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
2019-10-13 11:36:39	ga_s2bm_ard_3-2-1_50HPK_2019-10-13_final.stac-item
2019-10-16 11:44:23	ga_s2bm_ard_3-2-1_50KPU_2019-10-16_final.stac-item
2019-10-16 11:44:29	ga_s2bm_ard_3-2-1_50KPU_2019-10-16_final.stac-item
2019-10-16 11:44:39	ga_s2bm_ard_3-2-1_50JPT_2019-10-16_final.stac-item
2019-10-16 11:44:54	ga_s2bm_ard_3-2-1_50JPS_2019-10-16_final.stac-item
2019-10-16 11:45:08	ga_s2bm_ard_3-2-1_50JPR_2019-10-16_final.stac-item
2019-10-16 11:45:22	ga_s2bm_ard_3-2-1_50JPQ_2019-10-16_final.stac-item
2019-10-16 11:45:37	ga_s2bm_ard_3-2-1_50JPP_2019-10-16_final.stac-item
2019-10-16 11:45:51	ga_s2bm_ard_3-2-1_50JPN_2019-10-16_final.stac-item
2019-10-16 11:46:06	ga_s2bm_ard_3-2-1_50JPM_2019-10-16_final.stac-item
2019-10-16 11:46:20	ga_s2bm_ard_3-2-1_50JPL_2019-10-16_final.stac-item
2019-10-16 11:46:35	ga_s2bm_ard_3-2-1_50HPK_2019-10-16_final.stac-item
2019-10-16 11:46:49	ga_s2bm_ard_3-2-1_50HPJ_2019-10-16_final.stac-item
2019-10-16 11:47:03	ga_s2bm_ard_3-2-1_50HPH_2019-10-16_final.stac-item
2019-10-23 11:36:39	ga_s2bm_ard_3-2-1_50HPK_2019-10-23_final.stac-item
2019-10-23 11:36:53	ga_s2bm_ard_3-2-1_50HPJ_2019-10-23_final.stac-item
2019-10-23 11:37:08	ga_s2bm_ard_3-2-1_50HPH_2019-10-23_final.stac-item
2019-12-18 11:53:30	ga_s2bm_ard_3-2-1_50KPB_2019-12-18_final.stac-item
2019-12-18 11:53:45	ga_s2bm_ard_3-2-1_50KPA_2019-12-18_final.stac-item
2019-12-18 11:53:59	ga_s2bm_ard_3-2-1_50KPV_2019-12-18_final.stac-item
2019-12-18 11:54:14	ga_s2bm_ard_3-2-1_50KPU_2019-12-18_final.stac-item
2019-12-18 11:54:28	ga_s2bm_ard_3-2-1_50JPT_2019-12-18_final.stac-item
2019-12-18 11:54:43	ga_s2bm_ard_3-2-1_50JPS_2019-12-18_final.stac-item
2023-04-04 12:01:09	ga_s2bm_ard_3-2-1_50LPM_2023-04-04_nrt
2023-04-04 12:01:23	ga_s2bm_ard_3-2-1_50LPL_2023-04-04_nrt
2023-04-04 12:01:37	ga_s2bm_ard_3-2-1_50LPK_2023-04-04_nrt
2023-04-04 12:01:45	ga_s2bm_ard_3-2-1_50LPJ_2023-04-04_nrt
2023-04-05 11:37:24	ga_s2bm_ard_3-2-1_50HPG_2023-04-05_final
2023-04-05 11:37:34	ga_s2bm_ard_3-2-1_50HPF_2023-04-05_nrt
2023-04-08 11:44:25	ga_s2bm_ard_3-2-1_50KPU_2023-04-08_final
2023-04-08 11:44:40	ga_s2bm_ard_3-2-1_50JPT_2023-04-08_final
2023-04-08 11:44:55	ga_s2bm_ard_3-2-1_50JPS_2023-04-08_final
2023-04-08 11:46:36	ga_s2bm_ard_3-2-1_50HPK_2023-04-08_final
2023-04-08 11:46:50	ga_s2bm_ard_3-2-1_50HPJ_2023-04-08_final
2023-04-08 11:47:05	ga_s2bm_ard_3-2-1_50HPH_2023-04-08_final
2023-04-08 11:47:19	ga_s2bm_ard_3-2-1_50HPG_2023-04-08_final
2023-04-08 11:47:32	ga_s2bm_ard_3-2-1_50HPF_2023-04-08_nrt
2023-04-14 12:00:37	ga_s2bm_ard_3-2-1_50LPP_2023-04-14_nrt
2023-04-14 12:00:52	ga_s2bm_ard_3-2-1_50LPN_2023-04-14_nrt
2023-04-14 12:01:06	ga_s2bm_ard_3-2-1_50LPM_2023-04-14_nrt
2023-04-14 12:01:21	ga_s2bm_ard_3-2-1_50LPL_2023-04-14_nrt
2023-04-14 12:01:35	ga_s2bm_ard_3-2-1_50LPK_2023-04-14_nrt
2023-04-14 12:01:42	ga_s2bm_ard_3-2-1_50LPJ_2023-04-14_nrt
2023-04-15 11:36:53	ga_s2bm_ard_3-2-1_50HPJ_2023-04-15_final
2023-04-15 11:37:07	ga_s2bm_ard_3-2-1_50HPH_2023-04-15_final
2023-04-15 11:37:22	ga_s2bm_ard_3-2-1_50HPG_2023-04-15_final
2023-04-18 11:44:39	ga_s2bm_ard_3-2-1_50JPT_2023-04-18_final
2023-04-18 11:44:54	ga_s2bm_ard_3-2-1_50JPS_2023-04-18_final
2023-04-18 11:45:08	ga_s2bm_ard_3-2-1_50JPR_2023-04-18_final
2023-04-18 11:45:23	ga_s2bm_ard_3-2-1_50JPQ_2023-04-18_final
2023-04-18 11:45:37	ga_s2bm_ard_3-2-1_50JPP_2023-04-18_final
2023-04-18 11:45:52	ga_s2bm_ard_3-2-1_50JPN_2023-04-18_final
2023-04-21 11:51:28	ga_s2bm_ard_3-2-1_50LPL_2023-04-21_nrt
2023-04-21 11:54:35	ga_s2bm_ard_3-2-1_50JPT_2023-04-21_final
2023-04-21 11:54:50	ga_s2bm_ard_3-2-1_50JPS_2023-04-21_final
2023-04-24 12:00:39	ga_s2bm_ard_3-2-1_50LPP_2023-04-24_nrt
2023-04-24 12:00:53	ga_s2bm_ard_3-2-1_50LPN_2023-04-24_nrt
2023-04-24 12:01:08	ga_s2bm_ard_3-2-1_50LPM_2023-04-24_nrt
2023-04-24 12:01:22	ga_s2bm_ard_3-2-1_50LPL_2023-04-24_nrt
2023-04-24 12:01:36	ga_s2bm_ard_3-2-1_50LPK_2023-04-24_nrt
2023-04-24 12:01:44	ga_s2bm_ard_3-2-1_50LPJ_2023-04-24_nrt
2023-04-28 11:44:25	ga_s2bm_ard_3-2-1_50KPU_2023-04-28_final
2023-04-28 11:44:39	ga_s2bm_ard_3-2-1_50JPT_2023-04-28_final
2023-04-28 11:44:54	ga_s2bm_ard_3-2-1_50JPS_2023-04-28_final
2023-04-28 11:45:01	ga_s2bm_ard_3-2-1_50JPS_2023-04-28_final
2023-04-28 11:45:01	ga_s2bm_ard_3-2-1_50JPR_2023-04-28_final
2023-04-28 11:45:08	ga_s2bm_ard_3-2-1_50JPR_2023-04-28_final
2023-04-28 11:45:23	ga_s2bm_ard_3-2-1_50JPQ_2023-04-28_final
2023-04-28 11:45:37	ga_s2bm_ard_3-2-1_50JPP_2023-04-28_final
2023-04-28 11:45:52	ga_s2bm_ard_3-2-1_50JPN_2023-04-28_final
2023-04-28 11:46:06	ga_s2bm_ard_3-2-1_50JPM_2023-04-28_final
2023-04-28 11:46:21	ga_s2bm_ard_3-2-1_50JPL_2023-04-28_final
2023-04-28 11:46:35	ga_s2bm_ard_3-2-1_50HPK_2023-04-28_final
2023-04-28 11:46:50	ga_s2bm_ard_3-2-1_50HPJ_2023-04-28_final
2023-04-28 11:47:04	ga_s2bm_ard_3-2-1_50HPH_2023-04-28_final
2023-04-28 11:47:18	ga_s2bm_ard_3-2-1_50HPG_2023-04-28_final
2023-04-28 11:47:31	ga_s2bm_ard_3-2-1_50HPF_2023-04-28_nrt
2023-05-01 11:51:28	ga_s2bm_ard_3-2-1_50LPL_2023-05-01_nrt
2023-05-01 11:51:41	ga_s2bm_ard_3-2-1_50LPK_2023-05-01_nrt
2023-05-01 11:51:56	ga_s2bm_ard_3-2-1_50LPJ_2023-05-01_nrt
2023-05-01 11:52:11	ga_s2bm_ard_3-2-1_50LPH_2023-05-01_nrt
2023-05-01 11:52:25	ga_s2bm_ard_3-2-1_50KPG_2023-05-01_nrt
2023-05-01 11:52:40	ga_s2bm_ard_3-2-1_50KPF_2023-05-01_final
2023-05-01 11:52:54	ga_s2bm_ard_3-2-1_50KPE_2023-05-01_nrt
2023-05-01 11:53:09	ga_s2bm_ard_3-2-1_50KPD_2023-05-01_final
2023-05-01 11:53:23	ga_s2bm_ard_3-2-1_50KPC_2023-05-01_final
2023-05-01 11:53:38	ga_s2bm_ard_3-2-1_50KPB_2023-05-01_final
2023-05-01 11:53:50	ga_s2bm_ard_3-2-1_50KPA_2023-05-01_final
2023-05-01 11:53:56	ga_s2bm_ard_3-2-1_50KPA_2023-05-01_final
2023-05-01 11:54:07	ga_s2bm_ard_3-2-1_50KPV_2023-05-01_final
2023-05-01 11:54:21	ga_s2bm_ard_3-2-1_50KPU_2023-05-01_final
2023-05-01 11:54:35	ga_s2bm_ard_3-2-1_50JPT_2023-05-01_final
2023-05-01 11:54:50	ga_s2bm_ard_3-2-1_50JPS_2023-05-01_final
2023-05-18 11:44:15	ga_s2bm_ard_3-2-1_50KPV_2023-05-18_final
2023-05-18 11:44:28	ga_s2bm_ard_3-2-1_50KPU_2023-05-18_final
2023-05-18 11:44:42	ga_s2bm_ard_3-2-1_50JPT_2023-05-18_final
2023-05-18 11:45:55	ga_s2bm_ard_3-2-1_50JPN_2023-05-18_final
2023-05-18 11:46:09	ga_s2bm_ard_3-2-1_50JPM_2023-05-18_final
2023-05-18 11:46:38	ga_s2bm_ard_3-2-1_50HPK_2023-05-18_final
2023-05-18 11:46:53	ga_s2bm_ard_3-2-1_50HPJ_2023-05-18_final

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