Search ga_s2bm_ard_3 on 30th December 2023

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
2023-12-30 10:20:06	ga_s2bm_ard_3-2-1_55LBH_2023-12-30_nrt
2023-12-30 10:20:19	ga_s2bm_ard_3-2-1_55LBG_2023-12-30_nrt
2023-12-30 10:20:30	ga_s2bm_ard_3-2-1_55LBF_2023-12-30_nrt
2023-12-30 10:21:22	ga_s2bm_ard_3-2-1_54LVJ_2023-12-30_final
2023-12-30 10:21:30	ga_s2bm_ard_3-2-1_54LWH_2023-12-30_final
2023-12-30 10:21:38	ga_s2bm_ard_3-2-1_54KYG_2023-12-30_final
2023-12-30 12:00:41	ga_s2bm_ard_3-2-1_50LNP_2023-12-30_nrt
2023-12-30 12:00:45	ga_s2bm_ard_3-2-1_50LMP_2023-12-30_nrt
2023-12-30 12:00:49	ga_s2bm_ard_3-2-1_50LQN_2023-12-30_nrt
2023-12-30 12:00:52	ga_s2bm_ard_3-2-1_50LPN_2023-12-30_nrt
2023-12-30 12:00:56	ga_s2bm_ard_3-2-1_50LNN_2023-12-30_nrt
2023-12-30 12:00:59	ga_s2bm_ard_3-2-1_50LMN_2023-12-30_nrt
2023-12-30 12:01:03	ga_s2bm_ard_3-2-1_50LQM_2023-12-30_nrt
2023-12-30 12:01:07	ga_s2bm_ard_3-2-1_50LPM_2023-12-30_nrt
2023-12-30 12:01:10	ga_s2bm_ard_3-2-1_50LNM_2023-12-30_nrt
2023-12-30 12:01:13	ga_s2bm_ard_3-2-1_50LMM_2023-12-30_nrt
2023-12-30 12:01:17	ga_s2bm_ard_3-2-1_50LQL_2023-12-30_nrt
2023-12-30 12:01:21	ga_s2bm_ard_3-2-1_50LPL_2023-12-30_nrt
2023-12-30 12:01:25	ga_s2bm_ard_3-2-1_50LNL_2023-12-30_nrt
2023-12-30 12:01:28	ga_s2bm_ard_3-2-1_50LQK_2023-12-30_nrt
2023-12-30 12:01:28	ga_s2bm_ard_3-2-1_50LML_2023-12-30_nrt
2023-12-30 12:01:34	ga_s2bm_ard_3-2-1_50LPK_2023-12-30_nrt
2023-12-30 12:01:36	ga_s2bm_ard_3-2-1_50LNK_2023-12-30_nrt
2023-12-30 12:01:37	ga_s2bm_ard_3-2-1_50LMK_2023-12-30_nrt
2023-12-30 12:02:56	ga_s2bm_ard_3-2-1_50KNE_2023-12-30_nrt
2023-12-30 12:02:58	ga_s2bm_ard_3-2-1_50KME_2023-12-30_nrt
2023-12-30 12:02:59	ga_s2bm_ard_3-2-1_50KLE_2023-12-30_nrt
2023-12-30 12:03:02	ga_s2bm_ard_3-2-1_50KKE_2023-12-30_nrt
2023-12-30 12:03:06	ga_s2bm_ard_3-2-1_50KND_2023-12-30_nrt
2023-12-30 12:03:09	ga_s2bm_ard_3-2-1_50KMD_2023-12-30_nrt
2023-12-30 12:03:11	ga_s2bm_ard_3-2-1_50KLD_2023-12-30_nrt
2023-12-30 12:03:12	ga_s2bm_ard_3-2-1_50KKD_2023-12-30_nrt
2023-12-30 12:03:14	ga_s2bm_ard_3-2-1_49KHU_2023-12-30_nrt
2023-12-30 12:03:15	ga_s2bm_ard_3-2-1_50KND_2023-12-30_nrt
2023-12-30 12:03:15	ga_s2bm_ard_3-2-1_50KNC_2023-12-30_final
2023-12-30 12:03:16	ga_s2bm_ard_3-2-1_50KMC_2023-12-30_final
2023-12-30 12:03:16	ga_s2bm_ard_3-2-1_50KMD_2023-12-30_nrt
2023-12-30 12:03:17	ga_s2bm_ard_3-2-1_50KLD_2023-12-30_nrt
2023-12-30 12:03:19	ga_s2bm_ard_3-2-1_50KKD_2023-12-30_nrt
2023-12-30 12:03:20	ga_s2bm_ard_3-2-1_49KHU_2023-12-30_nrt
2023-12-30 12:03:21	ga_s2bm_ard_3-2-1_50KNC_2023-12-30_final
2023-12-30 12:03:24	ga_s2bm_ard_3-2-1_50KMC_2023-12-30_final
2023-12-30 12:03:28	ga_s2bm_ard_3-2-1_50KLC_2023-12-30_final
2023-12-30 12:03:31	ga_s2bm_ard_3-2-1_50KKC_2023-12-30_nrt
2023-12-30 12:03:31	ga_s2bm_ard_3-2-1_49KHT_2023-12-30_nrt
2023-12-30 12:03:34	ga_s2bm_ard_3-2-1_50KNB_2023-12-30_final
2023-12-30 12:03:39	ga_s2bm_ard_3-2-1_50KMB_2023-12-30_final
2023-12-30 12:03:42	ga_s2bm_ard_3-2-1_50KLB_2023-12-30_final
2023-12-30 12:03:45	ga_s2bm_ard_3-2-1_50KNA_2023-12-30_final
2023-12-30 12:03:45	ga_s2bm_ard_3-2-1_50KKB_2023-12-30_final
2023-12-30 12:03:46	ga_s2bm_ard_3-2-1_49KHS_2023-12-30_final
2023-12-30 12:03:53	ga_s2bm_ard_3-2-1_50KMA_2023-12-30_final
2023-12-30 12:03:57	ga_s2bm_ard_3-2-1_50KLA_2023-12-30_final
2023-12-30 12:04:00	ga_s2bm_ard_3-2-1_50KKA_2023-12-30_final
2023-12-30 12:04:00	ga_s2bm_ard_3-2-1_49KHR_2023-12-30_final
2023-12-30 12:04:08	ga_s2bm_ard_3-2-1_50KMV_2023-12-30_final
2023-12-30 12:04:11	ga_s2bm_ard_3-2-1_50KLV_2023-12-30_final
2023-12-30 12:04:14	ga_s2bm_ard_3-2-1_50KKV_2023-12-30_final
2023-12-30 12:04:15	ga_s2bm_ard_3-2-1_49KHQ_2023-12-30_final
2023-12-30 12:04:19	ga_s2bm_ard_3-2-1_49KGQ_2023-12-30_final
2023-12-30 12:04:23	ga_s2bm_ard_3-2-1_50KMU_2023-12-30_final
2023-12-30 12:04:26	ga_s2bm_ard_3-2-1_50KLU_2023-12-30_final
2023-12-30 12:04:29	ga_s2bm_ard_3-2-1_49KHP_2023-12-30_final
2023-12-30 12:04:29	ga_s2bm_ard_3-2-1_50KKU_2023-12-30_final
2023-12-30 12:04:32	ga_s2bm_ard_3-2-1_49KGP_2023-12-30_final
2023-12-30 12:04:37	ga_s2bm_ard_3-2-1_50JMT_2023-12-30_final
2023-12-30 12:04:40	ga_s2bm_ard_3-2-1_50JLT_2023-12-30_final
2023-12-30 12:04:44	ga_s2bm_ard_3-2-1_49JHN_2023-12-30_final
2023-12-30 12:04:44	ga_s2bm_ard_3-2-1_50JKT_2023-12-30_final
2023-12-30 12:04:47	ga_s2bm_ard_3-2-1_49JGN_2023-12-30_final
2023-12-30 12:04:50	ga_s2bm_ard_3-2-1_50JMS_2023-12-30_final
2023-12-30 12:04:55	ga_s2bm_ard_3-2-1_50JLS_2023-12-30_final
2023-12-30 12:04:58	ga_s2bm_ard_3-2-1_49JHM_2023-12-30_final
2023-12-30 12:04:58	ga_s2bm_ard_3-2-1_50JKS_2023-12-30_final
2023-12-30 12:05:00	ga_s2bm_ard_3-2-1_50JMR_2023-12-30_final
2023-12-30 12:05:02	ga_s2bm_ard_3-2-1_49JGM_2023-12-30_final
2023-12-30 12:05:09	ga_s2bm_ard_3-2-1_50JLR_2023-12-30_final
2023-12-30 12:05:09	ga_s2bm_ard_3-2-1_49JFM_2023-12-30_final
2023-12-30 12:05:13	ga_s2bm_ard_3-2-1_50JKR_2023-12-30_final
2023-12-30 12:05:16	ga_s2bm_ard_3-2-1_49JGL_2023-12-30_final
2023-12-30 12:05:20	ga_s2bm_ard_3-2-1_49JFL_2023-12-30_nrt
2023-12-30 12:05:24	ga_s2bm_ard_3-2-1_50JLQ_2023-12-30_final
2023-12-30 12:05:27	ga_s2bm_ard_3-2-1_50JKQ_2023-12-30_final
2023-12-30 12:05:30	ga_s2bm_ard_3-2-1_49JGK_2023-12-30_final
2023-12-30 12:05:34	ga_s2bm_ard_3-2-1_49JFK_2023-12-30_nrt
2023-12-30 12:05:39	ga_s2bm_ard_3-2-1_50JLP_2023-12-30_final
2023-12-30 12:05:42	ga_s2bm_ard_3-2-1_50JKP_2023-12-30_final
2023-12-30 12:05:45	ga_s2bm_ard_3-2-1_49JGJ_2023-12-30_final
2023-12-30 12:05:49	ga_s2bm_ard_3-2-1_49JFJ_2023-12-30_nrt
2023-12-30 12:05:53	ga_s2bm_ard_3-2-1_50JLN_2023-12-30_final
2023-12-30 12:05:56	ga_s2bm_ard_3-2-1_50JKN_2023-12-30_final
2023-12-30 12:05:59	ga_s2bm_ard_3-2-1_49JGH_2023-12-30_final
2023-12-30 12:06:03	ga_s2bm_ard_3-2-1_49JFH_2023-12-30_nrt
2023-12-30 12:06:05	ga_s2bm_ard_3-2-1_50JLM_2023-12-30_final
2023-12-30 12:06:11	ga_s2bm_ard_3-2-1_50JKM_2023-12-30_final
2023-12-30 12:06:14	ga_s2bm_ard_3-2-1_49JGG_2023-12-30_nrt
2023-12-30 12:06:17	ga_s2bm_ard_3-2-1_49JFG_2023-12-30_nrt
2023-12-30 12:06:24	ga_s2bm_ard_3-2-1_50JKL_2023-12-30_nrt
2023-12-30 12:06:26	ga_s2bm_ard_3-2-1_49JGF_2023-12-30_nrt
2023-12-30 12:06:27	ga_s2bm_ard_3-2-1_49JFF_2023-12-30_nrt

Return to ga_s2bm_ard_3 on 30th December 2023 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

2023

December

30th