Search ga_s2bm_ard_3 across December 2023

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
2023-12-01 09:49:33	ga_s2bm_ard_3-2-1_56LMP_2023-12-01_nrt
2023-12-01 09:49:37	ga_s2bm_ard_3-2-1_56LLP_2023-12-01_nrt
2023-12-01 09:49:40	ga_s2bm_ard_3-2-1_56LKP_2023-12-01_nrt
2023-12-01 09:49:42	ga_s2bm_ard_3-2-1_55LHJ_2023-12-01_nrt
2023-12-01 09:49:46	ga_s2bm_ard_3-2-1_56LMN_2023-12-01_nrt
2023-12-01 09:49:46	ga_s2bm_ard_3-2-1_55LGJ_2023-12-01_nrt
2023-12-01 09:49:52	ga_s2bm_ard_3-2-1_56LLN_2023-12-01_nrt
2023-12-01 09:49:55	ga_s2bm_ard_3-2-1_56LKN_2023-12-01_nrt
2023-12-01 09:49:56	ga_s2bm_ard_3-2-1_56LMM_2023-12-01_nrt
2023-12-01 09:49:56	ga_s2bm_ard_3-2-1_55LHH_2023-12-01_nrt
2023-12-01 09:49:59	ga_s2bm_ard_3-2-1_55LGH_2023-12-01_nrt
2023-12-01 09:50:02	ga_s2bm_ard_3-2-1_56LLM_2023-12-01_nrt
2023-12-01 09:50:03	ga_s2bm_ard_3-2-1_56LKM_2023-12-01_nrt
2023-12-01 09:50:04	ga_s2bm_ard_3-2-1_55LHG_2023-12-01_nrt
2023-12-01 09:50:05	ga_s2bm_ard_3-2-1_55LGG_2023-12-01_nrt
2023-12-01 09:50:56	ga_s2bm_ard_3-2-1_56LLJ_2023-12-01_nrt
2023-12-01 09:50:58	ga_s2bm_ard_3-2-1_56LKJ_2023-12-01_nrt
2023-12-01 09:50:59	ga_s2bm_ard_3-2-1_55LHD_2023-12-01_nrt
2023-12-01 09:51:00	ga_s2bm_ard_3-2-1_55LGD_2023-12-01_nrt
2023-12-01 09:51:01	ga_s2bm_ard_3-2-1_56LLH_2023-12-01_nrt
2023-12-01 09:51:02	ga_s2bm_ard_3-2-1_55LFD_2023-12-01_nrt
2023-12-01 09:51:07	ga_s2bm_ard_3-2-1_56LKH_2023-12-01_nrt
2023-12-01 09:51:09	ga_s2bm_ard_3-2-1_55LHC_2023-12-01_final
2023-12-01 09:51:12	ga_s2bm_ard_3-2-1_55LGC_2023-12-01_final
2023-12-01 09:51:16	ga_s2bm_ard_3-2-1_55LFC_2023-12-01_final
2023-12-01 09:51:22	ga_s2bm_ard_3-2-1_56KKG_2023-12-01_final
2023-12-01 09:51:23	ga_s2bm_ard_3-2-1_55KHB_2023-12-01_final
2023-12-01 09:51:27	ga_s2bm_ard_3-2-1_55KGB_2023-12-01_final
2023-12-01 09:51:30	ga_s2bm_ard_3-2-1_55KFB_2023-12-01_final
2023-12-01 09:51:37	ga_s2bm_ard_3-2-1_56KKF_2023-12-01_final
2023-12-01 09:51:38	ga_s2bm_ard_3-2-1_55KHA_2023-12-01_nrt
2023-12-01 09:51:41	ga_s2bm_ard_3-2-1_55KGA_2023-12-01_nrt
2023-12-01 09:51:45	ga_s2bm_ard_3-2-1_55KFA_2023-12-01_final
2023-12-01 09:51:50	ga_s2bm_ard_3-2-1_55KEA_2023-12-01_nrt
2023-12-01 09:51:51	ga_s2bm_ard_3-2-1_56KKE_2023-12-01_nrt
2023-12-01 09:51:52	ga_s2bm_ard_3-2-1_55KHV_2023-12-01_nrt
2023-12-01 09:51:56	ga_s2bm_ard_3-2-1_55KGV_2023-12-01_nrt
2023-12-01 09:51:59	ga_s2bm_ard_3-2-1_55KFV_2023-12-01_nrt
2023-12-01 09:52:03	ga_s2bm_ard_3-2-1_55KEV_2023-12-01_final
2023-12-01 09:52:05	ga_s2bm_ard_3-2-1_56KKD_2023-12-01_nrt
2023-12-01 09:52:07	ga_s2bm_ard_3-2-1_55KHU_2023-12-01_nrt
2023-12-01 09:52:10	ga_s2bm_ard_3-2-1_55KGU_2023-12-01_final
2023-12-01 09:52:14	ga_s2bm_ard_3-2-1_55KFU_2023-12-01_final
2023-12-01 09:52:16	ga_s2bm_ard_3-2-1_56KKC_2023-12-01_nrt
2023-12-01 09:52:17	ga_s2bm_ard_3-2-1_55KEU_2023-12-01_final
2023-12-01 09:52:21	ga_s2bm_ard_3-2-1_55KHT_2023-12-01_nrt
2023-12-01 09:52:25	ga_s2bm_ard_3-2-1_55KGT_2023-12-01_final
2023-12-01 09:52:28	ga_s2bm_ard_3-2-1_55KFT_2023-12-01_final
2023-12-01 09:52:32	ga_s2bm_ard_3-2-1_55KET_2023-12-01_final
2023-12-01 09:52:32	ga_s2bm_ard_3-2-1_55KHS_2023-12-01_final
2023-12-01 09:52:39	ga_s2bm_ard_3-2-1_55KGS_2023-12-01_final
2023-12-01 09:52:43	ga_s2bm_ard_3-2-1_55KFS_2023-12-01_final
2023-12-01 09:52:46	ga_s2bm_ard_3-2-1_55KES_2023-12-01_final
2023-12-01 11:35:09	ga_s2bm_ard_3-2-1_51JVK_2023-12-01_final
2023-12-01 11:35:18	ga_s2bm_ard_3-2-1_51JVJ_2023-12-01_final
2023-12-01 11:35:18	ga_s2bm_ard_3-2-1_51JVK_2023-12-01_final
2023-12-01 11:35:19	ga_s2bm_ard_3-2-1_51JUJ_2023-12-01_final
2023-12-01 11:35:21	ga_s2bm_ard_3-2-1_51JTK_2023-12-01_final
2023-12-01 11:35:30	ga_s2bm_ard_3-2-1_51JTJ_2023-12-01_final
2023-12-01 11:35:38	ga_s2bm_ard_3-2-1_51JVH_2023-12-01_final
2023-12-01 11:35:45	ga_s2bm_ard_3-2-1_51JTH_2023-12-01_final
2023-12-01 11:35:53	ga_s2bm_ard_3-2-1_51JVG_2023-12-01_final
2023-12-01 11:35:59	ga_s2bm_ard_3-2-1_51JTG_2023-12-01_final
2023-12-01 11:36:07	ga_s2bm_ard_3-2-1_51JVF_2023-12-01_final
2023-12-01 11:36:11	ga_s2bm_ard_3-2-1_51JUF_2023-12-01_final
2023-12-01 11:36:14	ga_s2bm_ard_3-2-1_51JTF_2023-12-01_final
2023-12-02 09:28:41	ga_s2bm_ard_3-2-1_55GFN_2023-12-01_final
2023-12-02 09:28:44	ga_s2bm_ard_3-2-1_55GEN_2023-12-01_final
2023-12-02 09:28:45	ga_s2bm_ard_3-2-1_56GLS_2023-12-01_nrt
2023-12-02 09:28:46	ga_s2bm_ard_3-2-1_55GGM_2023-12-01_nrt
2023-12-02 09:28:46	ga_s2bm_ard_3-2-1_55GGN_2023-12-01_nrt
2023-12-02 09:28:48	ga_s2bm_ard_3-2-1_55GFM_2023-12-01_nrt
2023-12-02 09:28:48	ga_s2bm_ard_3-2-1_55GFN_2023-12-01_final
2023-12-02 09:28:50	ga_s2bm_ard_3-2-1_55GEN_2023-12-01_final
2023-12-02 09:28:52	ga_s2bm_ard_3-2-1_56GLS_2023-12-01_nrt
2023-12-02 09:28:53	ga_s2bm_ard_3-2-1_55GGM_2023-12-01_nrt
2023-12-02 09:28:54	ga_s2bm_ard_3-2-1_55GFM_2023-12-01_nrt
2023-12-02 09:28:56	ga_s2bm_ard_3-2-1_55GEM_2023-12-01_final
2023-12-02 11:00:04	ga_s2bm_ard_3-2-1_53LMJ_2023-12-02_nrt
2023-12-02 11:00:08	ga_s2bm_ard_3-2-1_53LLJ_2023-12-02_nrt
2023-12-02 11:00:11	ga_s2bm_ard_3-2-1_53LKJ_2023-12-02_final
2023-12-02 11:00:13	ga_s2bm_ard_3-2-1_52LHP_2023-12-02_nrt
2023-12-02 11:00:19	ga_s2bm_ard_3-2-1_53LMH_2023-12-02_final
2023-12-02 11:00:23	ga_s2bm_ard_3-2-1_53LLH_2023-12-02_final
2023-12-02 11:00:26	ga_s2bm_ard_3-2-1_53LKH_2023-12-02_final
2023-12-02 11:00:27	ga_s2bm_ard_3-2-1_52LHN_2023-12-02_final
2023-12-02 11:00:33	ga_s2bm_ard_3-2-1_53LMG_2023-12-02_final
2023-12-02 11:00:34	ga_s2bm_ard_3-2-1_52LGN_2023-12-02_final
2023-12-02 11:00:37	ga_s2bm_ard_3-2-1_53LLG_2023-12-02_final
2023-12-02 11:00:40	ga_s2bm_ard_3-2-1_53LKG_2023-12-02_final
2023-12-02 11:00:42	ga_s2bm_ard_3-2-1_52LHM_2023-12-02_final
2023-12-02 11:00:46	ga_s2bm_ard_3-2-1_53LMF_2023-12-02_final
2023-12-02 11:00:46	ga_s2bm_ard_3-2-1_52LGM_2023-12-02_final
2023-12-02 11:00:51	ga_s2bm_ard_3-2-1_53LLF_2023-12-02_final
2023-12-02 11:00:55	ga_s2bm_ard_3-2-1_53LKF_2023-12-02_final
2023-12-02 11:00:55	ga_s2bm_ard_3-2-1_53LME_2023-12-02_final
2023-12-02 11:00:56	ga_s2bm_ard_3-2-1_52LHL_2023-12-02_final
2023-12-02 11:00:59	ga_s2bm_ard_3-2-1_52LGL_2023-12-02_final
2023-12-02 11:01:06	ga_s2bm_ard_3-2-1_53LLE_2023-12-02_final
2023-12-02 11:01:10	ga_s2bm_ard_3-2-1_53LKE_2023-12-02_final

Return to ga_s2bm_ard_3 across December 2023 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

2023

December

All days