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
2023-10-05 10:01:12	ga_s2bm_ard_3-2-1_55LCD_2023-10-05_final
2023-10-05 10:01:14	ga_s2bm_ard_3-2-1_55LFC_2023-10-05_final
2023-10-05 10:01:24	ga_s2bm_ard_3-2-1_55LCC_2023-10-05_final
2023-10-05 10:01:50	ga_s2bm_ard_3-2-1_55KDA_2023-10-05_final
2023-10-05 10:01:53	ga_s2bm_ard_3-2-1_55KCA_2023-10-05_final
2023-10-05 10:02:08	ga_s2bm_ard_3-2-1_55KCV_2023-10-05_final
2023-10-05 10:02:15	ga_s2bm_ard_3-2-1_55KBV_2023-10-05_final
2023-10-05 10:02:15	ga_s2bm_ard_3-2-1_55KEU_2023-10-05_final
2023-10-05 10:02:30	ga_s2bm_ard_3-2-1_55KET_2023-10-05_final
2023-10-05 10:02:42	ga_s2bm_ard_3-2-1_54KZC_2023-10-05_final
2023-10-05 10:03:03	ga_s2bm_ard_3-2-1_55KDR_2023-10-05_final
2023-10-05 10:03:06	ga_s2bm_ard_3-2-1_55KCR_2023-10-05_final
2023-10-05 10:03:12	ga_s2bm_ard_3-2-1_55KDQ_2023-10-05_final
2023-10-05 10:03:16	ga_s2bm_ard_3-2-1_54KZV_2023-10-05_final
2023-10-05 10:03:19	ga_s2bm_ard_3-2-1_55KDQ_2023-10-05_final
2023-10-05 10:03:24	ga_s2bm_ard_3-2-1_55KBQ_2023-10-05_final
2023-10-05 10:03:24	ga_s2bm_ard_3-2-1_54KZV_2023-10-05_final
2023-10-05 10:03:38	ga_s2bm_ard_3-2-1_54KZU_2023-10-05_final
2023-10-05 10:03:53	ga_s2bm_ard_3-2-1_55JBN_2023-10-05_final
2023-10-05 10:04:11	ga_s2bm_ard_3-2-1_54JYS_2023-10-05_final
2023-10-05 10:04:19	ga_s2bm_ard_3-2-1_55JCL_2023-10-05_final
2023-10-05 10:04:24	ga_s2bm_ard_3-2-1_55JDK_2023-10-05_final
2023-10-05 10:04:25	ga_s2bm_ard_3-2-1_54JYR_2023-10-05_final
2023-10-05 10:04:33	ga_s2bm_ard_3-2-1_55JCK_2023-10-05_final
2023-10-05 10:05:27	ga_s2bm_ard_3-2-1_54JXM_2023-10-05_final
2023-10-05 10:05:33	ga_s2bm_ard_3-2-1_54JWM_2023-10-05_final
2023-10-05 10:05:35	ga_s2bm_ard_3-2-1_55JBF_2023-10-05_final
2023-10-05 10:05:37	ga_s2bm_ard_3-2-1_54JYL_2023-10-05_final
2023-10-05 10:05:41	ga_s2bm_ard_3-2-1_54JXL_2023-10-05_final
2023-10-05 10:05:45	ga_s2bm_ard_3-2-1_54JWL_2023-10-05_final
2023-10-05 10:05:49	ga_s2bm_ard_3-2-1_55HBE_2023-10-05_final
2023-10-05 10:05:59	ga_s2bm_ard_3-2-1_54HWK_2023-10-05_final
2023-10-05 10:06:06	ga_s2bm_ard_3-2-1_54HYJ_2023-10-05_final
2023-10-05 10:06:20	ga_s2bm_ard_3-2-1_54HYH_2023-10-05_final
2023-10-05 10:06:33	ga_s2bm_ard_3-2-1_55HBB_2023-10-05_final
2023-10-05 10:06:53	ga_s2bm_ard_3-2-1_54HXF_2023-10-05_final
2023-10-05 10:07:08	ga_s2bm_ard_3-2-1_54HXE_2023-10-05_final
2023-10-05 10:07:14	ga_s2bm_ard_3-2-1_54HYD_2023-10-05_final
2023-10-05 10:07:30	ga_s2bm_ard_3-2-1_54HVD_2023-10-05_final
2023-10-05 10:07:41	ga_s2bm_ard_3-2-1_54HWC_2023-10-05_final
2023-10-05 10:07:43	ga_s2bm_ard_3-2-1_54HVC_2023-10-05_final
2023-10-05 11:41:18	ga_s2bm_ard_3-2-1_51LUF_2023-10-05_final
2023-10-05 11:41:59	ga_s2bm_ard_3-2-1_51LVC_2023-10-05_final
2023-10-05 11:42:27	ga_s2bm_ard_3-2-1_51KVA_2023-10-05_final
2023-10-05 11:42:46	ga_s2bm_ard_3-2-1_51KUV_2023-10-05_final
2023-10-05 11:43:00	ga_s2bm_ard_3-2-1_51KUU_2023-10-05_final
2023-10-05 11:43:03	ga_s2bm_ard_3-2-1_51KTU_2023-10-05_final
2023-10-05 11:43:09	ga_s2bm_ard_3-2-1_50KQD_2023-10-05_final
2023-10-05 11:43:11	ga_s2bm_ard_3-2-1_51KVT_2023-10-05_final
2023-10-05 11:43:18	ga_s2bm_ard_3-2-1_51KTT_2023-10-05_final
2023-10-05 11:43:18	ga_s2bm_ard_3-2-1_50KRC_2023-10-05_final
2023-10-05 11:43:22	ga_s2bm_ard_3-2-1_50KQC_2023-10-05_final
2023-10-05 11:43:25	ga_s2bm_ard_3-2-1_51KVS_2023-10-05_final
2023-10-05 11:43:32	ga_s2bm_ard_3-2-1_51KTS_2023-10-05_final
2023-10-05 11:43:33	ga_s2bm_ard_3-2-1_50KRB_2023-10-05_final
2023-10-05 11:43:47	ga_s2bm_ard_3-2-1_50KRA_2023-10-05_final
2023-10-05 11:44:34	ga_s2bm_ard_3-2-1_50JQT_2023-10-05_final
2023-10-05 11:44:45	ga_s2bm_ard_3-2-1_50JRS_2023-10-05_final
2023-10-05 11:45:36	ga_s2bm_ard_3-2-1_50JPP_2023-10-05_final
2023-10-05 11:45:50	ga_s2bm_ard_3-2-1_50JPN_2023-10-05_final
2023-10-05 11:45:54	ga_s2bm_ard_3-2-1_50JNN_2023-10-05_final
2023-10-05 11:46:01	ga_s2bm_ard_3-2-1_50JQM_2023-10-05_final
2023-10-05 11:46:05	ga_s2bm_ard_3-2-1_50JPM_2023-10-05_final
2023-10-05 11:46:08	ga_s2bm_ard_3-2-1_51JTF_2023-10-05_final
2023-10-05 11:46:08	ga_s2bm_ard_3-2-1_50JNM_2023-10-05_final
2023-10-05 11:46:13	ga_s2bm_ard_3-2-1_50JMM_2023-10-05_final
2023-10-05 11:46:15	ga_s2bm_ard_3-2-1_50JQL_2023-10-05_final
2023-10-05 11:46:41	ga_s2bm_ard_3-2-1_50HMK_2023-10-05_final
2023-10-05 11:46:53	ga_s2bm_ard_3-2-1_50HQH_2023-10-05_final
2023-10-05 11:46:55	ga_s2bm_ard_3-2-1_50HMJ_2023-10-05_final
2023-10-05 11:47:10	ga_s2bm_ard_3-2-1_50HMH_2023-10-05_final
2023-10-05 11:47:21	ga_s2bm_ard_3-2-1_50HNG_2023-10-05_final
2023-10-05 11:47:26	ga_s2bm_ard_3-2-1_50HLG_2023-10-05_final
2023-10-06 09:34:14	ga_s2bm_ard_3-2-1_56JKR_2023-10-06_final
2023-10-06 09:34:44	ga_s2bm_ard_3-2-1_56JNN_2023-10-06_final
2023-10-06 11:10:37	ga_s2bm_ard_3-2-1_52LHM_2023-10-06_final
2023-10-06 11:10:47	ga_s2bm_ard_3-2-1_52LHL_2023-10-06_final
2023-10-06 11:11:23	ga_s2bm_ard_3-2-1_52LFJ_2023-10-06_final
2023-10-06 11:13:29	ga_s2bm_ard_3-2-1_51KZS_2023-10-06_final
2023-10-06 11:15:29	ga_s2bm_ard_3-2-1_51JXJ_2023-10-06_final
2023-10-15 11:40:27	ga_s2bm_ard_3-2-1_51LXJ_2023-10-15_nrt
2023-10-15 11:40:31	ga_s2bm_ard_3-2-1_51LWJ_2023-10-15_nrt
2023-10-15 11:40:37	ga_s2bm_ard_3-2-1_51LUJ_2023-10-15_nrt
2023-10-15 11:40:42	ga_s2bm_ard_3-2-1_51LXH_2023-10-15_nrt
2023-10-15 11:40:45	ga_s2bm_ard_3-2-1_51LWH_2023-10-15_nrt
2023-10-15 11:40:51	ga_s2bm_ard_3-2-1_51LUH_2023-10-15_nrt
2023-10-15 11:40:56	ga_s2bm_ard_3-2-1_51LXG_2023-10-15_nrt
2023-10-15 11:41:06	ga_s2bm_ard_3-2-1_51LUG_2023-10-15_nrt
2023-10-15 11:41:09	ga_s2bm_ard_3-2-1_51LXF_2023-10-15_nrt
2023-10-15 11:41:19	ga_s2bm_ard_3-2-1_51LXE_2023-10-15_nrt
2023-10-15 11:47:37	ga_s2bm_ard_3-2-1_50HLF_2023-10-15_nrt
2023-10-16 09:30:07	ga_s2bm_ard_3-2-1_56LPM_2023-10-16_nrt
2023-10-16 09:32:32	ga_s2bm_ard_3-2-1_56KMC_2023-10-16_nrt
2023-10-16 09:33:12	ga_s2bm_ard_3-2-1_56KMV_2023-10-16_nrt
2023-10-16 09:36:39	ga_s2bm_ard_3-2-1_56HLF_2023-10-16_nrt
2023-10-16 09:36:53	ga_s2bm_ard_3-2-1_56HLE_2023-10-16_nrt
2023-10-16 09:36:57	ga_s2bm_ard_3-2-1_56HKE_2023-10-16_nrt
2023-10-16 09:37:06	ga_s2bm_ard_3-2-1_56HLD_2023-10-16_nrt
2023-10-16 09:37:12	ga_s2bm_ard_3-2-1_56HKD_2023-10-16_nrt
2023-10-16 09:37:26	ga_s2bm_ard_3-2-1_56HKC_2023-10-16_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