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
2018-07-28 11:45:46	ga_s2am_ard_3-2-1_50HMH_2018-07-28_final.stac-item
2018-07-28 11:45:46	ga_s2am_ard_3-2-1_50HMJ_2018-07-28_final.stac-item
2018-07-28 11:45:46	ga_s2am_ard_3-2-1_50HMK_2018-07-28_final.stac-item
2018-07-28 11:45:46	ga_s2am_ard_3-2-1_50HNH_2018-07-28_final.stac-item
2018-07-28 11:45:46	ga_s2am_ard_3-2-1_50JML_2018-07-28_final.stac-item
2018-07-28 11:45:46	ga_s2am_ard_3-2-1_50JMM_2018-07-28_final.stac-item
2018-07-31 11:53:40	ga_s2am_ard_3-2-1_50JMS_2018-07-31_final.stac-item
2018-07-31 11:53:40	ga_s2am_ard_3-2-1_50JMT_2018-07-31_final.stac-item
2018-08-03 12:02:50	ga_s2am_ard_3-2-1_50KMA_2018-08-03_final.stac-item
2018-08-03 12:02:50	ga_s2am_ard_3-2-1_50KMB_2018-08-03_final.stac-item
2018-08-03 12:02:50	ga_s2am_ard_3-2-1_50KMC_2018-08-03_final.stac-item
2023-05-23 11:46:16	ga_s2am_ard_3-2-1_50JMM_2023-05-23_final
2023-05-23 11:46:29	ga_s2am_ard_3-2-1_50JML_2023-05-23_final
2023-05-23 11:46:44	ga_s2am_ard_3-2-1_50HMK_2023-05-23_final
2023-05-23 11:46:58	ga_s2am_ard_3-2-1_50HMJ_2023-05-23_final
2023-05-23 11:47:09	ga_s2am_ard_3-2-1_50HNH_2023-05-23_final
2023-05-23 11:47:13	ga_s2am_ard_3-2-1_50HMH_2023-05-23_final
2023-05-23 11:47:24	ga_s2am_ard_3-2-1_50HNG_2023-05-23_final
2023-05-23 11:47:27	ga_s2am_ard_3-2-1_50HMG_2023-05-23_final
2023-05-23 11:47:34	ga_s2am_ard_3-2-1_50HNF_2023-05-23_nrt
2023-05-23 11:47:36	ga_s2am_ard_3-2-1_50HMF_2023-05-23_nrt
2023-05-26 11:53:34	ga_s2am_ard_3-2-1_50KMC_2023-05-26_final
2023-05-26 11:53:41	ga_s2am_ard_3-2-1_50KMB_2023-05-26_final
2023-05-26 11:53:48	ga_s2am_ard_3-2-1_50KMB_2023-05-26_final
2023-05-26 11:54:00	ga_s2am_ard_3-2-1_50KMA_2023-05-26_final
2023-05-26 11:54:15	ga_s2am_ard_3-2-1_50KMV_2023-05-26_final
2023-05-26 11:54:29	ga_s2am_ard_3-2-1_50KMU_2023-05-26_final
2023-05-26 11:54:44	ga_s2am_ard_3-2-1_50JMT_2023-05-26_final
2023-05-26 11:54:58	ga_s2am_ard_3-2-1_50JMS_2023-05-26_final
2023-05-26 11:55:13	ga_s2am_ard_3-2-1_50JMR_2023-05-26_final
2023-05-26 11:55:27	ga_s2am_ard_3-2-1_50JMQ_2023-05-26_final
2023-05-26 11:55:42	ga_s2am_ard_3-2-1_50JMP_2023-05-26_final
2023-05-26 11:55:56	ga_s2am_ard_3-2-1_50JMN_2023-05-26_final
2023-05-26 11:56:11	ga_s2am_ard_3-2-1_50JMM_2023-05-26_final
2023-05-26 11:56:25	ga_s2am_ard_3-2-1_50JML_2023-05-26_final
2023-05-26 11:56:40	ga_s2am_ard_3-2-1_50HMK_2023-05-26_final
2023-05-29 12:00:48	ga_s2am_ard_3-2-1_50LMP_2023-05-29_nrt
2023-05-29 12:01:01	ga_s2am_ard_3-2-1_50LMN_2023-05-29_nrt
2023-05-29 12:01:11	ga_s2am_ard_3-2-1_50LMM_2023-05-29_nrt
2023-05-29 12:01:18	ga_s2am_ard_3-2-1_50LMM_2023-05-29_nrt
2023-05-29 12:01:31	ga_s2am_ard_3-2-1_50LML_2023-05-29_nrt
2023-05-29 12:01:40	ga_s2am_ard_3-2-1_50LMK_2023-05-29_nrt
2023-05-29 12:03:00	ga_s2am_ard_3-2-1_50KME_2023-05-29_nrt
2023-05-29 12:03:12	ga_s2am_ard_3-2-1_50KMD_2023-05-29_nrt
2023-05-29 12:03:27	ga_s2am_ard_3-2-1_50KMC_2023-05-29_final
2023-05-29 12:03:41	ga_s2am_ard_3-2-1_50KMB_2023-05-29_final
2023-05-29 12:03:56	ga_s2am_ard_3-2-1_50KMA_2023-05-29_final
2023-06-22 11:46:16	ga_s2am_ard_3-2-1_50JMM_2023-06-22_final
2023-06-22 11:46:30	ga_s2am_ard_3-2-1_50JML_2023-06-22_final
2023-06-22 11:46:44	ga_s2am_ard_3-2-1_50HMK_2023-06-22_final
2023-06-22 11:46:59	ga_s2am_ard_3-2-1_50HMJ_2023-06-22_final
2023-06-22 11:47:10	ga_s2am_ard_3-2-1_50HNH_2023-06-22_final
2023-06-22 11:47:13	ga_s2am_ard_3-2-1_50HMH_2023-06-22_final
2023-06-22 11:47:24	ga_s2am_ard_3-2-1_50HNG_2023-06-22_final
2023-06-22 11:47:28	ga_s2am_ard_3-2-1_50HMG_2023-06-22_final
2023-06-22 11:47:33	ga_s2am_ard_3-2-1_50HNF_2023-06-22_nrt
2023-06-22 11:47:35	ga_s2am_ard_3-2-1_50HMF_2023-06-22_nrt
2023-06-25 11:53:34	ga_s2am_ard_3-2-1_50KMC_2023-06-25_final
2023-06-25 11:53:46	ga_s2am_ard_3-2-1_50KMB_2023-06-25_final
2023-06-25 11:53:59	ga_s2am_ard_3-2-1_50KMA_2023-06-25_final
2023-06-25 11:54:14	ga_s2am_ard_3-2-1_50KMV_2023-06-25_final
2023-06-25 11:54:29	ga_s2am_ard_3-2-1_50KMU_2023-06-25_final
2023-06-25 11:54:43	ga_s2am_ard_3-2-1_50JMT_2023-06-25_final
2023-06-25 11:54:58	ga_s2am_ard_3-2-1_50JMS_2023-06-25_final
2023-06-25 11:55:13	ga_s2am_ard_3-2-1_50JMR_2023-06-25_final
2023-06-25 11:55:27	ga_s2am_ard_3-2-1_50JMQ_2023-06-25_final
2023-06-28 12:00:48	ga_s2am_ard_3-2-1_50LMP_2023-06-28_nrt
2023-06-28 12:01:01	ga_s2am_ard_3-2-1_50LMN_2023-06-28_nrt
2023-06-28 12:01:16	ga_s2am_ard_3-2-1_50LMM_2023-06-28_nrt
2023-06-28 12:01:31	ga_s2am_ard_3-2-1_50LML_2023-06-28_nrt
2023-06-28 12:01:40	ga_s2am_ard_3-2-1_50LMK_2023-06-28_nrt
2023-06-28 12:03:01	ga_s2am_ard_3-2-1_50KME_2023-06-28_nrt
2023-06-28 12:03:13	ga_s2am_ard_3-2-1_50KMD_2023-06-28_nrt
2023-06-28 12:03:27	ga_s2am_ard_3-2-1_50KMC_2023-06-28_final
2023-06-28 12:03:41	ga_s2am_ard_3-2-1_50KMB_2023-06-28_final
2023-06-28 12:03:56	ga_s2am_ard_3-2-1_50KMA_2023-06-28_final
2023-07-02 11:46:17	ga_s2am_ard_3-2-1_50JMM_2023-07-02_final
2023-07-02 11:46:31	ga_s2am_ard_3-2-1_50JML_2023-07-02_final
2023-07-02 11:46:45	ga_s2am_ard_3-2-1_50HMK_2023-07-02_final
2023-07-02 11:47:00	ga_s2am_ard_3-2-1_50HMJ_2023-07-02_final
2023-07-02 11:47:11	ga_s2am_ard_3-2-1_50HNH_2023-07-02_final
2023-07-02 11:47:14	ga_s2am_ard_3-2-1_50HMH_2023-07-02_final
2023-07-02 11:47:25	ga_s2am_ard_3-2-1_50HNG_2023-07-02_final
2023-07-02 11:47:29	ga_s2am_ard_3-2-1_50HMG_2023-07-02_final
2023-07-02 11:47:34	ga_s2am_ard_3-2-1_50HNF_2023-07-02_nrt
2023-07-02 11:47:36	ga_s2am_ard_3-2-1_50HMF_2023-07-02_nrt
2023-07-05 11:53:36	ga_s2am_ard_3-2-1_50KMC_2023-07-05_final
2023-07-05 11:53:43	ga_s2am_ard_3-2-1_50KMB_2023-07-05_final
2023-07-05 11:53:50	ga_s2am_ard_3-2-1_50KMB_2023-07-05_final
2023-07-05 11:54:01	ga_s2am_ard_3-2-1_50KMA_2023-07-05_final
2023-07-05 11:54:16	ga_s2am_ard_3-2-1_50KMV_2023-07-05_final
2023-07-05 11:54:31	ga_s2am_ard_3-2-1_50KMU_2023-07-05_final
2023-07-05 11:54:45	ga_s2am_ard_3-2-1_50JMT_2023-07-05_final
2023-07-05 11:55:00	ga_s2am_ard_3-2-1_50JMS_2023-07-05_final
2023-07-05 11:55:15	ga_s2am_ard_3-2-1_50JMR_2023-07-05_final
2023-07-05 11:55:29	ga_s2am_ard_3-2-1_50JMQ_2023-07-05_final
2023-07-05 11:55:44	ga_s2am_ard_3-2-1_50JMP_2023-07-05_final
2023-07-05 11:55:58	ga_s2am_ard_3-2-1_50JMN_2023-07-05_final
2023-07-05 11:56:13	ga_s2am_ard_3-2-1_50JMM_2023-07-05_final
2023-07-05 11:56:27	ga_s2am_ard_3-2-1_50JML_2023-07-05_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