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
2019-06-04 10:20:29	ga_s2bm_ard_3-2-1_54LZM_2019-06-04_final.stac-item
2019-06-04 10:20:33	ga_s2bm_ard_3-2-1_54LYM_2019-06-04_final.stac-item
2019-06-04 10:20:36	ga_s2bm_ard_3-2-1_54LXM_2019-06-04_final.stac-item
2019-06-04 10:20:39	ga_s2bm_ard_3-2-1_54LWM_2019-06-04_final.stac-item
2019-06-07 10:30:34	ga_s2bm_ard_3-2-1_54LWM_2019-06-07_final.stac-item
2019-06-14 10:20:28	ga_s2bm_ard_3-2-1_54LZM_2019-06-14_final.stac-item
2019-06-14 10:20:32	ga_s2bm_ard_3-2-1_54LYM_2019-06-14_final.stac-item
2019-06-14 10:20:35	ga_s2bm_ard_3-2-1_54LXM_2019-06-14_final.stac-item
2019-06-14 10:20:38	ga_s2bm_ard_3-2-1_54LWM_2019-06-14_final.stac-item
2019-06-17 10:30:34	ga_s2bm_ard_3-2-1_54LWM_2019-06-17_final.stac-item
2019-06-24 10:20:29	ga_s2bm_ard_3-2-1_54LZM_2019-06-24_final.stac-item
2019-06-24 10:20:33	ga_s2bm_ard_3-2-1_54LYM_2019-06-24_final.stac-item
2019-06-24 10:20:36	ga_s2bm_ard_3-2-1_54LXM_2019-06-24_final.stac-item
2019-06-24 10:20:39	ga_s2bm_ard_3-2-1_54LWM_2019-06-24_final.stac-item
2019-06-27 10:30:34	ga_s2bm_ard_3-2-1_54LWM_2019-06-27_final.stac-item
2019-07-26 11:00:44	ga_s2bm_ard_3-2-1_53LMG_2019-07-26_final.stac-item
2019-07-26 11:00:48	ga_s2bm_ard_3-2-1_53LLG_2019-07-26_final.stac-item
2019-07-26 11:00:53	ga_s2bm_ard_3-2-1_52LHM_2019-07-26_final.stac-item
2019-07-26 11:00:57	ga_s2bm_ard_3-2-1_52LGM_2019-07-26_final.stac-item
2019-07-26 11:01:03	ga_s2bm_ard_3-2-1_52LHL_2019-07-26_final.stac-item
2019-08-08 11:10:44	ga_s2bm_ard_3-2-1_52LHM_2019-08-08_final.stac-item
2019-08-08 11:10:50	ga_s2bm_ard_3-2-1_52LGM_2019-08-08_final.stac-item
2019-08-25 11:00:41	ga_s2bm_ard_3-2-1_53LMG_2019-08-25_final.stac-item
2019-08-25 11:00:45	ga_s2bm_ard_3-2-1_53LLG_2019-08-25_final.stac-item
2019-08-25 11:00:48	ga_s2bm_ard_3-2-1_53LKG_2019-08-25_final.stac-item
2019-08-25 11:00:49	ga_s2bm_ard_3-2-1_52LHM_2019-08-25_final.stac-item
2019-08-25 11:00:53	ga_s2bm_ard_3-2-1_52LGM_2019-08-25_final.stac-item
2019-08-25 11:01:04	ga_s2bm_ard_3-2-1_52LHL_2019-08-25_final.stac-item
2019-08-27 11:41:05	ga_s2bm_ard_3-2-1_51LVG_2019-08-27_final.stac-item
2019-08-28 11:10:41	ga_s2bm_ard_3-2-1_52LHM_2019-08-28_final.stac-item
2019-08-28 11:10:47	ga_s2bm_ard_3-2-1_52LGM_2019-08-28_final.stac-item
2019-08-30 10:10:29	ga_s2bm_ard_3-2-1_54LZM_2019-08-30_final.stac-item
2019-08-30 10:10:44	ga_s2bm_ard_3-2-1_54LZL_2019-08-30_final.stac-item
2019-09-02 10:20:24	ga_s2bm_ard_3-2-1_54LZM_2019-09-02_final.stac-item
2019-09-02 10:20:28	ga_s2bm_ard_3-2-1_54LYM_2019-09-02_final.stac-item
2019-09-02 10:20:31	ga_s2bm_ard_3-2-1_54LXM_2019-09-02_final.stac-item
2019-09-02 10:20:34	ga_s2bm_ard_3-2-1_54LWM_2019-09-02_final.stac-item
2019-09-04 11:00:39	ga_s2bm_ard_3-2-1_53LMG_2019-09-04_final.stac-item
2019-09-04 11:00:43	ga_s2bm_ard_3-2-1_53LLG_2019-09-04_final.stac-item
2019-09-04 11:00:46	ga_s2bm_ard_3-2-1_53LKG_2019-09-04_final.stac-item
2019-09-04 11:00:47	ga_s2bm_ard_3-2-1_52LHM_2019-09-04_final.stac-item
2019-09-04 11:00:51	ga_s2bm_ard_3-2-1_52LGM_2019-09-04_final.stac-item
2019-09-04 11:01:02	ga_s2bm_ard_3-2-1_52LHL_2019-09-04_final.stac-item
2019-09-05 10:30:29	ga_s2bm_ard_3-2-1_54LWM_2019-09-05_final.stac-item
2019-09-06 11:41:03	ga_s2bm_ard_3-2-1_51LVG_2019-09-06_final.stac-item
2019-09-07 11:10:39	ga_s2bm_ard_3-2-1_52LHM_2019-09-07_final.stac-item
2019-09-07 11:10:45	ga_s2bm_ard_3-2-1_52LGM_2019-09-07_final.stac-item
2019-09-11 10:50:36	ga_s2bm_ard_3-2-1_53LPG_2019-09-11_final.stac-item
2019-09-11 10:50:39	ga_s2bm_ard_3-2-1_53LNG_2019-09-11_final.stac-item
2019-09-11 10:50:43	ga_s2bm_ard_3-2-1_53LMG_2019-09-11_final.stac-item
2019-09-11 10:50:49	ga_s2bm_ard_3-2-1_53LLG_2019-09-11_final.stac-item
2019-09-14 11:00:36	ga_s2bm_ard_3-2-1_53LMG_2019-09-14_final.stac-item
2019-09-14 11:00:40	ga_s2bm_ard_3-2-1_53LLG_2019-09-14_final.stac-item
2019-09-14 11:00:43	ga_s2bm_ard_3-2-1_53LKG_2019-09-14_final.stac-item
2019-09-14 11:00:45	ga_s2bm_ard_3-2-1_52LHM_2019-09-14_final.stac-item
2019-09-14 11:00:49	ga_s2bm_ard_3-2-1_52LGM_2019-09-14_final.stac-item
2019-09-14 11:00:59	ga_s2bm_ard_3-2-1_52LHL_2019-09-14_final.stac-item
2019-09-15 10:30:26	ga_s2bm_ard_3-2-1_54LWM_2019-09-15_final.stac-item
2019-09-16 11:41:00	ga_s2bm_ard_3-2-1_51LVG_2019-09-16_final.stac-item
2019-09-17 11:10:37	ga_s2bm_ard_3-2-1_52LHM_2019-09-17_final.stac-item
2019-09-17 11:10:43	ga_s2bm_ard_3-2-1_52LGM_2019-09-17_final.stac-item
2023-09-28 11:51:02	ga_s2bm_ard_3-2-1_51LUG_2023-09-28_nrt
2023-09-28 11:51:05	ga_s2bm_ard_3-2-1_51LTG_2023-09-28_nrt
2023-09-28 11:51:06	ga_s2bm_ard_3-2-1_50LRM_2023-09-28_nrt
2023-09-28 11:51:10	ga_s2bm_ard_3-2-1_50LQM_2023-09-28_nrt
2023-09-28 11:51:21	ga_s2bm_ard_3-2-1_50LRL_2023-09-28_nrt
2023-09-29 09:40:05	ga_s2bm_ard_3-2-1_56LPM_2023-09-29_nrt
2023-10-01 12:01:04	ga_s2bm_ard_3-2-1_50LQM_2023-10-01_nrt
2023-10-01 12:01:08	ga_s2bm_ard_3-2-1_50LPM_2023-10-01_nrt
2023-10-01 12:01:11	ga_s2bm_ard_3-2-1_50LNM_2023-10-01_nrt
2023-10-01 12:01:14	ga_s2bm_ard_3-2-1_50LMM_2023-10-01_nrt
2023-10-02 11:30:52	ga_s2bm_ard_3-2-1_52LBM_2023-10-02_nrt
2023-10-02 11:30:53	ga_s2bm_ard_3-2-1_51LZG_2023-10-02_nrt
2023-10-02 11:30:57	ga_s2bm_ard_3-2-1_51LYG_2023-10-02_nrt
2023-10-02 11:31:00	ga_s2bm_ard_3-2-1_51LXG_2023-10-02_nrt
2023-10-03 11:00:44	ga_s2bm_ard_3-2-1_53LKG_2023-10-03_final
2023-10-03 11:00:50	ga_s2bm_ard_3-2-1_52LGM_2023-10-03_final
2023-10-03 11:01:00	ga_s2bm_ard_3-2-1_52LHL_2023-10-03_final
2023-10-04 10:30:27	ga_s2bm_ard_3-2-1_54LWM_2023-10-04_final
2023-10-04 10:30:30	ga_s2bm_ard_3-2-1_54LVM_2023-10-04_final
2023-10-04 10:30:34	ga_s2bm_ard_3-2-1_54LUM_2023-10-04_nrt
2023-10-04 10:30:39	ga_s2bm_ard_3-2-1_54LTM_2023-10-04_nrt
2023-10-05 10:00:13	ga_s2bm_ard_3-2-1_55LGG_2023-10-05_nrt
2023-10-05 10:00:16	ga_s2bm_ard_3-2-1_55LFG_2023-10-05_nrt
2023-10-05 10:00:19	ga_s2bm_ard_3-2-1_55LEG_2023-10-05_nrt
2023-10-05 10:00:22	ga_s2bm_ard_3-2-1_55LDG_2023-10-05_nrt
2023-10-16 11:10:45	ga_s2bm_ard_3-2-1_52LGM_2023-10-16_final
2023-10-16 11:10:49	ga_s2bm_ard_3-2-1_52LFM_2023-10-16_final
2023-10-16 11:10:52	ga_s2bm_ard_3-2-1_52LEM_2023-10-16_nrt
2023-10-16 11:10:59	ga_s2bm_ard_3-2-1_52LDM_2023-10-16_nrt
2023-10-17 10:40:29	ga_s2bm_ard_3-2-1_54LUM_2023-10-17_nrt
2023-10-17 10:40:34	ga_s2bm_ard_3-2-1_54LTM_2023-10-17_nrt
2023-10-17 10:40:35	ga_s2bm_ard_3-2-1_53LRG_2023-10-17_nrt
2023-10-17 10:40:39	ga_s2bm_ard_3-2-1_53LQG_2023-10-17_final
2023-10-17 10:40:42	ga_s2bm_ard_3-2-1_53LPG_2023-10-17_final
2023-10-17 10:40:49	ga_s2bm_ard_3-2-1_53LRF_2023-10-17_nrt
2023-10-18 11:51:01	ga_s2bm_ard_3-2-1_51LUG_2023-10-18_nrt
2023-10-18 11:51:04	ga_s2bm_ard_3-2-1_51LTG_2023-10-18_nrt
2023-10-18 11:51:06	ga_s2bm_ard_3-2-1_50LRM_2023-10-18_nrt
2023-10-18 11:51:09	ga_s2bm_ard_3-2-1_50LQM_2023-10-18_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