2bm_ard_3-2-1_53JPG_2023-11-10_final
2023-11-10 10:25:51	ga_s2bm_ard_3-2-1_53JQF_2023-11-10_final
2023-11-10 10:25:59	ga_s2bm_ard_3-2-1_54HUK_2023-11-10_final
2023-11-10 10:26:17	ga_s2bm_ard_3-2-1_54HTJ_2023-11-10_final
2023-11-10 10:26:38	ga_s2bm_ard_3-2-1_53HPC_2023-11-10_final
2023-11-10 10:26:48	ga_s2bm_ard_3-2-1_53HQB_2023-11-10_final
2023-11-10 12:03:46	ga_s2bm_ard_3-2-1_50KKB_2023-11-10_final
2023-11-10 12:04:15	ga_s2bm_ard_3-2-1_49KHQ_2023-11-10_final
2023-11-10 12:04:23	ga_s2bm_ard_3-2-1_50KMU_2023-11-10_final
2023-11-10 12:04:26	ga_s2bm_ard_3-2-1_50KLU_2023-11-10_final
2023-11-10 12:04:44	ga_s2bm_ard_3-2-1_49JHN_2023-11-10_final
2023-11-10 12:05:39	ga_s2bm_ard_3-2-1_50JLP_2023-11-10_final
2023-11-10 12:06:11	ga_s2bm_ard_3-2-1_50JKM_2023-11-10_final
2023-11-11 09:53:01	ga_s2bm_ard_3-2-1_55KER_2023-11-11_final
2023-11-11 09:53:08	ga_s2bm_ard_3-2-1_55KER_2023-11-11_final
2023-11-11 09:53:10	ga_s2bm_ard_3-2-1_55KDR_2023-11-11_final
2023-11-11 09:53:23	ga_s2bm_ard_3-2-1_55KDQ_2023-11-11_final
2023-11-11 09:53:27	ga_s2bm_ard_3-2-1_55KGP_2023-11-11_final
2023-11-11 09:53:31	ga_s2bm_ard_3-2-1_55KFP_2023-11-11_final
2023-11-11 09:53:39	ga_s2bm_ard_3-2-1_55JGN_2023-11-11_final
2023-11-11 09:53:52	ga_s2bm_ard_3-2-1_55JDN_2023-11-11_final
2023-11-11 09:54:03	ga_s2bm_ard_3-2-1_55JEM_2023-11-11_final
2023-11-11 09:54:42	ga_s2bm_ard_3-2-1_55JFJ_2023-11-11_final
2023-11-11 09:54:52	ga_s2bm_ard_3-2-1_55JFH_2023-11-11_final
2023-11-11 09:55:19	ga_s2bm_ard_3-2-1_55JDG_2023-11-11_final
2023-11-11 09:55:30	ga_s2bm_ard_3-2-1_55JEF_2023-11-11_final
2023-11-11 09:55:34	ga_s2bm_ard_3-2-1_55JDF_2023-11-11_final
2023-11-11 09:55:44	ga_s2bm_ard_3-2-1_55HEE_2023-11-11_final
2023-11-11 09:55:55	ga_s2bm_ard_3-2-1_55HBE_2023-11-11_final
2023-11-11 09:56:11	ga_s2bm_ard_3-2-1_54HYJ_2023-11-11_final
2023-11-11 09:56:26	ga_s2bm_ard_3-2-1_54HYH_2023-11-11_final
2023-11-11 09:56:36	ga_s2bm_ard_3-2-1_55HCB_2023-11-11_final
2023-11-11 09:56:46	ga_s2bm_ard_3-2-1_55HDA_2023-11-11_final
2023-11-11 09:57:05	ga_s2bm_ard_3-2-1_55HCV_2023-11-11_final
2023-11-11 09:57:10	ga_s2bm_ard_3-2-1_55HDU_2023-11-11_final
2023-11-11 09:57:52	ga_s2bm_ard_3-2-1_55HBS_2023-11-11_final
2023-11-11 09:58:06	ga_s2bm_ard_3-2-1_55GBR_2023-11-11_final
2023-11-11 11:31:30	ga_s2bm_ard_3-2-1_52LBJ_2023-11-11_final
2023-11-11 11:32:09	ga_s2bm_ard_3-2-1_51KYB_2023-11-11_final
2023-11-11 11:32:13	ga_s2bm_ard_3-2-1_51KXB_2023-11-11_final
2023-11-11 11:32:27	ga_s2bm_ard_3-2-1_51KXA_2023-11-11_final
2023-11-11 11:32:38	ga_s2bm_ard_3-2-1_51KYV_2023-11-11_final
2023-11-11 11:32:45	ga_s2bm_ard_3-2-1_51KWV_2023-11-11_final
2023-11-11 11:33:03	ga_s2bm_ard_3-2-1_51KVU_2023-11-11_final
2023-11-11 11:33:25	ga_s2bm_ard_3-2-1_51KXS_2023-11-11_final
2023-11-11 11:33:40	ga_s2bm_ard_3-2-1_51KXR_2023-11-11_final
2023-11-11 11:34:04	ga_s2bm_ard_3-2-1_51KUQ_2023-11-11_final
2023-11-11 11:34:26	ga_s2bm_ard_3-2-1_51JWN_2023-11-11_final
2023-11-11 11:35:03	ga_s2bm_ard_3-2-1_51JUL_2023-11-11_final
2023-11-11 11:35:17	ga_s2bm_ard_3-2-1_51JUK_2023-11-11_final
2023-11-11 11:35:28	ga_s2bm_ard_3-2-1_51JVJ_2023-11-11_final
2023-11-11 11:36:28	ga_s2bm_ard_3-2-1_50JPL_2023-11-11_final
2023-11-11 11:36:39	ga_s2bm_ard_3-2-1_50HPK_2023-11-11_final
2023-11-12 09:24:29	ga_s2bm_ard_3-2-1_56JMQ_2023-11-11_final
2023-11-12 09:24:39	ga_s2bm_ard_3-2-1_56JNP_2023-11-11_final
2023-11-12 09:25:58	ga_s2bm_ard_3-2-1_56HLJ_2023-11-11_final
2023-11-12 09:26:13	ga_s2bm_ard_3-2-1_56HLH_2023-11-11_final
2023-11-12 09:26:33	ga_s2bm_ard_3-2-1_56HKG_2023-11-11_final
2023-11-20 12:00:48	ga_s2bm_ard_3-2-1_50LPN_2023-11-20_nrt
2023-11-20 12:01:03	ga_s2bm_ard_3-2-1_50LPM_2023-11-20_nrt
2023-11-20 12:01:13	ga_s2bm_ard_3-2-1_50LQL_2023-11-20_nrt
2023-11-20 12:01:17	ga_s2bm_ard_3-2-1_50LPL_2023-11-20_nrt
2023-11-20 12:02:54	ga_s2bm_ard_3-2-1_50KME_2023-11-20_nrt
2023-11-20 12:02:58	ga_s2bm_ard_3-2-1_50KKE_2023-11-20_nrt
2023-11-20 12:03:14	ga_s2bm_ard_3-2-1_49KHU_2023-11-20_nrt
2023-11-20 12:03:27	ga_s2bm_ard_3-2-1_49KHT_2023-11-20_nrt
2023-11-20 12:05:30	ga_s2bm_ard_3-2-1_49JFK_2023-11-20_nrt
2023-11-20 12:05:59	ga_s2bm_ard_3-2-1_49JFH_2023-11-20_nrt
2023-11-20 12:06:13	ga_s2bm_ard_3-2-1_49JFG_2023-11-20_nrt
2023-11-20 12:06:20	ga_s2bm_ard_3-2-1_50JKL_2023-11-20_nrt
2023-11-20 12:06:23	ga_s2bm_ard_3-2-1_49JFF_2023-11-20_nrt
2023-11-21 09:51:00	ga_s2bm_ard_3-2-1_55LGD_2023-11-21_nrt
2023-11-21 09:51:02	ga_s2bm_ard_3-2-1_55LFD_2023-11-21_nrt
2023-11-21 09:51:37	ga_s2bm_ard_3-2-1_55KHA_2023-11-21_nrt
2023-11-21 09:51:55	ga_s2bm_ard_3-2-1_55KGV_2023-11-21_nrt
2023-11-21 09:57:51	ga_s2bm_ard_3-2-1_54HXB_2023-11-21_nrt
2023-11-21 09:57:55	ga_s2bm_ard_3-2-1_54HWB_2023-11-21_nrt
2023-11-21 09:58:06	ga_s2bm_ard_3-2-1_54GXA_2023-11-21_nrt
2023-11-21 09:58:10	ga_s2bm_ard_3-2-1_54GWA_2023-11-21_nrt
2023-11-21 09:58:20	ga_s2bm_ard_3-2-1_54GXV_2023-11-21_nrt
2023-11-21 09:58:23	ga_s2bm_ard_3-2-1_54GWV_2023-11-21_nrt
2023-11-21 09:58:27	ga_s2bm_ard_3-2-1_54GXU_2023-11-21_nrt
2023-11-21 11:30:40	ga_s2bm_ard_3-2-1_51LXH_2023-11-21_nrt
2023-11-21 11:30:55	ga_s2bm_ard_3-2-1_51LXG_2023-11-21_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