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
2015-09-13 09:36:31	ga_s2am_ard_3-2-1_56HKF_2015-09-13_final.stac-item
2015-10-20 09:23:08	ga_s2am_ard_3-2-1_56HKF_2015-10-19_final.stac-item
2015-10-20 09:23:08	ga_s2am_ard_3-2-1_56HKG_2015-10-19_final.stac-item
2015-11-12 09:35:31	ga_s2am_ard_3-2-1_56HKF_2015-11-12_final.stac-item
2015-11-12 09:35:31	ga_s2am_ard_3-2-1_56HKG_2015-11-12_final.stac-item
2015-11-12 09:35:31	ga_s2am_ard_3-2-1_56HKH_2015-11-12_final.stac-item
2015-11-12 09:35:31	ga_s2am_ard_3-2-1_56HKJ_2015-11-12_final.stac-item
2015-11-12 09:35:31	ga_s2am_ard_3-2-1_56HKK_2015-11-12_final.stac-item
2015-11-18 09:47:56	ga_s2am_ard_3-2-1_56KKF_2015-11-18_final.stac-item
2015-11-18 09:47:56	ga_s2am_ard_3-2-1_56KKG_2015-11-18_final.stac-item
2015-11-18 09:47:56	ga_s2am_ard_3-2-1_55KHB_2015-11-18_final.stac-item
2015-11-18 09:47:56	ga_s2am_ard_3-2-1_55LHC_2015-11-18_final.stac-item
2015-11-28 09:47:27	ga_s2am_ard_3-2-1_56KKF_2015-11-28_final.stac-item
2015-11-28 09:47:27	ga_s2am_ard_3-2-1_56KKG_2015-11-28_final.stac-item
2015-11-28 09:47:27	ga_s2am_ard_3-2-1_55KHB_2015-11-28_final.stac-item
2015-11-28 09:47:27	ga_s2am_ard_3-2-1_55LHC_2015-11-28_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_56JKP_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_56JKQ_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_56JKR_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_56JKS_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_56JKT_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_56KKA_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_56KKB_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_56KKF_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_56KKU_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_56KKV_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_55JHM_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_55JHN_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_55KHP_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_55KHQ_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_55KHR_2018-02-12_final.stac-item
2018-02-12 09:41:05	ga_s2am_ard_3-2-1_55KHS_2018-02-12_final.stac-item
2018-02-16 09:23:21	ga_s2am_ard_3-2-1_56HKF_2018-02-15_final.stac-item
2018-02-16 09:23:21	ga_s2am_ard_3-2-1_56HKG_2018-02-15_final.stac-item
2018-02-22 09:41:05	ga_s2am_ard_3-2-1_56KKF_2018-02-22_final.stac-item
2019-05-31 09:51:42	ga_s2am_ard_3-2-1_56KKF_2019-05-31_final.stac-item
2019-06-01 09:26:34	ga_s2am_ard_3-2-1_56HKG_2019-05-31_final.stac-item
2019-06-01 09:26:47	ga_s2am_ard_3-2-1_56HKF_2019-05-31_final.stac-item
2019-06-11 09:26:34	ga_s2am_ard_3-2-1_56HKG_2019-06-10_final.stac-item
2019-06-11 09:26:47	ga_s2am_ard_3-2-1_56HKF_2019-06-10_final.stac-item
2023-10-24 09:42:57	ga_s2am_ard_3-2-1_56KKA_2023-10-24_final
2023-10-24 09:42:58	ga_s2am_ard_3-2-1_55KHR_2023-10-24_final
2023-10-24 09:43:12	ga_s2am_ard_3-2-1_56KKV_2023-10-24_final
2023-10-24 09:43:12	ga_s2am_ard_3-2-1_55KHQ_2023-10-24_final
2023-10-24 09:43:27	ga_s2am_ard_3-2-1_56KKU_2023-10-24_final
2023-10-24 09:43:27	ga_s2am_ard_3-2-1_55KHP_2023-10-24_final
2023-10-24 09:43:41	ga_s2am_ard_3-2-1_55JHN_2023-10-24_final
2023-10-24 09:43:41	ga_s2am_ard_3-2-1_56JKT_2023-10-24_final
2023-10-24 09:43:55	ga_s2am_ard_3-2-1_55JHM_2023-10-24_final
2023-10-24 09:43:56	ga_s2am_ard_3-2-1_56JKS_2023-10-24_final
2023-10-24 09:44:10	ga_s2am_ard_3-2-1_56JKR_2023-10-24_final
2023-10-24 09:44:25	ga_s2am_ard_3-2-1_56JKQ_2023-10-24_final
2023-10-24 09:44:39	ga_s2am_ard_3-2-1_56JKP_2023-10-24_final
2023-10-27 09:49:43	ga_s2am_ard_3-2-1_56LKP_2023-10-27_nrt
2023-10-27 09:49:45	ga_s2am_ard_3-2-1_55LHJ_2023-10-27_nrt
2023-10-27 09:49:58	ga_s2am_ard_3-2-1_56LKN_2023-10-27_nrt
2023-10-27 09:49:58	ga_s2am_ard_3-2-1_55LHH_2023-10-27_nrt
2023-10-27 09:50:05	ga_s2am_ard_3-2-1_56LKM_2023-10-27_nrt
2023-10-27 09:50:05	ga_s2am_ard_3-2-1_55LHG_2023-10-27_nrt
2023-10-27 09:51:01	ga_s2am_ard_3-2-1_56LKJ_2023-10-27_nrt
2023-10-27 09:51:02	ga_s2am_ard_3-2-1_55LHD_2023-10-27_nrt
2023-10-27 09:51:10	ga_s2am_ard_3-2-1_56LKH_2023-10-27_final
2023-10-27 09:51:12	ga_s2am_ard_3-2-1_55LHC_2023-10-27_final
2023-10-27 09:51:25	ga_s2am_ard_3-2-1_56KKG_2023-10-27_final
2023-10-27 09:51:26	ga_s2am_ard_3-2-1_55KHB_2023-10-27_final
2023-10-27 09:51:40	ga_s2am_ard_3-2-1_56KKF_2023-10-27_final
2023-10-27 09:51:40	ga_s2am_ard_3-2-1_55KHA_2023-10-27_final
2023-10-28 09:28:40	ga_s2am_ard_3-2-1_55GGN_2023-10-27_nrt
2023-10-28 09:28:52	ga_s2am_ard_3-2-1_56GLS_2023-10-27_nrt
2023-10-28 09:28:54	ga_s2am_ard_3-2-1_55GGM_2023-10-27_nrt
2023-11-13 09:42:13	ga_s2am_ard_3-2-1_56KKD_2023-11-13_nrt
2023-11-13 09:42:27	ga_s2am_ard_3-2-1_56KKC_2023-11-13_nrt
2023-11-13 09:42:42	ga_s2am_ard_3-2-1_56KKB_2023-11-13_final
2023-11-13 09:42:42	ga_s2am_ard_3-2-1_55KHS_2023-11-13_final
2023-11-13 09:42:56	ga_s2am_ard_3-2-1_56KKA_2023-11-13_final
2023-11-13 09:42:57	ga_s2am_ard_3-2-1_55KHR_2023-11-13_final
2023-11-13 09:43:11	ga_s2am_ard_3-2-1_56KKV_2023-11-13_final
2023-11-13 09:43:11	ga_s2am_ard_3-2-1_55KHQ_2023-11-13_final
2023-11-13 09:43:26	ga_s2am_ard_3-2-1_56KKU_2023-11-13_final
2023-11-13 09:43:26	ga_s2am_ard_3-2-1_55KHP_2023-11-13_final
2023-11-13 09:43:40	ga_s2am_ard_3-2-1_55JHN_2023-11-13_final
2023-11-13 09:43:40	ga_s2am_ard_3-2-1_56JKT_2023-11-13_final
2023-11-13 09:43:55	ga_s2am_ard_3-2-1_55JHM_2023-11-13_final
2023-11-13 09:43:55	ga_s2am_ard_3-2-1_56JKS_2023-11-13_final
2023-11-13 09:44:09	ga_s2am_ard_3-2-1_56JKR_2023-11-13_final
2023-11-13 09:44:24	ga_s2am_ard_3-2-1_56JKQ_2023-11-13_final
2023-11-17 09:28:37	ga_s2am_ard_3-2-1_55GGN_2023-11-16_nrt
2023-11-17 09:28:42	ga_s2am_ard_3-2-1_56GLT_2023-11-16_nrt
2023-11-17 09:28:43	ga_s2am_ard_3-2-1_55GGN_2023-11-16_nrt
2023-11-17 09:28:50	ga_s2am_ard_3-2-1_56GLS_2023-11-16_nrt
2023-11-17 09:28:53	ga_s2am_ard_3-2-1_55GGM_2023-11-16_nrt
2023-11-20 09:35:10	ga_s2am_ard_3-2-1_56JKM_2023-11-20_final
2023-11-20 09:35:24	ga_s2am_ard_3-2-1_56JKL_2023-11-20_final
2023-11-20 09:35:39	ga_s2am_ard_3-2-1_56HKK_2023-11-20_final
2023-11-20 09:35:54	ga_s2am_ard_3-2-1_56HKJ_2023-11-20_final
2023-11-20 09:36:08	ga_s2am_ard_3-2-1_56HKH_2023-11-20_final
2023-11-20 09:36:23	ga_s2am_ard_3-2-1_56HKG_2023-11-20_final
2023-11-20 09:36:37	ga_s2am_ard_3-2-1_56HKF_2023-11-20_final
2023-11-20 09:36:52	ga_s2am_ard_3-2-1_56HKE_2023-11-20_final
2023-11-20 09:37:06	ga_s2am_ard_3-2-1_56HKD_2023-11-20_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