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
2017-07-17 12:14:11	ga_s2bm_ard_3-2-1_49JFN_2017-07-17_final.stac-item
2018-02-12 12:13:14	ga_s2bm_ard_3-2-1_49JFN_2018-02-12_final.stac-item
2018-02-22 12:13:13	ga_s2bm_ard_3-2-1_49JFN_2018-02-22_final.stac-item
2018-06-02 12:13:22	ga_s2bm_ard_3-2-1_49JFN_2018-06-02_final.stac-item
2018-06-12 12:13:19	ga_s2bm_ard_3-2-1_49JFN_2018-06-12_final.stac-item
2018-06-22 12:14:14	ga_s2bm_ard_3-2-1_49JFN_2018-06-22_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
2019-07-07 12:14:54	ga_s2bm_ard_3-2-1_49JFN_2019-07-07_final.stac-item
2019-07-17 12:14:54	ga_s2bm_ard_3-2-1_49JFN_2019-07-17_final.stac-item
2019-08-26 12:14:50	ga_s2bm_ard_3-2-1_49JFN_2019-08-26_final.stac-item
2019-09-05 12:14:48	ga_s2bm_ard_3-2-1_49JFN_2019-09-05_final.stac-item
2019-09-15 12:14:45	ga_s2bm_ard_3-2-1_49JFN_2019-09-15_final.stac-item
2019-10-05 12:14:46	ga_s2bm_ard_3-2-1_49JFN_2019-10-05_final.stac-item
2019-12-14 12:14:40	ga_s2bm_ard_3-2-1_49JFN_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
2020-05-02 12:14:42	ga_s2bm_ard_3-2-1_49JFN_2020-05-02_final.stac-item
2020-05-12 12:14:44	ga_s2bm_ard_3-2-1_49JFN_2020-05-12_final.stac-item
2020-09-19 12:14:47	ga_s2bm_ard_3-2-1_49JFN_2020-09-19_final.stac-item
2020-09-29 12:14:48	ga_s2bm_ard_3-2-1_49JFN_2020-09-29_final.stac-item
2020-10-09 12:14:49	ga_s2bm_ard_3-2-1_49JFN_2020-10-09_final.stac-item
2020-10-19 12:14:49	ga_s2bm_ard_3-2-1_49JFN_2020-10-19_final.stac-item
2020-11-18 12:14:46	ga_s2bm_ard_3-2-1_49JFN_2020-11-18_final.stac-item
2020-11-28 12:14:45	ga_s2bm_ard_3-2-1_49JFN_2020-11-28_final.stac-item
2020-12-28 12:14:43	ga_s2bm_ard_3-2-1_49JFN_2020-12-28_final.stac-item
2021-01-07 12:14:44	ga_s2bm_ard_3-2-1_49JFN_2021-01-07_final.stac-item
2021-01-17 12:14:38	ga_s2bm_ard_3-2-1_49JFN_2021-01-17_final.stac-item
2021-01-17 12:14:45	ga_s2bm_ard_3-2-1_49JFN_2021-01-17_final.stac-item
2021-01-27 12:14:45	ga_s2bm_ard_3-2-1_49JFN_2021-01-27_final.stac-item
2021-03-28 12:14:44	ga_s2bm_ard_3-2-1_49JFN_2021-03-28_final.stac-item
2021-04-07 12:14:43	ga_s2bm_ard_3-2-1_49JFN_2021-04-07_final.stac-item
2021-06-06 12:14:46	ga_s2bm_ard_3-2-1_49JFN_2021-06-06_final.stac-item
2021-06-16 12:14:46	ga_s2bm_ard_3-2-1_49JFN_2021-06-16_final.stac-item
2021-06-26 12:14:46	ga_s2bm_ard_3-2-1_49JFN_2021-06-26_final.stac-item
2021-10-04 12:14:45	ga_s2bm_ard_3-2-1_49JFN_2021-10-04_final.stac-item
2021-10-24 12:14:42	ga_s2bm_ard_3-2-1_49JFN_2021-10-24_final.stac-item
2021-11-03 12:14:46	ga_s2bm_ard_3-2-1_49JFN_2021-11-03_final.stac-item
2021-11-13 12:14:44	ga_s2bm_ard_3-2-1_49JFN_2021-11-13_final.stac-item
2021-11-23 12:14:42	ga_s2bm_ard_3-2-1_49JFN_2021-11-23_final.stac-item
2021-12-03 12:14:41	ga_s2bm_ard_3-2-1_49JFN_2021-12-03_final.stac-item
2021-12-13 12:14:33	ga_s2bm_ard_3-2-1_49JFN_2021-12-13_final.stac-item
2021-12-13 12:14:40	ga_s2bm_ard_3-2-1_49JFN_2021-12-13_final.stac-item
2022-03-13 12:14:43	ga_s2bm_ard_3-2-1_49JFN_2022-03-13_final.stac-item
2022-03-23 12:14:43	ga_s2bm_ard_3-2-1_49JFN_2022-03-23_final.stac-item
2022-04-02 12:14:42	ga_s2bm_ard_3-2-1_49JFN_2022-04-02_final.stac-item
2022-07-01 12:14:50	ga_s2bm_ard_3-2-1_49JFN_2022-07-01_final.stac-item
2022-08-10 12:14:49	ga_s2bm_ard_3-2-1_49JFN_2022-08-10_final.stac-item
2022-10-19 12:14:45	ga_s2bm_ard_3-2-1_49JFN_2022-10-19_final.stac-item
2022-10-29 12:13:32	ga_s2bm_ard_3-2-1_49KFT_2022-10-29_nrt.stac-item
2022-10-29 12:13:46	ga_s2bm_ard_3-2-1_49KFS_2022-10-29_nrt.stac-item
2022-10-29 12:14:00	ga_s2bm_ard_3-2-1_49KFR_2022-10-29_nrt.stac-item
2022-10-29 12:14:15	ga_s2bm_ard_3-2-1_49KFQ_2022-10-29_nrt.stac-item
2022-10-29 12:14:29	ga_s2bm_ard_3-2-1_49KFP_2022-10-29_nrt.stac-item
2022-10-29 12:14:44	ga_s2bm_ard_3-2-1_49JFN_2022-10-29_final.stac-item
2022-11-08 12:14:44	ga_s2bm_ard_3-2-1_49JFN_2022-11-08_final.stac-item
2022-11-18 12:14:45	ga_s2bm_ard_3-2-1_49JFN_2022-11-18_final.stac-item
2022-11-28 12:14:44	ga_s2bm_ard_3-2-1_49JFN_2022-11-28_final.stac-item
2023-01-17 12:13:30	ga_s2bm_ard_3-2-1_49KFT_2023-01-17_nrt.stac-item
2023-01-17 12:13:44	ga_s2bm_ard_3-2-1_49KFS_2023-01-17_nrt.stac-item
2023-01-17 12:13:59	ga_s2bm_ard_3-2-1_49KFR_2023-01-17_nrt.stac-item
2023-01-17 12:14:13	ga_s2bm_ard_3-2-1_49KFQ_2023-01-17_nrt.stac-item
2023-01-17 12:14:28	ga_s2bm_ard_3-2-1_49KFP_2023-01-17_nrt.stac-item
2023-01-17 12:14:42	ga_s2bm_ard_3-2-1_49JFN_2023-01-17_final.stac-item
2023-01-27 12:13:31	ga_s2bm_ard_3-2-1_49KFT_2023-01-27_nrt.stac-item
2023-01-27 12:13:45	ga_s2bm_ard_3-2-1_49KFS_2023-01-27_nrt.stac-item
2023-01-27 12:14:00	ga_s2bm_ard_3-2-1_49KFR_2023-01-27_nrt.stac-item
2023-01-27 12:14:14	ga_s2bm_ard_3-2-1_49KFQ_2023-01-27_nrt.stac-item
2023-01-27 12:14:29	ga_s2bm_ard_3-2-1_49KFP_2023-01-27_nrt.stac-item
2023-01-27 12:14:43	ga_s2bm_ard_3-2-1_49JFN_2023-01-27_final.stac-item
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
2023-10-24 12:13:34	ga_s2bm_ard_3-2-1_49KFT_2023-10-24_nrt
2023-10-24 12:13:48	ga_s2bm_ard_3-2-1_49KFS_2023-10-24_nrt
2023-10-24 12:14:03	ga_s2bm_ard_3-2-1_49KFR_2023-10-24_nrt
2023-10-24 12:14:15	ga_s2bm_ard_3-2-1_49KFQ_2023-10-24_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
2023-11-03 12:13:50	ga_s2bm_ard_3-2-1_49KFS_2023-11-03_nrt
2023-11-13 12:13:33	ga_s2bm_ard_3-2-1_49KFT_2023-11-13_nrt
2023-11-13 12:13:48	ga_s2bm_ard_3-2-1_49KFS_2023-11-13_nrt
2023-11-13 12:14:02	ga_s2bm_ard_3-2-1_49KFR_2023-11-13_nrt
2023-11-13 12:14:17	ga_s2bm_ard_3-2-1_49KFQ_2023-11-13_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
2023-11-23 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
2024-04-01 12:13:32	ga_s2bm_ard_3-2-1_49KFT_2024-04-01_nrt
2024-04-01 12:13:46	ga_s2bm_ard_3-2-1_49KFS_2024-04-01_nrt
2024-04-01 12:14:01	ga_s2bm_ard_3-2-1_49KFR_2024-04-01_nrt
2024-04-01 12:14:15	ga_s2bm_ard_3-2-1_49KFQ_2024-04-01_nrt
2024-04-01 12:14:30	ga_s2bm_ard_3-2-1_49KFP_2024-04-01_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

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