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
2023-03-10 10:25:09	ga_s2am_ard_3-2-1_54JUN_2023-03-10_final
2023-03-10 10:25:23	ga_s2am_ard_3-2-1_54JUM_2023-03-10_final
2023-03-10 10:25:38	ga_s2am_ard_3-2-1_54JUL_2023-03-10_final
2023-03-10 10:25:52	ga_s2am_ard_3-2-1_54HUK_2023-03-10_final
2023-03-10 10:26:07	ga_s2am_ard_3-2-1_54HUJ_2023-03-10_final
2023-03-10 10:26:22	ga_s2am_ard_3-2-1_54HUH_2023-03-10_final
2023-03-10 10:26:35	ga_s2am_ard_3-2-1_54HUG_2023-03-10_final
2023-03-16 10:40:00	ga_s2am_ard_3-2-1_54LTP_2023-03-16_nrt
2023-03-16 10:40:14	ga_s2am_ard_3-2-1_54LTN_2023-03-16_nrt
2023-03-16 10:40:29	ga_s2am_ard_3-2-1_54LTM_2023-03-16_nrt
2023-03-16 10:40:41	ga_s2am_ard_3-2-1_54LTL_2023-03-16_nrt
2023-03-16 10:40:48	ga_s2am_ard_3-2-1_54LTL_2023-03-16_nrt
2023-03-17 10:16:00	ga_s2am_ard_3-2-1_54HUK_2023-03-17_final
2023-03-17 10:16:14	ga_s2am_ard_3-2-1_54HUJ_2023-03-17_final
2023-03-17 10:16:29	ga_s2am_ard_3-2-1_54HUH_2023-03-17_final
2023-03-17 10:16:43	ga_s2am_ard_3-2-1_54HUG_2023-03-17_final
2023-03-17 10:16:58	ga_s2am_ard_3-2-1_54HUF_2023-03-17_final
2023-03-17 10:17:12	ga_s2am_ard_3-2-1_54HUE_2023-03-17_final
2023-03-17 10:17:27	ga_s2am_ard_3-2-1_54HUD_2023-03-17_final
2023-03-17 10:17:37	ga_s2am_ard_3-2-1_54HUC_2023-03-17_nrt
2023-03-30 10:23:30	ga_s2am_ard_3-2-1_54KUV_2023-03-30_final
2023-03-30 10:23:35	ga_s2am_ard_3-2-1_54KTV_2023-03-30_final
2023-03-30 10:23:44	ga_s2am_ard_3-2-1_54KUU_2023-03-30_final
2023-03-30 10:23:48	ga_s2am_ard_3-2-1_54KTU_2023-03-30_final
2023-03-30 10:23:59	ga_s2am_ard_3-2-1_54JUT_2023-03-30_final
2023-03-30 10:24:02	ga_s2am_ard_3-2-1_54JTT_2023-03-30_final
2023-03-30 10:24:14	ga_s2am_ard_3-2-1_54JUS_2023-03-30_final
2023-03-30 10:24:17	ga_s2am_ard_3-2-1_54JTS_2023-03-30_final
2023-03-30 10:24:28	ga_s2am_ard_3-2-1_54JUR_2023-03-30_final
2023-03-30 10:24:43	ga_s2am_ard_3-2-1_54JUQ_2023-03-30_final
2023-03-30 10:24:57	ga_s2am_ard_3-2-1_54JUP_2023-03-30_final
2023-03-30 10:25:12	ga_s2am_ard_3-2-1_54JUN_2023-03-30_final
2023-03-30 10:25:26	ga_s2am_ard_3-2-1_54JUM_2023-03-30_final
2023-03-30 10:25:41	ga_s2am_ard_3-2-1_54JUL_2023-03-30_final
2023-03-30 10:25:55	ga_s2am_ard_3-2-1_54HUK_2023-03-30_final
2023-03-30 10:26:10	ga_s2am_ard_3-2-1_54HUJ_2023-03-30_final
2023-03-30 10:26:25	ga_s2am_ard_3-2-1_54HUH_2023-03-30_final
2023-03-30 10:26:38	ga_s2am_ard_3-2-1_54HUG_2023-03-30_final
2023-04-02 10:32:43	ga_s2am_ard_3-2-1_54KUC_2023-04-02_final
2023-04-02 10:32:46	ga_s2am_ard_3-2-1_54KTC_2023-04-02_final
2023-04-02 10:32:57	ga_s2am_ard_3-2-1_54KUB_2023-04-02_final
2023-04-02 10:33:01	ga_s2am_ard_3-2-1_54KTB_2023-04-02_final
2023-04-02 10:33:12	ga_s2am_ard_3-2-1_54KUA_2023-04-02_final
2023-04-02 10:33:15	ga_s2am_ard_3-2-1_54KTA_2023-04-02_final
2023-04-02 10:33:26	ga_s2am_ard_3-2-1_54KUV_2023-04-02_final
2023-04-02 10:33:30	ga_s2am_ard_3-2-1_54KTV_2023-04-02_final
2023-04-02 10:33:40	ga_s2am_ard_3-2-1_54KUU_2023-04-02_final
2023-04-02 10:33:44	ga_s2am_ard_3-2-1_54KTU_2023-04-02_final
2023-04-02 10:33:51	ga_s2am_ard_3-2-1_54JUT_2023-04-02_final
2023-04-02 10:33:59	ga_s2am_ard_3-2-1_54JTT_2023-04-02_final
2023-04-05 10:40:02	ga_s2am_ard_3-2-1_54LTP_2023-04-05_nrt
2023-04-05 10:40:17	ga_s2am_ard_3-2-1_54LTN_2023-04-05_nrt
2023-04-05 10:40:31	ga_s2am_ard_3-2-1_54LTM_2023-04-05_nrt
2023-04-05 10:40:43	ga_s2am_ard_3-2-1_54LTL_2023-04-05_nrt
2023-04-05 10:40:50	ga_s2am_ard_3-2-1_54LTL_2023-04-05_nrt
2023-04-06 10:17:29	ga_s2am_ard_3-2-1_54HUD_2023-04-06_final
2023-04-06 10:17:39	ga_s2am_ard_3-2-1_54HUC_2023-04-06_nrt
2023-04-09 10:23:18	ga_s2am_ard_3-2-1_54KUA_2023-04-09_final
2023-04-09 10:23:32	ga_s2am_ard_3-2-1_54KUV_2023-04-09_final
2023-04-09 10:23:47	ga_s2am_ard_3-2-1_54KUU_2023-04-09_final
2023-04-09 10:23:50	ga_s2am_ard_3-2-1_54KTU_2023-04-09_final
2023-04-09 10:24:01	ga_s2am_ard_3-2-1_54JUT_2023-04-09_final
2023-04-12 10:31:05	ga_s2am_ard_3-2-1_54LTK_2023-04-12_nrt
2023-04-12 10:31:20	ga_s2am_ard_3-2-1_54LTJ_2023-04-12_nrt
2023-04-12 10:31:34	ga_s2am_ard_3-2-1_54LTH_2023-04-12_nrt
2023-04-12 10:31:49	ga_s2am_ard_3-2-1_54KTG_2023-04-12_final
2023-04-12 10:32:00	ga_s2am_ard_3-2-1_54KUF_2023-04-12_final
2023-04-12 10:32:03	ga_s2am_ard_3-2-1_54KTF_2023-04-12_final
2023-04-12 10:32:15	ga_s2am_ard_3-2-1_54KUE_2023-04-12_final
2023-04-12 10:32:18	ga_s2am_ard_3-2-1_54KTE_2023-04-12_final
2023-04-12 10:32:29	ga_s2am_ard_3-2-1_54KUD_2023-04-12_final
2023-04-12 10:32:33	ga_s2am_ard_3-2-1_54KTD_2023-04-12_final
2023-04-12 10:32:44	ga_s2am_ard_3-2-1_54KUC_2023-04-12_final
2023-04-12 10:32:47	ga_s2am_ard_3-2-1_54KTC_2023-04-12_final
2023-04-12 10:32:58	ga_s2am_ard_3-2-1_54KUB_2023-04-12_final
2023-04-12 10:33:02	ga_s2am_ard_3-2-1_54KTB_2023-04-12_final
2023-04-12 10:33:13	ga_s2am_ard_3-2-1_54KUA_2023-04-12_final
2023-04-12 10:33:16	ga_s2am_ard_3-2-1_54KTA_2023-04-12_final
2023-04-12 10:33:28	ga_s2am_ard_3-2-1_54KUV_2023-04-12_final
2023-04-12 10:33:31	ga_s2am_ard_3-2-1_54KTV_2023-04-12_final
2023-04-12 10:33:41	ga_s2am_ard_3-2-1_54KUU_2023-04-12_final
2023-04-12 10:33:45	ga_s2am_ard_3-2-1_54KTU_2023-04-12_final
2023-04-12 10:33:52	ga_s2am_ard_3-2-1_54JUT_2023-04-12_final
2023-04-12 10:34:00	ga_s2am_ard_3-2-1_54JTT_2023-04-12_final
2023-04-15 10:40:02	ga_s2am_ard_3-2-1_54LTP_2023-04-15_nrt
2023-04-15 10:40:17	ga_s2am_ard_3-2-1_54LTN_2023-04-15_nrt
2023-04-15 10:40:31	ga_s2am_ard_3-2-1_54LTM_2023-04-15_nrt
2023-04-15 10:40:45	ga_s2am_ard_3-2-1_54LTL_2023-04-15_nrt
2023-06-01 10:30:54	ga_s2am_ard_3-2-1_54LTL_2023-06-01_nrt
2023-06-01 10:31:09	ga_s2am_ard_3-2-1_54LTK_2023-06-01_nrt
2023-06-01 10:31:24	ga_s2am_ard_3-2-1_54LTJ_2023-06-01_nrt
2023-06-01 10:31:38	ga_s2am_ard_3-2-1_54LTH_2023-06-01_nrt
2023-06-01 10:31:53	ga_s2am_ard_3-2-1_54KTG_2023-06-01_final
2023-06-01 10:32:06	ga_s2am_ard_3-2-1_54KTF_2023-06-01_final
2023-06-01 10:32:11	ga_s2am_ard_3-2-1_54KUF_2023-06-01_final
2023-06-01 10:32:12	ga_s2am_ard_3-2-1_54KTE_2023-06-01_final
2023-06-01 10:32:13	ga_s2am_ard_3-2-1_54KTF_2023-06-01_final
2023-06-04 10:40:06	ga_s2am_ard_3-2-1_54LTP_2023-06-04_nrt
2023-06-04 10:40:21	ga_s2am_ard_3-2-1_54LTN_2023-06-04_nrt
2023-06-04 10:40:35	ga_s2am_ard_3-2-1_54LTM_2023-06-04_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