Search ga_s2am_ard_3 on 3rd May 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
2023-05-03 08:31:42	ga_s2am_ard_3-2-1_57FVV_2023-05-02_final
2023-05-03 09:59:38	ga_s2am_ard_3-2-1_55LHJ_2023-05-03_nrt
2023-05-03 09:59:44	ga_s2am_ard_3-2-1_55LGJ_2023-05-03_nrt
2023-05-03 09:59:47	ga_s2am_ard_3-2-1_55LFJ_2023-05-03_nrt
2023-05-03 09:59:48	ga_s2am_ard_3-2-1_55LHH_2023-05-03_nrt
2023-05-03 09:59:51	ga_s2am_ard_3-2-1_55LEJ_2023-05-03_nrt
2023-05-03 09:59:58	ga_s2am_ard_3-2-1_55LGH_2023-05-03_nrt
2023-05-03 10:00:02	ga_s2am_ard_3-2-1_55LFH_2023-05-03_nrt
2023-05-03 10:00:05	ga_s2am_ard_3-2-1_55LEH_2023-05-03_nrt
2023-05-03 10:00:09	ga_s2am_ard_3-2-1_55LDH_2023-05-03_nrt
2023-05-03 10:00:13	ga_s2am_ard_3-2-1_55LGG_2023-05-03_nrt
2023-05-03 10:00:16	ga_s2am_ard_3-2-1_55LFG_2023-05-03_nrt
2023-05-03 10:00:20	ga_s2am_ard_3-2-1_55LEG_2023-05-03_nrt
2023-05-03 10:00:23	ga_s2am_ard_3-2-1_55LDG_2023-05-03_nrt
2023-05-03 10:00:27	ga_s2am_ard_3-2-1_55LGF_2023-05-03_nrt
2023-05-03 10:00:31	ga_s2am_ard_3-2-1_55LFF_2023-05-03_nrt
2023-05-03 10:00:34	ga_s2am_ard_3-2-1_55LEF_2023-05-03_nrt
2023-05-03 10:00:37	ga_s2am_ard_3-2-1_55LDF_2023-05-03_nrt
2023-05-03 10:00:41	ga_s2am_ard_3-2-1_55LGE_2023-05-03_nrt
2023-05-03 10:00:45	ga_s2am_ard_3-2-1_55LFE_2023-05-03_nrt
2023-05-03 10:00:49	ga_s2am_ard_3-2-1_55LEE_2023-05-03_nrt
2023-05-03 10:00:52	ga_s2am_ard_3-2-1_55LGD_2023-05-03_nrt
2023-05-03 10:00:52	ga_s2am_ard_3-2-1_55LDE_2023-05-03_nrt
2023-05-03 10:01:00	ga_s2am_ard_3-2-1_55LFD_2023-05-03_nrt
2023-05-03 10:01:03	ga_s2am_ard_3-2-1_55LED_2023-05-03_nrt
2023-05-03 10:01:07	ga_s2am_ard_3-2-1_55LDD_2023-05-03_nrt
2023-05-03 10:01:12	ga_s2am_ard_3-2-1_55LCD_2023-05-03_final
2023-05-03 10:01:14	ga_s2am_ard_3-2-1_55LFC_2023-05-03_final
2023-05-03 10:01:18	ga_s2am_ard_3-2-1_55LEC_2023-05-03_final
2023-05-03 10:01:21	ga_s2am_ard_3-2-1_55LDC_2023-05-03_nrt
2023-05-03 10:06:46	ga_s2am_ard_3-2-1_55HBA_2023-05-03_final
2023-05-03 10:06:50	ga_s2am_ard_3-2-1_54HYF_2023-05-03_final
2023-05-03 10:06:54	ga_s2am_ard_3-2-1_54HXF_2023-05-03_final
2023-05-03 10:07:01	ga_s2am_ard_3-2-1_54HVF_2023-05-03_final
2023-05-03 10:07:03	ga_s2am_ard_3-2-1_54HYE_2023-05-03_final
2023-05-03 10:07:08	ga_s2am_ard_3-2-1_54HXE_2023-05-03_final
2023-05-03 10:07:12	ga_s2am_ard_3-2-1_54HWE_2023-05-03_final
2023-05-03 10:07:16	ga_s2am_ard_3-2-1_54HVE_2023-05-03_final
2023-05-03 10:07:26	ga_s2am_ard_3-2-1_54HWD_2023-05-03_final
2023-05-03 10:07:30	ga_s2am_ard_3-2-1_54HVD_2023-05-03_final
2023-05-03 10:07:35	ga_s2am_ard_3-2-1_54HUD_2023-05-03_final
2023-05-03 10:07:41	ga_s2am_ard_3-2-1_54HWC_2023-05-03_final
2023-05-03 10:07:44	ga_s2am_ard_3-2-1_54HVC_2023-05-03_final
2023-05-03 10:07:46	ga_s2am_ard_3-2-1_54HUC_2023-05-03_nrt
2023-05-03 10:07:48	ga_s2am_ard_3-2-1_54HXB_2023-05-03_nrt
2023-05-03 10:07:49	ga_s2am_ard_3-2-1_54HWB_2023-05-03_nrt
2023-05-03 10:07:50	ga_s2am_ard_3-2-1_54HVB_2023-05-03_nrt
2023-05-03 11:40:25	ga_s2am_ard_3-2-1_51LXJ_2023-05-03_nrt
2023-05-03 11:40:29	ga_s2am_ard_3-2-1_51LWJ_2023-05-03_nrt
2023-05-03 11:40:32	ga_s2am_ard_3-2-1_51LVJ_2023-05-03_nrt
2023-05-03 11:40:35	ga_s2am_ard_3-2-1_51LUJ_2023-05-03_nrt
2023-05-03 11:40:40	ga_s2am_ard_3-2-1_51LXH_2023-05-03_nrt
2023-05-03 11:40:43	ga_s2am_ard_3-2-1_51LWH_2023-05-03_nrt
2023-05-03 11:40:46	ga_s2am_ard_3-2-1_51LVH_2023-05-03_nrt
2023-05-03 11:40:49	ga_s2am_ard_3-2-1_51LUH_2023-05-03_nrt
2023-05-03 11:40:54	ga_s2am_ard_3-2-1_51LXG_2023-05-03_nrt
2023-05-03 11:41:01	ga_s2am_ard_3-2-1_51LVG_2023-05-03_final
2023-05-03 11:41:04	ga_s2am_ard_3-2-1_51LUG_2023-05-03_nrt
2023-05-03 11:41:07	ga_s2am_ard_3-2-1_51LXF_2023-05-03_nrt
2023-05-03 11:41:12	ga_s2am_ard_3-2-1_51LWF_2023-05-03_nrt
2023-05-03 11:41:15	ga_s2am_ard_3-2-1_51LVF_2023-05-03_final
2023-05-03 11:41:17	ga_s2am_ard_3-2-1_51LXE_2023-05-03_nrt
2023-05-03 11:41:19	ga_s2am_ard_3-2-1_51LUF_2023-05-03_final
2023-05-03 11:41:26	ga_s2am_ard_3-2-1_51LWE_2023-05-03_final
2023-05-03 11:41:30	ga_s2am_ard_3-2-1_51LVE_2023-05-03_final
2023-05-03 11:41:33	ga_s2am_ard_3-2-1_51LUE_2023-05-03_final
2023-05-03 11:41:37	ga_s2am_ard_3-2-1_51LTE_2023-05-03_nrt
2023-05-03 11:41:41	ga_s2am_ard_3-2-1_51LWD_2023-05-03_nrt
2023-05-03 11:41:44	ga_s2am_ard_3-2-1_51LVD_2023-05-03_nrt
2023-05-03 11:41:48	ga_s2am_ard_3-2-1_51LUD_2023-05-03_final
2023-05-03 11:41:51	ga_s2am_ard_3-2-1_51LTD_2023-05-03_nrt
2023-05-03 11:41:56	ga_s2am_ard_3-2-1_51LWC_2023-05-03_final
2023-05-03 11:41:59	ga_s2am_ard_3-2-1_51LVC_2023-05-03_final
2023-05-03 11:42:02	ga_s2am_ard_3-2-1_51LUC_2023-05-03_final
2023-05-03 11:42:05	ga_s2am_ard_3-2-1_51LTC_2023-05-03_nrt
2023-05-03 11:42:08	ga_s2am_ard_3-2-1_50LRH_2023-05-03_nrt
2023-05-03 11:42:09	ga_s2am_ard_3-2-1_51KWB_2023-05-03_final
2023-05-03 11:42:13	ga_s2am_ard_3-2-1_51KVB_2023-05-03_final
2023-05-03 11:42:17	ga_s2am_ard_3-2-1_51KUB_2023-05-03_final
2023-05-03 11:42:20	ga_s2am_ard_3-2-1_51KTB_2023-05-03_nrt
2023-05-03 11:42:21	ga_s2am_ard_3-2-1_50KRG_2023-05-03_nrt
2023-05-03 11:42:21	ga_s2am_ard_3-2-1_51KWA_2023-05-03_final
2023-05-03 11:42:28	ga_s2am_ard_3-2-1_51KVA_2023-05-03_final
2023-05-03 11:42:31	ga_s2am_ard_3-2-1_51KUA_2023-05-03_final
2023-05-03 11:42:35	ga_s2am_ard_3-2-1_51KTA_2023-05-03_nrt
2023-05-03 11:42:35	ga_s2am_ard_3-2-1_50KRF_2023-05-03_nrt
2023-05-03 11:42:42	ga_s2am_ard_3-2-1_51KVV_2023-05-03_final
2023-05-03 11:42:46	ga_s2am_ard_3-2-1_51KUV_2023-05-03_final
2023-05-03 11:42:49	ga_s2am_ard_3-2-1_51KTV_2023-05-03_nrt
2023-05-03 11:42:50	ga_s2am_ard_3-2-1_50KRE_2023-05-03_nrt
2023-05-03 11:42:57	ga_s2am_ard_3-2-1_51KVU_2023-05-03_final
2023-05-03 11:43:00	ga_s2am_ard_3-2-1_51KUU_2023-05-03_final
2023-05-03 11:43:04	ga_s2am_ard_3-2-1_51KTU_2023-05-03_final
2023-05-03 11:43:04	ga_s2am_ard_3-2-1_50KRD_2023-05-03_final
2023-05-03 11:43:09	ga_s2am_ard_3-2-1_50KQD_2023-05-03_final
2023-05-03 11:43:12	ga_s2am_ard_3-2-1_51KVT_2023-05-03_final
2023-05-03 11:43:15	ga_s2am_ard_3-2-1_51KUT_2023-05-03_final
2023-05-03 11:43:18	ga_s2am_ard_3-2-1_51KTT_2023-05-03_final
2023-05-03 11:43:19	ga_s2am_ard_3-2-1_50KRC_2023-05-03_final
2023-05-03 11:43:22	ga_s2am_ard_3-2-1_50KQC_2023-05-03_final

Return to ga_s2am_ard_3 on 3rd May 2023 overview

ga_s2am_ard_3: Geoscience Australia Sentinel 2A MSI Analysis Ready Data Collection 3

Baseline satellite data

Land and vegetation

Inland water

Sea, ocean and coast

Hazards

Other products

2023

May

3rd