Search ga_s2am_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
2023-12-25 12:04:53	ga_s2am_ard_3-2-1_49JHM_2023-12-25_final
2023-12-25 12:04:54	ga_s2am_ard_3-2-1_50JKS_2023-12-25_final
2023-12-25 12:04:55	ga_s2am_ard_3-2-1_50JMR_2023-12-25_final
2023-12-25 12:04:57	ga_s2am_ard_3-2-1_49JGM_2023-12-25_final
2023-12-25 12:05:05	ga_s2am_ard_3-2-1_50JLR_2023-12-25_final
2023-12-25 12:05:09	ga_s2am_ard_3-2-1_50JKR_2023-12-25_final
2023-12-25 12:05:12	ga_s2am_ard_3-2-1_49JGL_2023-12-25_final
2023-12-25 12:05:16	ga_s2am_ard_3-2-1_49JFL_2023-12-25_final
2023-12-25 12:05:20	ga_s2am_ard_3-2-1_50JLQ_2023-12-25_final
2023-12-25 12:05:23	ga_s2am_ard_3-2-1_50JKQ_2023-12-25_final
2023-12-25 12:05:26	ga_s2am_ard_3-2-1_49JGK_2023-12-25_final
2023-12-25 12:05:29	ga_s2am_ard_3-2-1_49JFK_2023-12-25_nrt
2023-12-25 12:05:34	ga_s2am_ard_3-2-1_50JLP_2023-12-25_final
2023-12-25 12:05:38	ga_s2am_ard_3-2-1_50JKP_2023-12-25_final
2023-12-25 12:05:41	ga_s2am_ard_3-2-1_49JGJ_2023-12-25_final
2023-12-25 12:05:44	ga_s2am_ard_3-2-1_49JFJ_2023-12-25_nrt
2023-12-25 12:05:48	ga_s2am_ard_3-2-1_50JLN_2023-12-25_final
2023-12-25 12:05:52	ga_s2am_ard_3-2-1_50JKN_2023-12-25_final
2023-12-25 12:05:55	ga_s2am_ard_3-2-1_49JGH_2023-12-25_final
2023-12-25 12:05:59	ga_s2am_ard_3-2-1_49JFH_2023-12-25_nrt
2023-12-26 09:57:29	ga_s2am_ard_3-2-1_55HCT_2023-12-26_final
2023-12-26 09:57:33	ga_s2am_ard_3-2-1_55HBT_2023-12-26_final
2023-12-26 09:57:33	ga_s2am_ard_3-2-1_54HYC_2023-12-26_final
2023-12-26 09:57:42	ga_s2am_ard_3-2-1_54HWC_2023-12-26_final
2023-12-26 09:57:55	ga_s2am_ard_3-2-1_54HWB_2023-12-26_nrt
2023-12-26 09:57:58	ga_s2am_ard_3-2-1_55GCR_2023-12-26_final
2023-12-26 09:58:02	ga_s2am_ard_3-2-1_55GBR_2023-12-26_final
2023-12-26 09:58:02	ga_s2am_ard_3-2-1_54GYA_2023-12-26_final
2023-12-26 09:58:06	ga_s2am_ard_3-2-1_54GXA_2023-12-26_nrt
2023-12-26 09:58:10	ga_s2am_ard_3-2-1_54GWA_2023-12-26_nrt
2023-12-26 09:58:10	ga_s2am_ard_3-2-1_55GCQ_2023-12-26_final
2023-12-26 09:58:16	ga_s2am_ard_3-2-1_55GBQ_2023-12-26_final
2023-12-26 09:58:16	ga_s2am_ard_3-2-1_54GYV_2023-12-26_final
2023-12-26 09:58:20	ga_s2am_ard_3-2-1_55GCP_2023-12-26_final
2023-12-26 09:58:20	ga_s2am_ard_3-2-1_54GXV_2023-12-26_nrt
2023-12-26 09:58:23	ga_s2am_ard_3-2-1_54GWV_2023-12-26_nrt
2023-12-26 09:58:26	ga_s2am_ard_3-2-1_54GYU_2023-12-26_nrt
2023-12-26 09:58:28	ga_s2am_ard_3-2-1_54GXU_2023-12-26_nrt
2023-12-26 11:30:11	ga_s2am_ard_3-2-1_52LCP_2023-12-26_nrt
2023-12-26 11:30:18	ga_s2am_ard_3-2-1_52LBP_2023-12-26_nrt
2023-12-26 11:30:20	ga_s2am_ard_3-2-1_51LZJ_2023-12-26_nrt
2023-12-26 11:30:23	ga_s2am_ard_3-2-1_51LYJ_2023-12-26_nrt
2023-12-26 11:30:26	ga_s2am_ard_3-2-1_51LXJ_2023-12-26_nrt
2023-12-26 11:30:32	ga_s2am_ard_3-2-1_52LBN_2023-12-26_nrt
2023-12-26 11:30:34	ga_s2am_ard_3-2-1_51LZH_2023-12-26_nrt
2023-12-26 11:30:38	ga_s2am_ard_3-2-1_51LYH_2023-12-26_nrt
2023-12-26 11:30:41	ga_s2am_ard_3-2-1_51LXH_2023-12-26_nrt
2023-12-26 11:34:55	ga_s2am_ard_3-2-1_51JVL_2023-12-26_final
2023-12-26 11:34:58	ga_s2am_ard_3-2-1_51JUL_2023-12-26_final
2023-12-26 11:35:01	ga_s2am_ard_3-2-1_51JTL_2023-12-26_final
2023-12-26 11:35:05	ga_s2am_ard_3-2-1_51JWK_2023-12-26_final
2023-12-26 11:35:09	ga_s2am_ard_3-2-1_51JVK_2023-12-26_final
2023-12-26 11:35:12	ga_s2am_ard_3-2-1_51JUK_2023-12-26_final
2023-12-26 11:35:16	ga_s2am_ard_3-2-1_51JTK_2023-12-26_final
2023-12-26 11:35:16	ga_s2am_ard_3-2-1_51JWJ_2023-12-26_final
2023-12-26 11:35:23	ga_s2am_ard_3-2-1_51JVJ_2023-12-26_final
2023-12-26 11:35:27	ga_s2am_ard_3-2-1_51JUJ_2023-12-26_final
2023-12-26 11:35:30	ga_s2am_ard_3-2-1_51JTJ_2023-12-26_final
2023-12-26 11:35:34	ga_s2am_ard_3-2-1_50JQP_2023-12-26_final
2023-12-26 11:35:38	ga_s2am_ard_3-2-1_51JVH_2023-12-26_final
2023-12-26 11:35:42	ga_s2am_ard_3-2-1_51JUH_2023-12-26_final
2023-12-26 11:35:45	ga_s2am_ard_3-2-1_51JTH_2023-12-26_final
2023-12-26 11:35:47	ga_s2am_ard_3-2-1_50JQN_2023-12-26_final
2023-12-26 11:35:50	ga_s2am_ard_3-2-1_51JVG_2023-12-26_final
2023-12-26 11:35:52	ga_s2am_ard_3-2-1_51JUG_2023-12-26_final
2023-12-26 11:35:54	ga_s2am_ard_3-2-1_51JTG_2023-12-26_final
2023-12-26 11:35:55	ga_s2am_ard_3-2-1_50JQN_2023-12-26_final
2023-12-26 11:35:55	ga_s2am_ard_3-2-1_50JQM_2023-12-26_final
2023-12-26 11:35:57	ga_s2am_ard_3-2-1_51JVG_2023-12-26_final
2023-12-26 11:35:59	ga_s2am_ard_3-2-1_51JUG_2023-12-26_final
2023-12-26 11:36:01	ga_s2am_ard_3-2-1_51JTG_2023-12-26_final
2023-12-26 11:36:02	ga_s2am_ard_3-2-1_50JQM_2023-12-26_final
2023-12-26 11:36:07	ga_s2am_ard_3-2-1_51JVF_2023-12-26_final
2023-12-26 11:36:11	ga_s2am_ard_3-2-1_51JUF_2023-12-26_final
2023-12-26 11:36:14	ga_s2am_ard_3-2-1_51JTF_2023-12-26_final
2023-12-26 11:36:17	ga_s2am_ard_3-2-1_50JQL_2023-12-26_final
2023-12-26 11:36:20	ga_s2am_ard_3-2-1_51HVE_2023-12-26_final
2023-12-26 11:36:23	ga_s2am_ard_3-2-1_50JPL_2023-12-26_final
2023-12-26 11:36:25	ga_s2am_ard_3-2-1_51HUE_2023-12-26_final
2023-12-26 11:36:29	ga_s2am_ard_3-2-1_51HTE_2023-12-26_final
2023-12-26 11:36:30	ga_s2am_ard_3-2-1_51HVD_2023-12-26_final
2023-12-26 11:36:31	ga_s2am_ard_3-2-1_50HQK_2023-12-26_final
2023-12-26 11:36:35	ga_s2am_ard_3-2-1_50HPK_2023-12-26_final
2023-12-26 11:36:40	ga_s2am_ard_3-2-1_51HUD_2023-12-26_final
2023-12-26 11:36:43	ga_s2am_ard_3-2-1_51HTD_2023-12-26_final
2023-12-26 11:36:45	ga_s2am_ard_3-2-1_50HQJ_2023-12-26_final
2023-12-26 11:36:49	ga_s2am_ard_3-2-1_50HPJ_2023-12-26_final
2023-12-26 11:36:54	ga_s2am_ard_3-2-1_51HUC_2023-12-26_final
2023-12-26 11:36:58	ga_s2am_ard_3-2-1_51HTC_2023-12-26_final
2023-12-26 11:37:00	ga_s2am_ard_3-2-1_50HQH_2023-12-26_final
2023-12-26 11:37:04	ga_s2am_ard_3-2-1_50HPH_2023-12-26_final
2023-12-26 11:37:09	ga_s2am_ard_3-2-1_51HUB_2023-12-26_final
2023-12-26 11:37:12	ga_s2am_ard_3-2-1_51HTB_2023-12-26_final
2023-12-26 11:37:14	ga_s2am_ard_3-2-1_50HQG_2023-12-26_final
2023-12-26 11:37:18	ga_s2am_ard_3-2-1_50HPG_2023-12-26_final
2023-12-26 11:37:22	ga_s2am_ard_3-2-1_51HUA_2023-12-26_nrt
2023-12-26 11:37:23	ga_s2am_ard_3-2-1_50HNG_2023-12-26_final
2023-12-26 11:37:25	ga_s2am_ard_3-2-1_51HTA_2023-12-26_nrt
2023-12-26 11:37:25	ga_s2am_ard_3-2-1_50HQF_2023-12-26_nrt
2023-12-26 11:37:27	ga_s2am_ard_3-2-1_50HPF_2023-12-26_nrt

Return to ga_s2am_ard_3 whole collection 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

All years