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
2023-12-03 12:14:53	ga_s2bm_ard_3-2-1_49JGM_2023-12-03_final
2023-12-03 12:14:57	ga_s2bm_ard_3-2-1_49JFM_2023-12-03_final
2023-12-03 12:15:07	ga_s2bm_ard_3-2-1_49JGL_2023-12-03_final
2023-12-03 12:15:11	ga_s2bm_ard_3-2-1_49JFL_2023-12-03_nrt
2023-12-03 12:15:16	ga_s2bm_ard_3-2-1_49JGK_2023-12-03_final
2023-12-03 12:15:17	ga_s2bm_ard_3-2-1_49JFK_2023-12-03_nrt
2023-12-04 08:24:22	ga_s2bm_ard_3-2-1_59JKH_2023-12-03_final
2023-12-04 08:24:23	ga_s2bm_ard_3-2-1_58JGN_2023-12-03_final
2023-12-04 08:31:39	ga_s2bm_ard_3-2-1_57FVV_2023-12-03_final
2023-12-04 09:59:35	ga_s2bm_ard_3-2-1_55LHJ_2023-12-04_nrt
2023-12-04 09:59:41	ga_s2bm_ard_3-2-1_55LGJ_2023-12-04_nrt
2023-12-04 09:59:44	ga_s2bm_ard_3-2-1_55LFJ_2023-12-04_nrt
2023-12-04 09:59:45	ga_s2bm_ard_3-2-1_55LHH_2023-12-04_nrt
2023-12-04 09:59:47	ga_s2bm_ard_3-2-1_55LEJ_2023-12-04_nrt
2023-12-04 09:59:55	ga_s2bm_ard_3-2-1_55LGH_2023-12-04_nrt
2023-12-04 09:59:59	ga_s2bm_ard_3-2-1_55LFH_2023-12-04_nrt
2023-12-04 10:00:02	ga_s2bm_ard_3-2-1_55LEH_2023-12-04_nrt
2023-12-04 10:00:06	ga_s2bm_ard_3-2-1_55LDH_2023-12-04_nrt
2023-12-04 10:00:10	ga_s2bm_ard_3-2-1_55LGG_2023-12-04_nrt
2023-12-04 10:00:13	ga_s2bm_ard_3-2-1_55LFG_2023-12-04_nrt
2023-12-04 10:00:16	ga_s2bm_ard_3-2-1_55LEG_2023-12-04_nrt
2023-12-04 10:00:19	ga_s2bm_ard_3-2-1_55LDG_2023-12-04_nrt
2023-12-04 10:00:24	ga_s2bm_ard_3-2-1_55LGF_2023-12-04_nrt
2023-12-04 10:00:28	ga_s2bm_ard_3-2-1_55LFF_2023-12-04_nrt
2023-12-04 10:00:31	ga_s2bm_ard_3-2-1_55LEF_2023-12-04_nrt
2023-12-04 10:00:34	ga_s2bm_ard_3-2-1_55LDF_2023-12-04_nrt
2023-12-04 10:00:38	ga_s2bm_ard_3-2-1_55LGE_2023-12-04_nrt
2023-12-04 10:00:42	ga_s2bm_ard_3-2-1_55LFE_2023-12-04_nrt
2023-12-04 10:00:46	ga_s2bm_ard_3-2-1_55LEE_2023-12-04_nrt
2023-12-04 10:00:49	ga_s2bm_ard_3-2-1_55LGD_2023-12-04_nrt
2023-12-04 10:00:49	ga_s2bm_ard_3-2-1_55LDE_2023-12-04_nrt
2023-12-04 10:00:56	ga_s2bm_ard_3-2-1_55LFD_2023-12-04_nrt
2023-12-04 10:01:00	ga_s2bm_ard_3-2-1_55LED_2023-12-04_nrt
2023-12-04 10:01:03	ga_s2bm_ard_3-2-1_55LDD_2023-12-04_nrt
2023-12-04 10:01:09	ga_s2bm_ard_3-2-1_55LCD_2023-12-04_final
2023-12-04 10:01:11	ga_s2bm_ard_3-2-1_55LFC_2023-12-04_final
2023-12-04 10:01:15	ga_s2bm_ard_3-2-1_55LEC_2023-12-04_final
2023-12-04 10:01:18	ga_s2bm_ard_3-2-1_55LDC_2023-12-04_nrt
2023-12-04 10:01:21	ga_s2bm_ard_3-2-1_55LCC_2023-12-04_final
2023-12-04 10:01:26	ga_s2bm_ard_3-2-1_55KFB_2023-12-04_final
2023-12-04 10:01:29	ga_s2bm_ard_3-2-1_55KEB_2023-12-04_final
2023-12-04 10:01:32	ga_s2bm_ard_3-2-1_55KDB_2023-12-04_final
2023-12-04 10:01:35	ga_s2bm_ard_3-2-1_55KCB_2023-12-04_final
2023-12-04 10:01:40	ga_s2bm_ard_3-2-1_55KFA_2023-12-04_final
2023-12-04 10:01:44	ga_s2bm_ard_3-2-1_55KEA_2023-12-04_nrt
2023-12-04 10:01:47	ga_s2bm_ard_3-2-1_55KDA_2023-12-04_final
2023-12-04 10:01:50	ga_s2bm_ard_3-2-1_55KCA_2023-12-04_final
2023-12-04 10:01:52	ga_s2bm_ard_3-2-1_55KFV_2023-12-04_nrt
2023-12-04 10:01:58	ga_s2bm_ard_3-2-1_55KEV_2023-12-04_final
2023-12-04 10:02:02	ga_s2bm_ard_3-2-1_55KDV_2023-12-04_final
2023-12-04 10:02:02	ga_s2bm_ard_3-2-1_55KFU_2023-12-04_final
2023-12-04 10:02:05	ga_s2bm_ard_3-2-1_55KCV_2023-12-04_final
2023-12-04 10:02:12	ga_s2bm_ard_3-2-1_55KBV_2023-12-04_final
2023-12-04 10:02:12	ga_s2bm_ard_3-2-1_55KEU_2023-12-04_final
2023-12-04 10:02:16	ga_s2bm_ard_3-2-1_55KDU_2023-12-04_final
2023-12-04 10:02:19	ga_s2bm_ard_3-2-1_55KCU_2023-12-04_final
2023-12-04 10:02:23	ga_s2bm_ard_3-2-1_55KBU_2023-12-04_final
2023-12-04 10:02:27	ga_s2bm_ard_3-2-1_55KET_2023-12-04_final
2023-12-04 10:02:31	ga_s2bm_ard_3-2-1_55KDT_2023-12-04_final
2023-12-04 10:02:34	ga_s2bm_ard_3-2-1_55KCT_2023-12-04_final
2023-12-04 10:02:37	ga_s2bm_ard_3-2-1_55KBT_2023-12-04_final
2023-12-04 10:02:39	ga_s2bm_ard_3-2-1_54KZC_2023-12-04_final
2023-12-04 10:02:42	ga_s2bm_ard_3-2-1_55KES_2023-12-04_final
2023-12-04 10:02:45	ga_s2bm_ard_3-2-1_55KDS_2023-12-04_final
2023-12-04 10:02:48	ga_s2bm_ard_3-2-1_55KCS_2023-12-04_final
2023-12-04 10:02:51	ga_s2bm_ard_3-2-1_55KBS_2023-12-04_final
2023-12-04 10:02:52	ga_s2bm_ard_3-2-1_54KZB_2023-12-04_final
2023-12-04 10:02:55	ga_s2bm_ard_3-2-1_55KER_2023-12-04_final
2023-12-04 10:03:00	ga_s2bm_ard_3-2-1_55KDR_2023-12-04_final
2023-12-04 10:03:03	ga_s2bm_ard_3-2-1_55KCR_2023-12-04_final
2023-12-04 10:03:06	ga_s2bm_ard_3-2-1_55KEQ_2023-12-04_final
2023-12-04 10:03:06	ga_s2bm_ard_3-2-1_55KBR_2023-12-04_final
2023-12-04 10:03:06	ga_s2bm_ard_3-2-1_54KZA_2023-12-04_final
2023-12-04 10:03:14	ga_s2bm_ard_3-2-1_55KDQ_2023-12-04_final
2023-12-04 10:03:18	ga_s2bm_ard_3-2-1_55KCQ_2023-12-04_final
2023-12-04 10:03:21	ga_s2bm_ard_3-2-1_55KBQ_2023-12-04_final
2023-12-04 10:03:21	ga_s2bm_ard_3-2-1_54KZV_2023-12-04_final
2023-12-04 10:03:28	ga_s2bm_ard_3-2-1_55KDP_2023-12-04_final
2023-12-04 10:03:32	ga_s2bm_ard_3-2-1_55KCP_2023-12-04_final
2023-12-04 10:03:35	ga_s2bm_ard_3-2-1_55KBP_2023-12-04_final
2023-12-04 10:03:35	ga_s2bm_ard_3-2-1_54KZU_2023-12-04_final
2023-12-04 10:03:40	ga_s2bm_ard_3-2-1_54KYU_2023-12-04_final
2023-12-04 10:03:43	ga_s2bm_ard_3-2-1_55JDN_2023-12-04_final
2023-12-04 10:03:47	ga_s2bm_ard_3-2-1_55JCN_2023-12-04_final
2023-12-04 10:03:50	ga_s2bm_ard_3-2-1_54JZT_2023-12-04_final
2023-12-04 10:03:50	ga_s2bm_ard_3-2-1_55JBN_2023-12-04_final
2023-12-04 10:03:53	ga_s2bm_ard_3-2-1_54JYT_2023-12-04_final
2023-12-04 10:03:58	ga_s2bm_ard_3-2-1_55JDM_2023-12-04_final
2023-12-04 10:04:01	ga_s2bm_ard_3-2-1_55JCM_2023-12-04_final
2023-12-04 10:04:04	ga_s2bm_ard_3-2-1_54JZS_2023-12-04_final
2023-12-04 10:04:04	ga_s2bm_ard_3-2-1_55JBM_2023-12-04_final
2023-12-04 10:04:08	ga_s2bm_ard_3-2-1_54JYS_2023-12-04_final
2023-12-04 10:04:11	ga_s2bm_ard_3-2-1_55JDL_2023-12-04_final
2023-12-04 10:04:16	ga_s2bm_ard_3-2-1_55JCL_2023-12-04_final
2023-12-04 10:04:19	ga_s2bm_ard_3-2-1_55JBL_2023-12-04_final
2023-12-04 10:04:21	ga_s2bm_ard_3-2-1_55JDK_2023-12-04_final
2023-12-04 11:46:40	ga_s2bm_ard_3-2-1_50HQJ_2023-12-04_final
2023-12-04 11:46:50	ga_s2bm_ard_3-2-1_50HQH_2023-12-04_final
2023-12-04 11:47:25	ga_s2bm_ard_3-2-1_50HPF_2023-12-04_nrt
2023-12-04 11:47:27	ga_s2bm_ard_3-2-1_50HNF_2023-12-04_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