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
2023-10-04 10:29:40	ga_s2bm_ard_3-2-1_54LXQ_2023-10-04_final
2023-10-04 10:29:43	ga_s2bm_ard_3-2-1_54LWQ_2023-10-04_final
2023-10-04 10:30:59	ga_s2bm_ard_3-2-1_54LVK_2023-10-04_final
2023-10-04 10:31:07	ga_s2bm_ard_3-2-1_54LWJ_2023-10-04_final
2023-10-04 10:31:08	ga_s2bm_ard_3-2-1_54LVJ_2023-10-04_final
2023-10-04 10:31:10	ga_s2bm_ard_3-2-1_54LUJ_2023-10-04_final
2023-10-04 10:31:13	ga_s2bm_ard_3-2-1_54LWJ_2023-10-04_final
2023-10-04 10:31:15	ga_s2bm_ard_3-2-1_54LVJ_2023-10-04_final
2023-10-04 10:31:17	ga_s2bm_ard_3-2-1_54LUJ_2023-10-04_final
2023-10-04 10:31:32	ga_s2bm_ard_3-2-1_54LUH_2023-10-04_final
2023-10-04 10:31:43	ga_s2bm_ard_3-2-1_54KVG_2023-10-04_final
2023-10-04 10:31:49	ga_s2bm_ard_3-2-1_54KTG_2023-10-04_final
2023-10-04 10:31:57	ga_s2bm_ard_3-2-1_54KVF_2023-10-04_final
2023-10-04 10:32:01	ga_s2bm_ard_3-2-1_54KUF_2023-10-04_final
2023-10-04 10:32:05	ga_s2bm_ard_3-2-1_53KRA_2023-10-04_final
2023-10-04 10:32:11	ga_s2bm_ard_3-2-1_53KQA_2023-10-04_final
2023-10-04 10:32:15	ga_s2bm_ard_3-2-1_54KUE_2023-10-04_final
2023-10-04 10:32:19	ga_s2bm_ard_3-2-1_54KTE_2023-10-04_final
2023-10-04 10:32:35	ga_s2bm_ard_3-2-1_53KQU_2023-10-04_final
2023-10-04 10:32:40	ga_s2bm_ard_3-2-1_53KRT_2023-10-04_final
2023-10-04 10:32:42	ga_s2bm_ard_3-2-1_53KQU_2023-10-04_final
2023-10-04 10:32:48	ga_s2bm_ard_3-2-1_53KRT_2023-10-04_final
2023-10-04 10:33:03	ga_s2bm_ard_3-2-1_53KRS_2023-10-04_final
2023-10-04 10:33:06	ga_s2bm_ard_3-2-1_53KQS_2023-10-04_final
2023-10-04 10:33:17	ga_s2bm_ard_3-2-1_54KTA_2023-10-04_final
2023-10-04 10:33:32	ga_s2bm_ard_3-2-1_53KRQ_2023-10-04_final
2023-10-04 10:33:35	ga_s2bm_ard_3-2-1_53KQQ_2023-10-04_final
2023-10-04 10:33:38	ga_s2bm_ard_3-2-1_53KPQ_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 10:33:53	ga_s2bm_ard_3-2-1_53KPP_2023-10-04_final
2023-10-04 10:34:04	ga_s2bm_ard_3-2-1_53JQN_2023-10-04_final
2023-10-04 10:34:08	ga_s2bm_ard_3-2-1_53JPN_2023-10-04_final
2023-10-04 10:34:15	ga_s2bm_ard_3-2-1_53JRM_2023-10-04_final
2023-10-04 10:34:15	ga_s2bm_ard_3-2-1_54JTS_2023-10-04_final
2023-10-04 10:34:22	ga_s2bm_ard_3-2-1_53JPM_2023-10-04_final
2023-10-04 10:34:26	ga_s2bm_ard_3-2-1_53JNM_2023-10-04_final
2023-10-04 10:34:30	ga_s2bm_ard_3-2-1_54JTR_2023-10-04_final
2023-10-04 10:34:37	ga_s2bm_ard_3-2-1_53JPL_2023-10-04_final
2023-10-04 10:34:44	ga_s2bm_ard_3-2-1_54JTQ_2023-10-04_final
2023-10-04 10:34:47	ga_s2bm_ard_3-2-1_53JQK_2023-10-04_final
2023-10-04 10:34:51	ga_s2bm_ard_3-2-1_53JPK_2023-10-04_final
2023-10-04 10:35:02	ga_s2bm_ard_3-2-1_53JQJ_2023-10-04_final
2023-10-04 10:35:06	ga_s2bm_ard_3-2-1_53JPJ_2023-10-04_final
2023-10-04 10:35:16	ga_s2bm_ard_3-2-1_53JQH_2023-10-04_final
2023-10-04 10:35:24	ga_s2bm_ard_3-2-1_53JNH_2023-10-04_final
2023-10-04 10:35:27	ga_s2bm_ard_3-2-1_53JMH_2023-10-04_final
2023-10-04 10:35:38	ga_s2bm_ard_3-2-1_53JNG_2023-10-04_final
2023-10-04 10:35:49	ga_s2bm_ard_3-2-1_53JPF_2023-10-04_final
2023-10-04 10:35:53	ga_s2bm_ard_3-2-1_53JNF_2023-10-04_final
2023-10-04 10:35:56	ga_s2bm_ard_3-2-1_53JMF_2023-10-04_final
2023-10-04 10:36:07	ga_s2bm_ard_3-2-1_53HNE_2023-10-04_final
2023-10-04 10:36:11	ga_s2bm_ard_3-2-1_53HME_2023-10-04_final
2023-10-04 10:36:17	ga_s2bm_ard_3-2-1_53HLE_2023-10-04_final
2023-10-04 10:36:22	ga_s2bm_ard_3-2-1_53HND_2023-10-04_final
2023-10-04 10:36:25	ga_s2bm_ard_3-2-1_53HMD_2023-10-04_final
2023-10-04 10:36:29	ga_s2bm_ard_3-2-1_53HLD_2023-10-04_final
2023-10-04 10:36:32	ga_s2bm_ard_3-2-1_53HPC_2023-10-04_final
2023-10-04 10:36:59	ga_s2bm_ard_3-2-1_53HPA_2023-10-04_final
2023-10-04 12:13:40	ga_s2bm_ard_3-2-1_50KKB_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 12:13:59	ga_s2bm_ard_3-2-1_49KGR_2023-10-04_final
2023-10-04 12:14:09	ga_s2bm_ard_3-2-1_49KHQ_2023-10-04_final
2023-10-04 12:14:13	ga_s2bm_ard_3-2-1_49KGQ_2023-10-04_final
2023-10-04 12:14:27	ga_s2bm_ard_3-2-1_49KGP_2023-10-04_final
2023-10-04 12:14:42	ga_s2bm_ard_3-2-1_49JGN_2023-10-04_final
2023-10-04 12:15:11	ga_s2bm_ard_3-2-1_49JGL_2023-10-04_final
2023-10-04 12:15:20	ga_s2bm_ard_3-2-1_49JGK_2023-10-04_final
2023-10-05 10:03:43	ga_s2bm_ard_3-2-1_54KYU_2023-10-05_final
2023-10-05 11:42:21	ga_s2bm_ard_3-2-1_51KWA_2023-10-05_final
2023-10-05 11:44:10	ga_s2bm_ard_3-2-1_50KPV_2023-10-05_final
2023-10-05 11:44:13	ga_s2bm_ard_3-2-1_51KUP_2023-10-05_final
2023-10-05 11:44:38	ga_s2bm_ard_3-2-1_50JPT_2023-10-05_final
2023-10-05 11:44:51	ga_s2bm_ard_3-2-1_51JUL_2023-10-05_final
2023-10-05 11:45:11	ga_s2bm_ard_3-2-1_50JNR_2023-10-05_final
2023-10-05 11:46:30	ga_s2bm_ard_3-2-1_50HQK_2023-10-05_final
2023-10-05 11:47:14	ga_s2bm_ard_3-2-1_50HLH_2023-10-05_final
2023-10-14 10:31:04	ga_s2bm_ard_3-2-1_54LUK_2023-10-14_nrt
2023-10-14 10:31:14	ga_s2bm_ard_3-2-1_53LRE_2023-10-14_nrt
2023-10-14 12:10:30	ga_s2bm_ard_3-2-1_50LNP_2023-10-14_nrt
2023-10-14 12:10:31	ga_s2bm_ard_3-2-1_50LMP_2023-10-14_nrt
2023-10-14 12:13:25	ga_s2bm_ard_3-2-1_49KGU_2023-10-14_nrt
2023-10-14 12:13:30	ga_s2bm_ard_3-2-1_50KKC_2023-10-14_nrt
2023-10-14 12:13:32	ga_s2bm_ard_3-2-1_49KGT_2023-10-14_nrt
2023-10-14 12:13:46	ga_s2bm_ard_3-2-1_49KGS_2023-10-14_nrt
2023-10-14 12:15:16	ga_s2bm_ard_3-2-1_49JFL_2023-10-14_nrt
2023-10-14 12:15:22	ga_s2bm_ard_3-2-1_49JFK_2023-10-14_nrt
2023-10-15 09:59:49	ga_s2bm_ard_3-2-1_55LFJ_2023-10-15_nrt
2023-10-15 10:00:18	ga_s2bm_ard_3-2-1_55LFG_2023-10-15_nrt
2023-10-15 10:00:33	ga_s2bm_ard_3-2-1_55LFF_2023-10-15_nrt
2023-10-15 10:00:47	ga_s2bm_ard_3-2-1_55LFE_2023-10-15_nrt
2023-10-15 10:00:54	ga_s2bm_ard_3-2-1_55LGD_2023-10-15_nrt
2023-10-15 10:01:02	ga_s2bm_ard_3-2-1_55LFD_2023-10-15_nrt
2023-10-15 10:07:51	ga_s2bm_ard_3-2-1_54HXB_2023-10-15_nrt
2023-10-15 10:07:53	ga_s2bm_ard_3-2-1_54HWB_2023-10-15_nrt
2023-10-15 10:07:54	ga_s2bm_ard_3-2-1_54HVB_2023-10-15_nrt
2023-10-15 11:42:22	ga_s2bm_ard_3-2-1_51KTB_2023-10-15_nrt
2023-10-15 11:42:51	ga_s2bm_ard_3-2-1_51KTV_2023-10-15_nrt
2023-10-15 11:42:52	ga_s2bm_ard_3-2-1_50KRE_2023-10-15_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