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
2019-06-20 10:40:48	ga_s2bm_ard_3-2-1_53LPG_2019-06-20_final.stac-item
2019-07-26 11:00:37	ga_s2bm_ard_3-2-1_53LKH_2019-07-26_final.stac-item
2019-07-26 11:00:39	ga_s2bm_ard_3-2-1_52LHN_2019-07-26_final.stac-item
2019-07-26 11:00:45	ga_s2bm_ard_3-2-1_52LGN_2019-07-26_final.stac-item
2019-07-26 11:00:48	ga_s2bm_ard_3-2-1_53LLG_2019-07-26_final.stac-item
2019-07-26 11:00:53	ga_s2bm_ard_3-2-1_52LHM_2019-07-26_final.stac-item
2019-07-26 11:00:57	ga_s2bm_ard_3-2-1_52LGM_2019-07-26_final.stac-item
2019-08-08 11:10:32	ga_s2bm_ard_3-2-1_52LHN_2019-08-08_final.stac-item
2019-08-08 11:10:36	ga_s2bm_ard_3-2-1_52LGN_2019-08-08_final.stac-item
2019-08-08 11:10:44	ga_s2bm_ard_3-2-1_52LHM_2019-08-08_final.stac-item
2019-08-08 11:10:50	ga_s2bm_ard_3-2-1_52LGM_2019-08-08_final.stac-item
2019-11-24 10:30:04	ga_s2bm_ard_3-2-1_54LXN_2019-11-24_final.stac-item
2019-11-24 10:30:09	ga_s2bm_ard_3-2-1_54LWN_2019-11-24_final.stac-item
2019-11-24 10:30:15	ga_s2bm_ard_3-2-1_54LXM_2019-11-24_final.stac-item
2019-11-24 10:30:23	ga_s2bm_ard_3-2-1_54LWM_2019-11-24_final.stac-item
2019-11-28 10:10:07	ga_s2bm_ard_3-2-1_54LZN_2019-11-28_final.stac-item
2019-11-28 10:10:21	ga_s2bm_ard_3-2-1_54LZM_2019-11-28_final.stac-item
2023-10-03 11:00:23	ga_s2bm_ard_3-2-1_53LMH_2023-10-03_final
2023-10-03 11:00:26	ga_s2bm_ard_3-2-1_53LLH_2023-10-03_final
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 11:00:38	ga_s2bm_ard_3-2-1_52LGN_2023-10-03_final
2023-10-03 11:00:41	ga_s2bm_ard_3-2-1_53LLG_2023-10-03_final
2023-10-03 11:00:44	ga_s2bm_ard_3-2-1_53LKG_2023-10-03_final
2023-10-03 11:00:46	ga_s2bm_ard_3-2-1_52LHM_2023-10-03_final
2023-10-03 11:00:50	ga_s2bm_ard_3-2-1_52LGM_2023-10-03_final
2023-10-04 10:30:08	ga_s2bm_ard_3-2-1_54LXN_2023-10-04_final
2023-10-04 10:30:12	ga_s2bm_ard_3-2-1_54LWN_2023-10-04_final
2023-10-04 10:30:16	ga_s2bm_ard_3-2-1_54LVN_2023-10-04_nrt
2023-10-04 10:30:19	ga_s2bm_ard_3-2-1_54LXM_2023-10-04_final
2023-10-04 10:30:19	ga_s2bm_ard_3-2-1_54LUN_2023-10-04_nrt
2023-10-04 10:30:27	ga_s2bm_ard_3-2-1_54LWM_2023-10-04_final
2023-10-04 10:30:34	ga_s2bm_ard_3-2-1_54LUM_2023-10-04_nrt
2023-10-05 09:59:58	ga_s2bm_ard_3-2-1_55LGH_2023-10-05_nrt
2023-10-05 10:00:02	ga_s2bm_ard_3-2-1_55LFH_2023-10-05_nrt
2023-10-05 10:00:05	ga_s2bm_ard_3-2-1_55LEH_2023-10-05_nrt
2023-10-05 10:00:09	ga_s2bm_ard_3-2-1_55LDH_2023-10-05_nrt
2023-10-05 10:00:13	ga_s2bm_ard_3-2-1_55LGG_2023-10-05_nrt
2023-10-05 10:00:16	ga_s2bm_ard_3-2-1_55LFG_2023-10-05_nrt
2023-10-05 10:00:19	ga_s2bm_ard_3-2-1_55LEG_2023-10-05_nrt
2023-10-06 11:10:25	ga_s2bm_ard_3-2-1_52LHN_2023-10-06_final
2023-10-06 11:10:29	ga_s2bm_ard_3-2-1_52LGN_2023-10-06_final
2023-10-06 11:10:32	ga_s2bm_ard_3-2-1_52LFN_2023-10-06_final
2023-10-06 11:10:35	ga_s2bm_ard_3-2-1_52LEN_2023-10-06_nrt
2023-10-06 11:10:37	ga_s2bm_ard_3-2-1_52LHM_2023-10-06_final
2023-10-08 11:50:45	ga_s2bm_ard_3-2-1_51LUH_2023-10-08_nrt
2023-10-08 11:50:48	ga_s2bm_ard_3-2-1_51LTH_2023-10-08_nrt
2023-10-08 11:50:50	ga_s2bm_ard_3-2-1_50LRN_2023-10-08_nrt
2023-10-08 11:50:53	ga_s2bm_ard_3-2-1_50LQN_2023-10-08_nrt
2023-10-08 11:50:59	ga_s2bm_ard_3-2-1_51LUG_2023-10-08_nrt
2023-10-08 11:51:03	ga_s2bm_ard_3-2-1_51LTG_2023-10-08_nrt
2023-10-08 11:51:04	ga_s2bm_ard_3-2-1_50LRM_2023-10-08_nrt
2023-10-08 11:51:08	ga_s2bm_ard_3-2-1_50LQM_2023-10-08_nrt
2023-10-20 10:50:18	ga_s2bm_ard_3-2-1_53LQH_2023-10-20_nrt
2023-10-20 10:50:22	ga_s2bm_ard_3-2-1_53LPH_2023-10-20_final
2023-10-20 10:50:26	ga_s2bm_ard_3-2-1_53LNH_2023-10-20_final
2023-10-20 10:50:29	ga_s2bm_ard_3-2-1_53LMH_2023-10-20_final
2023-10-20 10:50:30	ga_s2bm_ard_3-2-1_53LQG_2023-10-20_final
2023-10-20 10:50:37	ga_s2bm_ard_3-2-1_53LPG_2023-10-20_final
2023-10-20 10:50:40	ga_s2bm_ard_3-2-1_53LNG_2023-10-20_final
2023-10-21 10:20:07	ga_s2bm_ard_3-2-1_55LBH_2023-10-21_nrt
2023-10-21 10:20:08	ga_s2bm_ard_3-2-1_54LZN_2023-10-21_final
2023-10-21 10:20:12	ga_s2bm_ard_3-2-1_54LYN_2023-10-21_final
2023-10-21 10:20:15	ga_s2bm_ard_3-2-1_54LXN_2023-10-21_final
2023-10-21 10:20:20	ga_s2bm_ard_3-2-1_54LWN_2023-10-21_final
2023-10-21 10:20:20	ga_s2bm_ard_3-2-1_55LBG_2023-10-21_nrt
2023-10-21 10:20:23	ga_s2bm_ard_3-2-1_54LZM_2023-10-21_final
2023-10-21 10:20:26	ga_s2bm_ard_3-2-1_54LYM_2023-10-21_final
2023-10-21 10:20:30	ga_s2bm_ard_3-2-1_54LXM_2023-10-21_final
2023-10-21 10:20:33	ga_s2bm_ard_3-2-1_54LWM_2023-10-21_final
2023-10-24 10:30:08	ga_s2bm_ard_3-2-1_54LXN_2023-10-24_final
2023-10-24 10:30:13	ga_s2bm_ard_3-2-1_54LWN_2023-10-24_final
2023-10-24 10:30:16	ga_s2bm_ard_3-2-1_54LVN_2023-10-24_nrt
2023-10-24 10:30:19	ga_s2bm_ard_3-2-1_54LXM_2023-10-24_final
2023-10-24 10:30:20	ga_s2bm_ard_3-2-1_54LUN_2023-10-24_nrt
2023-10-24 10:30:27	ga_s2bm_ard_3-2-1_54LWM_2023-10-24_final
2023-10-24 10:30:34	ga_s2bm_ard_3-2-1_54LUM_2023-10-24_nrt
2023-10-25 09:59:59	ga_s2bm_ard_3-2-1_55LGH_2023-10-25_nrt
2023-10-25 10:00:03	ga_s2bm_ard_3-2-1_55LFH_2023-10-25_nrt
2023-10-25 10:00:06	ga_s2bm_ard_3-2-1_55LEH_2023-10-25_nrt
2023-10-25 10:00:10	ga_s2bm_ard_3-2-1_55LDH_2023-10-25_nrt
2023-10-25 10:00:13	ga_s2bm_ard_3-2-1_55LGG_2023-10-25_nrt
2023-10-25 10:00:17	ga_s2bm_ard_3-2-1_55LFG_2023-10-25_nrt
2023-10-25 10:00:20	ga_s2bm_ard_3-2-1_55LEG_2023-10-25_nrt
2023-10-25 11:40:40	ga_s2bm_ard_3-2-1_51LXH_2023-10-25_nrt
2023-10-25 11:40:44	ga_s2bm_ard_3-2-1_51LWH_2023-10-25_nrt
2023-10-25 11:40:47	ga_s2bm_ard_3-2-1_51LVH_2023-10-25_nrt
2023-10-25 11:40:50	ga_s2bm_ard_3-2-1_51LUH_2023-10-25_nrt
2023-10-25 11:40:55	ga_s2bm_ard_3-2-1_51LXG_2023-10-25_nrt
2023-10-25 11:40:58	ga_s2bm_ard_3-2-1_51LWG_2023-10-25_final
2023-10-25 11:41:04	ga_s2bm_ard_3-2-1_51LUG_2023-10-25_nrt
2023-10-26 09:29:55	ga_s2bm_ard_3-2-1_56LPN_2023-10-25_nrt
2023-10-26 09:30:05	ga_s2bm_ard_3-2-1_56LPM_2023-10-26_nrt
2023-10-26 11:10:26	ga_s2bm_ard_3-2-1_52LHN_2023-10-26_final
2023-10-26 11:10:30	ga_s2bm_ard_3-2-1_52LGN_2023-10-26_final
2023-10-26 11:10:33	ga_s2bm_ard_3-2-1_52LFN_2023-10-26_final
2023-10-26 11:10:36	ga_s2bm_ard_3-2-1_52LEN_2023-10-26_nrt
2023-10-26 11:10:38	ga_s2bm_ard_3-2-1_52LHM_2023-10-26_final
2023-10-26 11:10:44	ga_s2bm_ard_3-2-1_52LGM_2023-10-26_final
2023-10-26 11:10:48	ga_s2bm_ard_3-2-1_52LFM_2023-10-26_final

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