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
2023-02-01 10:29:38	ga_s2am_ard_3-2-1_54LXQ_2023-02-01_final
2023-02-01 12:14:17	ga_s2am_ard_3-2-1_50KKU_2023-02-01_final
2023-02-01 12:14:25	ga_s2am_ard_3-2-1_49KGP_2023-02-01_final
2023-02-02 10:04:24	ga_s2am_ard_3-2-1_54JYR_2023-02-02_final
2023-02-02 10:04:50	ga_s2am_ard_3-2-1_55JBJ_2023-02-02_final
2023-02-02 10:05:37	ga_s2am_ard_3-2-1_55HCE_2023-02-02_final
2023-02-02 10:05:50	ga_s2am_ard_3-2-1_54HYK_2023-02-02_final
2023-02-02 10:05:54	ga_s2am_ard_3-2-1_54HXK_2023-02-02_final
2023-02-02 10:05:57	ga_s2am_ard_3-2-1_54HWK_2023-02-02_final
2023-02-02 10:06:33	ga_s2am_ard_3-2-1_54HYG_2023-02-02_final
2023-02-02 11:43:28	ga_s2am_ard_3-2-1_51KUS_2023-02-02_final
2023-02-02 11:43:32	ga_s2am_ard_3-2-1_50KRB_2023-02-02_final
2023-02-02 11:44:09	ga_s2am_ard_3-2-1_50KPV_2023-02-02_final
2023-02-02 11:44:22	ga_s2am_ard_3-2-1_50KPU_2023-02-02_final
2023-02-02 11:45:02	ga_s2am_ard_3-2-1_50JQR_2023-02-02_final
2023-02-02 11:45:16	ga_s2am_ard_3-2-1_50JQQ_2023-02-02_final
2023-02-02 11:45:23	ga_s2am_ard_3-2-1_50JNQ_2023-02-02_final
2023-02-02 11:45:49	ga_s2am_ard_3-2-1_50JPN_2023-02-02_final
2023-02-02 11:46:03	ga_s2am_ard_3-2-1_50JPM_2023-02-02_final
2023-02-02 11:46:14	ga_s2am_ard_3-2-1_50JQL_2023-02-02_final
2023-02-02 11:46:54	ga_s2am_ard_3-2-1_50HMJ_2023-02-02_final
2023-02-02 11:47:14	ga_s2am_ard_3-2-1_50HMH_2023-02-02_final
2023-02-05 10:10:39	ga_s2am_ard_3-2-1_54LZL_2023-02-05_final
2023-02-05 10:12:24	ga_s2am_ard_3-2-1_54KYD_2023-02-05_final
2023-02-05 10:12:43	ga_s2am_ard_3-2-1_54KZB_2023-02-05_final
2023-02-05 10:12:50	ga_s2am_ard_3-2-1_54KZB_2023-02-05_final
2023-02-05 10:14:16	ga_s2am_ard_3-2-1_54JVS_2023-02-05_final
2023-02-05 10:14:38	ga_s2am_ard_3-2-1_54JXQ_2023-02-05_final
2023-02-05 10:15:36	ga_s2am_ard_3-2-1_54JXL_2023-02-05_final
2023-02-05 10:15:54	ga_s2am_ard_3-2-1_54HWK_2023-02-05_final
2023-02-05 10:16:51	ga_s2am_ard_3-2-1_54HWF_2023-02-05_final
2023-02-05 10:17:02	ga_s2am_ard_3-2-1_54HWE_2023-02-05_final
2023-02-05 10:17:03	ga_s2am_ard_3-2-1_53HQA_2023-02-05_final
2023-02-05 11:53:09	ga_s2am_ard_3-2-1_50KRC_2023-02-05_final
2023-02-05 11:53:34	ga_s2am_ard_3-2-1_50KPB_2023-02-05_final
2023-02-05 11:54:43	ga_s2am_ard_3-2-1_50JLT_2023-02-05_final
2023-02-05 11:55:19	ga_s2am_ard_3-2-1_50JNQ_2023-02-05_final
2023-02-05 11:56:03	ga_s2am_ard_3-2-1_50JNM_2023-02-05_final
2023-02-06 09:46:01	ga_s2am_ard_3-2-1_55HED_2023-02-06_final
2023-02-06 09:46:16	ga_s2am_ard_3-2-1_55HEC_2023-02-06_final
2023-02-06 09:46:43	ga_s2am_ard_3-2-1_55HFA_2023-02-06_final
2023-02-06 09:47:13	ga_s2am_ard_3-2-1_55HEU_2023-02-06_final
2023-02-06 09:47:28	ga_s2am_ard_3-2-1_55HBU_2023-02-06_final
2023-02-06 09:48:07	ga_s2am_ard_3-2-1_55GBR_2023-02-06_final
2023-02-06 09:48:29	ga_s2am_ard_3-2-1_55GDP_2023-02-06_final
2023-02-06 11:21:31	ga_s2am_ard_3-2-1_52LDJ_2023-02-06_final
2023-02-06 11:22:03	ga_s2am_ard_3-2-1_52KCG_2023-02-06_final
2023-02-06 11:22:07	ga_s2am_ard_3-2-1_52KBG_2023-02-06_final
2023-02-06 11:23:15	ga_s2am_ard_3-2-1_52KCB_2023-02-06_final
2023-02-06 11:23:34	ga_s2am_ard_3-2-1_52KBA_2023-02-06_final
2023-02-06 11:25:33	ga_s2am_ard_3-2-1_51JYH_2023-02-06_final
2023-02-06 11:25:59	ga_s2am_ard_3-2-1_51JVG_2023-02-06_final
2023-02-06 11:26:39	ga_s2am_ard_3-2-1_51HWD_2023-02-06_final
2023-02-06 11:27:01	ga_s2am_ard_3-2-1_51HUC_2023-02-06_final
2023-02-07 10:51:27	ga_s2am_ard_3-2-1_53LLD_2023-02-07_final
2023-02-07 10:51:53	ga_s2am_ard_3-2-1_53KMB_2023-02-07_final
2023-02-07 10:52:27	ga_s2am_ard_3-2-1_53KMV_2023-02-07_final
2023-02-07 10:53:38	ga_s2am_ard_3-2-1_53KLQ_2023-02-07_final
2023-02-08 10:20:34	ga_s2am_ard_3-2-1_54LZL_2023-02-08_final
2023-02-08 10:20:59	ga_s2am_ard_3-2-1_54LWK_2023-02-08_final
2023-02-08 10:21:36	ga_s2am_ard_3-2-1_54KYG_2023-02-08_final
2023-02-08 10:22:41	ga_s2am_ard_3-2-1_54KWC_2023-02-08_final
2023-02-08 10:22:55	ga_s2am_ard_3-2-1_54KWB_2023-02-08_final
2023-02-08 10:22:59	ga_s2am_ard_3-2-1_54KVB_2023-02-08_final
2023-02-08 10:23:39	ga_s2am_ard_3-2-1_53KRQ_2023-02-08_final
2023-02-08 10:24:12	ga_s2am_ard_3-2-1_54JUS_2023-02-08_final
2023-02-08 10:24:14	ga_s2am_ard_3-2-1_54JTS_2023-02-08_final
2023-02-08 10:24:20	ga_s2am_ard_3-2-1_54JTS_2023-02-08_final
2023-02-08 10:24:26	ga_s2am_ard_3-2-1_54JVR_2023-02-08_final
2023-02-08 10:24:29	ga_s2am_ard_3-2-1_54JUR_2023-02-08_final
2023-02-08 10:25:45	ga_s2am_ard_3-2-1_54JTL_2023-02-08_final
2023-02-08 10:25:56	ga_s2am_ard_3-2-1_54HUK_2023-02-08_final
2023-02-08 12:04:27	ga_s2am_ard_3-2-1_49KHP_2023-02-08_final
2023-02-08 12:04:30	ga_s2am_ard_3-2-1_49KGP_2023-02-08_final
2023-02-08 12:04:45	ga_s2am_ard_3-2-1_49JGN_2023-02-08_final
2023-02-08 12:04:59	ga_s2am_ard_3-2-1_49JGM_2023-02-08_final
2023-02-08 12:05:22	ga_s2am_ard_3-2-1_50JLQ_2023-02-08_final
2023-02-08 12:05:43	ga_s2am_ard_3-2-1_49JGJ_2023-02-08_final
2023-02-08 12:06:03	ga_s2am_ard_3-2-1_50JLM_2023-02-08_final
2023-02-28 12:00:21	ga_s2am_ard_3-2-1_50LRP_2023-02-28_nrt
2023-02-28 12:00:31	ga_s2am_ard_3-2-1_50LQP_2023-02-28_nrt
2023-02-28 12:00:37	ga_s2am_ard_3-2-1_50LNP_2023-02-28_nrt
2023-02-28 12:00:42	ga_s2am_ard_3-2-1_50LMP_2023-02-28_nrt
2023-02-28 12:00:45	ga_s2am_ard_3-2-1_50LQN_2023-02-28_nrt
2023-02-28 12:00:52	ga_s2am_ard_3-2-1_50LNN_2023-02-28_nrt
2023-02-28 12:00:55	ga_s2am_ard_3-2-1_50LMN_2023-02-28_nrt
2023-02-28 12:01:07	ga_s2am_ard_3-2-1_50LNM_2023-02-28_nrt
2023-02-28 12:01:10	ga_s2am_ard_3-2-1_50LMM_2023-02-28_nrt
2023-02-28 12:01:21	ga_s2am_ard_3-2-1_50LNL_2023-02-28_nrt
2023-02-28 12:01:24	ga_s2am_ard_3-2-1_50LML_2023-02-28_nrt
2023-02-28 12:01:34	ga_s2am_ard_3-2-1_50LNK_2023-02-28_nrt
2023-02-28 12:01:36	ga_s2am_ard_3-2-1_50LMK_2023-02-28_nrt
2023-02-28 12:01:39	ga_s2am_ard_3-2-1_50LLK_2023-02-28_nrt
2023-02-28 12:01:39	ga_s2am_ard_3-2-1_50LPJ_2023-02-28_nrt
2023-02-28 12:02:57	ga_s2am_ard_3-2-1_50KLE_2023-02-28_nrt
2023-02-28 12:03:09	ga_s2am_ard_3-2-1_50KLD_2023-02-28_nrt
2023-02-28 12:05:30	ga_s2am_ard_3-2-1_49JFK_2023-02-28_nrt
2023-02-28 12:05:45	ga_s2am_ard_3-2-1_49JFJ_2023-02-28_nrt
2023-02-28 12:05:59	ga_s2am_ard_3-2-1_49JFH_2023-02-28_nrt
2023-02-28 12:06:10	ga_s2am_ard_3-2-1_49JGG_2023-02-28_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