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
2023-10-28 10:10:00	ga_s2bm_ard_3-2-1_54LZP_2023-10-28_final
2023-10-28 10:10:13	ga_s2bm_ard_3-2-1_54LZN_2023-10-28_final
2023-10-28 10:11:02	ga_s2bm_ard_3-2-1_54LYK_2023-10-28_final
2023-10-28 10:11:14	ga_s2bm_ard_3-2-1_54LYJ_2023-10-28_final
2023-10-28 10:11:39	ga_s2bm_ard_3-2-1_55KBB_2023-10-28_final
2023-10-28 10:11:53	ga_s2bm_ard_3-2-1_55KBA_2023-10-28_final
2023-10-28 10:12:12	ga_s2bm_ard_3-2-1_54KYE_2023-10-28_final
2023-10-28 10:12:16	ga_s2bm_ard_3-2-1_54KXE_2023-10-28_final
2023-10-28 10:12:27	ga_s2bm_ard_3-2-1_54KYD_2023-10-28_final
2023-10-28 10:12:30	ga_s2bm_ard_3-2-1_54KXD_2023-10-28_final
2023-10-28 10:12:51	ga_s2bm_ard_3-2-1_55KBS_2023-10-28_final
2023-10-28 10:12:52	ga_s2bm_ard_3-2-1_54KZB_2023-10-28_final
2023-10-28 10:13:01	ga_s2bm_ard_3-2-1_55KBR_2023-10-28_final
2023-10-28 10:13:01	ga_s2bm_ard_3-2-1_54KZA_2023-10-28_final
2023-10-28 10:13:05	ga_s2bm_ard_3-2-1_54KXA_2023-10-28_final
2023-10-28 10:13:08	ga_s2bm_ard_3-2-1_54KZA_2023-10-28_final
2023-10-28 10:13:08	ga_s2bm_ard_3-2-1_55KBR_2023-10-28_final
2023-10-28 10:13:13	ga_s2bm_ard_3-2-1_54KXA_2023-10-28_final
2023-10-28 10:13:21	ga_s2bm_ard_3-2-1_54KZV_2023-10-28_final
2023-10-28 10:13:31	ga_s2bm_ard_3-2-1_54KWV_2023-10-28_final
2023-10-28 10:13:35	ga_s2bm_ard_3-2-1_54KZU_2023-10-28_final
2023-10-28 10:13:47	ga_s2bm_ard_3-2-1_54JZT_2023-10-28_final
2023-10-28 10:14:01	ga_s2bm_ard_3-2-1_54JWT_2023-10-28_final
2023-10-28 10:14:15	ga_s2bm_ard_3-2-1_54JWS_2023-10-28_final
2023-10-28 10:14:26	ga_s2bm_ard_3-2-1_54JXR_2023-10-28_final
2023-10-28 10:14:36	ga_s2bm_ard_3-2-1_54JYQ_2023-10-28_final
2023-10-28 10:14:40	ga_s2bm_ard_3-2-1_54JXQ_2023-10-28_final
2023-10-28 10:14:49	ga_s2bm_ard_3-2-1_54JYP_2023-10-28_final
2023-10-28 10:15:02	ga_s2bm_ard_3-2-1_54JVP_2023-10-28_final
2023-10-28 10:15:09	ga_s2bm_ard_3-2-1_54JUP_2023-10-28_final
2023-10-28 10:15:16	ga_s2bm_ard_3-2-1_54JVN_2023-10-28_final
2023-10-28 10:15:42	ga_s2bm_ard_3-2-1_54JWL_2023-10-28_final
2023-10-28 10:15:56	ga_s2bm_ard_3-2-1_54HWK_2023-10-28_final
2023-10-28 10:16:00	ga_s2bm_ard_3-2-1_54HVK_2023-10-28_final
2023-10-28 10:16:03	ga_s2bm_ard_3-2-1_54HUK_2023-10-28_final
2023-10-28 10:16:15	ga_s2bm_ard_3-2-1_54HVJ_2023-10-28_final
2023-10-28 10:16:22	ga_s2bm_ard_3-2-1_54HTJ_2023-10-28_final
2023-10-28 10:16:53	ga_s2bm_ard_3-2-1_53HQB_2023-10-28_final
2023-10-28 10:17:27	ga_s2bm_ard_3-2-1_54HVD_2023-10-28_final
2023-10-28 10:17:38	ga_s2bm_ard_3-2-1_54HVC_2023-10-28_final
2023-10-28 11:51:15	ga_s2bm_ard_3-2-1_51LUF_2023-10-28_final
2023-10-28 11:52:21	ga_s2bm_ard_3-2-1_50KQG_2023-10-28_final
2023-10-28 11:52:36	ga_s2bm_ard_3-2-1_50KQF_2023-10-28_final
2023-10-28 11:52:50	ga_s2bm_ard_3-2-1_50KQE_2023-10-28_final
2023-10-28 11:52:55	ga_s2bm_ard_3-2-1_51KTU_2023-10-28_final
2023-10-28 11:53:00	ga_s2bm_ard_3-2-1_50KRD_2023-10-28_final
2023-10-28 11:53:04	ga_s2bm_ard_3-2-1_50KQD_2023-10-28_final
2023-10-28 11:53:19	ga_s2bm_ard_3-2-1_50KQC_2023-10-28_final
2023-10-28 11:53:37	ga_s2bm_ard_3-2-1_50KPB_2023-10-28_final
2023-10-28 11:53:51	ga_s2bm_ard_3-2-1_50KPA_2023-10-28_final
2023-10-28 11:54:09	ga_s2bm_ard_3-2-1_50KNV_2023-10-28_final
2023-10-28 11:54:13	ga_s2bm_ard_3-2-1_50KMV_2023-10-28_final
2023-10-28 11:54:27	ga_s2bm_ard_3-2-1_50KMU_2023-10-28_final
2023-10-28 11:54:35	ga_s2bm_ard_3-2-1_50JPT_2023-10-28_final
2023-10-28 11:54:35	ga_s2bm_ard_3-2-1_50KLU_2023-10-28_final
2023-10-28 11:54:39	ga_s2bm_ard_3-2-1_50JNT_2023-10-28_final
2023-10-28 11:54:49	ga_s2bm_ard_3-2-1_50JPS_2023-10-28_final
2023-10-28 11:54:56	ga_s2bm_ard_3-2-1_50JMS_2023-10-28_final
2023-10-28 11:55:29	ga_s2bm_ard_3-2-1_50JLQ_2023-10-28_final
2023-10-28 11:55:43	ga_s2bm_ard_3-2-1_50JLP_2023-10-28_final
2023-10-28 11:55:51	ga_s2bm_ard_3-2-1_50JNN_2023-10-28_final
2023-10-28 11:55:54	ga_s2bm_ard_3-2-1_50JMN_2023-10-28_final
2023-10-28 11:56:06	ga_s2bm_ard_3-2-1_50JNM_2023-10-28_final
2023-10-28 11:56:09	ga_s2bm_ard_3-2-1_50JMM_2023-10-28_final
2023-10-28 11:56:15	ga_s2bm_ard_3-2-1_50JKM_2023-10-28_final
2023-10-28 11:56:38	ga_s2bm_ard_3-2-1_50HMK_2023-10-28_final
2023-10-28 11:57:24	ga_s2bm_ard_3-2-1_50HLG_2023-10-28_final
2023-11-07 11:50:32	ga_s2bm_ard_3-2-1_51LUJ_2023-11-07_nrt
2023-11-07 11:50:37	ga_s2bm_ard_3-2-1_50LRP_2023-11-07_nrt
2023-11-07 11:50:40	ga_s2bm_ard_3-2-1_50LQP_2023-11-07_nrt
2023-11-07 11:50:47	ga_s2bm_ard_3-2-1_51LUH_2023-11-07_nrt
2023-11-07 11:50:52	ga_s2bm_ard_3-2-1_50LRN_2023-11-07_nrt
2023-11-07 11:50:55	ga_s2bm_ard_3-2-1_50LQN_2023-11-07_nrt
2023-11-07 11:51:01	ga_s2bm_ard_3-2-1_51LUG_2023-11-07_nrt
2023-11-07 11:51:06	ga_s2bm_ard_3-2-1_50LRM_2023-11-07_nrt
2023-11-07 11:51:21	ga_s2bm_ard_3-2-1_50LRL_2023-11-07_nrt
2023-11-07 11:51:35	ga_s2bm_ard_3-2-1_50LRK_2023-11-07_nrt
2023-11-07 11:51:48	ga_s2bm_ard_3-2-1_51LTD_2023-11-07_nrt
2023-11-07 11:51:50	ga_s2bm_ard_3-2-1_50LRJ_2023-11-07_nrt
2023-11-07 11:52:03	ga_s2bm_ard_3-2-1_51LTC_2023-11-07_nrt
2023-11-07 11:52:04	ga_s2bm_ard_3-2-1_50LRH_2023-11-07_nrt
2023-11-07 11:52:18	ga_s2bm_ard_3-2-1_50KRG_2023-11-07_nrt
2023-11-07 11:52:33	ga_s2bm_ard_3-2-1_50KRF_2023-11-07_nrt
2023-11-07 11:52:47	ga_s2bm_ard_3-2-1_50KRE_2023-11-07_nrt
2023-11-07 11:56:31	ga_s2bm_ard_3-2-1_50JKL_2023-11-07_nrt
2023-11-07 11:56:46	ga_s2bm_ard_3-2-1_50HKK_2023-11-07_nrt
2023-11-07 11:56:48	ga_s2bm_ard_3-2-1_49HGE_2023-11-07_nrt
2023-11-07 11:57:00	ga_s2bm_ard_3-2-1_50HKJ_2023-11-07_nrt
2023-11-07 11:57:02	ga_s2bm_ard_3-2-1_49HGD_2023-11-07_nrt
2023-11-07 11:57:15	ga_s2bm_ard_3-2-1_50HKH_2023-11-07_nrt
2023-11-07 11:57:17	ga_s2bm_ard_3-2-1_49HGC_2023-11-07_nrt
2023-11-07 11:57:25	ga_s2bm_ard_3-2-1_49HFC_2023-11-07_nrt
2023-11-07 11:57:26	ga_s2bm_ard_3-2-1_50HKG_2023-11-07_nrt
2023-11-07 11:57:27	ga_s2bm_ard_3-2-1_49HGB_2023-11-07_nrt
2023-11-07 11:57:29	ga_s2bm_ard_3-2-1_49HFB_2023-11-07_nrt
2023-11-08 09:48:40	ga_s2bm_ard_3-2-1_54GYU_2023-11-08_nrt
2023-11-08 09:48:54	ga_s2bm_ard_3-2-1_54GYT_2023-11-08_nrt
2023-11-08 09:49:02	ga_s2bm_ard_3-2-1_54GYS_2023-11-08_nrt
2023-11-08 11:20:18	ga_s2bm_ard_3-2-1_52LEP_2023-11-08_nrt
2023-11-08 11:20:47	ga_s2bm_ard_3-2-1_52LEM_2023-11-08_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