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
2023-09-13 11:57:31	ga_s2am_ard_3-2-1_49HFB_2023-09-13_nrt
2023-09-13 11:57:32	ga_s2am_ard_3-2-1_50HMF_2023-09-13_nrt
2023-09-14 09:44:12	ga_s2am_ard_3-2-1_55JGM_2023-09-14_final
2023-09-14 09:44:13	ga_s2am_ard_3-2-1_55JFM_2023-09-14_final
2023-09-14 09:44:14	ga_s2am_ard_3-2-1_56JLR_2023-09-14_final
2023-09-14 09:44:17	ga_s2am_ard_3-2-1_56JKR_2023-09-14_final
2023-09-14 09:44:19	ga_s2am_ard_3-2-1_55JGL_2023-09-14_final
2023-09-14 09:44:20	ga_s2am_ard_3-2-1_56JLQ_2023-09-14_final
2023-09-14 09:44:22	ga_s2am_ard_3-2-1_55JFL_2023-09-14_final
2023-09-14 09:44:29	ga_s2am_ard_3-2-1_56JKQ_2023-09-14_final
2023-09-14 09:44:29	ga_s2am_ard_3-2-1_55JEL_2023-09-14_final
2023-09-14 09:44:32	ga_s2am_ard_3-2-1_55JGK_2023-09-14_final
2023-09-14 09:44:36	ga_s2am_ard_3-2-1_55JFK_2023-09-14_final
2023-09-14 09:44:40	ga_s2am_ard_3-2-1_55JEK_2023-09-14_final
2023-09-14 09:44:47	ga_s2am_ard_3-2-1_55JGJ_2023-09-14_final
2023-09-14 09:44:50	ga_s2am_ard_3-2-1_55JFJ_2023-09-14_final
2023-09-14 09:45:01	ga_s2am_ard_3-2-1_55JGH_2023-09-14_final
2023-09-14 09:45:05	ga_s2am_ard_3-2-1_55JFH_2023-09-14_final
2023-09-14 09:45:15	ga_s2am_ard_3-2-1_55JGG_2023-09-14_final
2023-09-14 09:45:23	ga_s2am_ard_3-2-1_55JEG_2023-09-14_final
2023-09-14 09:45:30	ga_s2am_ard_3-2-1_55JGF_2023-09-14_final
2023-09-14 09:45:30	ga_s2am_ard_3-2-1_55JDG_2023-09-14_final
2023-09-14 09:45:41	ga_s2am_ard_3-2-1_55JDF_2023-09-14_final
2023-09-14 09:45:44	ga_s2am_ard_3-2-1_55HGE_2023-09-14_final
2023-09-14 09:45:48	ga_s2am_ard_3-2-1_55HFE_2023-09-14_final
2023-09-14 09:45:55	ga_s2am_ard_3-2-1_55HDE_2023-09-14_final
2023-09-14 09:45:59	ga_s2am_ard_3-2-1_55HGD_2023-09-14_final
2023-09-14 09:46:03	ga_s2am_ard_3-2-1_55HFD_2023-09-14_final
2023-09-14 09:46:06	ga_s2am_ard_3-2-1_55HED_2023-09-14_final
2023-09-14 09:46:10	ga_s2am_ard_3-2-1_55HDD_2023-09-14_final
2023-09-14 09:46:11	ga_s2am_ard_3-2-1_55HGC_2023-09-14_final
2023-09-14 09:46:17	ga_s2am_ard_3-2-1_55HFC_2023-09-14_final
2023-09-14 09:46:21	ga_s2am_ard_3-2-1_55HEC_2023-09-14_final
2023-09-14 09:46:21	ga_s2am_ard_3-2-1_55HGB_2023-09-14_final
2023-09-14 09:46:25	ga_s2am_ard_3-2-1_55HDC_2023-09-14_final
2023-09-14 09:46:31	ga_s2am_ard_3-2-1_55HFB_2023-09-14_final
2023-09-14 09:46:32	ga_s2am_ard_3-2-1_55HCC_2023-09-14_final
2023-09-14 09:46:35	ga_s2am_ard_3-2-1_55HEB_2023-09-14_final
2023-09-14 09:46:39	ga_s2am_ard_3-2-1_55HDB_2023-09-14_final
2023-09-14 09:46:43	ga_s2am_ard_3-2-1_55HCB_2023-09-14_final
2023-09-14 09:46:46	ga_s2am_ard_3-2-1_55HFA_2023-09-14_final
2023-09-14 09:46:50	ga_s2am_ard_3-2-1_55HEA_2023-09-14_final
2023-09-14 09:46:53	ga_s2am_ard_3-2-1_55HDA_2023-09-14_final
2023-09-14 09:46:57	ga_s2am_ard_3-2-1_55HCA_2023-09-14_final
2023-09-14 09:47:01	ga_s2am_ard_3-2-1_55HFV_2023-09-14_final
2023-09-14 09:47:04	ga_s2am_ard_3-2-1_55HEV_2023-09-14_final
2023-09-14 09:47:08	ga_s2am_ard_3-2-1_55HDV_2023-09-14_final
2023-09-14 09:47:11	ga_s2am_ard_3-2-1_55HCV_2023-09-14_final
2023-09-14 09:47:13	ga_s2am_ard_3-2-1_55HFU_2023-09-14_final
2023-09-14 09:47:19	ga_s2am_ard_3-2-1_55HEU_2023-09-14_final
2023-09-14 09:47:22	ga_s2am_ard_3-2-1_55HDU_2023-09-14_final
2023-09-14 09:47:23	ga_s2am_ard_3-2-1_55HFT_2023-09-14_final
2023-09-14 09:48:47	ga_s2am_ard_3-2-1_54GXU_2023-09-14_nrt
2023-09-14 09:48:49	ga_s2am_ard_3-2-1_55GDN_2023-09-14_final
2023-09-14 09:48:53	ga_s2am_ard_3-2-1_55GCN_2023-09-14_final
2023-09-14 09:48:56	ga_s2am_ard_3-2-1_54GYT_2023-09-14_nrt
2023-09-14 09:48:59	ga_s2am_ard_3-2-1_54GXT_2023-09-14_nrt
2023-09-14 09:49:01	ga_s2am_ard_3-2-1_55GDM_2023-09-14_final
2023-09-14 09:49:03	ga_s2am_ard_3-2-1_55GCM_2023-09-14_final
2023-09-14 09:49:04	ga_s2am_ard_3-2-1_54GYS_2023-09-14_nrt
2023-09-14 09:49:06	ga_s2am_ard_3-2-1_54GXS_2023-09-14_nrt
2023-09-14 11:20:20	ga_s2am_ard_3-2-1_52LEP_2023-09-14_nrt
2023-09-14 11:20:24	ga_s2am_ard_3-2-1_52LDP_2023-09-14_nrt
2023-09-14 11:20:27	ga_s2am_ard_3-2-1_52LCP_2023-09-14_nrt
2023-09-14 11:20:30	ga_s2am_ard_3-2-1_52LBP_2023-09-14_nrt
2023-09-14 11:20:35	ga_s2am_ard_3-2-1_52LEN_2023-09-14_nrt
2023-09-14 11:20:38	ga_s2am_ard_3-2-1_52LDN_2023-09-14_nrt
2023-09-14 11:20:42	ga_s2am_ard_3-2-1_52LCN_2023-09-14_nrt
2023-09-14 11:20:44	ga_s2am_ard_3-2-1_52LBN_2023-09-14_nrt
2023-09-14 11:20:49	ga_s2am_ard_3-2-1_52LEM_2023-09-14_nrt
2023-09-14 11:20:51	ga_s2am_ard_3-2-1_51LZH_2023-09-14_nrt
2023-09-14 11:20:53	ga_s2am_ard_3-2-1_52LDM_2023-09-14_nrt
2023-09-14 11:20:56	ga_s2am_ard_3-2-1_52LCM_2023-09-14_nrt
2023-09-14 11:20:59	ga_s2am_ard_3-2-1_52LBM_2023-09-14_nrt
2023-09-14 11:21:01	ga_s2am_ard_3-2-1_51LZG_2023-09-14_nrt
2023-09-14 11:21:02	ga_s2am_ard_3-2-1_52LEL_2023-09-14_final
2023-09-14 11:21:07	ga_s2am_ard_3-2-1_52LDL_2023-09-14_nrt
2023-09-14 11:21:11	ga_s2am_ard_3-2-1_52LCL_2023-09-14_nrt
2023-09-14 11:21:12	ga_s2am_ard_3-2-1_52LEK_2023-09-14_final
2023-09-14 11:21:14	ga_s2am_ard_3-2-1_52LBL_2023-09-14_nrt
2023-09-14 11:21:15	ga_s2am_ard_3-2-1_51LZF_2023-09-14_nrt
2023-09-14 11:21:22	ga_s2am_ard_3-2-1_52LDK_2023-09-14_nrt
2023-09-14 11:21:25	ga_s2am_ard_3-2-1_52LCK_2023-09-14_final
2023-09-14 11:21:28	ga_s2am_ard_3-2-1_52LBK_2023-09-14_final
2023-09-14 11:21:30	ga_s2am_ard_3-2-1_51LZE_2023-09-14_final
2023-09-14 11:21:36	ga_s2am_ard_3-2-1_52LDJ_2023-09-14_final
2023-09-14 11:21:40	ga_s2am_ard_3-2-1_52LCJ_2023-09-14_final
2023-09-14 11:21:43	ga_s2am_ard_3-2-1_52LBJ_2023-09-14_final
2023-09-14 11:21:44	ga_s2am_ard_3-2-1_51LZD_2023-09-14_final
2023-09-14 11:21:51	ga_s2am_ard_3-2-1_52LDH_2023-09-14_final
2023-09-14 11:21:54	ga_s2am_ard_3-2-1_52LCH_2023-09-14_final
2023-09-14 11:21:58	ga_s2am_ard_3-2-1_52LBH_2023-09-14_final
2023-09-14 11:21:59	ga_s2am_ard_3-2-1_51LZC_2023-09-14_final
2023-09-14 11:22:03	ga_s2am_ard_3-2-1_51LYC_2023-09-14_final
2023-09-14 11:22:05	ga_s2am_ard_3-2-1_52KDG_2023-09-14_final
2023-09-14 11:22:09	ga_s2am_ard_3-2-1_52KCG_2023-09-14_final
2023-09-14 11:22:12	ga_s2am_ard_3-2-1_52KBG_2023-09-14_final
2023-09-14 11:22:13	ga_s2am_ard_3-2-1_51KZB_2023-09-14_final
2023-09-14 11:22:16	ga_s2am_ard_3-2-1_51KYB_2023-09-14_final
2023-09-14 11:22:16	ga_s2am_ard_3-2-1_52KDF_2023-09-14_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