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
2018-10-09 11:00:38	ga_s2bm_ard_3-2-1_52JDS_2018-10-09_final.stac-item
2018-10-09 11:00:38	ga_s2bm_ard_3-2-1_52JDT_2018-10-09_final.stac-item
2018-10-09 11:00:38	ga_s2bm_ard_3-2-1_52KDU_2018-10-09_final.stac-item
2018-10-09 11:00:38	ga_s2bm_ard_3-2-1_52KDV_2018-10-09_final.stac-item
2018-10-12 11:07:09	ga_s2bm_ard_3-2-1_52LDJ_2018-10-12_final.stac-item
2018-10-12 11:11:18	ga_s2bm_ard_3-2-1_52KDA_2018-10-12_final.stac-item
2018-10-12 11:11:18	ga_s2bm_ard_3-2-1_52KDB_2018-10-12_final.stac-item
2018-10-12 11:11:18	ga_s2bm_ard_3-2-1_52KDC_2018-10-12_final.stac-item
2018-10-12 11:11:18	ga_s2bm_ard_3-2-1_52KDD_2018-10-12_final.stac-item
2018-10-12 11:11:18	ga_s2bm_ard_3-2-1_52KDE_2018-10-12_final.stac-item
2018-10-12 11:11:18	ga_s2bm_ard_3-2-1_52KDF_2018-10-12_final.stac-item
2018-10-12 11:11:18	ga_s2bm_ard_3-2-1_52KDG_2018-10-12_final.stac-item
2018-10-12 11:11:18	ga_s2bm_ard_3-2-1_52KDV_2018-10-12_final.stac-item
2018-10-12 11:11:18	ga_s2bm_ard_3-2-1_52LDH_2018-10-12_final.stac-item
2018-10-12 11:11:18	ga_s2bm_ard_3-2-1_52LDJ_2018-10-12_final.stac-item
2018-10-12 11:11:18	ga_s2bm_ard_3-2-1_52JDT_2018-10-12_final.stac-item
2018-10-12 11:11:18	ga_s2bm_ard_3-2-1_52KDU_2018-10-12_final.stac-item
2019-05-07 11:05:28	ga_s2bm_ard_3-2-1_52JDP_2019-05-07_final.stac-item
2019-05-07 11:05:42	ga_s2bm_ard_3-2-1_52JDN_2019-05-07_final.stac-item
2019-05-07 11:05:53	ga_s2bm_ard_3-2-1_52JEM_2019-05-07_final.stac-item
2019-05-07 11:05:57	ga_s2bm_ard_3-2-1_52JDM_2019-05-07_final.stac-item
2019-05-07 11:06:07	ga_s2bm_ard_3-2-1_52JEL_2019-05-07_final.stac-item
2019-05-07 11:06:11	ga_s2bm_ard_3-2-1_52JDL_2019-05-07_final.stac-item
2019-05-30 11:11:45	ga_s2bm_ard_3-2-1_52LDJ_2019-05-30_final.stac-item
2019-05-30 11:12:00	ga_s2bm_ard_3-2-1_52LDH_2019-05-30_final.stac-item
2019-05-30 11:12:14	ga_s2bm_ard_3-2-1_52KDG_2019-05-30_final.stac-item
2019-05-30 11:12:29	ga_s2bm_ard_3-2-1_52KDF_2019-05-30_final.stac-item
2019-05-30 11:12:44	ga_s2bm_ard_3-2-1_52KDE_2019-05-30_final.stac-item
2019-05-30 11:12:58	ga_s2bm_ard_3-2-1_52KDD_2019-05-30_final.stac-item
2019-05-30 11:13:13	ga_s2bm_ard_3-2-1_52KDC_2019-05-30_final.stac-item
2019-05-30 11:13:20	ga_s2bm_ard_3-2-1_52KDC_2019-05-30_final.stac-item
2019-05-30 11:13:20	ga_s2bm_ard_3-2-1_52KDB_2019-05-30_final.stac-item
2019-05-30 11:13:27	ga_s2bm_ard_3-2-1_52KDB_2019-05-30_final.stac-item
2019-05-30 11:13:42	ga_s2bm_ard_3-2-1_52KDA_2019-05-30_final.stac-item
2019-05-30 11:13:56	ga_s2bm_ard_3-2-1_52KDV_2019-05-30_final.stac-item
2019-05-30 11:14:11	ga_s2bm_ard_3-2-1_52KDU_2019-05-30_final.stac-item
2023-03-27 11:03:56	ga_s2bm_ard_3-2-1_52KDV_2023-03-27_final
2023-03-27 11:04:09	ga_s2bm_ard_3-2-1_52KDU_2023-03-27_final
2023-03-27 11:04:24	ga_s2bm_ard_3-2-1_52JDT_2023-03-27_final
2023-03-27 11:04:39	ga_s2bm_ard_3-2-1_52JDS_2023-03-27_final
2023-03-27 11:04:53	ga_s2bm_ard_3-2-1_52JDR_2023-03-27_final
2023-03-27 11:05:08	ga_s2bm_ard_3-2-1_52JDQ_2023-03-27_final
2023-03-27 11:05:22	ga_s2bm_ard_3-2-1_52JDP_2023-03-27_final
2023-03-27 11:05:37	ga_s2bm_ard_3-2-1_52JDN_2023-03-27_final
2023-03-27 11:05:48	ga_s2bm_ard_3-2-1_52JEM_2023-03-27_final
2023-03-27 11:05:51	ga_s2bm_ard_3-2-1_52JDM_2023-03-27_final
2023-03-27 11:06:02	ga_s2bm_ard_3-2-1_52JEL_2023-03-27_final
2023-03-27 11:06:06	ga_s2bm_ard_3-2-1_52JDL_2023-03-27_final
2023-03-30 11:11:00	ga_s2bm_ard_3-2-1_52LDM_2023-03-30_nrt
2023-03-30 11:11:12	ga_s2bm_ard_3-2-1_52LDL_2023-03-30_nrt
2023-03-30 11:11:25	ga_s2bm_ard_3-2-1_52LDK_2023-03-30_nrt
2023-03-30 11:11:33	ga_s2bm_ard_3-2-1_52LDK_2023-03-30_nrt
2023-03-30 11:11:33	ga_s2bm_ard_3-2-1_52LDJ_2023-03-30_final
2023-03-30 11:11:40	ga_s2bm_ard_3-2-1_52LDJ_2023-03-30_final
2023-03-30 11:11:55	ga_s2bm_ard_3-2-1_52LDH_2023-03-30_final
2023-03-30 11:12:09	ga_s2bm_ard_3-2-1_52KDG_2023-03-30_final
2023-03-30 11:12:24	ga_s2bm_ard_3-2-1_52KDF_2023-03-30_final
2023-03-30 11:12:38	ga_s2bm_ard_3-2-1_52KDE_2023-03-30_final
2023-03-30 11:12:53	ga_s2bm_ard_3-2-1_52KDD_2023-03-30_final
2023-03-30 11:13:08	ga_s2bm_ard_3-2-1_52KDC_2023-03-30_final
2023-03-30 11:13:22	ga_s2bm_ard_3-2-1_52KDB_2023-03-30_final
2023-03-30 11:13:37	ga_s2bm_ard_3-2-1_52KDA_2023-03-30_final
2023-03-30 11:13:51	ga_s2bm_ard_3-2-1_52KDV_2023-03-30_final
2023-03-30 11:14:06	ga_s2bm_ard_3-2-1_52KDU_2023-03-30_final
2023-03-30 11:14:20	ga_s2bm_ard_3-2-1_52JDT_2023-03-30_final
2023-04-02 11:20:23	ga_s2bm_ard_3-2-1_52LDP_2023-04-02_nrt
2023-04-02 11:20:37	ga_s2bm_ard_3-2-1_52LDN_2023-04-02_nrt
2023-04-02 11:20:52	ga_s2bm_ard_3-2-1_52LDM_2023-04-02_nrt
2023-04-02 11:21:06	ga_s2bm_ard_3-2-1_52LDL_2023-04-02_nrt
2023-04-02 11:21:21	ga_s2bm_ard_3-2-1_52LDK_2023-04-02_nrt
2023-04-03 10:56:21	ga_s2bm_ard_3-2-1_52HEK_2023-04-03_nrt
2023-04-03 10:56:25	ga_s2bm_ard_3-2-1_52HDK_2023-04-03_final
2023-04-06 11:03:56	ga_s2bm_ard_3-2-1_52KDV_2023-04-06_final
2023-04-06 11:04:09	ga_s2bm_ard_3-2-1_52KDU_2023-04-06_final
2023-04-06 11:04:24	ga_s2bm_ard_3-2-1_52JDT_2023-04-06_final
2023-04-06 11:04:39	ga_s2bm_ard_3-2-1_52JDS_2023-04-06_final
2023-04-06 11:04:53	ga_s2bm_ard_3-2-1_52JDR_2023-04-06_final
2023-04-16 11:03:55	ga_s2bm_ard_3-2-1_52KDV_2023-04-16_final
2023-04-16 11:04:08	ga_s2bm_ard_3-2-1_52KDU_2023-04-16_final
2023-04-16 11:04:22	ga_s2bm_ard_3-2-1_52JDT_2023-04-16_final
2023-04-16 11:04:37	ga_s2bm_ard_3-2-1_52JDS_2023-04-16_final
2023-04-16 11:04:52	ga_s2bm_ard_3-2-1_52JDR_2023-04-16_final
2023-04-16 11:05:06	ga_s2bm_ard_3-2-1_52JDQ_2023-04-16_final
2023-04-16 11:05:21	ga_s2bm_ard_3-2-1_52JDP_2023-04-16_final
2023-04-19 11:10:59	ga_s2bm_ard_3-2-1_52LDM_2023-04-19_nrt
2023-04-19 11:11:10	ga_s2bm_ard_3-2-1_52LDL_2023-04-19_nrt
2023-04-19 11:11:24	ga_s2bm_ard_3-2-1_52LDK_2023-04-19_nrt
2023-04-19 11:11:31	ga_s2bm_ard_3-2-1_52LDK_2023-04-19_nrt
2023-04-19 11:11:31	ga_s2bm_ard_3-2-1_52LDJ_2023-04-19_final
2023-04-19 11:11:39	ga_s2bm_ard_3-2-1_52LDJ_2023-04-19_final
2023-04-19 11:11:53	ga_s2bm_ard_3-2-1_52LDH_2023-04-19_final
2023-04-19 11:12:08	ga_s2bm_ard_3-2-1_52KDG_2023-04-19_final
2023-04-19 11:12:22	ga_s2bm_ard_3-2-1_52KDF_2023-04-19_final
2023-04-19 11:12:37	ga_s2bm_ard_3-2-1_52KDE_2023-04-19_final
2023-04-19 11:12:51	ga_s2bm_ard_3-2-1_52KDD_2023-04-19_final
2023-04-22 11:20:22	ga_s2bm_ard_3-2-1_52LDP_2023-04-22_nrt
2023-04-22 11:20:43	ga_s2bm_ard_3-2-1_52LDN_2023-04-22_nrt
2023-04-22 11:20:51	ga_s2bm_ard_3-2-1_52LDM_2023-04-22_nrt
2023-04-22 11:21:19	ga_s2bm_ard_3-2-1_52LDK_2023-04-22_nrt
2023-04-23 10:56:20	ga_s2bm_ard_3-2-1_52HEK_2023-04-23_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