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
2022-10-09 12:14:39	ga_s2bm_ard_3-2-1_49JGN_2022-10-09_final.stac-item
2022-10-09 12:14:43	ga_s2bm_ard_3-2-1_49JFN_2022-10-09_final.stac-item
2022-10-09 12:14:54	ga_s2bm_ard_3-2-1_49JGM_2022-10-09_final.stac-item
2022-10-09 12:14:58	ga_s2bm_ard_3-2-1_49JFM_2022-10-09_final.stac-item
2022-10-09 12:15:08	ga_s2bm_ard_3-2-1_49JGL_2022-10-09_final.stac-item
2022-10-09 12:15:18	ga_s2bm_ard_3-2-1_49JGK_2022-10-09_final.stac-item
2022-10-10 10:01:34	ga_s2bm_ard_3-2-1_55KDB_2022-10-10_final.stac-item
2022-10-10 10:01:36	ga_s2bm_ard_3-2-1_55KCB_2022-10-10_final.stac-item
2022-10-10 10:01:48	ga_s2bm_ard_3-2-1_55KDA_2022-10-10_final.stac-item
2022-10-10 10:01:51	ga_s2bm_ard_3-2-1_55KCA_2022-10-10_final.stac-item
2022-10-10 10:02:06	ga_s2bm_ard_3-2-1_55KCV_2022-10-10_final.stac-item
2022-10-10 10:02:13	ga_s2bm_ard_3-2-1_55KBV_2022-10-10_final.stac-item
2022-10-10 10:02:20	ga_s2bm_ard_3-2-1_55KCU_2022-10-10_final.stac-item
2022-10-10 10:02:24	ga_s2bm_ard_3-2-1_55KBU_2022-10-10_final.stac-item
2022-10-10 10:02:35	ga_s2bm_ard_3-2-1_55KCT_2022-10-10_final.stac-item
2022-10-10 10:02:38	ga_s2bm_ard_3-2-1_55KBT_2022-10-10_final.stac-item
2022-10-10 10:02:40	ga_s2bm_ard_3-2-1_54KZC_2022-10-10_final.stac-item
2022-10-10 10:02:50	ga_s2bm_ard_3-2-1_55KCS_2022-10-10_final.stac-item
2022-10-10 10:02:53	ga_s2bm_ard_3-2-1_55KBS_2022-10-10_final.stac-item
2022-10-10 10:02:53	ga_s2bm_ard_3-2-1_54KZB_2022-10-10_final.stac-item
2022-10-10 10:03:04	ga_s2bm_ard_3-2-1_55KCR_2022-10-10_final.stac-item
2022-10-10 10:03:07	ga_s2bm_ard_3-2-1_55KBR_2022-10-10_final.stac-item
2022-10-10 10:03:07	ga_s2bm_ard_3-2-1_54KZA_2022-10-10_final.stac-item
2022-10-10 10:03:19	ga_s2bm_ard_3-2-1_55KCQ_2022-10-10_final.stac-item
2022-10-10 10:03:22	ga_s2bm_ard_3-2-1_55KBQ_2022-10-10_final.stac-item
2022-10-10 10:03:30	ga_s2bm_ard_3-2-1_55KDP_2022-10-10_final.stac-item
2022-10-10 10:03:33	ga_s2bm_ard_3-2-1_55KCP_2022-10-10_final.stac-item
2022-10-10 10:03:36	ga_s2bm_ard_3-2-1_55KBP_2022-10-10_final.stac-item
2022-10-10 10:03:41	ga_s2bm_ard_3-2-1_54KYU_2022-10-10_final.stac-item
2022-10-10 10:03:44	ga_s2bm_ard_3-2-1_55JDN_2022-10-10_final.stac-item
2022-10-10 10:03:48	ga_s2bm_ard_3-2-1_55JCN_2022-10-10_final.stac-item
2022-10-10 10:03:51	ga_s2bm_ard_3-2-1_54JZT_2022-10-10_final.stac-item
2022-10-10 10:03:51	ga_s2bm_ard_3-2-1_55JBN_2022-10-10_final.stac-item
2022-10-10 10:03:54	ga_s2bm_ard_3-2-1_54JYT_2022-10-10_final.stac-item
2022-10-10 10:03:59	ga_s2bm_ard_3-2-1_55JDM_2022-10-10_final.stac-item
2022-10-10 10:04:02	ga_s2bm_ard_3-2-1_55JCM_2022-10-10_final.stac-item
2022-10-10 10:04:05	ga_s2bm_ard_3-2-1_54JZS_2022-10-10_final.stac-item
2022-10-10 11:43:42	ga_s2bm_ard_3-2-1_51KUR_2022-10-10_final.stac-item
2022-10-10 11:43:45	ga_s2bm_ard_3-2-1_51KTR_2022-10-10_final.stac-item
2022-10-10 11:43:45	ga_s2bm_ard_3-2-1_50KRA_2022-10-10_final.stac-item
2022-10-10 11:43:49	ga_s2bm_ard_3-2-1_50KQA_2022-10-10_final.stac-item
2022-10-10 11:43:56	ga_s2bm_ard_3-2-1_51KUQ_2022-10-10_final.stac-item
2022-10-10 11:44:00	ga_s2bm_ard_3-2-1_51KTQ_2022-10-10_final.stac-item
2022-10-10 11:44:00	ga_s2bm_ard_3-2-1_50KRV_2022-10-10_final.stac-item
2022-10-10 11:44:04	ga_s2bm_ard_3-2-1_50KQV_2022-10-10_final.stac-item
2022-10-10 11:44:08	ga_s2bm_ard_3-2-1_50KPV_2022-10-10_final.stac-item
2022-10-10 11:44:11	ga_s2bm_ard_3-2-1_51KUP_2022-10-10_final.stac-item
2022-10-10 11:44:14	ga_s2bm_ard_3-2-1_50KRU_2022-10-10_final.stac-item
2022-10-10 11:44:14	ga_s2bm_ard_3-2-1_51KTP_2022-10-10_final.stac-item
2022-10-10 11:44:18	ga_s2bm_ard_3-2-1_50KQU_2022-10-10_final.stac-item
2022-10-10 11:44:21	ga_s2bm_ard_3-2-1_50KPU_2022-10-10_final.stac-item
2022-10-10 11:44:25	ga_s2bm_ard_3-2-1_51JUN_2022-10-10_final.stac-item
2022-10-10 11:44:29	ga_s2bm_ard_3-2-1_50JRT_2022-10-10_final.stac-item
2022-10-10 11:44:29	ga_s2bm_ard_3-2-1_51JTN_2022-10-10_final.stac-item
2022-10-10 11:44:33	ga_s2bm_ard_3-2-1_50JQT_2022-10-10_final.stac-item
2022-10-10 11:44:36	ga_s2bm_ard_3-2-1_50JPT_2022-10-10_final.stac-item
2022-10-10 11:44:39	ga_s2bm_ard_3-2-1_51JUM_2022-10-10_final.stac-item
2022-10-10 11:44:43	ga_s2bm_ard_3-2-1_50JRS_2022-10-10_final.stac-item
2022-10-10 11:44:43	ga_s2bm_ard_3-2-1_51JTM_2022-10-10_final.stac-item
2022-10-10 11:44:47	ga_s2bm_ard_3-2-1_50JQS_2022-10-10_final.stac-item
2022-10-10 11:44:49	ga_s2bm_ard_3-2-1_51JUL_2022-10-10_final.stac-item
2022-10-10 11:44:51	ga_s2bm_ard_3-2-1_50JPS_2022-10-10_final.stac-item
2022-10-10 11:44:58	ga_s2bm_ard_3-2-1_51JTL_2022-10-10_final.stac-item
2022-10-10 11:45:01	ga_s2bm_ard_3-2-1_50JQR_2022-10-10_final.stac-item
2022-10-10 11:45:05	ga_s2bm_ard_3-2-1_50JPR_2022-10-10_final.stac-item
2022-10-10 11:45:09	ga_s2bm_ard_3-2-1_50JNR_2022-10-10_final.stac-item
2022-10-10 11:45:12	ga_s2bm_ard_3-2-1_51JTK_2022-10-10_final.stac-item
2022-10-10 11:45:16	ga_s2bm_ard_3-2-1_50JQQ_2022-10-10_final.stac-item
2022-10-10 11:45:19	ga_s2bm_ard_3-2-1_50JPQ_2022-10-10_final.stac-item
2022-10-10 11:45:23	ga_s2bm_ard_3-2-1_50JNQ_2022-10-10_final.stac-item
2022-10-10 11:45:27	ga_s2bm_ard_3-2-1_51JTJ_2022-10-10_final.stac-item
2022-10-10 11:45:30	ga_s2bm_ard_3-2-1_50JQP_2022-10-10_final.stac-item
2022-10-10 11:45:34	ga_s2bm_ard_3-2-1_50JPP_2022-10-10_final.stac-item
2022-10-10 11:45:37	ga_s2bm_ard_3-2-1_50JNP_2022-10-10_final.stac-item
2022-10-10 11:45:42	ga_s2bm_ard_3-2-1_51JTH_2022-10-10_final.stac-item
2022-10-10 11:45:45	ga_s2bm_ard_3-2-1_50JQN_2022-10-10_final.stac-item
2022-10-10 11:45:48	ga_s2bm_ard_3-2-1_50JPN_2022-10-10_final.stac-item
2022-10-10 11:45:52	ga_s2bm_ard_3-2-1_50JNN_2022-10-10_final.stac-item
2022-10-10 11:45:55	ga_s2bm_ard_3-2-1_51JTG_2022-10-10_final.stac-item
2022-10-10 11:45:59	ga_s2bm_ard_3-2-1_50JQM_2022-10-10_final.stac-item
2022-10-10 11:46:03	ga_s2bm_ard_3-2-1_50JPM_2022-10-10_final.stac-item
2022-10-10 11:46:06	ga_s2bm_ard_3-2-1_51JTF_2022-10-10_final.stac-item
2022-10-10 11:46:06	ga_s2bm_ard_3-2-1_50JNM_2022-10-10_final.stac-item
2022-10-10 11:46:11	ga_s2bm_ard_3-2-1_50JMM_2022-10-10_final.stac-item
2022-10-10 11:46:13	ga_s2bm_ard_3-2-1_50JQL_2022-10-10_final.stac-item
2022-10-10 11:46:17	ga_s2bm_ard_3-2-1_50JPL_2022-10-10_final.stac-item
2022-10-10 11:46:21	ga_s2bm_ard_3-2-1_50JNL_2022-10-10_final.stac-item
2022-10-10 11:46:24	ga_s2bm_ard_3-2-1_50JML_2022-10-10_final.stac-item
2022-10-10 11:46:28	ga_s2bm_ard_3-2-1_50HQK_2022-10-10_final.stac-item
2022-10-10 11:46:32	ga_s2bm_ard_3-2-1_50HPK_2022-10-10_final.stac-item
2022-10-10 11:46:36	ga_s2bm_ard_3-2-1_50HNK_2022-10-10_final.stac-item
2022-10-10 11:46:39	ga_s2bm_ard_3-2-1_50HMK_2022-10-10_final.stac-item
2022-10-10 11:46:41	ga_s2bm_ard_3-2-1_50HQJ_2022-10-10_final.stac-item
2022-10-10 11:46:46	ga_s2bm_ard_3-2-1_50HPJ_2022-10-10_final.stac-item
2022-10-10 11:46:50	ga_s2bm_ard_3-2-1_50HNJ_2022-10-10_final.stac-item
2022-10-10 11:46:51	ga_s2bm_ard_3-2-1_50HQH_2022-10-10_final.stac-item
2022-10-10 11:46:54	ga_s2bm_ard_3-2-1_50HMJ_2022-10-10_final.stac-item
2022-10-10 11:47:00	ga_s2bm_ard_3-2-1_50HPH_2022-10-10_final.stac-item
2022-10-10 11:47:05	ga_s2bm_ard_3-2-1_50HNH_2022-10-10_final.stac-item
2022-10-10 11:47:08	ga_s2bm_ard_3-2-1_50HMH_2022-10-10_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

All years