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
2023-09-23 11:55:44	ga_s2am_ard_3-2-1_50JLP_2023-09-23_final
2023-09-23 11:55:48	ga_s2am_ard_3-2-1_50JKP_2023-09-23_final
2023-09-23 11:55:48	ga_s2am_ard_3-2-1_50JNN_2023-09-23_final
2023-09-23 11:55:50	ga_s2am_ard_3-2-1_50JMN_2023-09-23_final
2023-09-23 11:55:51	ga_s2am_ard_3-2-1_50JLP_2023-09-23_final
2023-09-23 11:55:52	ga_s2am_ard_3-2-1_50JLN_2023-09-23_final
2023-09-23 11:55:53	ga_s2am_ard_3-2-1_50JKP_2023-09-23_final
2023-09-23 11:55:53	ga_s2am_ard_3-2-1_50JKN_2023-09-23_final
2023-09-23 11:55:55	ga_s2am_ard_3-2-1_50JNN_2023-09-23_final
2023-09-23 11:55:57	ga_s2am_ard_3-2-1_50JMN_2023-09-23_final
2023-09-23 11:55:58	ga_s2am_ard_3-2-1_50JLN_2023-09-23_final
2023-09-23 11:56:01	ga_s2am_ard_3-2-1_50JKN_2023-09-23_final
2023-09-23 11:56:06	ga_s2am_ard_3-2-1_50JNM_2023-09-23_final
2023-09-23 11:56:09	ga_s2am_ard_3-2-1_50JMM_2023-09-23_final
2023-09-23 11:56:13	ga_s2am_ard_3-2-1_50JLM_2023-09-23_final
2023-09-23 11:56:16	ga_s2am_ard_3-2-1_50JKM_2023-09-23_final
2023-09-23 11:56:20	ga_s2am_ard_3-2-1_50JNL_2023-09-23_final
2023-09-23 11:56:24	ga_s2am_ard_3-2-1_50JML_2023-09-23_final
2023-09-23 11:56:27	ga_s2am_ard_3-2-1_50JLL_2023-09-23_final
2023-09-23 11:56:30	ga_s2am_ard_3-2-1_50JKL_2023-09-23_nrt
2023-09-23 11:56:31	ga_s2am_ard_3-2-1_50HNK_2023-09-23_final
2023-09-23 11:56:35	ga_s2am_ard_3-2-1_49JGF_2023-09-23_nrt
2023-09-23 11:56:38	ga_s2am_ard_3-2-1_50HMK_2023-09-23_final
2023-09-23 11:56:42	ga_s2am_ard_3-2-1_50HLK_2023-09-23_final
2023-09-23 11:56:45	ga_s2am_ard_3-2-1_50HKK_2023-09-23_nrt
2023-09-23 11:56:47	ga_s2am_ard_3-2-1_49HGE_2023-09-23_nrt
2023-09-23 11:56:53	ga_s2am_ard_3-2-1_50HMJ_2023-09-23_final
2023-09-23 11:56:56	ga_s2am_ard_3-2-1_50HLJ_2023-09-23_final
2023-09-23 11:57:00	ga_s2am_ard_3-2-1_50HKJ_2023-09-23_nrt
2023-09-23 11:57:02	ga_s2am_ard_3-2-1_49HGD_2023-09-23_nrt
2023-09-23 11:57:08	ga_s2am_ard_3-2-1_50HMH_2023-09-23_final
2023-09-23 11:57:11	ga_s2am_ard_3-2-1_50HLH_2023-09-23_final
2023-09-23 11:57:14	ga_s2am_ard_3-2-1_50HKH_2023-09-23_nrt
2023-09-23 11:57:16	ga_s2am_ard_3-2-1_49HGC_2023-09-23_nrt
2023-09-23 11:57:22	ga_s2am_ard_3-2-1_50HMG_2023-09-23_final
2023-09-23 11:57:24	ga_s2am_ard_3-2-1_49HFC_2023-09-23_nrt
2023-09-23 11:57:24	ga_s2am_ard_3-2-1_50HLG_2023-09-23_final
2023-09-23 11:57:26	ga_s2am_ard_3-2-1_50HKG_2023-09-23_nrt
2023-09-23 11:57:27	ga_s2am_ard_3-2-1_49HGB_2023-09-23_nrt
2023-09-23 11:57:29	ga_s2am_ard_3-2-1_49HFB_2023-09-23_nrt
2023-09-23 11:57:29	ga_s2am_ard_3-2-1_50HMF_2023-09-23_nrt
2023-09-23 11:57:30	ga_s2am_ard_3-2-1_50HLF_2023-09-23_nrt
2023-09-24 09:39:37	ga_s2am_ard_3-2-1_56LPP_2023-09-24_nrt
2023-09-24 09:39:41	ga_s2am_ard_3-2-1_56LNP_2023-09-24_nrt
2023-09-24 09:39:44	ga_s2am_ard_3-2-1_56LMP_2023-09-24_nrt
2023-09-24 09:39:50	ga_s2am_ard_3-2-1_56LLP_2023-09-24_nrt
2023-09-24 09:39:51	ga_s2am_ard_3-2-1_56LPN_2023-09-24_nrt
2023-09-24 09:39:55	ga_s2am_ard_3-2-1_56LNN_2023-09-24_nrt
2023-09-24 09:39:58	ga_s2am_ard_3-2-1_56LMN_2023-09-24_nrt
2023-09-24 09:40:02	ga_s2am_ard_3-2-1_56LLN_2023-09-24_nrt
2023-09-24 09:40:05	ga_s2am_ard_3-2-1_56LPM_2023-09-24_nrt
2023-09-24 09:40:07	ga_s2am_ard_3-2-1_56LNM_2023-09-24_nrt
2023-09-24 09:40:09	ga_s2am_ard_3-2-1_56LMM_2023-09-24_nrt
2023-09-24 09:40:10	ga_s2am_ard_3-2-1_56LLM_2023-09-24_nrt
2023-09-24 09:40:12	ga_s2am_ard_3-2-1_56LPL_2023-09-24_nrt
2023-09-24 09:41:16	ga_s2am_ard_3-2-1_56LNH_2023-09-24_nrt
2023-09-24 09:41:18	ga_s2am_ard_3-2-1_56LMH_2023-09-24_nrt
2023-09-24 09:41:19	ga_s2am_ard_3-2-1_56LLH_2023-09-24_nrt
2023-09-24 09:41:20	ga_s2am_ard_3-2-1_56LKH_2023-09-24_nrt
2023-09-24 09:41:23	ga_s2am_ard_3-2-1_56KNG_2023-09-24_nrt
2023-09-24 09:41:26	ga_s2am_ard_3-2-1_56KMG_2023-09-24_final
2023-09-24 09:41:29	ga_s2am_ard_3-2-1_56KLG_2023-09-24_final
2023-09-24 09:41:32	ga_s2am_ard_3-2-1_56KKG_2023-09-24_final
2023-09-24 09:41:34	ga_s2am_ard_3-2-1_55KHB_2023-09-24_final
2023-09-24 09:41:36	ga_s2am_ard_3-2-1_56KNF_2023-09-24_nrt
2023-09-24 09:41:40	ga_s2am_ard_3-2-1_56KMF_2023-09-24_final
2023-09-24 09:41:43	ga_s2am_ard_3-2-1_56KLF_2023-09-24_final
2023-09-24 09:41:46	ga_s2am_ard_3-2-1_56KKF_2023-09-24_final
2023-09-24 09:41:47	ga_s2am_ard_3-2-1_55KHA_2023-09-24_nrt
2023-09-24 09:41:48	ga_s2am_ard_3-2-1_56KNE_2023-09-24_nrt
2023-09-24 09:41:54	ga_s2am_ard_3-2-1_56KME_2023-09-24_final
2023-09-24 09:47:45	ga_s2am_ard_3-2-1_55HES_2023-09-24_final
2023-09-24 09:48:39	ga_s2am_ard_3-2-1_54GYU_2023-09-24_nrt
2023-09-24 09:48:40	ga_s2am_ard_3-2-1_55GBP_2023-09-24_nrt
2023-09-24 09:48:44	ga_s2am_ard_3-2-1_54GXU_2023-09-24_nrt
2023-09-24 09:48:50	ga_s2am_ard_3-2-1_55GCN_2023-09-24_final
2023-09-24 09:48:51	ga_s2am_ard_3-2-1_54GYT_2023-09-24_nrt
2023-09-24 09:48:53	ga_s2am_ard_3-2-1_54GXT_2023-09-24_nrt
2023-09-24 09:48:58	ga_s2am_ard_3-2-1_54GYT_2023-09-24_nrt
2023-09-24 09:49:00	ga_s2am_ard_3-2-1_54GXT_2023-09-24_nrt
2023-09-24 09:49:02	ga_s2am_ard_3-2-1_54GYS_2023-09-24_nrt
2023-09-24 09:49:04	ga_s2am_ard_3-2-1_54GXS_2023-09-24_nrt
2023-09-24 11:20:18	ga_s2am_ard_3-2-1_52LEP_2023-09-24_nrt
2023-09-24 11:20:22	ga_s2am_ard_3-2-1_52LDP_2023-09-24_nrt
2023-09-24 11:20:25	ga_s2am_ard_3-2-1_52LCP_2023-09-24_nrt
2023-09-24 11:20:28	ga_s2am_ard_3-2-1_52LBP_2023-09-24_nrt
2023-09-24 11:20:33	ga_s2am_ard_3-2-1_52LEN_2023-09-24_nrt
2023-09-24 11:20:36	ga_s2am_ard_3-2-1_52LDN_2023-09-24_nrt
2023-09-24 11:20:39	ga_s2am_ard_3-2-1_52LCN_2023-09-24_nrt
2023-09-24 11:20:42	ga_s2am_ard_3-2-1_52LBN_2023-09-24_nrt
2023-09-24 11:20:47	ga_s2am_ard_3-2-1_52LEM_2023-09-24_nrt
2023-09-24 11:20:51	ga_s2am_ard_3-2-1_52LDM_2023-09-24_nrt
2023-09-24 11:20:54	ga_s2am_ard_3-2-1_52LCM_2023-09-24_nrt
2023-09-24 11:20:57	ga_s2am_ard_3-2-1_52LBM_2023-09-24_nrt
2023-09-24 11:20:59	ga_s2am_ard_3-2-1_51LZG_2023-09-24_nrt
2023-09-24 11:21:05	ga_s2am_ard_3-2-1_52LDL_2023-09-24_nrt
2023-09-24 11:21:08	ga_s2am_ard_3-2-1_52LCL_2023-09-24_nrt
2023-09-24 11:21:12	ga_s2am_ard_3-2-1_52LBL_2023-09-24_nrt
2023-09-24 11:21:13	ga_s2am_ard_3-2-1_51LZF_2023-09-24_nrt
2023-09-24 11:21:19	ga_s2am_ard_3-2-1_52LDK_2023-09-24_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