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
2023-11-03 10:30:21	ga_s2bm_ard_3-2-1_54LXM_2023-11-03_final
2023-11-03 10:30:29	ga_s2bm_ard_3-2-1_54LWM_2023-11-03_final
2023-11-03 10:30:33	ga_s2bm_ard_3-2-1_54LVM_2023-11-03_final
2023-11-03 10:30:43	ga_s2bm_ard_3-2-1_54LWL_2023-11-03_final
2023-11-03 10:30:47	ga_s2bm_ard_3-2-1_54LVL_2023-11-03_final
2023-11-03 10:31:23	ga_s2bm_ard_3-2-1_54LWH_2023-11-03_final
2023-11-03 10:31:27	ga_s2bm_ard_3-2-1_54LWH_2023-11-03_final
2023-11-03 10:31:49	ga_s2bm_ard_3-2-1_54KUG_2023-11-03_final
2023-11-03 10:31:52	ga_s2bm_ard_3-2-1_54KTG_2023-11-03_final
2023-11-03 10:31:53	ga_s2bm_ard_3-2-1_53KRB_2023-11-03_final
2023-11-03 10:32:07	ga_s2bm_ard_3-2-1_53KRA_2023-11-03_final
2023-11-03 10:32:13	ga_s2bm_ard_3-2-1_53KQA_2023-11-03_final
2023-11-03 10:32:28	ga_s2bm_ard_3-2-1_54KVD_2023-11-03_final
2023-11-03 10:32:32	ga_s2bm_ard_3-2-1_54KUD_2023-11-03_final
2023-11-03 10:32:35	ga_s2bm_ard_3-2-1_54KTD_2023-11-03_final
2023-11-03 10:32:46	ga_s2bm_ard_3-2-1_54KUC_2023-11-03_final
2023-11-03 10:32:50	ga_s2bm_ard_3-2-1_54KTC_2023-11-03_final
2023-11-03 10:32:50	ga_s2bm_ard_3-2-1_53KRT_2023-11-03_final
2023-11-03 10:32:52	ga_s2bm_ard_3-2-1_53KQT_2023-11-03_final
2023-11-03 10:32:55	ga_s2bm_ard_3-2-1_54KUC_2023-11-03_final
2023-11-03 10:32:56	ga_s2bm_ard_3-2-1_53KRS_2023-11-03_final
2023-11-03 10:32:57	ga_s2bm_ard_3-2-1_54KTC_2023-11-03_final
2023-11-03 10:32:57	ga_s2bm_ard_3-2-1_53KRT_2023-11-03_final
2023-11-03 10:32:57	ga_s2bm_ard_3-2-1_54KTB_2023-11-03_final
2023-11-03 10:32:58	ga_s2bm_ard_3-2-1_53KQT_2023-11-03_final
2023-11-03 10:33:04	ga_s2bm_ard_3-2-1_54KTB_2023-11-03_final
2023-11-03 10:33:05	ga_s2bm_ard_3-2-1_53KRS_2023-11-03_final
2023-11-03 10:33:15	ga_s2bm_ard_3-2-1_53KPS_2023-11-03_final
2023-11-03 10:33:16	ga_s2bm_ard_3-2-1_54KUA_2023-11-03_final
2023-11-03 10:33:23	ga_s2bm_ard_3-2-1_53KQR_2023-11-03_final
2023-11-03 10:33:26	ga_s2bm_ard_3-2-1_53KPR_2023-11-03_final
2023-11-03 10:33:34	ga_s2bm_ard_3-2-1_54KTV_2023-11-03_final
2023-11-03 10:33:41	ga_s2bm_ard_3-2-1_53KPQ_2023-11-03_final
2023-11-03 10:33:44	ga_s2bm_ard_3-2-1_54KUU_2023-11-03_final
2023-11-03 10:33:48	ga_s2bm_ard_3-2-1_54KTU_2023-11-03_final
2023-11-03 10:33:48	ga_s2bm_ard_3-2-1_53KRP_2023-11-03_final
2023-11-03 10:33:54	ga_s2bm_ard_3-2-1_54JUT_2023-11-03_final
2023-11-03 10:33:55	ga_s2bm_ard_3-2-1_53KPP_2023-11-03_final
2023-11-03 10:34:02	ga_s2bm_ard_3-2-1_53JRN_2023-11-03_final
2023-11-03 10:34:02	ga_s2bm_ard_3-2-1_54JTT_2023-11-03_final
2023-11-03 10:34:10	ga_s2bm_ard_3-2-1_53JPN_2023-11-03_final
2023-11-03 10:34:16	ga_s2bm_ard_3-2-1_53JNN_2023-11-03_final
2023-11-03 10:34:21	ga_s2bm_ard_3-2-1_53JQM_2023-11-03_final
2023-11-03 10:34:24	ga_s2bm_ard_3-2-1_53JPM_2023-11-03_final
2023-11-03 10:34:28	ga_s2bm_ard_3-2-1_53JNM_2023-11-03_final
2023-11-03 10:34:35	ga_s2bm_ard_3-2-1_53JQL_2023-11-03_final
2023-11-03 10:34:39	ga_s2bm_ard_3-2-1_53JPL_2023-11-03_final
2023-11-03 10:34:42	ga_s2bm_ard_3-2-1_53JNL_2023-11-03_final
2023-11-03 10:34:46	ga_s2bm_ard_3-2-1_54JTQ_2023-11-03_final
2023-11-03 10:34:50	ga_s2bm_ard_3-2-1_53JQK_2023-11-03_final
2023-11-03 10:34:53	ga_s2bm_ard_3-2-1_53JPK_2023-11-03_final
2023-11-03 10:34:57	ga_s2bm_ard_3-2-1_53JNK_2023-11-03_final
2023-11-03 10:35:00	ga_s2bm_ard_3-2-1_54JTP_2023-11-03_final
2023-11-03 10:35:04	ga_s2bm_ard_3-2-1_53JQJ_2023-11-03_final
2023-11-03 10:35:08	ga_s2bm_ard_3-2-1_53JPJ_2023-11-03_final
2023-11-03 10:35:11	ga_s2bm_ard_3-2-1_53JNJ_2023-11-03_final
2023-11-03 10:35:18	ga_s2bm_ard_3-2-1_53JQH_2023-11-03_final
2023-11-03 10:35:43	ga_s2bm_ard_3-2-1_53JMG_2023-11-03_final
2023-11-03 10:35:51	ga_s2bm_ard_3-2-1_53JPF_2023-11-03_final
2023-11-03 10:35:58	ga_s2bm_ard_3-2-1_53JMF_2023-11-03_final
2023-11-03 10:35:59	ga_s2bm_ard_3-2-1_53HQE_2023-11-03_final
2023-11-03 10:36:05	ga_s2bm_ard_3-2-1_53HPE_2023-11-03_final
2023-11-03 10:36:09	ga_s2bm_ard_3-2-1_53HNE_2023-11-03_final
2023-11-03 10:36:20	ga_s2bm_ard_3-2-1_53HPD_2023-11-03_final
2023-11-03 10:36:24	ga_s2bm_ard_3-2-1_53HND_2023-11-03_final
2023-11-03 10:36:38	ga_s2bm_ard_3-2-1_53HNC_2023-11-03_final
2023-11-03 10:36:42	ga_s2bm_ard_3-2-1_53HMC_2023-11-03_final
2023-11-03 10:36:53	ga_s2bm_ard_3-2-1_53HNB_2023-11-03_final
2023-11-03 10:36:56	ga_s2bm_ard_3-2-1_53HMB_2023-11-03_final
2023-11-03 10:37:01	ga_s2bm_ard_3-2-1_53HPA_2023-11-03_final
2023-11-03 12:13:57	ga_s2bm_ard_3-2-1_50KKA_2023-11-03_final
2023-11-03 12:14:01	ga_s2bm_ard_3-2-1_49KGR_2023-11-03_final
2023-11-03 12:14:11	ga_s2bm_ard_3-2-1_49KHQ_2023-11-03_final
2023-11-03 12:14:24	ga_s2bm_ard_3-2-1_49KHP_2023-11-03_final
2023-11-03 12:14:33	ga_s2bm_ard_3-2-1_49JHN_2023-11-03_final
2023-11-03 12:14:44	ga_s2bm_ard_3-2-1_49JGN_2023-11-03_final
2023-11-03 12:14:48	ga_s2bm_ard_3-2-1_49JFN_2023-11-03_final
2023-11-03 12:14:59	ga_s2bm_ard_3-2-1_49JGM_2023-11-03_final
2023-11-03 12:15:02	ga_s2bm_ard_3-2-1_49JFM_2023-11-03_final
2023-11-13 10:31:36	ga_s2bm_ard_3-2-1_53LRC_2023-11-13_nrt
2023-11-13 12:13:26	ga_s2bm_ard_3-2-1_49KFU_2023-11-13_nrt
2023-11-13 12:13:29	ga_s2bm_ard_3-2-1_49KHT_2023-11-13_nrt
2023-11-13 12:13:31	ga_s2bm_ard_3-2-1_49KGT_2023-11-13_nrt
2023-11-13 12:13:33	ga_s2bm_ard_3-2-1_49KFT_2023-11-13_nrt
2023-11-13 12:13:44	ga_s2bm_ard_3-2-1_49KGS_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 12:15:14	ga_s2bm_ard_3-2-1_49JFL_2023-11-13_nrt
2023-11-13 12:15:21	ga_s2bm_ard_3-2-1_49JFK_2023-11-13_nrt
2023-11-14 09:59:39	ga_s2bm_ard_3-2-1_55LHJ_2023-11-14_nrt
2023-11-14 09:59:48	ga_s2bm_ard_3-2-1_55LHH_2023-11-14_nrt
2023-11-14 10:00:05	ga_s2bm_ard_3-2-1_55LEH_2023-11-14_nrt
2023-11-14 10:00:20	ga_s2bm_ard_3-2-1_55LEG_2023-11-14_nrt
2023-11-14 10:00:34	ga_s2bm_ard_3-2-1_55LEF_2023-11-14_nrt
2023-11-14 10:00:49	ga_s2bm_ard_3-2-1_55LEE_2023-11-14_nrt
2023-11-14 10:01:03	ga_s2bm_ard_3-2-1_55LED_2023-11-14_nrt
2023-11-14 10:07:47	ga_s2bm_ard_3-2-1_54HUC_2023-11-14_nrt
2023-11-14 10:07:52	ga_s2bm_ard_3-2-1_54HVB_2023-11-14_nrt
2023-11-14 10:07:53	ga_s2bm_ard_3-2-1_54HUB_2023-11-14_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