Search ga_s2bm_ard_3 on 22nd April 2019

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
2019-04-22 10:13:50	ga_s2bm_ard_3-2-1_55JBN_2019-04-22_final.stac-item
2019-04-22 10:14:02	ga_s2bm_ard_3-2-1_54JXT_2019-04-22_final.stac-item
2019-04-22 10:14:06	ga_s2bm_ard_3-2-1_54JWT_2019-04-22_final.stac-item
2019-04-22 10:14:12	ga_s2bm_ard_3-2-1_54JVT_2019-04-22_final.stac-item
2019-04-22 10:14:17	ga_s2bm_ard_3-2-1_54JXS_2019-04-22_final.stac-item
2019-04-22 10:14:20	ga_s2bm_ard_3-2-1_54JWS_2019-04-22_final.stac-item
2019-04-22 10:14:24	ga_s2bm_ard_3-2-1_54JVS_2019-04-22_final.stac-item
2019-04-22 10:14:27	ga_s2bm_ard_3-2-1_54JYR_2019-04-22_final.stac-item
2019-04-22 10:14:31	ga_s2bm_ard_3-2-1_54JXR_2019-04-22_final.stac-item
2019-04-22 10:14:34	ga_s2bm_ard_3-2-1_54JWR_2019-04-22_final.stac-item
2019-04-22 10:14:38	ga_s2bm_ard_3-2-1_54JVR_2019-04-22_final.stac-item
2019-04-22 10:14:41	ga_s2bm_ard_3-2-1_54JYQ_2019-04-22_final.stac-item
2019-04-22 10:14:45	ga_s2bm_ard_3-2-1_54JXQ_2019-04-22_final.stac-item
2019-04-22 10:14:49	ga_s2bm_ard_3-2-1_54JWQ_2019-04-22_final.stac-item
2019-04-22 10:14:52	ga_s2bm_ard_3-2-1_54JVQ_2019-04-22_final.stac-item
2019-04-22 10:14:54	ga_s2bm_ard_3-2-1_54JYP_2019-04-22_final.stac-item
2019-04-22 10:15:00	ga_s2bm_ard_3-2-1_54JXP_2019-04-22_final.stac-item
2019-04-22 10:15:04	ga_s2bm_ard_3-2-1_54JWP_2019-04-22_final.stac-item
2019-04-22 10:15:04	ga_s2bm_ard_3-2-1_54JYN_2019-04-22_final.stac-item
2019-04-22 10:15:07	ga_s2bm_ard_3-2-1_54JVP_2019-04-22_final.stac-item
2019-04-22 10:15:14	ga_s2bm_ard_3-2-1_54JUP_2019-04-22_final.stac-item
2019-04-22 10:15:14	ga_s2bm_ard_3-2-1_54JXN_2019-04-22_final.stac-item
2019-04-22 10:15:18	ga_s2bm_ard_3-2-1_54JWN_2019-04-22_final.stac-item
2019-04-22 10:15:21	ga_s2bm_ard_3-2-1_54JVN_2019-04-22_final.stac-item
2019-04-22 10:15:25	ga_s2bm_ard_3-2-1_54JUN_2019-04-22_final.stac-item
2019-04-22 10:15:29	ga_s2bm_ard_3-2-1_54JXM_2019-04-22_final.stac-item
2019-04-22 10:15:33	ga_s2bm_ard_3-2-1_54JWM_2019-04-22_final.stac-item
2019-04-22 10:15:36	ga_s2bm_ard_3-2-1_54JVM_2019-04-22_final.stac-item
2019-04-22 10:15:39	ga_s2bm_ard_3-2-1_54JUM_2019-04-22_final.stac-item
2019-04-22 10:15:43	ga_s2bm_ard_3-2-1_54JXL_2019-04-22_final.stac-item
2019-04-22 10:15:47	ga_s2bm_ard_3-2-1_54JWL_2019-04-22_final.stac-item
2019-04-22 10:15:51	ga_s2bm_ard_3-2-1_54JVL_2019-04-22_final.stac-item
2019-04-22 10:15:54	ga_s2bm_ard_3-2-1_54JUL_2019-04-22_final.stac-item
2019-04-22 10:15:56	ga_s2bm_ard_3-2-1_54HXK_2019-04-22_final.stac-item
2019-04-22 10:16:01	ga_s2bm_ard_3-2-1_54HWK_2019-04-22_final.stac-item
2019-04-22 10:16:05	ga_s2bm_ard_3-2-1_54HVK_2019-04-22_final.stac-item
2019-04-22 10:16:06	ga_s2bm_ard_3-2-1_54HXJ_2019-04-22_final.stac-item
2019-04-22 10:16:08	ga_s2bm_ard_3-2-1_54HUK_2019-04-22_final.stac-item
2019-04-22 10:16:16	ga_s2bm_ard_3-2-1_54HWJ_2019-04-22_final.stac-item
2019-04-22 10:16:20	ga_s2bm_ard_3-2-1_54HVJ_2019-04-22_final.stac-item
2019-04-22 10:16:23	ga_s2bm_ard_3-2-1_54HUJ_2019-04-22_final.stac-item
2019-04-22 10:16:27	ga_s2bm_ard_3-2-1_54HTJ_2019-04-22_final.stac-item
2019-04-22 10:16:30	ga_s2bm_ard_3-2-1_54HWH_2019-04-22_final.stac-item
2019-04-22 10:16:34	ga_s2bm_ard_3-2-1_54HVH_2019-04-22_final.stac-item
2019-04-22 10:16:37	ga_s2bm_ard_3-2-1_54HUH_2019-04-22_final.stac-item
2019-04-22 10:16:40	ga_s2bm_ard_3-2-1_54HTH_2019-04-22_final.stac-item
2019-04-22 10:16:45	ga_s2bm_ard_3-2-1_54HWG_2019-04-22_final.stac-item
2019-04-22 10:16:49	ga_s2bm_ard_3-2-1_54HVG_2019-04-22_final.stac-item
2019-04-22 10:16:52	ga_s2bm_ard_3-2-1_54HUG_2019-04-22_final.stac-item
2019-04-22 10:16:55	ga_s2bm_ard_3-2-1_54HTG_2019-04-22_final.stac-item
2019-04-22 10:16:58	ga_s2bm_ard_3-2-1_53HQB_2019-04-22_final.stac-item
2019-04-22 10:16:59	ga_s2bm_ard_3-2-1_54HWF_2019-04-22_final.stac-item
2019-04-22 10:17:03	ga_s2bm_ard_3-2-1_54HVF_2019-04-22_final.stac-item
2019-04-22 10:17:07	ga_s2bm_ard_3-2-1_54HUF_2019-04-22_final.stac-item
2019-04-22 10:17:10	ga_s2bm_ard_3-2-1_54HWE_2019-04-22_final.stac-item
2019-04-22 10:17:10	ga_s2bm_ard_3-2-1_54HTF_2019-04-22_final.stac-item
2019-04-22 10:17:11	ga_s2bm_ard_3-2-1_53HQA_2019-04-22_final.stac-item
2019-04-22 10:17:17	ga_s2bm_ard_3-2-1_54HVE_2019-04-22_final.stac-item
2019-04-22 10:17:32	ga_s2bm_ard_3-2-1_54HVD_2019-04-22_final.stac-item
2019-04-22 11:51:20	ga_s2bm_ard_3-2-1_51LUF_2019-04-22_final.stac-item
2019-04-22 11:51:33	ga_s2bm_ard_3-2-1_51LUE_2019-04-22_final.stac-item
2019-04-22 11:53:00	ga_s2bm_ard_3-2-1_51KTU_2019-04-22_final.stac-item
2019-04-22 11:53:05	ga_s2bm_ard_3-2-1_50KRD_2019-04-22_final.stac-item
2019-04-22 11:53:17	ga_s2bm_ard_3-2-1_50KRC_2019-04-22_final.stac-item
2019-04-22 11:53:24	ga_s2bm_ard_3-2-1_50KQC_2019-04-22_final.stac-item
2019-04-22 11:53:27	ga_s2bm_ard_3-2-1_50KPC_2019-04-22_final.stac-item
2019-04-22 11:53:31	ga_s2bm_ard_3-2-1_50KNC_2019-04-22_final.stac-item
2019-04-22 11:53:37	ga_s2bm_ard_3-2-1_50KMC_2019-04-22_final.stac-item
2019-04-22 11:53:38	ga_s2bm_ard_3-2-1_50KQB_2019-04-22_final.stac-item
2019-04-22 11:53:42	ga_s2bm_ard_3-2-1_50KPB_2019-04-22_final.stac-item
2019-04-22 11:53:45	ga_s2bm_ard_3-2-1_50KNB_2019-04-22_final.stac-item
2019-04-22 11:53:49	ga_s2bm_ard_3-2-1_50KMB_2019-04-22_final.stac-item
2019-04-22 11:53:53	ga_s2bm_ard_3-2-1_50KQA_2019-04-22_final.stac-item
2019-04-22 11:53:56	ga_s2bm_ard_3-2-1_50KPA_2019-04-22_final.stac-item
2019-04-22 11:54:00	ga_s2bm_ard_3-2-1_50KNA_2019-04-22_final.stac-item
2019-04-22 11:54:03	ga_s2bm_ard_3-2-1_50KMA_2019-04-22_final.stac-item
2019-04-22 11:54:07	ga_s2bm_ard_3-2-1_50KQV_2019-04-22_final.stac-item
2019-04-22 11:54:11	ga_s2bm_ard_3-2-1_50KPV_2019-04-22_final.stac-item
2019-04-22 11:54:14	ga_s2bm_ard_3-2-1_50KNV_2019-04-22_final.stac-item
2019-04-22 11:54:18	ga_s2bm_ard_3-2-1_50KMV_2019-04-22_final.stac-item
2019-04-22 11:54:20	ga_s2bm_ard_3-2-1_50KQU_2019-04-22_final.stac-item
2019-04-22 11:54:25	ga_s2bm_ard_3-2-1_50KPU_2019-04-22_final.stac-item
2019-04-22 11:54:29	ga_s2bm_ard_3-2-1_50KNU_2019-04-22_final.stac-item
2019-04-22 11:54:29	ga_s2bm_ard_3-2-1_50JQT_2019-04-22_final.stac-item
2019-04-22 11:54:32	ga_s2bm_ard_3-2-1_50KMU_2019-04-22_final.stac-item
2019-04-22 11:54:40	ga_s2bm_ard_3-2-1_50KLU_2019-04-22_final.stac-item
2019-04-22 11:54:40	ga_s2bm_ard_3-2-1_50JPT_2019-04-22_final.stac-item
2019-04-22 11:54:44	ga_s2bm_ard_3-2-1_50JNT_2019-04-22_final.stac-item
2019-04-22 11:54:47	ga_s2bm_ard_3-2-1_50JMT_2019-04-22_final.stac-item
2019-04-22 11:54:51	ga_s2bm_ard_3-2-1_50JLT_2019-04-22_final.stac-item
2019-04-22 11:54:52	ga_s2bm_ard_3-2-1_50JPS_2019-04-22_final.stac-item
2019-04-22 11:54:54	ga_s2bm_ard_3-2-1_50JNS_2019-04-22_final.stac-item
2019-04-22 11:54:56	ga_s2bm_ard_3-2-1_50JMS_2019-04-22_final.stac-item
2019-04-22 11:54:56	ga_s2bm_ard_3-2-1_50JLT_2019-04-22_final.stac-item
2019-04-22 11:54:57	ga_s2bm_ard_3-2-1_50JLS_2019-04-22_final.stac-item
2019-04-22 11:54:59	ga_s2bm_ard_3-2-1_50JPS_2019-04-22_final.stac-item
2019-04-22 11:55:01	ga_s2bm_ard_3-2-1_50JNS_2019-04-22_final.stac-item
2019-04-22 11:55:02	ga_s2bm_ard_3-2-1_50JMS_2019-04-22_final.stac-item
2019-04-22 11:55:04	ga_s2bm_ard_3-2-1_50JLS_2019-04-22_final.stac-item
2019-04-22 11:55:09	ga_s2bm_ard_3-2-1_50JPR_2019-04-22_final.stac-item

Return to ga_s2bm_ard_3 on 22nd April 2019 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

2019

April

22nd