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

96 results

Time (local)	Label
2023-07-30 10:10:04	ga_s2bm_ard_3-2-1_55LEH_2023-07-30_nrt
2023-07-30 10:10:23	ga_s2bm_ard_3-2-1_55LDG_2023-07-30_nrt
2023-07-30 10:10:30	ga_s2bm_ard_3-2-1_55LBG_2023-07-30_nrt
2023-07-30 11:50:29	ga_s2bm_ard_3-2-1_51LVJ_2023-07-30_nrt
2023-07-30 11:50:35	ga_s2bm_ard_3-2-1_51LUJ_2023-07-30_nrt
2023-07-30 11:50:39	ga_s2bm_ard_3-2-1_51LTJ_2023-07-30_nrt
2023-07-30 11:50:40	ga_s2bm_ard_3-2-1_50LRP_2023-07-30_nrt
2023-07-30 11:50:43	ga_s2bm_ard_3-2-1_50LQP_2023-07-30_nrt
2023-07-30 11:50:50	ga_s2bm_ard_3-2-1_51LUH_2023-07-30_nrt
2023-07-30 11:50:53	ga_s2bm_ard_3-2-1_51LTH_2023-07-30_nrt
2023-07-30 11:50:55	ga_s2bm_ard_3-2-1_50LRN_2023-07-30_nrt
2023-07-30 11:50:58	ga_s2bm_ard_3-2-1_50LQN_2023-07-30_nrt
2023-07-30 11:51:04	ga_s2bm_ard_3-2-1_51LUG_2023-07-30_nrt
2023-07-30 11:51:08	ga_s2bm_ard_3-2-1_51LTG_2023-07-30_nrt
2023-07-30 11:51:13	ga_s2bm_ard_3-2-1_50LQM_2023-07-30_nrt
2023-07-30 11:51:22	ga_s2bm_ard_3-2-1_51LTF_2023-07-30_nrt
2023-07-30 11:51:24	ga_s2bm_ard_3-2-1_50LRL_2023-07-30_nrt
2023-07-30 11:51:32	ga_s2bm_ard_3-2-1_50LPL_2023-07-30_nrt
2023-07-30 11:51:38	ga_s2bm_ard_3-2-1_50LRK_2023-07-30_nrt
2023-07-30 11:51:41	ga_s2bm_ard_3-2-1_50LQK_2023-07-30_nrt
2023-07-30 11:51:45	ga_s2bm_ard_3-2-1_50LPK_2023-07-30_nrt
2023-07-30 11:51:51	ga_s2bm_ard_3-2-1_51LTD_2023-07-30_nrt
2023-07-30 11:51:53	ga_s2bm_ard_3-2-1_50LRJ_2023-07-30_nrt
2023-07-30 11:51:56	ga_s2bm_ard_3-2-1_50LQJ_2023-07-30_nrt
2023-07-30 11:51:59	ga_s2bm_ard_3-2-1_50LPJ_2023-07-30_nrt
2023-07-30 11:52:07	ga_s2bm_ard_3-2-1_50LRH_2023-07-30_nrt
2023-07-30 11:52:11	ga_s2bm_ard_3-2-1_50LQH_2023-07-30_nrt
2023-07-30 11:52:20	ga_s2bm_ard_3-2-1_51KTB_2023-07-30_nrt
2023-07-30 11:52:21	ga_s2bm_ard_3-2-1_50KRG_2023-07-30_nrt
2023-07-30 11:52:35	ga_s2bm_ard_3-2-1_51KTA_2023-07-30_nrt
2023-07-30 11:52:36	ga_s2bm_ard_3-2-1_50KRF_2023-07-30_nrt
2023-07-30 11:52:46	ga_s2bm_ard_3-2-1_50KNF_2023-07-30_nrt
2023-07-30 11:52:48	ga_s2bm_ard_3-2-1_51KTV_2023-07-30_nrt
2023-07-30 11:52:50	ga_s2bm_ard_3-2-1_50KRE_2023-07-30_nrt
2023-07-30 11:52:57	ga_s2bm_ard_3-2-1_50KPE_2023-07-30_nrt
2023-07-30 11:53:00	ga_s2bm_ard_3-2-1_50KNE_2023-07-30_nrt
2023-07-30 11:53:15	ga_s2bm_ard_3-2-1_50KND_2023-07-30_nrt
2023-07-30 11:56:34	ga_s2bm_ard_3-2-1_50JKL_2023-07-30_nrt
2023-07-30 11:56:49	ga_s2bm_ard_3-2-1_50HKK_2023-07-30_nrt
2023-07-30 11:56:51	ga_s2bm_ard_3-2-1_49HGE_2023-07-30_nrt
2023-07-30 11:57:03	ga_s2bm_ard_3-2-1_50HKJ_2023-07-30_nrt
2023-07-30 11:57:05	ga_s2bm_ard_3-2-1_49HGD_2023-07-30_nrt
2023-07-30 11:57:18	ga_s2bm_ard_3-2-1_50HKH_2023-07-30_nrt
2023-07-30 11:57:20	ga_s2bm_ard_3-2-1_49HGC_2023-07-30_nrt
2023-07-30 11:57:28	ga_s2bm_ard_3-2-1_49HFC_2023-07-30_nrt
2023-07-30 11:57:29	ga_s2bm_ard_3-2-1_50HKG_2023-07-30_nrt
2023-07-30 11:57:30	ga_s2bm_ard_3-2-1_49HGB_2023-07-30_nrt
2023-07-30 11:57:32	ga_s2bm_ard_3-2-1_49HFB_2023-07-30_nrt
2023-07-30 11:57:32	ga_s2bm_ard_3-2-1_50HMF_2023-07-30_nrt
2023-07-31 09:39:40	ga_s2bm_ard_3-2-1_56LPP_2023-07-31_nrt
2023-07-31 09:39:44	ga_s2bm_ard_3-2-1_56LNP_2023-07-31_nrt
2023-07-31 09:39:47	ga_s2bm_ard_3-2-1_56LMP_2023-07-31_nrt
2023-07-31 09:39:54	ga_s2bm_ard_3-2-1_56LLP_2023-07-31_nrt
2023-07-31 09:39:55	ga_s2bm_ard_3-2-1_56LPN_2023-07-31_nrt
2023-07-31 09:39:59	ga_s2bm_ard_3-2-1_56LNN_2023-07-31_nrt
2023-07-31 09:40:02	ga_s2bm_ard_3-2-1_56LMN_2023-07-31_nrt
2023-07-31 09:40:05	ga_s2bm_ard_3-2-1_56LLN_2023-07-31_nrt
2023-07-31 09:40:08	ga_s2bm_ard_3-2-1_56LPM_2023-07-31_nrt
2023-07-31 09:40:10	ga_s2bm_ard_3-2-1_56LNM_2023-07-31_nrt
2023-07-31 09:40:12	ga_s2bm_ard_3-2-1_56LMM_2023-07-31_nrt
2023-07-31 09:40:13	ga_s2bm_ard_3-2-1_56LLM_2023-07-31_nrt
2023-07-31 09:40:15	ga_s2bm_ard_3-2-1_56LPL_2023-07-31_nrt
2023-07-31 09:41:20	ga_s2bm_ard_3-2-1_56LNH_2023-07-31_nrt
2023-07-31 09:41:21	ga_s2bm_ard_3-2-1_56LMH_2023-07-31_nrt
2023-07-31 09:41:22	ga_s2bm_ard_3-2-1_56LLH_2023-07-31_nrt
2023-07-31 09:41:24	ga_s2bm_ard_3-2-1_56LKH_2023-07-31_nrt
2023-07-31 09:41:26	ga_s2bm_ard_3-2-1_56KNG_2023-07-31_nrt
2023-07-31 09:41:39	ga_s2bm_ard_3-2-1_56KNF_2023-07-31_nrt
2023-07-31 09:41:50	ga_s2bm_ard_3-2-1_55KHA_2023-07-31_nrt
2023-07-31 09:41:52	ga_s2bm_ard_3-2-1_56KNE_2023-07-31_nrt
2023-07-31 09:42:04	ga_s2bm_ard_3-2-1_56KKE_2023-07-31_nrt
2023-07-31 09:42:05	ga_s2bm_ard_3-2-1_55KHV_2023-07-31_nrt
2023-07-31 09:42:20	ga_s2bm_ard_3-2-1_55KHU_2023-07-31_nrt
2023-07-31 09:42:27	ga_s2bm_ard_3-2-1_56KMC_2023-07-31_nrt
2023-07-31 09:42:30	ga_s2bm_ard_3-2-1_56KLC_2023-07-31_nrt
2023-07-31 09:42:33	ga_s2bm_ard_3-2-1_56KKC_2023-07-31_nrt
2023-07-31 09:42:34	ga_s2bm_ard_3-2-1_55KHT_2023-07-31_nrt
2023-07-31 09:43:06	ga_s2bm_ard_3-2-1_56KMV_2023-07-31_nrt
2023-07-31 09:48:43	ga_s2bm_ard_3-2-1_54GYU_2023-07-31_nrt
2023-07-31 09:48:43	ga_s2bm_ard_3-2-1_55GBP_2023-07-31_nrt
2023-07-31 09:48:48	ga_s2bm_ard_3-2-1_54GXU_2023-07-31_nrt
2023-07-31 09:48:54	ga_s2bm_ard_3-2-1_54GYT_2023-07-31_nrt
2023-07-31 09:48:56	ga_s2bm_ard_3-2-1_54GXT_2023-07-31_nrt
2023-07-31 09:49:01	ga_s2bm_ard_3-2-1_54GYT_2023-07-31_nrt
2023-07-31 09:49:03	ga_s2bm_ard_3-2-1_54GXT_2023-07-31_nrt
2023-07-31 09:49:05	ga_s2bm_ard_3-2-1_54GYS_2023-07-31_nrt
2023-07-31 09:49:07	ga_s2bm_ard_3-2-1_54GXS_2023-07-31_nrt
2023-07-31 11:20:21	ga_s2bm_ard_3-2-1_52LEP_2023-07-31_nrt
2023-07-31 11:20:25	ga_s2bm_ard_3-2-1_52LDP_2023-07-31_nrt
2023-07-31 11:20:28	ga_s2bm_ard_3-2-1_52LCP_2023-07-31_nrt
2023-07-31 11:20:36	ga_s2bm_ard_3-2-1_52LEN_2023-07-31_nrt
2023-07-31 11:20:54	ga_s2bm_ard_3-2-1_52LDM_2023-07-31_nrt
2023-07-31 11:21:00	ga_s2bm_ard_3-2-1_52LBM_2023-07-31_nrt
2023-07-31 11:21:08	ga_s2bm_ard_3-2-1_52LDL_2023-07-31_nrt
2023-07-31 11:21:15	ga_s2bm_ard_3-2-1_52LBL_2023-07-31_nrt
2023-07-31 11:27:21	ga_s2bm_ard_3-2-1_51HWA_2023-07-31_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