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
2023-10-03 11:00:30	ga_s2bm_ard_3-2-1_53LKH_2023-10-03_final
2023-10-03 11:00:31	ga_s2bm_ard_3-2-1_52LHN_2023-10-03_final
2023-10-03 11:00:37	ga_s2bm_ard_3-2-1_53LMG_2023-10-03_final
2023-10-03 11:00:44	ga_s2bm_ard_3-2-1_53LKG_2023-10-03_final
2023-10-03 11:00:49	ga_s2bm_ard_3-2-1_53LMF_2023-10-03_final
2023-10-03 11:00:55	ga_s2bm_ard_3-2-1_53LLF_2023-10-03_final
2023-10-03 11:00:59	ga_s2bm_ard_3-2-1_53LKF_2023-10-03_final
2023-10-03 11:01:03	ga_s2bm_ard_3-2-1_52LGL_2023-10-03_final
2023-10-03 11:01:10	ga_s2bm_ard_3-2-1_53LLE_2023-10-03_final
2023-10-03 11:01:15	ga_s2bm_ard_3-2-1_52LHK_2023-10-03_final
2023-10-03 11:01:18	ga_s2bm_ard_3-2-1_52LGK_2023-10-03_final
2023-10-03 11:01:29	ga_s2bm_ard_3-2-1_52LHJ_2023-10-03_final
2023-10-03 11:01:33	ga_s2bm_ard_3-2-1_52LGJ_2023-10-03_final
2023-10-03 11:01:40	ga_s2bm_ard_3-2-1_52LFJ_2023-10-03_final
2023-10-03 11:01:44	ga_s2bm_ard_3-2-1_52LHH_2023-10-03_final
2023-10-03 11:01:47	ga_s2bm_ard_3-2-1_52LGH_2023-10-03_final
2023-10-03 11:01:51	ga_s2bm_ard_3-2-1_52LFH_2023-10-03_final
2023-10-03 11:01:57	ga_s2bm_ard_3-2-1_53KKB_2023-10-03_final
2023-10-03 11:01:58	ga_s2bm_ard_3-2-1_52KHG_2023-10-03_final
2023-10-03 11:02:05	ga_s2bm_ard_3-2-1_52KFG_2023-10-03_final
2023-10-03 11:02:13	ga_s2bm_ard_3-2-1_52KHF_2023-10-03_final
2023-10-03 11:02:16	ga_s2bm_ard_3-2-1_52KGF_2023-10-03_final
2023-10-03 11:02:19	ga_s2bm_ard_3-2-1_52KFF_2023-10-03_final
2023-10-03 11:02:26	ga_s2bm_ard_3-2-1_53KKV_2023-10-03_final
2023-10-03 11:02:27	ga_s2bm_ard_3-2-1_52KHE_2023-10-03_final
2023-10-03 11:02:30	ga_s2bm_ard_3-2-1_52KGE_2023-10-03_final
2023-10-03 11:02:33	ga_s2bm_ard_3-2-1_52KFE_2023-10-03_final
2023-10-03 11:02:38	ga_s2bm_ard_3-2-1_52KGE_2023-10-03_final
2023-10-03 11:02:39	ga_s2bm_ard_3-2-1_52KFD_2023-10-03_final
2023-10-03 11:02:39	ga_s2bm_ard_3-2-1_52KFE_2023-10-03_final
2023-10-03 11:02:45	ga_s2bm_ard_3-2-1_52KGD_2023-10-03_final
2023-10-03 11:02:48	ga_s2bm_ard_3-2-1_52KFD_2023-10-03_final
2023-10-03 11:02:53	ga_s2bm_ard_3-2-1_52KED_2023-10-03_final
2023-10-03 11:02:56	ga_s2bm_ard_3-2-1_52KHC_2023-10-03_final
2023-10-03 11:02:59	ga_s2bm_ard_3-2-1_52KGC_2023-10-03_final
2023-10-03 11:03:06	ga_s2bm_ard_3-2-1_52KEC_2023-10-03_final
2023-10-03 11:03:09	ga_s2bm_ard_3-2-1_53KKS_2023-10-03_final
2023-10-03 11:03:14	ga_s2bm_ard_3-2-1_52KGB_2023-10-03_final
2023-10-03 11:03:24	ga_s2bm_ard_3-2-1_52KHA_2023-10-03_final
2023-10-03 11:03:35	ga_s2bm_ard_3-2-1_52KEA_2023-10-03_final
2023-10-03 11:03:43	ga_s2bm_ard_3-2-1_52KGV_2023-10-03_final
2023-10-03 11:03:50	ga_s2bm_ard_3-2-1_52KEV_2023-10-03_final
2023-10-03 11:03:55	ga_s2bm_ard_3-2-1_52KDV_2023-10-03_final
2023-10-03 11:04:04	ga_s2bm_ard_3-2-1_52KEU_2023-10-03_final
2023-10-03 11:04:08	ga_s2bm_ard_3-2-1_52KDU_2023-10-03_final
2023-10-03 11:04:15	ga_s2bm_ard_3-2-1_52JFT_2023-10-03_final
2023-10-03 11:04:19	ga_s2bm_ard_3-2-1_52JET_2023-10-03_final
2023-10-03 11:04:30	ga_s2bm_ard_3-2-1_52JFS_2023-10-03_final
2023-10-03 11:04:33	ga_s2bm_ard_3-2-1_52JES_2023-10-03_final
2023-10-03 11:04:48	ga_s2bm_ard_3-2-1_52JER_2023-10-03_final
2023-10-03 11:04:51	ga_s2bm_ard_3-2-1_52JDR_2023-10-03_final
2023-10-03 11:05:03	ga_s2bm_ard_3-2-1_52JEQ_2023-10-03_final
2023-10-03 11:05:06	ga_s2bm_ard_3-2-1_52JDQ_2023-10-03_final
2023-10-03 11:05:13	ga_s2bm_ard_3-2-1_52JFP_2023-10-03_final
2023-10-03 11:05:17	ga_s2bm_ard_3-2-1_52JEP_2023-10-03_final
2023-10-03 11:05:20	ga_s2bm_ard_3-2-1_52JDP_2023-10-03_final
2023-10-03 11:05:23	ga_s2bm_ard_3-2-1_52JCP_2023-10-03_final
2023-10-03 11:05:27	ga_s2bm_ard_3-2-1_52JFN_2023-10-03_final
2023-10-03 11:05:31	ga_s2bm_ard_3-2-1_52JEN_2023-10-03_final
2023-10-03 11:05:35	ga_s2bm_ard_3-2-1_52JDN_2023-10-03_final
2023-10-03 11:05:38	ga_s2bm_ard_3-2-1_52JCN_2023-10-03_final
2023-10-03 11:05:46	ga_s2bm_ard_3-2-1_52JEM_2023-10-03_final
2023-10-03 11:05:50	ga_s2bm_ard_3-2-1_52JDM_2023-10-03_final
2023-10-03 11:05:53	ga_s2bm_ard_3-2-1_52JCM_2023-10-03_final
2023-10-03 11:05:59	ga_s2bm_ard_3-2-1_52JBM_2023-10-03_final
2023-10-03 11:06:00	ga_s2bm_ard_3-2-1_52JEL_2023-10-03_final
2023-10-03 11:06:04	ga_s2bm_ard_3-2-1_52JDL_2023-10-03_final
2023-10-03 11:06:07	ga_s2bm_ard_3-2-1_52JCL_2023-10-03_final
2023-10-03 11:06:11	ga_s2bm_ard_3-2-1_52JBL_2023-10-03_final
2023-10-03 11:06:19	ga_s2bm_ard_3-2-1_52HDK_2023-10-03_final
2023-10-03 11:06:22	ga_s2bm_ard_3-2-1_52HCK_2023-10-03_final
2023-10-03 11:06:25	ga_s2bm_ard_3-2-1_52HBK_2023-10-03_final
2023-10-04 10:30:56	ga_s2bm_ard_3-2-1_54LWK_2023-10-04_final
2023-10-04 10:32:23	ga_s2bm_ard_3-2-1_53KQV_2023-10-04_final
2023-10-04 10:33:17	ga_s2bm_ard_3-2-1_54KTA_2023-10-04_final
2023-10-04 10:33:28	ga_s2bm_ard_3-2-1_54KUV_2023-10-04_final
2023-10-04 10:33:46	ga_s2bm_ard_3-2-1_54KTU_2023-10-04_final
2023-10-04 10:33:50	ga_s2bm_ard_3-2-1_53KQP_2023-10-04_final
2023-10-04 10:34:14	ga_s2bm_ard_3-2-1_53JNN_2023-10-04_final
2023-10-04 10:34:33	ga_s2bm_ard_3-2-1_53JQL_2023-10-04_final
2023-10-04 10:34:40	ga_s2bm_ard_3-2-1_53JNL_2023-10-04_final
2023-10-04 10:35:16	ga_s2bm_ard_3-2-1_53JMJ_2023-10-04_final
2023-10-04 10:35:41	ga_s2bm_ard_3-2-1_53JMG_2023-10-04_final
2023-10-04 10:35:56	ga_s2bm_ard_3-2-1_53JMF_2023-10-04_final
2023-10-04 10:35:57	ga_s2bm_ard_3-2-1_53HQE_2023-10-04_final
2023-10-04 10:36:03	ga_s2bm_ard_3-2-1_53HPE_2023-10-04_final
2023-10-04 10:36:22	ga_s2bm_ard_3-2-1_53HND_2023-10-04_final
2023-10-04 12:13:55	ga_s2bm_ard_3-2-1_50KKA_2023-10-04_final
2023-10-04 12:13:55	ga_s2bm_ard_3-2-1_49KHR_2023-10-04_final
2023-10-04 12:14:13	ga_s2bm_ard_3-2-1_49KGQ_2023-10-04_final
2023-10-04 12:14:32	ga_s2bm_ard_3-2-1_49JHN_2023-10-04_final
2023-10-14 10:30:40	ga_s2bm_ard_3-2-1_54LTM_2023-10-14_nrt
2023-10-14 10:30:53	ga_s2bm_ard_3-2-1_54LTL_2023-10-14_nrt
2023-10-14 10:31:06	ga_s2bm_ard_3-2-1_54LTK_2023-10-14_nrt
2023-10-14 10:31:13	ga_s2bm_ard_3-2-1_54LTK_2023-10-14_nrt
2023-10-14 10:31:22	ga_s2bm_ard_3-2-1_54LTJ_2023-10-14_nrt
2023-10-14 10:31:37	ga_s2bm_ard_3-2-1_54LTH_2023-10-14_nrt
2023-10-14 10:36:44	ga_s2bm_ard_3-2-1_53HLC_2023-10-14_nrt
2023-10-14 10:36:59	ga_s2bm_ard_3-2-1_53HLB_2023-10-14_nrt
2023-10-14 10:37:04	ga_s2bm_ard_3-2-1_53HNA_2023-10-14_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