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
2019-06-23 11:47:14	ga_s2am_ard_3-2-1_50HMH_2019-06-23_final.stac-item
2019-06-23 11:47:18	ga_s2am_ard_3-2-1_50HLH_2019-06-23_final.stac-item
2019-07-09 12:05:13	ga_s2am_ard_3-2-1_50JLR_2019-07-09_final.stac-item
2023-10-30 11:47:09	ga_s2am_ard_3-2-1_50HMH_2023-10-30_final
2023-10-30 11:47:14	ga_s2am_ard_3-2-1_50HLH_2023-10-30_final
2023-10-30 11:47:24	ga_s2am_ard_3-2-1_50HMG_2023-10-30_final
2023-10-30 11:47:26	ga_s2am_ard_3-2-1_50HLG_2023-10-30_final
2023-11-02 11:54:43	ga_s2am_ard_3-2-1_50JLT_2023-11-02_final
2023-11-02 11:54:57	ga_s2am_ard_3-2-1_50JLS_2023-11-02_final
2023-11-02 11:55:11	ga_s2am_ard_3-2-1_50JLR_2023-11-02_final
2023-11-02 11:55:26	ga_s2am_ard_3-2-1_50JLQ_2023-11-02_final
2023-11-02 11:55:41	ga_s2am_ard_3-2-1_50JLP_2023-11-02_final
2023-11-02 11:55:49	ga_s2am_ard_3-2-1_50JLN_2023-11-02_final
2023-11-02 11:55:55	ga_s2am_ard_3-2-1_50JLN_2023-11-02_final
2023-11-02 11:56:10	ga_s2am_ard_3-2-1_50JLM_2023-11-02_final
2023-11-02 11:56:24	ga_s2am_ard_3-2-1_50JLL_2023-11-02_final
2023-11-02 11:56:39	ga_s2am_ard_3-2-1_50HLK_2023-11-02_final
2023-11-02 11:56:53	ga_s2am_ard_3-2-1_50HLJ_2023-11-02_final
2023-11-02 11:57:04	ga_s2am_ard_3-2-1_50HMH_2023-11-02_final
2023-11-02 11:57:08	ga_s2am_ard_3-2-1_50HLH_2023-11-02_final
2023-11-02 11:57:19	ga_s2am_ard_3-2-1_50HMG_2023-11-02_final
2023-11-02 11:57:21	ga_s2am_ard_3-2-1_50HLG_2023-11-02_final
2023-11-02 11:57:26	ga_s2am_ard_3-2-1_50HMF_2023-11-02_nrt
2023-11-02 11:57:28	ga_s2am_ard_3-2-1_50HLF_2023-11-02_nrt
2023-11-05 12:02:58	ga_s2am_ard_3-2-1_50KLE_2023-11-05_nrt
2023-11-05 12:03:11	ga_s2am_ard_3-2-1_50KLD_2023-11-05_nrt
2023-11-05 12:03:26	ga_s2am_ard_3-2-1_50KLC_2023-11-05_final
2023-11-05 12:03:40	ga_s2am_ard_3-2-1_50KLB_2023-11-05_final
2023-11-05 12:03:55	ga_s2am_ard_3-2-1_50KLA_2023-11-05_final
2023-11-05 12:04:09	ga_s2am_ard_3-2-1_50KLV_2023-11-05_final
2023-11-09 11:47:09	ga_s2am_ard_3-2-1_50HMH_2023-11-09_final
2023-11-09 11:47:14	ga_s2am_ard_3-2-1_50HLH_2023-11-09_final
2023-11-09 11:47:23	ga_s2am_ard_3-2-1_50HMG_2023-11-09_final
2023-11-09 11:47:26	ga_s2am_ard_3-2-1_50HLG_2023-11-09_final
2023-11-09 11:47:31	ga_s2am_ard_3-2-1_50HMF_2023-11-09_nrt
2023-11-09 11:47:32	ga_s2am_ard_3-2-1_50HLF_2023-11-09_nrt
2023-11-12 11:54:44	ga_s2am_ard_3-2-1_50JLT_2023-11-12_final
2023-11-12 11:54:57	ga_s2am_ard_3-2-1_50JLS_2023-11-12_final
2023-11-12 11:55:12	ga_s2am_ard_3-2-1_50JLR_2023-11-12_final
2023-11-12 11:55:27	ga_s2am_ard_3-2-1_50JLQ_2023-11-12_final
2023-11-12 11:55:41	ga_s2am_ard_3-2-1_50JLP_2023-11-12_final
2023-11-12 11:55:56	ga_s2am_ard_3-2-1_50JLN_2023-11-12_final
2023-11-12 11:56:10	ga_s2am_ard_3-2-1_50JLM_2023-11-12_final
2023-11-12 11:56:25	ga_s2am_ard_3-2-1_50JLL_2023-11-12_final
2023-11-12 11:56:39	ga_s2am_ard_3-2-1_50HLK_2023-11-12_final
2023-11-12 11:56:54	ga_s2am_ard_3-2-1_50HLJ_2023-11-12_final
2023-11-12 11:57:05	ga_s2am_ard_3-2-1_50HMH_2023-11-12_final
2023-11-12 11:57:09	ga_s2am_ard_3-2-1_50HLH_2023-11-12_final
2023-11-12 11:57:19	ga_s2am_ard_3-2-1_50HMG_2023-11-12_final
2023-11-12 11:57:22	ga_s2am_ard_3-2-1_50HLG_2023-11-12_final
2023-11-19 11:47:07	ga_s2am_ard_3-2-1_50HMH_2023-11-19_final
2023-11-19 11:47:12	ga_s2am_ard_3-2-1_50HLH_2023-11-19_final
2023-11-19 11:47:21	ga_s2am_ard_3-2-1_50HMG_2023-11-19_final
2023-11-19 11:47:24	ga_s2am_ard_3-2-1_50HLG_2023-11-19_final
2023-11-19 11:47:29	ga_s2am_ard_3-2-1_50HMF_2023-11-19_nrt
2023-11-19 11:47:30	ga_s2am_ard_3-2-1_50HLF_2023-11-19_nrt
2023-11-29 11:47:02	ga_s2am_ard_3-2-1_50HMH_2023-11-29_final
2023-11-29 11:47:06	ga_s2am_ard_3-2-1_50HLH_2023-11-29_final
2023-11-29 11:47:16	ga_s2am_ard_3-2-1_50HMG_2023-11-29_final
2023-11-29 11:47:20	ga_s2am_ard_3-2-1_50HLG_2023-11-29_final
2023-11-29 11:47:25	ga_s2am_ard_3-2-1_50HMF_2023-11-29_final
2023-11-29 11:47:26	ga_s2am_ard_3-2-1_50HLF_2023-11-29_nrt
2023-12-02 11:54:36	ga_s2am_ard_3-2-1_50JLT_2023-12-02_final
2023-12-02 11:54:50	ga_s2am_ard_3-2-1_50JLS_2023-12-02_final
2023-12-02 11:55:05	ga_s2am_ard_3-2-1_50JLR_2023-12-02_final
2023-12-02 11:55:19	ga_s2am_ard_3-2-1_50JLQ_2023-12-02_final
2023-12-02 11:55:34	ga_s2am_ard_3-2-1_50JLP_2023-12-02_final
2023-12-02 11:55:48	ga_s2am_ard_3-2-1_50JLN_2023-12-02_final
2023-12-02 11:56:03	ga_s2am_ard_3-2-1_50JLM_2023-12-02_final
2023-12-02 11:56:17	ga_s2am_ard_3-2-1_50JLL_2023-12-02_final
2023-12-02 11:56:32	ga_s2am_ard_3-2-1_50HLK_2023-12-02_final
2023-12-02 11:56:47	ga_s2am_ard_3-2-1_50HLJ_2023-12-02_final
2023-12-02 11:56:58	ga_s2am_ard_3-2-1_50HMH_2023-12-02_final
2023-12-02 11:57:01	ga_s2am_ard_3-2-1_50HLH_2023-12-02_final
2023-12-02 11:57:12	ga_s2am_ard_3-2-1_50HMG_2023-12-02_final
2023-12-02 11:57:14	ga_s2am_ard_3-2-1_50HLG_2023-12-02_final
2023-12-02 11:57:19	ga_s2am_ard_3-2-1_50HMF_2023-12-02_final
2023-12-02 11:57:20	ga_s2am_ard_3-2-1_50HLF_2023-12-02_nrt
2023-12-05 12:02:48	ga_s2am_ard_3-2-1_50KLE_2023-12-05_nrt
2023-12-05 12:02:54	ga_s2am_ard_3-2-1_50KLE_2023-12-05_nrt
2023-12-05 12:03:05	ga_s2am_ard_3-2-1_50KLD_2023-12-05_nrt
2023-12-05 12:03:20	ga_s2am_ard_3-2-1_50KLC_2023-12-05_final
2023-12-05 12:03:34	ga_s2am_ard_3-2-1_50KLB_2023-12-05_final
2023-12-05 12:03:49	ga_s2am_ard_3-2-1_50KLA_2023-12-05_final
2023-12-05 12:04:03	ga_s2am_ard_3-2-1_50KLV_2023-12-05_final
2023-12-05 12:04:18	ga_s2am_ard_3-2-1_50KLU_2023-12-05_final
2023-12-05 12:04:32	ga_s2am_ard_3-2-1_50JLT_2023-12-05_final
2023-12-05 12:04:47	ga_s2am_ard_3-2-1_50JLS_2023-12-05_final
2023-12-05 12:05:01	ga_s2am_ard_3-2-1_50JLR_2023-12-05_final
2023-12-09 11:47:04	ga_s2am_ard_3-2-1_50HMH_2023-12-09_final
2023-12-09 11:47:09	ga_s2am_ard_3-2-1_50HLH_2023-12-09_final
2023-12-09 11:47:19	ga_s2am_ard_3-2-1_50HMG_2023-12-09_final
2023-12-09 11:47:21	ga_s2am_ard_3-2-1_50HLG_2023-12-09_final
2023-12-12 11:54:40	ga_s2am_ard_3-2-1_50JLT_2023-12-12_final
2023-12-12 11:54:53	ga_s2am_ard_3-2-1_50JLS_2023-12-12_final
2023-12-12 11:55:08	ga_s2am_ard_3-2-1_50JLR_2023-12-12_final
2023-12-12 11:55:23	ga_s2am_ard_3-2-1_50JLQ_2023-12-12_final
2023-12-12 11:55:37	ga_s2am_ard_3-2-1_50JLP_2023-12-12_final
2023-12-12 11:55:52	ga_s2am_ard_3-2-1_50JLN_2023-12-12_final
2023-12-12 11:56:06	ga_s2am_ard_3-2-1_50JLM_2023-12-12_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