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
2023-09-15 10:01:14	ga_s2bm_ard_3-2-1_55LCD_2023-09-15_final
2023-09-15 10:01:17	ga_s2bm_ard_3-2-1_55LFC_2023-09-15_final
2023-09-15 10:01:20	ga_s2bm_ard_3-2-1_55LEC_2023-09-15_final
2023-09-15 10:01:27	ga_s2bm_ard_3-2-1_55LCC_2023-09-15_final
2023-09-15 10:01:35	ga_s2bm_ard_3-2-1_55KEB_2023-09-15_final
2023-09-15 10:01:38	ga_s2bm_ard_3-2-1_55KDB_2023-09-15_final
2023-09-15 10:01:45	ga_s2bm_ard_3-2-1_55KFA_2023-09-15_final
2023-09-15 10:01:53	ga_s2bm_ard_3-2-1_55KDA_2023-09-15_final
2023-09-15 10:02:04	ga_s2bm_ard_3-2-1_55KEV_2023-09-15_final
2023-09-15 10:02:11	ga_s2bm_ard_3-2-1_55KCV_2023-09-15_final
2023-09-15 10:02:25	ga_s2bm_ard_3-2-1_55KCU_2023-09-15_final
2023-09-15 10:02:33	ga_s2bm_ard_3-2-1_55KET_2023-09-15_final
2023-09-15 10:02:45	ga_s2bm_ard_3-2-1_55KBT_2023-09-15_final
2023-09-15 10:02:57	ga_s2bm_ard_3-2-1_55KBS_2023-09-15_final
2023-09-15 10:03:09	ga_s2bm_ard_3-2-1_55KCR_2023-09-15_final
2023-09-15 10:03:12	ga_s2bm_ard_3-2-1_55KEQ_2023-09-15_final
2023-09-15 10:03:23	ga_s2bm_ard_3-2-1_55KCQ_2023-09-15_final
2023-09-15 10:03:34	ga_s2bm_ard_3-2-1_55KDP_2023-09-15_final
2023-09-15 10:03:38	ga_s2bm_ard_3-2-1_55KCP_2023-09-15_final
2023-09-15 10:03:52	ga_s2bm_ard_3-2-1_55JCN_2023-09-15_final
2023-09-15 10:04:03	ga_s2bm_ard_3-2-1_55JDM_2023-09-15_final
2023-09-15 10:04:07	ga_s2bm_ard_3-2-1_55JCM_2023-09-15_final
2023-09-15 10:04:36	ga_s2bm_ard_3-2-1_55JCK_2023-09-15_final
2023-09-15 10:04:42	ga_s2bm_ard_3-2-1_54JYQ_2023-09-15_final
2023-09-15 10:04:57	ga_s2bm_ard_3-2-1_54JYP_2023-09-15_final
2023-09-15 10:05:40	ga_s2bm_ard_3-2-1_54JYL_2023-09-15_final
2023-09-15 10:05:48	ga_s2bm_ard_3-2-1_54JWL_2023-09-15_final
2023-09-15 10:05:52	ga_s2bm_ard_3-2-1_55HBE_2023-09-15_final
2023-09-15 10:06:02	ga_s2bm_ard_3-2-1_54HWK_2023-09-15_final
2023-09-15 10:06:31	ga_s2bm_ard_3-2-1_54HWH_2023-09-15_final
2023-09-15 10:07:03	ga_s2bm_ard_3-2-1_54HVF_2023-09-15_final
2023-09-15 10:07:11	ga_s2bm_ard_3-2-1_54HXE_2023-09-15_final
2023-09-15 10:07:37	ga_s2bm_ard_3-2-1_54HUD_2023-09-15_final
2023-09-15 11:41:00	ga_s2bm_ard_3-2-1_51LWG_2023-09-15_final
2023-09-15 11:41:03	ga_s2bm_ard_3-2-1_51LVG_2023-09-15_final
2023-09-15 11:41:18	ga_s2bm_ard_3-2-1_51LVF_2023-09-15_final
2023-09-15 11:41:29	ga_s2bm_ard_3-2-1_51LWE_2023-09-15_final
2023-09-15 11:41:33	ga_s2bm_ard_3-2-1_51LVE_2023-09-15_final
2023-09-15 11:41:36	ga_s2bm_ard_3-2-1_51LUE_2023-09-15_final
2023-09-15 11:41:50	ga_s2bm_ard_3-2-1_51LUD_2023-09-15_final
2023-09-15 11:42:12	ga_s2bm_ard_3-2-1_51KWB_2023-09-15_final
2023-09-15 11:42:19	ga_s2bm_ard_3-2-1_51KUB_2023-09-15_final
2023-09-15 11:42:24	ga_s2bm_ard_3-2-1_51KWA_2023-09-15_final
2023-09-15 11:42:34	ga_s2bm_ard_3-2-1_51KUA_2023-09-15_final
2023-09-15 11:43:07	ga_s2bm_ard_3-2-1_50KRD_2023-09-15_final
2023-09-15 11:43:14	ga_s2bm_ard_3-2-1_51KVT_2023-09-15_final
2023-09-15 11:43:21	ga_s2bm_ard_3-2-1_50KRC_2023-09-15_final
2023-09-15 11:43:50	ga_s2bm_ard_3-2-1_50KRA_2023-09-15_final
2023-09-15 11:44:05	ga_s2bm_ard_3-2-1_50KRV_2023-09-15_final
2023-09-15 11:44:43	ga_s2bm_ard_3-2-1_51JUM_2023-09-15_final
2023-09-15 11:44:48	ga_s2bm_ard_3-2-1_51JTM_2023-09-15_final
2023-09-15 11:45:02	ga_s2bm_ard_3-2-1_51JTL_2023-09-15_final
2023-09-15 11:45:06	ga_s2bm_ard_3-2-1_50JQR_2023-09-15_final
2023-09-15 11:45:32	ga_s2bm_ard_3-2-1_51JTJ_2023-09-15_final
2023-09-15 11:45:39	ga_s2bm_ard_3-2-1_50JPP_2023-09-15_final
2023-09-15 11:45:49	ga_s2bm_ard_3-2-1_50JQN_2023-09-15_final
2023-09-15 11:45:57	ga_s2bm_ard_3-2-1_50JNN_2023-09-15_final
2023-09-15 11:46:33	ga_s2bm_ard_3-2-1_50HQK_2023-09-15_final
2023-09-15 11:46:37	ga_s2bm_ard_3-2-1_50HPK_2023-09-15_final
2023-09-15 11:46:44	ga_s2bm_ard_3-2-1_50HMK_2023-09-15_final
2023-09-15 11:46:51	ga_s2bm_ard_3-2-1_50HPJ_2023-09-15_final
2023-09-15 11:47:13	ga_s2bm_ard_3-2-1_50HMH_2023-09-15_final
2023-09-15 11:47:27	ga_s2bm_ard_3-2-1_50HMG_2023-09-15_final
2023-09-15 11:47:29	ga_s2bm_ard_3-2-1_50HLG_2023-09-15_final
2023-09-25 10:00:05	ga_s2bm_ard_3-2-1_55LFH_2023-09-25_nrt
2023-09-25 11:40:32	ga_s2bm_ard_3-2-1_51LWJ_2023-09-25_nrt
2023-09-25 11:40:43	ga_s2bm_ard_3-2-1_51LXH_2023-09-25_nrt
2023-09-25 11:40:46	ga_s2bm_ard_3-2-1_51LWH_2023-09-25_nrt
2023-09-25 11:40:49	ga_s2bm_ard_3-2-1_51LVH_2023-09-25_nrt
2023-09-25 11:41:15	ga_s2bm_ard_3-2-1_51LWF_2023-09-25_nrt
2023-09-25 11:41:20	ga_s2bm_ard_3-2-1_51LXE_2023-09-25_nrt
2023-09-25 11:41:47	ga_s2bm_ard_3-2-1_51LVD_2023-09-25_nrt
2023-09-25 11:41:54	ga_s2bm_ard_3-2-1_51LTD_2023-09-25_nrt
2023-09-25 11:42:08	ga_s2bm_ard_3-2-1_51LTC_2023-09-25_nrt
2023-09-25 11:42:23	ga_s2bm_ard_3-2-1_51KTB_2023-09-25_nrt
2023-09-25 11:42:24	ga_s2bm_ard_3-2-1_50KRG_2023-09-25_nrt
2023-09-25 11:42:38	ga_s2bm_ard_3-2-1_51KTA_2023-09-25_nrt
2023-09-25 11:47:32	ga_s2bm_ard_3-2-1_50HPF_2023-09-25_nrt
2023-09-25 11:47:33	ga_s2bm_ard_3-2-1_50HNF_2023-09-25_nrt
2023-09-26 09:33:33	ga_s2bm_ard_3-2-1_56JPT_2023-09-26_nrt
2023-09-26 09:33:43	ga_s2bm_ard_3-2-1_56JPS_2023-09-26_nrt
2023-09-26 09:36:54	ga_s2bm_ard_3-2-1_56HLE_2023-09-26_nrt
2023-09-26 09:36:58	ga_s2bm_ard_3-2-1_56HKE_2023-09-26_nrt
2023-09-26 09:37:07	ga_s2bm_ard_3-2-1_56HLD_2023-09-26_nrt
2023-09-26 09:37:13	ga_s2bm_ard_3-2-1_56HKD_2023-09-26_nrt
2023-09-26 09:37:27	ga_s2bm_ard_3-2-1_56HKC_2023-09-26_nrt
2023-09-26 09:37:28	ga_s2bm_ard_3-2-1_55HGT_2023-09-26_nrt
2023-09-26 09:37:42	ga_s2bm_ard_3-2-1_56HKB_2023-09-26_nrt
2023-09-26 09:37:42	ga_s2bm_ard_3-2-1_55HGS_2023-09-26_nrt
2023-09-26 09:37:57	ga_s2bm_ard_3-2-1_55GGR_2023-09-26_nrt
2023-09-26 09:38:11	ga_s2bm_ard_3-2-1_55GGQ_2023-09-26_nrt
2023-09-26 09:38:22	ga_s2bm_ard_3-2-1_55GGP_2023-09-26_nrt
2023-09-26 11:10:14	ga_s2bm_ard_3-2-1_52LHP_2023-09-26_nrt
2023-09-26 11:10:18	ga_s2bm_ard_3-2-1_52LGP_2023-09-26_nrt
2023-09-26 11:10:21	ga_s2bm_ard_3-2-1_52LFP_2023-09-26_nrt
2023-09-26 11:10:24	ga_s2bm_ard_3-2-1_52LEP_2023-09-26_nrt
2023-09-26 11:10:39	ga_s2bm_ard_3-2-1_52LEN_2023-09-26_nrt
2023-09-26 11:10:53	ga_s2bm_ard_3-2-1_52LEM_2023-09-26_nrt
2023-09-26 11:16:31	ga_s2bm_ard_3-2-1_52HCJ_2023-09-26_nrt
2023-09-26 11:16:38	ga_s2bm_ard_3-2-1_52HBJ_2023-09-26_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