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
2023-02-23 11:16:09	ga_s2am_ard_3-2-1_51JXF_2023-02-23_final
2023-02-25 11:53:22	ga_s2am_ard_3-2-1_50KNC_2023-02-25_final
2023-02-25 11:53:29	ga_s2am_ard_3-2-1_50KQB_2023-02-25_final
2023-02-25 11:53:43	ga_s2am_ard_3-2-1_50KQA_2023-02-25_final
2023-02-25 11:53:48	ga_s2am_ard_3-2-1_50KMA_2023-02-25_final
2023-02-25 11:53:50	ga_s2am_ard_3-2-1_50KQV_2023-02-25_final
2023-02-25 11:53:51	ga_s2am_ard_3-2-1_50KQA_2023-02-25_final
2023-02-25 11:53:55	ga_s2am_ard_3-2-1_50KMA_2023-02-25_final
2023-02-25 11:53:58	ga_s2am_ard_3-2-1_50KQV_2023-02-25_final
2023-02-25 11:54:20	ga_s2am_ard_3-2-1_50KNU_2023-02-25_final
2023-02-25 11:54:24	ga_s2am_ard_3-2-1_50KMU_2023-02-25_final
2023-02-25 11:54:31	ga_s2am_ard_3-2-1_50JPT_2023-02-25_final
2023-02-25 11:54:45	ga_s2am_ard_3-2-1_50JPS_2023-02-25_final
2023-02-25 11:54:49	ga_s2am_ard_3-2-1_50JNS_2023-02-25_final
2023-02-25 11:54:55	ga_s2am_ard_3-2-1_50JLS_2023-02-25_final
2023-02-25 11:55:00	ga_s2am_ard_3-2-1_50JPR_2023-02-25_final
2023-02-25 11:55:04	ga_s2am_ard_3-2-1_50JNR_2023-02-25_final
2023-02-25 11:55:07	ga_s2am_ard_3-2-1_50JMR_2023-02-25_final
2023-02-25 11:55:10	ga_s2am_ard_3-2-1_50JLR_2023-02-25_final
2023-02-25 11:55:13	ga_s2am_ard_3-2-1_50JPQ_2023-02-25_final
2023-02-25 11:55:18	ga_s2am_ard_3-2-1_50JNQ_2023-02-25_final
2023-02-25 11:55:24	ga_s2am_ard_3-2-1_50JPP_2023-02-25_final
2023-02-25 11:55:25	ga_s2am_ard_3-2-1_50JLQ_2023-02-25_final
2023-02-25 11:55:32	ga_s2am_ard_3-2-1_50JNP_2023-02-25_final
2023-02-25 11:55:44	ga_s2am_ard_3-2-1_50JKP_2023-02-25_final
2023-02-25 11:55:47	ga_s2am_ard_3-2-1_50JNN_2023-02-25_final
2023-02-25 11:55:51	ga_s2am_ard_3-2-1_50JMN_2023-02-25_final
2023-02-25 11:55:57	ga_s2am_ard_3-2-1_50JKN_2023-02-25_final
2023-02-25 11:56:02	ga_s2am_ard_3-2-1_50JNM_2023-02-25_final
2023-02-25 11:56:05	ga_s2am_ard_3-2-1_50JMM_2023-02-25_final
2023-02-25 11:56:09	ga_s2am_ard_3-2-1_50JLM_2023-02-25_final
2023-02-25 11:56:11	ga_s2am_ard_3-2-1_50JKM_2023-02-25_final
2023-02-25 11:56:16	ga_s2am_ard_3-2-1_50JNL_2023-02-25_final
2023-02-25 11:56:20	ga_s2am_ard_3-2-1_50JML_2023-02-25_final
2023-02-25 11:56:23	ga_s2am_ard_3-2-1_50JLL_2023-02-25_final
2023-02-25 11:56:34	ga_s2am_ard_3-2-1_50HMK_2023-02-25_final
2023-02-25 11:56:49	ga_s2am_ard_3-2-1_50HMJ_2023-02-25_final
2023-02-25 11:56:52	ga_s2am_ard_3-2-1_50HLJ_2023-02-25_final
2023-02-25 11:57:03	ga_s2am_ard_3-2-1_50HMH_2023-02-25_final
2023-02-25 11:57:07	ga_s2am_ard_3-2-1_50HLH_2023-02-25_final
2023-02-26 11:24:20	ga_s2am_ard_3-2-1_51JYN_2023-02-26_final
2023-02-26 11:24:24	ga_s2am_ard_3-2-1_51JXN_2023-02-26_final
2023-02-26 11:25:01	ga_s2am_ard_3-2-1_51JVL_2023-02-26_final
2023-02-26 11:25:50	ga_s2am_ard_3-2-1_51JXG_2023-02-26_final
2023-02-26 11:25:58	ga_s2am_ard_3-2-1_51JVG_2023-02-26_final
2023-02-26 11:26:05	ga_s2am_ard_3-2-1_51JXF_2023-02-26_final
2023-02-26 11:27:10	ga_s2am_ard_3-2-1_51HUB_2023-02-26_final
2023-02-28 12:03:17	ga_s2am_ard_3-2-1_50KNC_2023-02-28_final
2023-02-28 12:03:49	ga_s2am_ard_3-2-1_50KMA_2023-02-28_final
2023-02-28 12:03:53	ga_s2am_ard_3-2-1_50KLA_2023-02-28_final
2023-02-28 12:04:11	ga_s2am_ard_3-2-1_50KKV_2023-02-28_final
2023-02-28 12:04:19	ga_s2am_ard_3-2-1_50KMU_2023-02-28_final
2023-02-28 12:04:24	ga_s2am_ard_3-2-1_49KHP_2023-02-28_final
2023-02-28 12:04:24	ga_s2am_ard_3-2-1_50KKU_2023-02-28_final
2023-02-28 12:04:30	ga_s2am_ard_3-2-1_49JHN_2023-02-28_final
2023-02-28 12:04:31	ga_s2am_ard_3-2-1_49KHP_2023-02-28_final
2023-02-28 12:04:31	ga_s2am_ard_3-2-1_50KKU_2023-02-28_final
2023-02-28 12:04:40	ga_s2am_ard_3-2-1_49JHN_2023-02-28_final
2023-02-28 12:04:40	ga_s2am_ard_3-2-1_50JKT_2023-02-28_final
2023-02-28 12:04:43	ga_s2am_ard_3-2-1_49JGN_2023-02-28_final
2023-02-28 12:04:46	ga_s2am_ard_3-2-1_50JMS_2023-02-28_final
2023-02-28 12:04:51	ga_s2am_ard_3-2-1_50JLS_2023-02-28_final
2023-02-28 12:04:54	ga_s2am_ard_3-2-1_49JHM_2023-02-28_final
2023-02-28 12:04:56	ga_s2am_ard_3-2-1_50JMR_2023-02-28_final
2023-02-28 12:05:06	ga_s2am_ard_3-2-1_50JLR_2023-02-28_final
2023-02-28 12:05:12	ga_s2am_ard_3-2-1_49JGL_2023-02-28_final
2023-02-28 12:05:20	ga_s2am_ard_3-2-1_50JLQ_2023-02-28_final
2023-02-28 12:05:27	ga_s2am_ard_3-2-1_49JGK_2023-02-28_final
2023-02-28 12:05:35	ga_s2am_ard_3-2-1_50JLP_2023-02-28_final
2023-02-28 12:05:38	ga_s2am_ard_3-2-1_50JKP_2023-02-28_final
2023-02-28 12:05:41	ga_s2am_ard_3-2-1_49JGJ_2023-02-28_final
2023-02-28 12:05:53	ga_s2am_ard_3-2-1_50JKN_2023-02-28_final
2023-02-28 12:06:02	ga_s2am_ard_3-2-1_50JLM_2023-02-28_final
2023-02-28 12:06:12	ga_s2am_ard_3-2-1_50JLL_2023-02-28_final
2023-03-01 11:34:13	ga_s2am_ard_3-2-1_51JXN_2023-03-01_final
2023-03-01 11:34:34	ga_s2am_ard_3-2-1_50JRT_2023-03-01_final
2023-03-01 11:34:40	ga_s2am_ard_3-2-1_51JVM_2023-03-01_final
2023-03-01 11:34:44	ga_s2am_ard_3-2-1_51JUM_2023-03-01_final
2023-03-01 11:34:46	ga_s2am_ard_3-2-1_50JRS_2023-03-01_final
2023-03-01 11:34:55	ga_s2am_ard_3-2-1_51JVL_2023-03-01_final
2023-03-01 11:35:31	ga_s2am_ard_3-2-1_51JTJ_2023-03-01_final
2023-03-01 11:35:34	ga_s2am_ard_3-2-1_50JQP_2023-03-01_final
2023-03-01 11:35:53	ga_s2am_ard_3-2-1_51JVG_2023-03-01_final
2023-03-01 11:36:02	ga_s2am_ard_3-2-1_50JQM_2023-03-01_final
2023-03-01 11:36:29	ga_s2am_ard_3-2-1_51HTE_2023-03-01_final
2023-03-01 11:36:46	ga_s2am_ard_3-2-1_50HQJ_2023-03-01_final
2023-03-01 11:37:00	ga_s2am_ard_3-2-1_50HQH_2023-03-01_final
2023-03-02 11:06:34	ga_s2am_ard_3-2-1_51HYD_2023-03-02_final
2023-03-03 12:13:35	ga_s2am_ard_3-2-1_50KKB_2023-03-03_final
2023-03-03 12:13:42	ga_s2am_ard_3-2-1_49KHR_2023-03-03_final
2023-03-03 12:13:43	ga_s2am_ard_3-2-1_50KKB_2023-03-03_final
2023-03-03 12:13:51	ga_s2am_ard_3-2-1_49KHR_2023-03-03_final
2023-03-03 12:13:55	ga_s2am_ard_3-2-1_49KGR_2023-03-03_final
2023-03-03 12:14:04	ga_s2am_ard_3-2-1_50KKV_2023-03-03_final
2023-03-03 12:14:06	ga_s2am_ard_3-2-1_49KHQ_2023-03-03_final
2023-03-03 12:14:16	ga_s2am_ard_3-2-1_50KKU_2023-03-03_final
2023-03-03 12:14:18	ga_s2am_ard_3-2-1_49KHP_2023-03-03_final
2023-03-03 12:14:38	ga_s2am_ard_3-2-1_49JGN_2023-03-03_final
2023-03-03 12:14:42	ga_s2am_ard_3-2-1_49JFN_2023-03-03_final
2023-03-03 12:14:57	ga_s2am_ard_3-2-1_49JFM_2023-03-03_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