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 10:19:40	ga_s2bm_ard_3-2-1_54LZQ_2023-11-10_final
2023-11-10 10:20:20	ga_s2bm_ard_3-2-1_54LWN_2023-11-10_final
2023-11-10 10:20:44	ga_s2bm_ard_3-2-1_54LXL_2023-11-10_final
2023-11-10 10:20:51	ga_s2bm_ard_3-2-1_54LZK_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 10:21:09	ga_s2bm_ard_3-2-1_54LYJ_2023-11-10_final
2023-11-10 10:21:31	ga_s2bm_ard_3-2-1_54LWH_2023-11-10_final
2023-11-10 10:21:46	ga_s2bm_ard_3-2-1_54KWG_2023-11-10_final
2023-11-10 10:22:05	ga_s2bm_ard_3-2-1_54KYE_2023-11-10_final
2023-11-10 10:22:11	ga_s2bm_ard_3-2-1_54KXE_2023-11-10_final
2023-11-10 10:22:15	ga_s2bm_ard_3-2-1_54KYD_2023-11-10_final
2023-11-10 10:22:47	ga_s2bm_ard_3-2-1_54KVC_2023-11-10_final
2023-11-10 10:22:50	ga_s2bm_ard_3-2-1_54KUC_2023-11-10_final
2023-11-10 10:23:02	ga_s2bm_ard_3-2-1_54KVB_2023-11-10_final
2023-11-10 10:23:13	ga_s2bm_ard_3-2-1_54KWA_2023-11-10_final
2023-11-10 10:23:31	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 10:23:42	ga_s2bm_ard_3-2-1_54KWU_2023-11-10_final
2023-11-10 10:23:45	ga_s2bm_ard_3-2-1_54KVU_2023-11-10_final
2023-11-10 10:24:03	ga_s2bm_ard_3-2-1_54JUT_2023-11-10_final
2023-11-10 10:24:14	ga_s2bm_ard_3-2-1_54JVS_2023-11-10_final
2023-11-10 10:24:18	ga_s2bm_ard_3-2-1_54JUS_2023-11-10_final
2023-11-10 10:24:46	ga_s2bm_ard_3-2-1_53JQK_2023-11-10_final
2023-11-10 10:25:19	ga_s2bm_ard_3-2-1_54JTN_2023-11-10_final
2023-11-10 10:25:34	ga_s2bm_ard_3-2-1_54JTM_2023-11-10_final
2023-11-10 10:25:42	ga_s2bm_ard_3-2-1_53JPG_2023-11-10_final
2023-11-10 10:25:45	ga_s2bm_ard_3-2-1_54JUL_2023-11-10_final
2023-11-10 10:26:42	ga_s2bm_ard_3-2-1_54HUG_2023-11-10_final
2023-11-10 10:26:52	ga_s2bm_ard_3-2-1_54HUF_2023-11-10_final
2023-11-10 10:27:01	ga_s2bm_ard_3-2-1_54HTF_2023-11-10_final
2023-11-10 10:27:03	ga_s2bm_ard_3-2-1_53HQA_2023-11-10_final
2023-11-10 10:27:13	ga_s2bm_ard_3-2-1_54HTE_2023-11-10_final
2023-11-10 10:27:14	ga_s2bm_ard_3-2-1_53HQV_2023-11-10_final
2023-11-10 10:27:16	ga_s2bm_ard_3-2-1_53HPV_2023-11-10_final
2023-11-10 12:04:00	ga_s2bm_ard_3-2-1_50KKA_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 09:53:59	ga_s2bm_ard_3-2-1_55JCN_2023-11-11_final
2023-11-11 09:54:10	ga_s2bm_ard_3-2-1_55JCM_2023-11-11_final
2023-11-11 09:54:21	ga_s2bm_ard_3-2-1_55JDL_2023-11-11_final
2023-11-11 09:55:23	ga_s2bm_ard_3-2-1_55JCG_2023-11-11_final
2023-11-11 09:55:44	ga_s2bm_ard_3-2-1_55HEE_2023-11-11_final
2023-11-11 09:56:46	ga_s2bm_ard_3-2-1_55HDA_2023-11-11_final
2023-11-11 09:57:10	ga_s2bm_ard_3-2-1_55HDU_2023-11-11_final
2023-11-11 09:57:23	ga_s2bm_ard_3-2-1_55HBU_2023-11-11_final
2023-11-11 09:57:37	ga_s2bm_ard_3-2-1_55HBT_2023-11-11_final
2023-11-11 09:58:02	ga_s2bm_ard_3-2-1_55GCR_2023-11-11_final
2023-11-11 09:58:15	ga_s2bm_ard_3-2-1_55GCQ_2023-11-11_final
2023-11-11 11:31:19	ga_s2bm_ard_3-2-1_52LBK_2023-11-11_final
2023-11-11 11:31:50	ga_s2bm_ard_3-2-1_51LZC_2023-11-11_final
2023-11-11 11:32:00	ga_s2bm_ard_3-2-1_51KZB_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 11:32:30	ga_s2bm_ard_3-2-1_51KWA_2023-11-11_final
2023-11-11 11:32:45	ga_s2bm_ard_3-2-1_51KWV_2023-11-11_final
2023-11-11 11:33:10	ga_s2bm_ard_3-2-1_51KXT_2023-11-11_final
2023-11-11 11:33:35	ga_s2bm_ard_3-2-1_51KUS_2023-11-11_final
2023-11-11 11:34:26	ga_s2bm_ard_3-2-1_51JWN_2023-11-11_final
2023-11-11 11:35:38	ga_s2bm_ard_3-2-1_50JQP_2023-11-11_final
2023-11-11 11:35:52	ga_s2bm_ard_3-2-1_50JQN_2023-11-11_final
2023-11-11 11:36:33	ga_s2bm_ard_3-2-1_51HTE_2023-11-11_final
2023-11-11 11:36:35	ga_s2bm_ard_3-2-1_51HVD_2023-11-11_final
2023-11-11 11:36:39	ga_s2bm_ard_3-2-1_50HPK_2023-11-11_final
2023-11-12 09:28:44	ga_s2bm_ard_3-2-1_55GEN_2023-11-11_final
2023-11-12 09:28:51	ga_s2bm_ard_3-2-1_55GEN_2023-11-11_final
2023-11-20 12:00:37	ga_s2bm_ard_3-2-1_50LNP_2023-11-20_nrt
2023-11-20 12:00:41	ga_s2bm_ard_3-2-1_50LMP_2023-11-20_nrt
2023-11-20 12:00:51	ga_s2bm_ard_3-2-1_50LNN_2023-11-20_nrt
2023-11-20 12:01:09	ga_s2bm_ard_3-2-1_50LMM_2023-11-20_nrt
2023-11-20 12:01:21	ga_s2bm_ard_3-2-1_50LNL_2023-11-20_nrt
2023-11-20 12:01:24	ga_s2bm_ard_3-2-1_50LML_2023-11-20_nrt
2023-11-20 12:01:30	ga_s2bm_ard_3-2-1_50LPK_2023-11-20_nrt
2023-11-20 12:01:31	ga_s2bm_ard_3-2-1_50LNK_2023-11-20_nrt
2023-11-20 12:01:33	ga_s2bm_ard_3-2-1_50LMK_2023-11-20_nrt
2023-11-20 12:02:54	ga_s2bm_ard_3-2-1_50KME_2023-11-20_nrt
2023-11-20 12:03:14	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 09:51:37	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 09:52:20	ga_s2bm_ard_3-2-1_55KHT_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 09:58:25	ga_s2bm_ard_3-2-1_55GBP_2023-11-21_nrt
2023-11-21 11:30:17	ga_s2bm_ard_3-2-1_52LBP_2023-11-21_nrt
2023-11-21 11:30:23	ga_s2bm_ard_3-2-1_51LYJ_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