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
2023-11-23 12:14:08	ga_s2bm_ard_3-2-1_49KGQ_2023-11-23_final
2023-11-23 12:14:11	ga_s2bm_ard_3-2-1_49KFQ_2023-11-23_nrt
2023-11-23 12:14:14	ga_s2bm_ard_3-2-1_50KKU_2023-11-23_final
2023-11-23 12:14:16	ga_s2bm_ard_3-2-1_49KHP_2023-11-23_final
2023-11-23 12:14:22	ga_s2bm_ard_3-2-1_49KGP_2023-11-23_final
2023-11-23 12:14:26	ga_s2bm_ard_3-2-1_49KFP_2023-11-23_nrt
2023-11-23 12:14:26	ga_s2bm_ard_3-2-1_49JHN_2023-11-23_final
2023-11-23 12:14:36	ga_s2bm_ard_3-2-1_49JGN_2023-11-23_final
2023-11-23 12:14:40	ga_s2bm_ard_3-2-1_49JFN_2023-11-23_final
2023-11-23 12:14:51	ga_s2bm_ard_3-2-1_49JGM_2023-11-23_final
2023-11-23 12:14:55	ga_s2bm_ard_3-2-1_49JFM_2023-11-23_final
2023-11-23 12:15:06	ga_s2bm_ard_3-2-1_49JGL_2023-11-23_final
2023-11-23 12:15:09	ga_s2bm_ard_3-2-1_49JFL_2023-11-23_nrt
2023-11-23 12:15:14	ga_s2bm_ard_3-2-1_49JGK_2023-11-23_final
2023-11-23 12:15:15	ga_s2bm_ard_3-2-1_49JFK_2023-11-23_nrt
2023-11-24 09:59:33	ga_s2bm_ard_3-2-1_55LHJ_2023-11-24_nrt
2023-11-24 09:59:38	ga_s2bm_ard_3-2-1_55LGJ_2023-11-24_nrt
2023-11-24 09:59:42	ga_s2bm_ard_3-2-1_55LFJ_2023-11-24_nrt
2023-11-24 09:59:43	ga_s2bm_ard_3-2-1_55LHH_2023-11-24_nrt
2023-11-24 09:59:45	ga_s2bm_ard_3-2-1_55LEJ_2023-11-24_nrt
2023-11-24 09:59:53	ga_s2bm_ard_3-2-1_55LGH_2023-11-24_nrt
2023-11-24 09:59:57	ga_s2bm_ard_3-2-1_55LFH_2023-11-24_nrt
2023-11-24 10:00:00	ga_s2bm_ard_3-2-1_55LEH_2023-11-24_nrt
2023-11-24 10:00:04	ga_s2bm_ard_3-2-1_55LDH_2023-11-24_nrt
2023-11-24 10:00:07	ga_s2bm_ard_3-2-1_55LGG_2023-11-24_nrt
2023-11-24 10:00:11	ga_s2bm_ard_3-2-1_55LFG_2023-11-24_nrt
2023-11-24 10:00:14	ga_s2bm_ard_3-2-1_55LEG_2023-11-24_nrt
2023-11-24 10:00:17	ga_s2bm_ard_3-2-1_55LDG_2023-11-24_nrt
2023-11-24 10:07:40	ga_s2bm_ard_3-2-1_54HUC_2023-11-24_nrt
2023-11-24 11:40:56	ga_s2bm_ard_3-2-1_51LVG_2023-11-24_final
2023-11-24 11:40:59	ga_s2bm_ard_3-2-1_51LUG_2023-11-24_nrt
2023-11-24 11:41:02	ga_s2bm_ard_3-2-1_51LXF_2023-11-24_nrt
2023-11-24 11:41:07	ga_s2bm_ard_3-2-1_51LWF_2023-11-24_nrt
2023-11-24 11:41:10	ga_s2bm_ard_3-2-1_51LVF_2023-11-24_final
2023-11-24 11:41:12	ga_s2bm_ard_3-2-1_51LXE_2023-11-24_nrt
2023-11-24 11:41:13	ga_s2bm_ard_3-2-1_51LUF_2023-11-24_final
2023-11-24 11:41:21	ga_s2bm_ard_3-2-1_51LWE_2023-11-24_final
2023-11-24 11:41:25	ga_s2bm_ard_3-2-1_51LVE_2023-11-24_final
2023-11-24 11:41:28	ga_s2bm_ard_3-2-1_51LUE_2023-11-24_final
2023-11-24 11:41:32	ga_s2bm_ard_3-2-1_51LTE_2023-11-24_nrt
2023-11-24 11:41:35	ga_s2bm_ard_3-2-1_51LWD_2023-11-24_nrt
2023-11-24 11:41:39	ga_s2bm_ard_3-2-1_51LVD_2023-11-24_nrt
2023-11-24 11:41:42	ga_s2bm_ard_3-2-1_51LUD_2023-11-24_final
2023-11-24 11:41:45	ga_s2bm_ard_3-2-1_51LTD_2023-11-24_nrt
2023-11-24 11:41:50	ga_s2bm_ard_3-2-1_51LWC_2023-11-24_final
2023-11-24 11:41:54	ga_s2bm_ard_3-2-1_51LVC_2023-11-24_final
2023-11-24 11:41:57	ga_s2bm_ard_3-2-1_51LUC_2023-11-24_final
2023-11-24 11:42:00	ga_s2bm_ard_3-2-1_51LTC_2023-11-24_nrt
2023-11-24 11:42:03	ga_s2bm_ard_3-2-1_50LRH_2023-11-24_nrt
2023-11-24 11:42:04	ga_s2bm_ard_3-2-1_51KWB_2023-11-24_final
2023-11-24 11:42:08	ga_s2bm_ard_3-2-1_51KVB_2023-11-24_final
2023-11-24 11:42:11	ga_s2bm_ard_3-2-1_51KUB_2023-11-24_final
2023-11-24 11:42:15	ga_s2bm_ard_3-2-1_51KTB_2023-11-24_nrt
2023-11-24 11:42:15	ga_s2bm_ard_3-2-1_50KRG_2023-11-24_nrt
2023-11-24 11:42:17	ga_s2bm_ard_3-2-1_51KWA_2023-11-24_final
2023-11-24 11:42:22	ga_s2bm_ard_3-2-1_51KVA_2023-11-24_final
2023-11-24 11:42:26	ga_s2bm_ard_3-2-1_51KUA_2023-11-24_final
2023-11-24 11:42:29	ga_s2bm_ard_3-2-1_51KTA_2023-11-24_nrt
2023-11-24 11:42:30	ga_s2bm_ard_3-2-1_50KRF_2023-11-24_nrt
2023-11-24 11:42:37	ga_s2bm_ard_3-2-1_51KVV_2023-11-24_final
2023-11-24 11:42:41	ga_s2bm_ard_3-2-1_51KUV_2023-11-24_final
2023-11-24 11:42:44	ga_s2bm_ard_3-2-1_51KTV_2023-11-24_nrt
2023-11-24 11:42:45	ga_s2bm_ard_3-2-1_50KRE_2023-11-24_nrt
2023-11-24 11:45:24	ga_s2bm_ard_3-2-1_51JTJ_2023-11-24_final
2023-11-24 11:45:27	ga_s2bm_ard_3-2-1_50JQP_2023-11-24_final
2023-11-24 11:45:31	ga_s2bm_ard_3-2-1_50JPP_2023-11-24_final
2023-11-24 11:45:34	ga_s2bm_ard_3-2-1_50JNP_2023-11-24_final
2023-11-24 11:45:38	ga_s2bm_ard_3-2-1_51JTH_2023-11-24_final
2023-11-24 11:45:41	ga_s2bm_ard_3-2-1_50JQN_2023-11-24_final
2023-11-24 11:45:45	ga_s2bm_ard_3-2-1_50JPN_2023-11-24_final
2023-11-24 11:45:49	ga_s2bm_ard_3-2-1_50JNN_2023-11-24_final
2023-11-24 11:45:52	ga_s2bm_ard_3-2-1_51JTG_2023-11-24_final
2023-11-24 11:45:56	ga_s2bm_ard_3-2-1_50JQM_2023-11-24_final
2023-11-24 11:46:00	ga_s2bm_ard_3-2-1_50JPM_2023-11-24_final
2023-11-24 11:46:03	ga_s2bm_ard_3-2-1_51JTF_2023-11-24_final
2023-11-24 11:46:03	ga_s2bm_ard_3-2-1_50JNM_2023-11-24_final
2023-11-24 11:46:08	ga_s2bm_ard_3-2-1_50JMM_2023-11-24_final
2023-11-24 11:46:10	ga_s2bm_ard_3-2-1_50JQL_2023-11-24_final
2023-11-24 11:46:14	ga_s2bm_ard_3-2-1_50JPL_2023-11-24_final
2023-11-24 11:46:18	ga_s2bm_ard_3-2-1_50JNL_2023-11-24_final
2023-11-24 11:46:21	ga_s2bm_ard_3-2-1_50JML_2023-11-24_final
2023-11-24 11:46:25	ga_s2bm_ard_3-2-1_50HQK_2023-11-24_final
2023-11-24 11:46:29	ga_s2bm_ard_3-2-1_50HPK_2023-11-24_final
2023-11-24 11:46:32	ga_s2bm_ard_3-2-1_50HNK_2023-11-24_final
2023-11-24 11:46:36	ga_s2bm_ard_3-2-1_50HMK_2023-11-24_final
2023-11-24 11:46:38	ga_s2bm_ard_3-2-1_50HQJ_2023-11-24_final
2023-11-24 11:46:43	ga_s2bm_ard_3-2-1_50HPJ_2023-11-24_final
2023-11-24 11:46:47	ga_s2bm_ard_3-2-1_50HNJ_2023-11-24_final
2023-11-24 11:46:48	ga_s2bm_ard_3-2-1_50HQH_2023-11-24_final
2023-11-24 11:46:50	ga_s2bm_ard_3-2-1_50HMJ_2023-11-24_final
2023-11-24 11:46:57	ga_s2bm_ard_3-2-1_50HPH_2023-11-24_final
2023-11-24 11:47:01	ga_s2bm_ard_3-2-1_50HNH_2023-11-24_final
2023-11-24 11:47:05	ga_s2bm_ard_3-2-1_50HMH_2023-11-24_final
2023-11-24 11:47:09	ga_s2bm_ard_3-2-1_50HLH_2023-11-24_final
2023-11-24 11:47:12	ga_s2bm_ard_3-2-1_50HPG_2023-11-24_final
2023-11-24 11:47:16	ga_s2bm_ard_3-2-1_50HNG_2023-11-24_final
2023-11-24 11:47:19	ga_s2bm_ard_3-2-1_50HMG_2023-11-24_final
2023-11-24 11:47:21	ga_s2bm_ard_3-2-1_50HLG_2023-11-24_final
2023-11-24 11:47:23	ga_s2bm_ard_3-2-1_50HPF_2023-11-24_nrt
2023-11-24 11:47:25	ga_s2bm_ard_3-2-1_50HNF_2023-11-24_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