Search ga_s2bm_ard_3 on 10th 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
2019-04-10 09:32:53	ga_s2bm_ard_3-2-1_56KLB_2019-04-10_final.stac-item
2019-04-10 09:33:00	ga_s2bm_ard_3-2-1_56KMA_2019-04-10_final.stac-item
2019-04-10 09:33:03	ga_s2bm_ard_3-2-1_56KLA_2019-04-10_final.stac-item
2019-04-10 09:33:17	ga_s2bm_ard_3-2-1_56KLV_2019-04-10_final.stac-item
2019-04-10 09:33:29	ga_s2bm_ard_3-2-1_56KMU_2019-04-10_final.stac-item
2019-04-10 09:33:32	ga_s2bm_ard_3-2-1_56KLU_2019-04-10_final.stac-item
2019-04-10 09:33:40	ga_s2bm_ard_3-2-1_56JNT_2019-04-10_final.stac-item
2019-04-10 09:33:43	ga_s2bm_ard_3-2-1_56JMT_2019-04-10_final.stac-item
2019-04-10 09:33:47	ga_s2bm_ard_3-2-1_56JLT_2019-04-10_final.stac-item
2019-04-10 09:33:54	ga_s2bm_ard_3-2-1_56JKT_2019-04-10_final.stac-item
2019-04-10 09:33:54	ga_s2bm_ard_3-2-1_56JNS_2019-04-10_final.stac-item
2019-04-10 09:33:58	ga_s2bm_ard_3-2-1_56JMS_2019-04-10_final.stac-item
2019-04-10 09:34:01	ga_s2bm_ard_3-2-1_56JLS_2019-04-10_final.stac-item
2019-04-10 09:34:05	ga_s2bm_ard_3-2-1_55JHM_2019-04-10_final.stac-item
2019-04-10 09:34:05	ga_s2bm_ard_3-2-1_56JKS_2019-04-10_final.stac-item
2019-04-10 09:34:12	ga_s2bm_ard_3-2-1_56JMR_2019-04-10_final.stac-item
2019-04-10 09:34:16	ga_s2bm_ard_3-2-1_56JLR_2019-04-10_final.stac-item
2019-04-10 09:34:19	ga_s2bm_ard_3-2-1_56JKR_2019-04-10_final.stac-item
2019-04-10 09:34:27	ga_s2bm_ard_3-2-1_56JMQ_2019-04-10_final.stac-item
2019-04-10 09:34:30	ga_s2bm_ard_3-2-1_56JLQ_2019-04-10_final.stac-item
2019-04-10 09:34:33	ga_s2bm_ard_3-2-1_56JKQ_2019-04-10_final.stac-item
2019-04-10 09:34:41	ga_s2bm_ard_3-2-1_56JMP_2019-04-10_final.stac-item
2019-04-10 09:34:45	ga_s2bm_ard_3-2-1_56JLP_2019-04-10_final.stac-item
2019-04-10 09:34:48	ga_s2bm_ard_3-2-1_56JNN_2019-04-10_final.stac-item
2019-04-10 09:34:48	ga_s2bm_ard_3-2-1_56JKP_2019-04-10_final.stac-item
2019-04-10 09:34:54	ga_s2bm_ard_3-2-1_55JGJ_2019-04-10_final.stac-item
2019-04-10 09:34:56	ga_s2bm_ard_3-2-1_56JMN_2019-04-10_final.stac-item
2019-04-10 09:34:59	ga_s2bm_ard_3-2-1_56JLN_2019-04-10_final.stac-item
2019-04-10 09:35:03	ga_s2bm_ard_3-2-1_56JKN_2019-04-10_final.stac-item
2019-04-10 09:35:06	ga_s2bm_ard_3-2-1_55JGH_2019-04-10_final.stac-item
2019-04-10 09:35:10	ga_s2bm_ard_3-2-1_56JMM_2019-04-10_final.stac-item
2019-04-10 09:35:46	ga_s2bm_ard_3-2-1_56HKK_2019-04-10_final.stac-item
2019-04-10 09:35:57	ga_s2bm_ard_3-2-1_56HLJ_2019-04-10_final.stac-item
2019-04-10 09:36:01	ga_s2bm_ard_3-2-1_56HKJ_2019-04-10_final.stac-item
2019-04-10 09:36:16	ga_s2bm_ard_3-2-1_56HKH_2019-04-10_final.stac-item
2019-04-10 09:36:30	ga_s2bm_ard_3-2-1_56HKG_2019-04-10_final.stac-item
2019-04-10 09:36:36	ga_s2bm_ard_3-2-1_55HFB_2019-04-10_final
2019-04-10 09:36:45	ga_s2bm_ard_3-2-1_56HKF_2019-04-10_final.stac-item
2019-04-10 09:38:44	ga_s2bm_ard_3-2-1_55GCP_2019-04-10_final.stac-item
2019-04-10 09:38:53	ga_s2bm_ard_3-2-1_55GDN_2019-04-10_final.stac-item
2019-04-10 09:38:57	ga_s2bm_ard_3-2-1_55GCN_2019-04-10_final.stac-item
2019-04-10 11:10:29	ga_s2bm_ard_3-2-1_52LHN_2019-04-10_final.stac-item
2019-04-10 11:10:33	ga_s2bm_ard_3-2-1_52LGN_2019-04-10_final.stac-item
2019-04-10 11:10:41	ga_s2bm_ard_3-2-1_52LHM_2019-04-10_final.stac-item
2019-04-10 11:10:47	ga_s2bm_ard_3-2-1_52LGM_2019-04-10_final.stac-item
2019-04-10 11:11:02	ga_s2bm_ard_3-2-1_52LGL_2019-04-10_final.stac-item
2019-04-10 11:11:06	ga_s2bm_ard_3-2-1_52LFL_2019-04-10_final.stac-item
2019-04-10 11:11:17	ga_s2bm_ard_3-2-1_52LGK_2019-04-10_final.stac-item
2019-04-10 11:11:20	ga_s2bm_ard_3-2-1_52LFK_2019-04-10_final.stac-item
2019-04-10 11:11:31	ga_s2bm_ard_3-2-1_52LGJ_2019-04-10_final.stac-item
2019-04-10 11:11:35	ga_s2bm_ard_3-2-1_52LFJ_2019-04-10_final.stac-item
2019-04-10 11:11:38	ga_s2bm_ard_3-2-1_52LEJ_2019-04-10_final.stac-item
2019-04-10 11:11:41	ga_s2bm_ard_3-2-1_52LDJ_2019-04-10_final.stac-item
2019-04-10 11:11:44	ga_s2bm_ard_3-2-1_52LGH_2019-04-10_final.stac-item
2019-04-10 11:11:49	ga_s2bm_ard_3-2-1_52LFH_2019-04-10_final.stac-item
2019-04-10 11:11:52	ga_s2bm_ard_3-2-1_52LEH_2019-04-10_final.stac-item
2019-04-10 11:11:54	ga_s2bm_ard_3-2-1_52KGG_2019-04-10_final.stac-item
2019-04-10 11:11:56	ga_s2bm_ard_3-2-1_52LDH_2019-04-10_final.stac-item
2019-04-10 11:12:03	ga_s2bm_ard_3-2-1_52KFG_2019-04-10_final.stac-item
2019-04-10 11:12:07	ga_s2bm_ard_3-2-1_52KEG_2019-04-10_final.stac-item
2019-04-10 11:12:10	ga_s2bm_ard_3-2-1_52KDG_2019-04-10_final.stac-item
2019-04-10 11:12:15	ga_s2bm_ard_3-2-1_52KCG_2019-04-10_final.stac-item
2019-04-10 11:12:18	ga_s2bm_ard_3-2-1_52KFF_2019-04-10_final.stac-item
2019-04-10 11:12:21	ga_s2bm_ard_3-2-1_52KEF_2019-04-10_final.stac-item
2019-04-10 11:12:25	ga_s2bm_ard_3-2-1_52KDF_2019-04-10_final.stac-item
2019-04-10 11:12:28	ga_s2bm_ard_3-2-1_52KCF_2019-04-10_final.stac-item
2019-04-10 11:12:33	ga_s2bm_ard_3-2-1_52KFE_2019-04-10_final.stac-item
2019-04-10 11:12:36	ga_s2bm_ard_3-2-1_52KEE_2019-04-10_final.stac-item
2019-04-10 11:12:39	ga_s2bm_ard_3-2-1_52KDE_2019-04-10_final.stac-item
2019-04-10 11:12:42	ga_s2bm_ard_3-2-1_52KCE_2019-04-10_final.stac-item
2019-04-10 11:12:46	ga_s2bm_ard_3-2-1_52KFD_2019-04-10_final.stac-item
2019-04-10 11:12:50	ga_s2bm_ard_3-2-1_52KED_2019-04-10_final.stac-item
2019-04-10 11:12:54	ga_s2bm_ard_3-2-1_52KDD_2019-04-10_final.stac-item
2019-04-10 11:12:57	ga_s2bm_ard_3-2-1_52KCD_2019-04-10_final.stac-item
2019-04-10 11:12:58	ga_s2bm_ard_3-2-1_52KFC_2019-04-10_final.stac-item
2019-04-10 11:13:05	ga_s2bm_ard_3-2-1_52KEC_2019-04-10_final.stac-item
2019-04-10 11:13:08	ga_s2bm_ard_3-2-1_52KDC_2019-04-10_final.stac-item
2019-04-10 11:13:11	ga_s2bm_ard_3-2-1_52KCC_2019-04-10_final.stac-item
2019-04-10 11:13:14	ga_s2bm_ard_3-2-1_52KEB_2019-04-10_final.stac-item
2019-04-10 11:13:15	ga_s2bm_ard_3-2-1_52KBC_2019-04-10_final.stac-item
2019-04-10 11:13:16	ga_s2bm_ard_3-2-1_52KDC_2019-04-10_final.stac-item
2019-04-10 11:13:16	ga_s2bm_ard_3-2-1_52KDB_2019-04-10_final.stac-item
2019-04-10 11:13:17	ga_s2bm_ard_3-2-1_52KCC_2019-04-10_final.stac-item
2019-04-10 11:13:18	ga_s2bm_ard_3-2-1_52KCB_2019-04-10_final.stac-item
2019-04-10 11:13:19	ga_s2bm_ard_3-2-1_52KBC_2019-04-10_final.stac-item
2019-04-10 11:13:21	ga_s2bm_ard_3-2-1_52KEB_2019-04-10_final.stac-item
2019-04-10 11:13:23	ga_s2bm_ard_3-2-1_52KDB_2019-04-10_final.stac-item
2019-04-10 11:13:26	ga_s2bm_ard_3-2-1_52KCB_2019-04-10_final.stac-item
2019-04-10 11:13:30	ga_s2bm_ard_3-2-1_52KBB_2019-04-10_final.stac-item
2019-04-10 11:13:33	ga_s2bm_ard_3-2-1_51KZS_2019-04-10_final.stac-item
2019-04-10 11:13:34	ga_s2bm_ard_3-2-1_52KEA_2019-04-10_final.stac-item
2019-04-10 11:13:38	ga_s2bm_ard_3-2-1_52KDA_2019-04-10_final.stac-item
2019-04-10 11:13:41	ga_s2bm_ard_3-2-1_52KCA_2019-04-10_final.stac-item
2019-04-10 11:13:44	ga_s2bm_ard_3-2-1_52KBA_2019-04-10_final.stac-item
2019-04-10 11:13:44	ga_s2bm_ard_3-2-1_51KZR_2019-04-10_final.stac-item
2019-04-10 11:13:48	ga_s2bm_ard_3-2-1_52KEV_2019-04-10_final.stac-item
2019-04-10 11:13:52	ga_s2bm_ard_3-2-1_52KDV_2019-04-10_final.stac-item
2019-04-10 11:13:55	ga_s2bm_ard_3-2-1_52KCV_2019-04-10_final.stac-item
2019-04-10 11:16:41	ga_s2bm_ard_3-2-1_51HYD_2019-04-10_final.stac-item
2019-04-10 11:16:45	ga_s2bm_ard_3-2-1_51HXD_2019-04-10_final.stac-item

Return to ga_s2bm_ard_3 on 10th 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

10th