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
2019-01-08 10:35:26	ga_s2bm_ard_3-2-1_53JQG_2019-01-08_final.stac-item
2019-01-11 10:41:19	ga_s2bm_ard_3-2-1_53LQD_2019-01-11_final.stac-item
2019-01-11 10:41:33	ga_s2bm_ard_3-2-1_53LQC_2019-01-11_final
2019-01-11 10:41:48	ga_s2bm_ard_3-2-1_53KQB_2019-01-11_final
2019-01-11 10:42:02	ga_s2bm_ard_3-2-1_53KQA_2019-01-11_final.stac-item
2019-01-11 10:42:17	ga_s2bm_ard_3-2-1_53KQV_2019-01-11_final.stac-item
2019-01-11 10:42:31	ga_s2bm_ard_3-2-1_53KQU_2019-01-11_final.stac-item
2019-03-22 10:41:20	ga_s2bm_ard_3-2-1_53LQD_2019-03-22_final.stac-item
2019-03-22 10:41:34	ga_s2bm_ard_3-2-1_53LQC_2019-03-22_final
2019-03-26 10:26:04	ga_s2bm_ard_3-2-1_53HQE_2019-03-26_final.stac-item
2019-03-26 10:26:19	ga_s2bm_ard_3-2-1_53HQD_2019-03-26_final.stac-item
2019-03-26 10:26:33	ga_s2bm_ard_3-2-1_53HQC_2019-03-26_final.stac-item
2019-03-26 10:26:45	ga_s2bm_ard_3-2-1_54HTG_2019-03-26_final.stac-item
2019-03-26 10:26:47	ga_s2bm_ard_3-2-1_53HQB_2019-03-26_final.stac-item
2019-03-26 10:27:00	ga_s2bm_ard_3-2-1_54HTF_2019-03-26_final.stac-item
2019-03-26 10:27:02	ga_s2bm_ard_3-2-1_53HQA_2019-03-26_final.stac-item
2019-04-11 10:41:24	ga_s2bm_ard_3-2-1_53LQD_2019-04-11_final.stac-item
2019-04-11 10:41:39	ga_s2bm_ard_3-2-1_53LQC_2019-04-11_final
2019-04-11 10:41:53	ga_s2bm_ard_3-2-1_53KQB_2019-04-11_final
2019-04-11 10:42:05	ga_s2bm_ard_3-2-1_53KQA_2019-04-11_final.stac-item
2019-04-11 10:42:11	ga_s2bm_ard_3-2-1_53KQA_2019-04-11_final.stac-item
2019-04-11 10:42:22	ga_s2bm_ard_3-2-1_53KQV_2019-04-11_final.stac-item
2019-04-11 10:42:36	ga_s2bm_ard_3-2-1_53KQU_2019-04-11_final.stac-item
2019-04-15 10:26:23	ga_s2bm_ard_3-2-1_53HQD_2019-04-15_final.stac-item
2019-04-15 10:26:38	ga_s2bm_ard_3-2-1_53HQC_2019-04-15_final.stac-item
2019-04-15 10:26:50	ga_s2bm_ard_3-2-1_54HTG_2019-04-15_final.stac-item
2019-04-15 10:26:52	ga_s2bm_ard_3-2-1_53HQB_2019-04-15_final.stac-item
2019-04-15 10:27:04	ga_s2bm_ard_3-2-1_54HTF_2019-04-15_final.stac-item
2019-04-15 10:27:06	ga_s2bm_ard_3-2-1_53HQA_2019-04-15_final.stac-item
2019-05-28 10:33:00	ga_s2bm_ard_3-2-1_53KQT_2019-05-28_final.stac-item
2019-05-28 10:33:14	ga_s2bm_ard_3-2-1_53KQS_2019-05-28_final.stac-item
2019-05-28 10:33:29	ga_s2bm_ard_3-2-1_53KQR_2019-05-28_final.stac-item
2019-05-28 10:33:43	ga_s2bm_ard_3-2-1_53KQQ_2019-05-28_final.stac-item
2019-05-28 10:33:58	ga_s2bm_ard_3-2-1_53KQP_2019-05-28_final.stac-item
2019-05-28 10:34:12	ga_s2bm_ard_3-2-1_53JQN_2019-05-28_final.stac-item
2019-05-28 10:34:27	ga_s2bm_ard_3-2-1_53JQM_2019-05-28_final.stac-item
2019-05-28 10:34:41	ga_s2bm_ard_3-2-1_53JQL_2019-05-28_final.stac-item
2019-05-28 10:34:55	ga_s2bm_ard_3-2-1_53JQK_2019-05-28_final.stac-item
2019-05-28 10:35:10	ga_s2bm_ard_3-2-1_53JQJ_2019-05-28_final.stac-item
2023-10-27 10:40:08	ga_s2bm_ard_3-2-1_53LQJ_2023-10-27_nrt
2023-10-27 10:40:23	ga_s2bm_ard_3-2-1_53LQH_2023-10-27_nrt
2023-10-27 10:40:31	ga_s2bm_ard_3-2-1_53LQG_2023-10-27_final
2023-10-27 10:40:38	ga_s2bm_ard_3-2-1_53LQG_2023-10-27_final
2023-10-27 10:40:52	ga_s2bm_ard_3-2-1_53LQF_2023-10-27_final
2023-10-27 10:41:06	ga_s2bm_ard_3-2-1_53LQE_2023-10-27_final
2023-10-28 10:17:19	ga_s2bm_ard_3-2-1_54HTE_2023-10-28_final
2023-10-28 10:17:21	ga_s2bm_ard_3-2-1_53HQV_2023-10-28_final
2023-10-28 10:17:34	ga_s2bm_ard_3-2-1_54HTD_2023-10-28_nrt
2023-10-28 10:17:35	ga_s2bm_ard_3-2-1_53HQU_2023-10-28_nrt
2023-10-28 10:17:41	ga_s2bm_ard_3-2-1_54HTC_2023-10-28_nrt
2023-10-28 10:17:41	ga_s2bm_ard_3-2-1_53HQT_2023-10-28_nrt
2023-10-31 10:24:54	ga_s2bm_ard_3-2-1_53JQK_2023-10-31_final
2023-10-31 10:25:08	ga_s2bm_ard_3-2-1_53JQJ_2023-10-31_final
2023-10-31 10:25:22	ga_s2bm_ard_3-2-1_53JQH_2023-10-31_final
2023-10-31 10:25:37	ga_s2bm_ard_3-2-1_53JQG_2023-10-31_final
2023-10-31 10:25:51	ga_s2bm_ard_3-2-1_53JQF_2023-10-31_final
2023-10-31 10:26:06	ga_s2bm_ard_3-2-1_53HQE_2023-10-31_final
2023-10-31 10:26:20	ga_s2bm_ard_3-2-1_53HQD_2023-10-31_final
2023-10-31 10:26:34	ga_s2bm_ard_3-2-1_53HQC_2023-10-31_final
2023-10-31 10:26:47	ga_s2bm_ard_3-2-1_54HTG_2023-10-31_final
2023-10-31 10:26:49	ga_s2bm_ard_3-2-1_53HQB_2023-10-31_final
2023-10-31 10:27:01	ga_s2bm_ard_3-2-1_54HTF_2023-10-31_final
2023-10-31 10:27:03	ga_s2bm_ard_3-2-1_53HQA_2023-10-31_final
2023-11-03 10:32:25	ga_s2bm_ard_3-2-1_53KQV_2023-11-03_final
2023-11-03 10:32:39	ga_s2bm_ard_3-2-1_53KQU_2023-11-03_final
2023-11-03 10:32:52	ga_s2bm_ard_3-2-1_53KQT_2023-11-03_final
2023-11-06 10:40:10	ga_s2bm_ard_3-2-1_53LQJ_2023-11-06_nrt
2023-11-06 10:40:25	ga_s2bm_ard_3-2-1_53LQH_2023-11-06_nrt
2023-11-06 10:40:39	ga_s2bm_ard_3-2-1_53LQG_2023-11-06_final
2023-11-06 10:40:54	ga_s2bm_ard_3-2-1_53LQF_2023-11-06_final
2023-11-06 10:41:29	ga_s2bm_ard_3-2-1_53LQC_2023-11-06_final
2023-11-06 10:41:37	ga_s2bm_ard_3-2-1_53LQC_2023-11-06_final
2023-11-06 10:41:52	ga_s2bm_ard_3-2-1_53KQB_2023-11-06_final
2023-11-06 10:42:06	ga_s2bm_ard_3-2-1_53KQA_2023-11-06_final
2023-11-06 10:42:20	ga_s2bm_ard_3-2-1_53KQV_2023-11-06_final
2023-11-06 10:42:35	ga_s2bm_ard_3-2-1_53KQU_2023-11-06_final
2023-11-07 10:17:21	ga_s2bm_ard_3-2-1_54HTE_2023-11-07_final
2023-11-10 10:24:53	ga_s2bm_ard_3-2-1_53JQK_2023-11-10_final
2023-11-13 10:33:07	ga_s2bm_ard_3-2-1_53KQS_2023-11-13_final
2023-11-13 10:33:21	ga_s2bm_ard_3-2-1_53KQR_2023-11-13_final
2023-11-13 10:33:35	ga_s2bm_ard_3-2-1_53KQQ_2023-11-13_final
2023-11-13 10:33:44	ga_s2bm_ard_3-2-1_53KQP_2023-11-13_final
2023-11-13 10:33:51	ga_s2bm_ard_3-2-1_53KQP_2023-11-13_final
2023-11-13 10:34:04	ga_s2bm_ard_3-2-1_53JQN_2023-11-13_final
2023-11-13 10:34:19	ga_s2bm_ard_3-2-1_53JQM_2023-11-13_final
2023-11-13 10:34:33	ga_s2bm_ard_3-2-1_53JQL_2023-11-13_final
2023-11-13 10:34:48	ga_s2bm_ard_3-2-1_53JQK_2023-11-13_final
2023-11-13 10:35:02	ga_s2bm_ard_3-2-1_53JQJ_2023-11-13_final
2023-11-13 10:35:16	ga_s2bm_ard_3-2-1_53JQH_2023-11-13_final
2023-11-13 10:35:31	ga_s2bm_ard_3-2-1_53JQG_2023-11-13_final
2023-11-16 10:40:07	ga_s2bm_ard_3-2-1_53LQJ_2023-11-16_nrt
2023-11-16 10:40:21	ga_s2bm_ard_3-2-1_53LQH_2023-11-16_nrt
2023-11-16 10:40:36	ga_s2bm_ard_3-2-1_53LQG_2023-11-16_final
2023-11-16 10:40:50	ga_s2bm_ard_3-2-1_53LQF_2023-11-16_final
2023-11-16 10:41:05	ga_s2bm_ard_3-2-1_53LQE_2023-11-16_final
2023-11-16 10:41:19	ga_s2bm_ard_3-2-1_53LQD_2023-11-16_final
2023-11-17 10:17:17	ga_s2bm_ard_3-2-1_54HTE_2023-11-17_final
2023-11-17 10:17:18	ga_s2bm_ard_3-2-1_53HQV_2023-11-17_final
2023-11-17 10:17:31	ga_s2bm_ard_3-2-1_54HTD_2023-11-17_nrt
2023-11-17 10:17:32	ga_s2bm_ard_3-2-1_53HQU_2023-11-17_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