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
2019-04-08 09:47:23	ga_s2am_ard_3-2-1_55HCU_2019-04-08_final.stac-item
2019-04-08 09:47:31	ga_s2am_ard_3-2-1_55HBU_2019-04-08_final.stac-item
2019-04-08 09:47:37	ga_s2am_ard_3-2-1_55HCT_2019-04-08_final.stac-item
2019-04-11 09:55:13	ga_s2am_ard_3-2-1_55JBH_2019-04-11_final.stac-item
2019-04-11 09:55:26	ga_s2am_ard_3-2-1_55JBG_2019-04-11_final.stac-item
2019-04-11 09:55:41	ga_s2am_ard_3-2-1_55JBF_2019-04-11_final.stac-item
2019-04-11 09:55:55	ga_s2am_ard_3-2-1_55HBE_2019-04-11_final.stac-item
2019-04-11 09:56:10	ga_s2am_ard_3-2-1_55HBD_2019-04-11_final.stac-item
2019-07-29 10:19:43	ga_s2am_ard_3-2-1_54LZQ_2019-07-29_final.stac-item
2019-07-29 10:19:57	ga_s2am_ard_3-2-1_54LZP_2019-07-29_final.stac-item
2019-07-29 10:20:11	ga_s2am_ard_3-2-1_54LZN_2019-07-29_final.stac-item
2019-08-08 10:19:42	ga_s2am_ard_3-2-1_54LZQ_2019-08-08_final.stac-item
2019-08-08 10:19:56	ga_s2am_ard_3-2-1_54LZP_2019-08-08_final.stac-item
2019-08-08 10:20:10	ga_s2am_ard_3-2-1_54LZN_2019-08-08_final.stac-item
2019-08-12 10:02:43	ga_s2am_ard_3-2-1_55KBT_2019-08-12_final.stac-item
2019-08-12 10:02:45	ga_s2am_ard_3-2-1_54KZC_2019-08-12_final.stac-item
2019-08-12 10:02:58	ga_s2am_ard_3-2-1_55KBS_2019-08-12_final.stac-item
2019-08-12 10:02:58	ga_s2am_ard_3-2-1_54KZB_2019-08-12_final.stac-item
2019-08-12 10:03:13	ga_s2am_ard_3-2-1_55KBR_2019-08-12_final.stac-item
2019-08-12 10:03:13	ga_s2am_ard_3-2-1_54KZA_2019-08-12_final.stac-item
2019-08-12 10:03:27	ga_s2am_ard_3-2-1_55KBQ_2019-08-12_final.stac-item
2019-08-12 10:03:27	ga_s2am_ard_3-2-1_54KZV_2019-08-12_final.stac-item
2019-08-12 10:03:42	ga_s2am_ard_3-2-1_55KBP_2019-08-12_final.stac-item
2019-08-12 10:03:42	ga_s2am_ard_3-2-1_54KZU_2019-08-12_final.stac-item
2019-08-12 10:03:56	ga_s2am_ard_3-2-1_54JZT_2019-08-12_final.stac-item
2019-08-12 10:03:56	ga_s2am_ard_3-2-1_55JBN_2019-08-12_final.stac-item
2019-08-12 10:04:11	ga_s2am_ard_3-2-1_54JZS_2019-08-12_final.stac-item
2019-08-12 10:04:11	ga_s2am_ard_3-2-1_55JBM_2019-08-12_final.stac-item
2019-08-12 10:04:26	ga_s2am_ard_3-2-1_55JBL_2019-08-12_final.stac-item
2019-08-12 10:04:40	ga_s2am_ard_3-2-1_55JBK_2019-08-12_final.stac-item
2019-08-12 10:04:55	ga_s2am_ard_3-2-1_55JBJ_2019-08-12_final.stac-item
2019-08-12 10:05:09	ga_s2am_ard_3-2-1_55JBH_2019-08-12_final.stac-item
2019-08-12 10:05:24	ga_s2am_ard_3-2-1_55JBG_2019-08-12_final.stac-item
2019-08-12 10:05:38	ga_s2am_ard_3-2-1_55JBF_2019-08-12_final.stac-item
2019-08-12 10:05:53	ga_s2am_ard_3-2-1_55HBE_2019-08-12_final.stac-item
2023-09-16 10:19:27	ga_s2am_ard_3-2-1_54LZR_2023-09-16_final
2023-09-16 10:19:39	ga_s2am_ard_3-2-1_55LBK_2023-09-16_final
2023-09-16 10:19:41	ga_s2am_ard_3-2-1_54LZQ_2023-09-16_final
2023-09-16 10:19:54	ga_s2am_ard_3-2-1_55LBJ_2023-09-16_final
2023-09-16 10:19:56	ga_s2am_ard_3-2-1_54LZP_2023-09-16_final
2023-09-16 10:20:09	ga_s2am_ard_3-2-1_55LBH_2023-09-16_nrt
2023-09-16 10:20:10	ga_s2am_ard_3-2-1_54LZN_2023-09-16_final
2023-09-17 09:56:12	ga_s2am_ard_3-2-1_55HBD_2023-09-17_final
2023-09-17 09:57:10	ga_s2am_ard_3-2-1_55HBV_2023-09-17_final
2023-09-17 09:57:21	ga_s2am_ard_3-2-1_55HCU_2023-09-17_final
2023-09-17 09:57:25	ga_s2am_ard_3-2-1_55HBU_2023-09-17_final
2023-09-17 09:57:36	ga_s2am_ard_3-2-1_55HCT_2023-09-17_final
2023-09-17 09:57:39	ga_s2am_ard_3-2-1_55HBT_2023-09-17_final
2023-09-17 09:57:50	ga_s2am_ard_3-2-1_55HCS_2023-09-17_final
2023-09-17 09:57:54	ga_s2am_ard_3-2-1_55HBS_2023-09-17_final
2023-09-17 09:58:04	ga_s2am_ard_3-2-1_55GCR_2023-09-17_final
2023-09-17 09:58:08	ga_s2am_ard_3-2-1_55GBR_2023-09-17_final
2023-09-17 09:58:17	ga_s2am_ard_3-2-1_55GCQ_2023-09-17_final
2023-09-17 09:58:23	ga_s2am_ard_3-2-1_55GBQ_2023-09-17_final
2023-09-20 10:02:42	ga_s2am_ard_3-2-1_55KBT_2023-09-20_final
2023-09-20 10:02:44	ga_s2am_ard_3-2-1_54KZC_2023-09-20_final
2023-09-20 10:02:57	ga_s2am_ard_3-2-1_55KBS_2023-09-20_final
2023-09-20 10:02:57	ga_s2am_ard_3-2-1_54KZB_2023-09-20_final
2023-09-20 10:03:11	ga_s2am_ard_3-2-1_55KBR_2023-09-20_final
2023-09-20 10:03:12	ga_s2am_ard_3-2-1_54KZA_2023-09-20_final
2023-09-20 10:03:26	ga_s2am_ard_3-2-1_55KBQ_2023-09-20_final
2023-09-20 10:03:26	ga_s2am_ard_3-2-1_54KZV_2023-09-20_final
2023-09-20 10:03:40	ga_s2am_ard_3-2-1_55KBP_2023-09-20_final
2023-09-20 10:03:40	ga_s2am_ard_3-2-1_54KZU_2023-09-20_final
2023-09-20 10:03:55	ga_s2am_ard_3-2-1_54JZT_2023-09-20_final
2023-09-20 10:03:55	ga_s2am_ard_3-2-1_55JBN_2023-09-20_final
2023-09-20 10:04:09	ga_s2am_ard_3-2-1_54JZS_2023-09-20_final
2023-09-20 10:04:10	ga_s2am_ard_3-2-1_55JBM_2023-09-20_final
2023-09-20 10:04:24	ga_s2am_ard_3-2-1_55JBL_2023-09-20_final
2023-09-20 10:04:39	ga_s2am_ard_3-2-1_55JBK_2023-09-20_final
2023-09-20 10:04:53	ga_s2am_ard_3-2-1_55JBJ_2023-09-20_final
2023-09-20 10:05:08	ga_s2am_ard_3-2-1_55JBH_2023-09-20_final
2023-09-20 10:05:20	ga_s2am_ard_3-2-1_55JBG_2023-09-20_final
2023-09-23 10:09:57	ga_s2am_ard_3-2-1_55LBJ_2023-09-23_nrt
2023-09-23 10:10:00	ga_s2am_ard_3-2-1_54LZP_2023-09-23_final
2023-09-23 10:10:12	ga_s2am_ard_3-2-1_55LBH_2023-09-23_nrt
2023-09-23 10:10:13	ga_s2am_ard_3-2-1_54LZN_2023-09-23_final
2023-09-23 10:10:26	ga_s2am_ard_3-2-1_55LBG_2023-09-23_nrt
2023-09-23 10:10:27	ga_s2am_ard_3-2-1_54LZM_2023-09-23_final
2023-09-23 10:10:40	ga_s2am_ard_3-2-1_55LBF_2023-09-23_nrt
2023-09-23 10:10:42	ga_s2am_ard_3-2-1_54LZL_2023-09-23_final
2023-09-23 10:10:55	ga_s2am_ard_3-2-1_55LBE_2023-09-23_final
2023-09-23 10:10:57	ga_s2am_ard_3-2-1_54LZK_2023-09-23_final
2023-09-23 10:11:10	ga_s2am_ard_3-2-1_55LBD_2023-09-23_final
2023-09-23 10:11:11	ga_s2am_ard_3-2-1_54LZJ_2023-09-23_final
2023-09-23 10:11:24	ga_s2am_ard_3-2-1_55LBC_2023-09-23_final
2023-09-23 10:11:26	ga_s2am_ard_3-2-1_54LZH_2023-09-23_final
2023-09-23 10:11:39	ga_s2am_ard_3-2-1_55KBB_2023-09-23_final
2023-09-23 10:11:40	ga_s2am_ard_3-2-1_54KZG_2023-09-23_final
2023-10-30 10:02:39	ga_s2am_ard_3-2-1_55KBT_2023-10-30_final
2023-10-30 10:02:41	ga_s2am_ard_3-2-1_54KZC_2023-10-30_final
2023-10-30 10:02:54	ga_s2am_ard_3-2-1_55KBS_2023-10-30_final
2023-10-30 10:02:54	ga_s2am_ard_3-2-1_54KZB_2023-10-30_final
2023-10-30 10:03:09	ga_s2am_ard_3-2-1_55KBR_2023-10-30_final
2023-10-30 10:03:09	ga_s2am_ard_3-2-1_54KZA_2023-10-30_final
2023-10-30 10:03:23	ga_s2am_ard_3-2-1_55KBQ_2023-10-30_final
2023-10-30 10:03:23	ga_s2am_ard_3-2-1_54KZV_2023-10-30_final
2023-10-30 10:03:38	ga_s2am_ard_3-2-1_55KBP_2023-10-30_final
2023-10-30 10:03:38	ga_s2am_ard_3-2-1_54KZU_2023-10-30_final
2023-10-30 10:03:52	ga_s2am_ard_3-2-1_54JZT_2023-10-30_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