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
2022-05-13 11:46:46	ga_s2bm_ard_3-2-1_50HPJ_2022-05-13_final.stac-item
2022-05-13 11:46:50	ga_s2bm_ard_3-2-1_50HNJ_2022-05-13_final.stac-item
2022-05-13 11:46:51	ga_s2bm_ard_3-2-1_50HQH_2022-05-13_final.stac-item
2022-05-13 11:46:54	ga_s2bm_ard_3-2-1_50HMJ_2022-05-13_final.stac-item
2022-05-13 11:47:01	ga_s2bm_ard_3-2-1_50HPH_2022-05-13_final.stac-item
2022-05-13 11:47:05	ga_s2bm_ard_3-2-1_50HNH_2022-05-13_final.stac-item
2022-05-13 11:47:08	ga_s2bm_ard_3-2-1_50HMH_2022-05-13_final.stac-item
2022-05-13 11:47:12	ga_s2bm_ard_3-2-1_50HLH_2022-05-13_final.stac-item
2022-05-14 09:32:43	ga_s2bm_ard_3-2-1_56KNB_2022-05-14_final
2022-05-14 09:32:45	ga_s2bm_ard_3-2-1_56KMB_2022-05-14_final
2022-05-14 09:32:47	ga_s2bm_ard_3-2-1_56KLB_2022-05-14_final.stac-item
2022-05-14 09:32:51	ga_s2bm_ard_3-2-1_56KNA_2022-05-14_final
2022-05-14 09:32:54	ga_s2bm_ard_3-2-1_56KMA_2022-05-14_final.stac-item
2022-05-14 09:32:57	ga_s2bm_ard_3-2-1_56KLA_2022-05-14_final.stac-item
2022-05-14 09:33:11	ga_s2bm_ard_3-2-1_56KLV_2022-05-14_final.stac-item
2022-05-14 09:33:23	ga_s2bm_ard_3-2-1_56KMU_2022-05-14_final.stac-item
2022-05-14 09:33:26	ga_s2bm_ard_3-2-1_56KLU_2022-05-14_final.stac-item
2022-05-14 09:33:34	ga_s2bm_ard_3-2-1_56JNT_2022-05-14_final.stac-item
2022-05-14 09:33:38	ga_s2bm_ard_3-2-1_56JMT_2022-05-14_final.stac-item
2022-05-14 09:33:41	ga_s2bm_ard_3-2-1_56JLT_2022-05-14_final.stac-item
2022-05-14 09:33:48	ga_s2bm_ard_3-2-1_56JKT_2022-05-14_final.stac-item
2022-05-14 09:33:48	ga_s2bm_ard_3-2-1_56JNS_2022-05-14_final.stac-item
2022-05-14 09:33:52	ga_s2bm_ard_3-2-1_56JMS_2022-05-14_final.stac-item
2022-05-14 09:33:55	ga_s2bm_ard_3-2-1_56JLS_2022-05-14_final.stac-item
2022-05-14 09:33:59	ga_s2bm_ard_3-2-1_55JHM_2022-05-14_final.stac-item
2022-05-14 09:33:59	ga_s2bm_ard_3-2-1_56JKS_2022-05-14_final.stac-item
2022-05-14 09:34:07	ga_s2bm_ard_3-2-1_56JMR_2022-05-14_final.stac-item
2022-05-14 09:34:10	ga_s2bm_ard_3-2-1_56JLR_2022-05-14_final.stac-item
2022-05-14 09:34:13	ga_s2bm_ard_3-2-1_56JKR_2022-05-14_final.stac-item
2022-05-14 09:34:21	ga_s2bm_ard_3-2-1_56JMQ_2022-05-14_final.stac-item
2022-05-14 09:34:25	ga_s2bm_ard_3-2-1_56JLQ_2022-05-14_final.stac-item
2022-05-14 09:34:28	ga_s2bm_ard_3-2-1_56JKQ_2022-05-14_final.stac-item
2022-05-14 09:34:36	ga_s2bm_ard_3-2-1_56JMP_2022-05-14_final.stac-item
2022-05-14 09:34:39	ga_s2bm_ard_3-2-1_56JLP_2022-05-14_final.stac-item
2022-05-14 09:34:42	ga_s2bm_ard_3-2-1_56JNN_2022-05-14_final.stac-item
2022-05-14 09:34:42	ga_s2bm_ard_3-2-1_56JKP_2022-05-14_final.stac-item
2022-05-14 09:34:49	ga_s2bm_ard_3-2-1_55JGJ_2022-05-14_final.stac-item
2022-05-14 09:34:50	ga_s2bm_ard_3-2-1_56JMN_2022-05-14_final.stac-item
2022-05-14 09:34:54	ga_s2bm_ard_3-2-1_56JLN_2022-05-14_final.stac-item
2022-05-14 09:34:57	ga_s2bm_ard_3-2-1_56JKN_2022-05-14_final.stac-item
2022-05-14 09:35:00	ga_s2bm_ard_3-2-1_55JGH_2022-05-14_final.stac-item
2022-05-14 09:35:05	ga_s2bm_ard_3-2-1_56JMM_2022-05-14_final.stac-item
2022-05-14 09:35:08	ga_s2bm_ard_3-2-1_56JLM_2022-05-14_final.stac-item
2022-05-14 09:35:11	ga_s2bm_ard_3-2-1_56JKM_2022-05-14_final.stac-item
2022-05-14 09:35:14	ga_s2bm_ard_3-2-1_55JGG_2022-05-14_final.stac-item
2022-05-14 09:35:19	ga_s2bm_ard_3-2-1_56JML_2022-05-14_final.stac-item
2022-05-14 09:35:23	ga_s2bm_ard_3-2-1_56JLL_2022-05-14_final.stac-item
2022-05-14 09:35:26	ga_s2bm_ard_3-2-1_56JKL_2022-05-14_final.stac-item
2022-05-14 09:35:28	ga_s2bm_ard_3-2-1_55JGF_2022-05-14_final.stac-item
2022-05-14 09:35:34	ga_s2bm_ard_3-2-1_56HMK_2022-05-14_final.stac-item
2022-05-14 09:35:37	ga_s2bm_ard_3-2-1_56HLK_2022-05-14_final.stac-item
2022-05-14 09:35:41	ga_s2bm_ard_3-2-1_56HKK_2022-05-14_final.stac-item
2022-05-14 09:35:43	ga_s2bm_ard_3-2-1_55HGE_2022-05-14_final.stac-item
2022-05-14 09:35:47	ga_s2bm_ard_3-2-1_56HMJ_2022-05-14_final.stac-item
2022-05-14 09:35:48	ga_s2bm_ard_3-2-1_55HFE_2022-05-14_final.stac-item
2022-05-14 09:35:52	ga_s2bm_ard_3-2-1_56HLJ_2022-05-14_final.stac-item
2022-05-14 09:35:55	ga_s2bm_ard_3-2-1_56HKJ_2022-05-14_final.stac-item
2022-05-14 09:35:57	ga_s2bm_ard_3-2-1_55HGD_2022-05-14_final.stac-item
2022-05-14 09:36:01	ga_s2bm_ard_3-2-1_55HFD_2022-05-14_final.stac-item
2022-05-14 09:36:10	ga_s2bm_ard_3-2-1_56HKH_2022-05-14_final.stac-item
2022-05-14 09:36:12	ga_s2bm_ard_3-2-1_55HGC_2022-05-14_final.stac-item
2022-05-14 09:36:15	ga_s2bm_ard_3-2-1_55HFC_2022-05-14_final.stac-item
2022-05-14 11:13:35	ga_s2bm_ard_3-2-1_52KCA_2022-05-14_final.stac-item
2022-05-14 11:13:38	ga_s2bm_ard_3-2-1_52KBA_2022-05-14_final.stac-item
2022-05-14 11:13:53	ga_s2bm_ard_3-2-1_52KBV_2022-05-14_final.stac-item
2022-05-14 11:14:06	ga_s2bm_ard_3-2-1_52JET_2022-05-14_final.stac-item
2022-05-14 11:14:07	ga_s2bm_ard_3-2-1_52KBU_2022-05-14_final.stac-item
2022-05-14 11:14:15	ga_s2bm_ard_3-2-1_52JDT_2022-05-14_final.stac-item
2022-05-14 11:14:19	ga_s2bm_ard_3-2-1_52JCT_2022-05-14_final.stac-item
2022-05-14 11:14:22	ga_s2bm_ard_3-2-1_52JBT_2022-05-14_final.stac-item
2022-05-14 11:14:30	ga_s2bm_ard_3-2-1_52JDS_2022-05-14_final.stac-item
2022-05-14 11:14:33	ga_s2bm_ard_3-2-1_52JCS_2022-05-14_final.stac-item
2022-05-14 11:14:36	ga_s2bm_ard_3-2-1_52JBS_2022-05-14_final.stac-item
2022-05-14 11:14:45	ga_s2bm_ard_3-2-1_52JDR_2022-05-14_final.stac-item
2022-05-14 11:14:48	ga_s2bm_ard_3-2-1_52JCR_2022-05-14_final.stac-item
2022-05-14 11:14:51	ga_s2bm_ard_3-2-1_52JBR_2022-05-14_final.stac-item
2022-05-14 11:14:54	ga_s2bm_ard_3-2-1_51JYL_2022-05-14_final.stac-item
2022-05-14 11:14:58	ga_s2bm_ard_3-2-1_52JDQ_2022-05-14_final.stac-item
2022-05-14 11:15:02	ga_s2bm_ard_3-2-1_52JCQ_2022-05-14_final.stac-item
2022-05-14 11:15:06	ga_s2bm_ard_3-2-1_52JBQ_2022-05-14_final.stac-item
2022-05-14 11:15:09	ga_s2bm_ard_3-2-1_51JYK_2022-05-14_final.stac-item
2022-05-14 11:15:11	ga_s2bm_ard_3-2-1_52JDP_2022-05-14_final.stac-item
2022-05-14 11:15:17	ga_s2bm_ard_3-2-1_52JCP_2022-05-14_final.stac-item
2022-05-14 11:15:20	ga_s2bm_ard_3-2-1_52JBP_2022-05-14_final.stac-item
2022-05-14 11:15:23	ga_s2bm_ard_3-2-1_51JYJ_2022-05-14_final.stac-item
2022-05-14 11:15:28	ga_s2bm_ard_3-2-1_51JXJ_2022-05-14_final.stac-item
2022-05-14 11:15:31	ga_s2bm_ard_3-2-1_52JCN_2022-05-14_final.stac-item
2022-05-14 11:15:35	ga_s2bm_ard_3-2-1_52JBN_2022-05-14_final.stac-item
2022-05-14 11:15:38	ga_s2bm_ard_3-2-1_51JYH_2022-05-14_final.stac-item
2022-05-14 11:15:41	ga_s2bm_ard_3-2-1_51JXH_2022-05-14_final.stac-item
2022-05-14 11:15:46	ga_s2bm_ard_3-2-1_52JCM_2022-05-14_final.stac-item
2022-05-14 11:15:49	ga_s2bm_ard_3-2-1_52JBM_2022-05-14_final.stac-item
2022-05-14 11:15:52	ga_s2bm_ard_3-2-1_51JYG_2022-05-14_final.stac-item
2022-05-14 11:15:56	ga_s2bm_ard_3-2-1_51JXG_2022-05-14_final.stac-item
2022-05-14 11:16:01	ga_s2bm_ard_3-2-1_52JCL_2022-05-14_final.stac-item
2022-05-14 11:16:04	ga_s2bm_ard_3-2-1_52JBL_2022-05-14_final.stac-item
2022-05-14 11:16:06	ga_s2bm_ard_3-2-1_51JYF_2022-05-14_final.stac-item
2022-05-14 11:16:10	ga_s2bm_ard_3-2-1_51JXF_2022-05-14_final.stac-item
2022-05-14 11:16:18	ga_s2bm_ard_3-2-1_51JWF_2022-05-14_final.stac-item
2022-05-14 11:16:18	ga_s2bm_ard_3-2-1_52HBK_2022-05-14_final.stac-item

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

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