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
2017-07-07 12:14:42	ga_s2bm_ard_3-2-1_49JFM_2017-07-07_final.stac-item
2017-07-07 12:14:42	ga_s2bm_ard_3-2-1_49JFN_2017-07-07_final.stac-item
2017-07-17 12:14:11	ga_s2bm_ard_3-2-1_49JFM_2017-07-17_final.stac-item
2017-07-17 12:14:11	ga_s2bm_ard_3-2-1_49JFN_2017-07-17_final.stac-item
2017-07-27 12:14:41	ga_s2bm_ard_3-2-1_49JFM_2017-07-27_final.stac-item
2017-07-27 12:14:41	ga_s2bm_ard_3-2-1_49JFN_2017-07-27_final.stac-item
2017-11-14 12:13:08	ga_s2bm_ard_3-2-1_49JFM_2017-11-14_final.stac-item
2017-11-14 12:13:08	ga_s2bm_ard_3-2-1_49JFN_2017-11-14_final.stac-item
2017-11-24 12:13:09	ga_s2bm_ard_3-2-1_49JFM_2017-11-24_final.stac-item
2017-11-24 12:13:09	ga_s2bm_ard_3-2-1_49JFN_2017-11-24_final.stac-item
2017-12-14 12:13:09	ga_s2bm_ard_3-2-1_49JFM_2017-12-14_final.stac-item
2017-12-14 12:13:09	ga_s2bm_ard_3-2-1_49JFN_2017-12-14_final.stac-item
2017-12-24 12:13:09	ga_s2bm_ard_3-2-1_49JFM_2017-12-24_final.stac-item
2017-12-24 12:13:09	ga_s2bm_ard_3-2-1_49JFN_2017-12-24_final.stac-item
2018-01-23 12:13:14	ga_s2bm_ard_3-2-1_49JFM_2018-01-23_final.stac-item
2018-01-23 12:13:14	ga_s2bm_ard_3-2-1_49JFN_2018-01-23_final.stac-item
2018-02-02 12:13:14	ga_s2bm_ard_3-2-1_49JFM_2018-02-02_final.stac-item
2018-02-02 12:13:14	ga_s2bm_ard_3-2-1_49JFN_2018-02-02_final.stac-item
2018-03-04 12:13:52	ga_s2bm_ard_3-2-1_49JFM_2018-03-04_final.stac-item
2018-03-04 12:13:52	ga_s2bm_ard_3-2-1_49JFN_2018-03-04_final.stac-item
2018-03-14 12:13:13	ga_s2bm_ard_3-2-1_49JFM_2018-03-14_final.stac-item
2018-03-14 12:13:13	ga_s2bm_ard_3-2-1_49JFN_2018-03-14_final.stac-item
2018-03-24 12:13:16	ga_s2bm_ard_3-2-1_49JFM_2018-03-24_final.stac-item
2018-03-24 12:13:16	ga_s2bm_ard_3-2-1_49JFN_2018-03-24_final.stac-item
2018-04-03 12:13:18	ga_s2bm_ard_3-2-1_49JFM_2018-04-03_final.stac-item
2018-04-03 12:13:18	ga_s2bm_ard_3-2-1_49JFN_2018-04-03_final.stac-item
2018-04-13 12:13:19	ga_s2bm_ard_3-2-1_49JFM_2018-04-13_final.stac-item
2018-04-13 12:13:19	ga_s2bm_ard_3-2-1_49JFN_2018-04-13_final.stac-item
2018-07-02 12:13:20	ga_s2bm_ard_3-2-1_49JFM_2018-07-02_final.stac-item
2018-07-02 12:13:20	ga_s2bm_ard_3-2-1_49JFN_2018-07-02_final.stac-item
2018-07-12 12:13:20	ga_s2bm_ard_3-2-1_49JFM_2018-07-12_final.stac-item
2018-07-12 12:13:20	ga_s2bm_ard_3-2-1_49JFN_2018-07-12_final.stac-item
2018-11-29 12:14:41	ga_s2bm_ard_3-2-1_49JFN_2018-11-29_final.stac-item
2018-11-29 12:14:56	ga_s2bm_ard_3-2-1_49JFM_2018-11-29_final.stac-item
2018-12-09 12:14:39	ga_s2bm_ard_3-2-1_49JFN_2018-12-09_final.stac-item
2018-12-09 12:14:54	ga_s2bm_ard_3-2-1_49JFM_2018-12-09_final.stac-item
2018-12-19 12:14:40	ga_s2bm_ard_3-2-1_49JFN_2018-12-19_final.stac-item
2018-12-19 12:14:54	ga_s2bm_ard_3-2-1_49JFM_2018-12-19_final.stac-item
2018-12-29 12:14:42	ga_s2bm_ard_3-2-1_49JFN_2018-12-29_final.stac-item
2018-12-29 12:14:57	ga_s2bm_ard_3-2-1_49JFM_2018-12-29_final.stac-item
2019-06-07 12:14:53	ga_s2bm_ard_3-2-1_49JFN_2019-06-07_final.stac-item
2019-06-27 12:14:54	ga_s2bm_ard_3-2-1_49JFN_2019-06-27_final.stac-item
2019-06-27 12:15:08	ga_s2bm_ard_3-2-1_49JFM_2019-06-27_final.stac-item
2019-08-26 12:14:50	ga_s2bm_ard_3-2-1_49JFN_2019-08-26_final.stac-item
2019-08-26 12:15:04	ga_s2bm_ard_3-2-1_49JFM_2019-08-26_final.stac-item
2019-09-15 12:14:45	ga_s2bm_ard_3-2-1_49JFN_2019-09-15_final.stac-item
2019-09-15 12:15:00	ga_s2bm_ard_3-2-1_49JFM_2019-09-15_final.stac-item
2019-12-04 12:14:39	ga_s2bm_ard_3-2-1_49JFN_2019-12-04_final.stac-item
2019-12-04 12:14:54	ga_s2bm_ard_3-2-1_49JFM_2019-12-04_final.stac-item
2019-12-14 12:14:40	ga_s2bm_ard_3-2-1_49JFN_2019-12-14_final.stac-item
2019-12-14 12:14:54	ga_s2bm_ard_3-2-1_49JFM_2019-12-14_final.stac-item
2019-12-24 12:14:40	ga_s2bm_ard_3-2-1_49JFN_2019-12-24_final.stac-item
2019-12-24 12:14:55	ga_s2bm_ard_3-2-1_49JFM_2019-12-24_final.stac-item
2020-02-12 12:14:40	ga_s2bm_ard_3-2-1_49JFN_2020-02-12_final.stac-item
2020-02-12 12:14:55	ga_s2bm_ard_3-2-1_49JFM_2020-02-12_final.stac-item
2020-02-22 12:14:42	ga_s2bm_ard_3-2-1_49JFN_2020-02-22_final.stac-item
2020-02-22 12:14:56	ga_s2bm_ard_3-2-1_49JFM_2020-02-22_final.stac-item
2020-03-03 12:14:43	ga_s2bm_ard_3-2-1_49JFN_2020-03-03_final.stac-item
2020-03-03 12:14:57	ga_s2bm_ard_3-2-1_49JFM_2020-03-03_final.stac-item
2020-03-13 12:14:43	ga_s2bm_ard_3-2-1_49JFN_2020-03-13_final.stac-item
2020-03-13 12:14:58	ga_s2bm_ard_3-2-1_49JFM_2020-03-13_final.stac-item
2023-09-04 12:13:37	ga_s2bm_ard_3-2-1_49KFT_2023-09-04_nrt
2023-09-04 12:13:52	ga_s2bm_ard_3-2-1_49KFS_2023-09-04_nrt
2023-09-04 12:14:06	ga_s2bm_ard_3-2-1_49KFR_2023-09-04_nrt
2023-09-04 12:14:21	ga_s2bm_ard_3-2-1_49KFQ_2023-09-04_nrt
2023-09-04 12:14:35	ga_s2bm_ard_3-2-1_49KFP_2023-09-04_nrt
2023-09-04 12:14:50	ga_s2bm_ard_3-2-1_49JFN_2023-09-04_final
2023-09-04 12:15:04	ga_s2bm_ard_3-2-1_49JFM_2023-09-04_final
2023-09-04 12:15:18	ga_s2bm_ard_3-2-1_49JFL_2023-09-04_nrt
2023-10-14 12:13:35	ga_s2bm_ard_3-2-1_49KFT_2023-10-14_nrt
2023-10-14 12:13:49	ga_s2bm_ard_3-2-1_49KFS_2023-10-14_nrt
2023-10-14 12:14:04	ga_s2bm_ard_3-2-1_49KFR_2023-10-14_nrt
2023-10-14 12:14:18	ga_s2bm_ard_3-2-1_49KFQ_2023-10-14_nrt
2023-10-14 12:14:33	ga_s2bm_ard_3-2-1_49KFP_2023-10-14_nrt
2023-10-14 12:14:48	ga_s2bm_ard_3-2-1_49JFN_2023-10-14_final
2023-10-14 12:15:02	ga_s2bm_ard_3-2-1_49JFM_2023-10-14_final
2023-10-14 12:15:16	ga_s2bm_ard_3-2-1_49JFL_2023-10-14_nrt
2023-10-24 12:14:22	ga_s2bm_ard_3-2-1_49KFQ_2023-10-24_nrt
2023-10-24 12:14:32	ga_s2bm_ard_3-2-1_49KFP_2023-10-24_nrt
2023-10-24 12:14:46	ga_s2bm_ard_3-2-1_49JFN_2023-10-24_final
2023-10-24 12:15:01	ga_s2bm_ard_3-2-1_49JFM_2023-10-24_final
2023-10-24 12:15:15	ga_s2bm_ard_3-2-1_49JFL_2023-10-24_nrt
2023-11-03 12:13:35	ga_s2bm_ard_3-2-1_49KFT_2023-11-03_nrt
2023-11-03 12:13:50	ga_s2bm_ard_3-2-1_49KFS_2023-11-03_nrt
2023-11-03 12:14:04	ga_s2bm_ard_3-2-1_49KFR_2023-11-03_nrt
2023-11-03 12:14:19	ga_s2bm_ard_3-2-1_49KFQ_2023-11-03_nrt
2023-11-03 12:14:33	ga_s2bm_ard_3-2-1_49KFP_2023-11-03_nrt
2023-11-03 12:14:48	ga_s2bm_ard_3-2-1_49JFN_2023-11-03_final
2023-11-03 12:15:16	ga_s2bm_ard_3-2-1_49JFL_2023-11-03_nrt
2023-11-13 12:14:31	ga_s2bm_ard_3-2-1_49KFP_2023-11-13_nrt
2023-11-13 12:14:46	ga_s2bm_ard_3-2-1_49JFN_2023-11-13_final
2023-11-13 12:15:00	ga_s2bm_ard_3-2-1_49JFM_2023-11-13_final
2023-11-13 12:15:14	ga_s2bm_ard_3-2-1_49JFL_2023-11-13_nrt
2023-11-23 12:13:28	ga_s2bm_ard_3-2-1_49KFT_2023-11-23_nrt
2023-11-23 12:13:42	ga_s2bm_ard_3-2-1_49KFS_2023-11-23_nrt
2023-11-23 12:13:57	ga_s2bm_ard_3-2-1_49KFR_2023-11-23_nrt
2023-11-23 12:14:11	ga_s2bm_ard_3-2-1_49KFQ_2023-11-23_nrt
2023-11-23 12:14:26	ga_s2bm_ard_3-2-1_49KFP_2023-11-23_nrt
2023-11-23 12:14:40	ga_s2bm_ard_3-2-1_49JFN_2023-11-23_final
2023-11-23 12:14:55	ga_s2bm_ard_3-2-1_49JFM_2023-11-23_final

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