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
2023-05-09 12:03:41	ga_s2am_ard_3-2-1_50KLB_2023-05-09_final
2023-05-10 11:31:24	ga_s2am_ard_3-2-1_51LYE_2023-05-10_final
2023-05-10 11:31:38	ga_s2am_ard_3-2-1_51LYD_2023-05-10_final
2023-05-10 11:31:42	ga_s2am_ard_3-2-1_51LXD_2023-05-10_final
2023-05-10 11:32:11	ga_s2am_ard_3-2-1_51KXB_2023-05-10_final
2023-05-10 11:32:21	ga_s2am_ard_3-2-1_51KYA_2023-05-10_final
2023-05-10 11:32:25	ga_s2am_ard_3-2-1_51KXA_2023-05-10_final
2023-05-10 11:32:43	ga_s2am_ard_3-2-1_51KWV_2023-05-10_final
2023-05-10 11:33:01	ga_s2am_ard_3-2-1_51KYT_2023-05-10_final
2023-05-10 11:33:15	ga_s2am_ard_3-2-1_51KVT_2023-05-10_final
2023-05-10 11:33:44	ga_s2am_ard_3-2-1_51KVR_2023-05-10_final
2023-05-10 11:33:59	ga_s2am_ard_3-2-1_51KVQ_2023-05-10_final
2023-05-10 11:34:02	ga_s2am_ard_3-2-1_51KUQ_2023-05-10_final
2023-05-10 11:34:10	ga_s2am_ard_3-2-1_51KWP_2023-05-10_final
2023-05-10 11:34:43	ga_s2am_ard_3-2-1_51JVM_2023-05-10_final
2023-05-10 11:34:46	ga_s2am_ard_3-2-1_51JUM_2023-05-10_final
2023-05-10 11:34:57	ga_s2am_ard_3-2-1_51JVL_2023-05-10_final
2023-05-10 11:35:33	ga_s2am_ard_3-2-1_51JTJ_2023-05-10_final
2023-05-10 11:35:36	ga_s2am_ard_3-2-1_50JQP_2023-05-10_final
2023-05-10 11:35:50	ga_s2am_ard_3-2-1_50JQN_2023-05-10_final
2023-05-10 11:35:55	ga_s2am_ard_3-2-1_51JVG_2023-05-10_final
2023-05-10 11:36:05	ga_s2am_ard_3-2-1_50JQM_2023-05-10_final
2023-05-10 11:36:23	ga_s2am_ard_3-2-1_51HVE_2023-05-10_final
2023-05-10 11:36:31	ga_s2am_ard_3-2-1_51HTE_2023-05-10_final
2023-05-10 11:36:37	ga_s2am_ard_3-2-1_50HPK_2023-05-10_final
2023-05-10 11:36:48	ga_s2am_ard_3-2-1_50HQJ_2023-05-10_final
2023-05-10 11:36:51	ga_s2am_ard_3-2-1_50HPJ_2023-05-10_final
2023-05-10 11:37:12	ga_s2am_ard_3-2-1_51HUB_2023-05-10_final
2023-05-10 11:37:23	ga_s2am_ard_3-2-1_50HNG_2023-05-10_final
2023-05-10 11:37:24	ga_s2am_ard_3-2-1_50HQG_2023-05-10_final
2023-05-11 11:04:35	ga_s2am_ard_3-2-1_52JDS_2023-05-11_final
2023-05-11 11:04:55	ga_s2am_ard_3-2-1_52JCR_2023-05-11_final
2023-05-11 11:05:07	ga_s2am_ard_3-2-1_52JCQ_2023-05-11_final
2023-05-11 11:05:30	ga_s2am_ard_3-2-1_52JEN_2023-05-11_final
2023-05-11 11:05:57	ga_s2am_ard_3-2-1_52JBM_2023-05-11_final
2023-05-12 12:13:39	ga_s2am_ard_3-2-1_49KHS_2023-05-12_final
2023-05-12 12:13:53	ga_s2am_ard_3-2-1_50KKA_2023-05-12_final
2023-05-12 12:13:54	ga_s2am_ard_3-2-1_49KHR_2023-05-12_final
2023-05-12 12:13:57	ga_s2am_ard_3-2-1_49KGR_2023-05-12_final
2023-05-12 12:14:07	ga_s2am_ard_3-2-1_50KKV_2023-05-12_final
2023-05-12 12:14:18	ga_s2am_ard_3-2-1_50KKU_2023-05-12_final
2023-05-12 12:14:40	ga_s2am_ard_3-2-1_49JGN_2023-05-12_final
2023-05-12 12:14:44	ga_s2am_ard_3-2-1_49JFN_2023-05-12_final
2023-05-12 12:14:55	ga_s2am_ard_3-2-1_49JGM_2023-05-12_final
2023-05-12 12:14:59	ga_s2am_ard_3-2-1_49JFM_2023-05-12_final
2023-05-12 12:15:18	ga_s2am_ard_3-2-1_49JGK_2023-05-12_final
2023-05-13 11:41:47	ga_s2am_ard_3-2-1_51LUD_2023-05-13_final
2023-05-13 11:43:00	ga_s2am_ard_3-2-1_51KUU_2023-05-13_final
2023-05-13 11:43:03	ga_s2am_ard_3-2-1_51KTU_2023-05-13_final
2023-05-13 11:43:14	ga_s2am_ard_3-2-1_51KUT_2023-05-13_final
2023-05-13 11:43:18	ga_s2am_ard_3-2-1_51KTT_2023-05-13_final
2023-05-13 11:43:18	ga_s2am_ard_3-2-1_50KRC_2023-05-13_final
2023-05-13 11:43:33	ga_s2am_ard_3-2-1_50KRB_2023-05-13_final
2023-05-13 11:43:35	ga_s2am_ard_3-2-1_51KVR_2023-05-13_final
2023-05-13 11:43:36	ga_s2am_ard_3-2-1_50KQB_2023-05-13_final
2023-05-13 11:43:47	ga_s2am_ard_3-2-1_50KRA_2023-05-13_final
2023-05-13 11:43:51	ga_s2am_ard_3-2-1_50KQA_2023-05-13_final
2023-05-13 11:43:58	ga_s2am_ard_3-2-1_51KUQ_2023-05-13_final
2023-05-13 11:44:02	ga_s2am_ard_3-2-1_50KRV_2023-05-13_final
2023-05-13 11:44:05	ga_s2am_ard_3-2-1_50KQV_2023-05-13_final
2023-05-13 11:44:10	ga_s2am_ard_3-2-1_50KPV_2023-05-13_final
2023-05-13 11:44:30	ga_s2am_ard_3-2-1_50JRT_2023-05-13_final
2023-05-13 11:44:34	ga_s2am_ard_3-2-1_50JQT_2023-05-13_final
2023-05-13 11:44:37	ga_s2am_ard_3-2-1_50JPT_2023-05-13_final
2023-05-13 11:44:43	ga_s2am_ard_3-2-1_51JTM_2023-05-13_final
2023-05-13 11:44:47	ga_s2am_ard_3-2-1_50JPS_2023-05-13_final
2023-05-13 11:44:48	ga_s2am_ard_3-2-1_51JUM_2023-05-13_final
2023-05-13 11:44:50	ga_s2am_ard_3-2-1_51JTM_2023-05-13_final
2023-05-13 11:44:54	ga_s2am_ard_3-2-1_50JPS_2023-05-13_final
2023-05-13 11:45:21	ga_s2am_ard_3-2-1_50JPQ_2023-05-13_final
2023-05-13 11:45:24	ga_s2am_ard_3-2-1_50JNQ_2023-05-13_final
2023-05-13 11:45:29	ga_s2am_ard_3-2-1_51JTJ_2023-05-13_final
2023-05-13 11:45:32	ga_s2am_ard_3-2-1_50JQP_2023-05-13_final
2023-05-13 11:45:39	ga_s2am_ard_3-2-1_50JNP_2023-05-13_final
2023-05-13 11:45:46	ga_s2am_ard_3-2-1_50JQN_2023-05-13_final
2023-05-13 11:46:00	ga_s2am_ard_3-2-1_50JQM_2023-05-13_final
2023-05-13 11:46:08	ga_s2am_ard_3-2-1_50JNM_2023-05-13_final
2023-05-13 11:46:19	ga_s2am_ard_3-2-1_50JPL_2023-05-13_final
2023-05-13 11:46:22	ga_s2am_ard_3-2-1_50JNL_2023-05-13_final
2023-05-13 11:46:33	ga_s2am_ard_3-2-1_50HPK_2023-05-13_final
2023-05-13 11:46:37	ga_s2am_ard_3-2-1_50HNK_2023-05-13_final
2023-05-13 11:46:40	ga_s2am_ard_3-2-1_50HMK_2023-05-13_final
2023-05-13 11:46:43	ga_s2am_ard_3-2-1_50HQJ_2023-05-13_final
2023-05-13 11:46:48	ga_s2am_ard_3-2-1_50HPJ_2023-05-13_final
2023-05-13 11:46:51	ga_s2am_ard_3-2-1_50HNJ_2023-05-13_final
2023-05-13 11:47:06	ga_s2am_ard_3-2-1_50HNH_2023-05-13_final
2023-05-13 11:47:14	ga_s2am_ard_3-2-1_50HLH_2023-05-13_final
2023-05-13 11:47:20	ga_s2am_ard_3-2-1_50HNG_2023-05-13_final
2023-05-13 11:47:24	ga_s2am_ard_3-2-1_50HMG_2023-05-13_final
2023-05-14 11:13:54	ga_s2am_ard_3-2-1_51KZQ_2023-05-14_final
2023-05-14 11:14:31	ga_s2am_ard_3-2-1_52JDS_2023-05-14_final
2023-05-14 11:14:41	ga_s2am_ard_3-2-1_51JYM_2023-05-14_final
2023-05-14 11:14:49	ga_s2am_ard_3-2-1_52JCR_2023-05-14_final
2023-05-14 11:14:56	ga_s2am_ard_3-2-1_51JYL_2023-05-14_final
2023-05-14 11:15:04	ga_s2am_ard_3-2-1_52JCQ_2023-05-14_final
2023-05-14 11:15:36	ga_s2am_ard_3-2-1_52JBN_2023-05-14_final
2023-05-14 11:15:51	ga_s2am_ard_3-2-1_52JBM_2023-05-14_final
2023-05-14 11:16:12	ga_s2am_ard_3-2-1_51JXF_2023-05-14_final
2023-05-14 11:16:16	ga_s2am_ard_3-2-1_52HCK_2023-05-14_final
2023-05-14 11:16:55	ga_s2am_ard_3-2-1_51HXC_2023-05-14_final

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