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
2018-11-24 10:35:01	ga_s2am_ard_3-2-1_53JNJ_2018-11-24_final.stac-item
2018-11-24 10:35:08	ga_s2am_ard_3-2-1_53JMJ_2018-11-24_final.stac-item
2018-11-24 10:35:16	ga_s2am_ard_3-2-1_53JNH_2018-11-24_final.stac-item
2018-11-24 10:35:19	ga_s2am_ard_3-2-1_53JMH_2018-11-24_final.stac-item
2018-11-24 10:35:30	ga_s2am_ard_3-2-1_53JNG_2018-11-24_final.stac-item
2018-11-24 10:35:33	ga_s2am_ard_3-2-1_53JMG_2018-11-24_final.stac-item
2018-11-24 10:35:45	ga_s2am_ard_3-2-1_53JNF_2018-11-24_final.stac-item
2018-11-24 10:35:48	ga_s2am_ard_3-2-1_53JMF_2018-11-24_final.stac-item
2018-11-24 10:35:59	ga_s2am_ard_3-2-1_53HNE_2018-11-24_final.stac-item
2018-11-24 10:36:03	ga_s2am_ard_3-2-1_53HME_2018-11-24_final.stac-item
2018-11-24 10:36:14	ga_s2am_ard_3-2-1_53HND_2018-11-24_final.stac-item
2018-11-24 10:36:17	ga_s2am_ard_3-2-1_53HMD_2018-11-24_final.stac-item
2018-11-24 10:36:28	ga_s2am_ard_3-2-1_53HNC_2018-11-24_final.stac-item
2018-11-24 10:36:43	ga_s2am_ard_3-2-1_53HNB_2018-11-24_final.stac-item
2019-05-09 10:50:45	ga_s2am_ard_3-2-1_53LMG_2019-05-09_final.stac-item
2019-05-09 10:51:00	ga_s2am_ard_3-2-1_53LMF_2019-05-09_final.stac-item
2019-05-09 10:51:14	ga_s2am_ard_3-2-1_53LME_2019-05-09_final.stac-item
2019-05-09 10:51:29	ga_s2am_ard_3-2-1_53LMD_2019-05-09_final.stac-item
2019-05-09 10:51:40	ga_s2am_ard_3-2-1_53LNC_2019-05-09_final.stac-item
2019-05-09 10:51:44	ga_s2am_ard_3-2-1_53LMC_2019-05-09_final.stac-item
2019-05-09 10:51:54	ga_s2am_ard_3-2-1_53KNB_2019-05-09_final.stac-item
2019-05-09 10:51:58	ga_s2am_ard_3-2-1_53KMB_2019-05-09_final.stac-item
2019-05-09 10:52:09	ga_s2am_ard_3-2-1_53KNA_2019-05-09_final.stac-item
2019-05-09 10:52:13	ga_s2am_ard_3-2-1_53KMA_2019-05-09_final.stac-item
2019-05-09 10:52:24	ga_s2am_ard_3-2-1_53KNV_2019-05-09_final.stac-item
2019-05-09 10:52:27	ga_s2am_ard_3-2-1_53KMV_2019-05-09_final.stac-item
2019-05-09 10:52:37	ga_s2am_ard_3-2-1_53KNU_2019-05-09_final.stac-item
2019-05-09 10:52:42	ga_s2am_ard_3-2-1_53KMU_2019-05-09_final.stac-item
2019-05-09 10:52:49	ga_s2am_ard_3-2-1_53KNT_2019-05-09_final.stac-item
2019-05-09 10:52:56	ga_s2am_ard_3-2-1_53KMT_2019-05-09_final.stac-item
2019-05-19 10:50:45	ga_s2am_ard_3-2-1_53LMG_2019-05-19_final.stac-item
2019-05-19 10:51:00	ga_s2am_ard_3-2-1_53LMF_2019-05-19_final.stac-item
2019-05-19 10:51:14	ga_s2am_ard_3-2-1_53LME_2019-05-19_final.stac-item
2019-05-19 10:51:29	ga_s2am_ard_3-2-1_53LMD_2019-05-19_final.stac-item
2019-05-19 10:51:40	ga_s2am_ard_3-2-1_53LNC_2019-05-19_final.stac-item
2019-05-19 10:51:43	ga_s2am_ard_3-2-1_53LMC_2019-05-19_final.stac-item
2019-05-19 10:51:54	ga_s2am_ard_3-2-1_53KNB_2019-05-19_final.stac-item
2019-05-19 10:51:58	ga_s2am_ard_3-2-1_53KMB_2019-05-19_final.stac-item
2019-05-19 10:52:09	ga_s2am_ard_3-2-1_53KNA_2019-05-19_final.stac-item
2019-05-19 10:52:12	ga_s2am_ard_3-2-1_53KMA_2019-05-19_final.stac-item
2019-05-19 10:52:24	ga_s2am_ard_3-2-1_53KNV_2019-05-19_final.stac-item
2019-05-19 10:52:27	ga_s2am_ard_3-2-1_53KMV_2019-05-19_final.stac-item
2019-05-19 10:52:37	ga_s2am_ard_3-2-1_53KNU_2019-05-19_final.stac-item
2019-05-19 10:52:41	ga_s2am_ard_3-2-1_53KMU_2019-05-19_final.stac-item
2019-05-19 10:52:49	ga_s2am_ard_3-2-1_53KNT_2019-05-19_final.stac-item
2019-05-19 10:52:56	ga_s2am_ard_3-2-1_53KMT_2019-05-19_final.stac-item
2023-09-09 10:35:20	ga_s2am_ard_3-2-1_53JMJ_2023-09-09_final
2023-09-09 10:35:28	ga_s2am_ard_3-2-1_53JNH_2023-09-09_final
2023-09-09 10:35:32	ga_s2am_ard_3-2-1_53JMH_2023-09-09_final
2023-09-09 10:35:43	ga_s2am_ard_3-2-1_53JNG_2023-09-09_final
2023-09-09 10:35:46	ga_s2am_ard_3-2-1_53JMG_2023-09-09_final
2023-09-09 10:35:57	ga_s2am_ard_3-2-1_53JNF_2023-09-09_final
2023-09-09 10:36:01	ga_s2am_ard_3-2-1_53JMF_2023-09-09_final
2023-09-09 10:36:12	ga_s2am_ard_3-2-1_53HNE_2023-09-09_final
2023-09-09 10:36:59	ga_s2am_ard_3-2-1_53HMB_2023-09-09_final
2023-09-09 10:37:07	ga_s2am_ard_3-2-1_53HNA_2023-09-09_nrt
2023-09-09 10:37:09	ga_s2am_ard_3-2-1_53HMA_2023-09-09_nrt
2023-09-12 10:42:58	ga_s2am_ard_3-2-1_53KNT_2023-09-12_final
2023-09-12 10:43:01	ga_s2am_ard_3-2-1_53KMT_2023-09-12_final
2023-09-12 10:43:13	ga_s2am_ard_3-2-1_53KNS_2023-09-12_final
2023-09-12 10:43:16	ga_s2am_ard_3-2-1_53KMS_2023-09-12_final
2023-09-12 10:43:27	ga_s2am_ard_3-2-1_53KNR_2023-09-12_final
2023-09-12 10:43:31	ga_s2am_ard_3-2-1_53KMR_2023-09-12_final
2023-09-12 10:43:42	ga_s2am_ard_3-2-1_53KNQ_2023-09-12_final
2023-09-12 10:43:45	ga_s2am_ard_3-2-1_53KMQ_2023-09-12_final
2023-09-12 10:43:56	ga_s2am_ard_3-2-1_53KNP_2023-09-12_final
2023-09-12 10:44:00	ga_s2am_ard_3-2-1_53KMP_2023-09-12_final
2023-09-12 10:44:11	ga_s2am_ard_3-2-1_53JNN_2023-09-12_final
2023-09-12 10:44:14	ga_s2am_ard_3-2-1_53JMN_2023-09-12_final
2023-09-12 10:44:25	ga_s2am_ard_3-2-1_53JNM_2023-09-12_final
2023-09-12 10:44:29	ga_s2am_ard_3-2-1_53JMM_2023-09-12_final
2023-09-12 10:44:40	ga_s2am_ard_3-2-1_53JNL_2023-09-12_final
2023-09-12 10:44:44	ga_s2am_ard_3-2-1_53JML_2023-09-12_final
2023-09-12 10:44:54	ga_s2am_ard_3-2-1_53JNK_2023-09-12_final
2023-09-12 10:44:58	ga_s2am_ard_3-2-1_53JMK_2023-09-12_final
2023-09-12 10:45:09	ga_s2am_ard_3-2-1_53JNJ_2023-09-12_final
2023-09-12 10:45:13	ga_s2am_ard_3-2-1_53JMJ_2023-09-12_final
2023-09-12 10:45:23	ga_s2am_ard_3-2-1_53JNH_2023-09-12_final
2023-09-12 10:45:27	ga_s2am_ard_3-2-1_53JMH_2023-09-12_final
2023-09-12 10:45:36	ga_s2am_ard_3-2-1_53JNG_2023-09-12_final
2023-09-12 10:45:41	ga_s2am_ard_3-2-1_53JMG_2023-09-12_final
2023-09-12 10:45:46	ga_s2am_ard_3-2-1_53JNF_2023-09-12_final
2023-09-12 10:45:56	ga_s2am_ard_3-2-1_53JMF_2023-09-12_final
2023-09-15 10:50:16	ga_s2am_ard_3-2-1_53LMJ_2023-09-15_nrt
2023-09-15 10:50:31	ga_s2am_ard_3-2-1_53LMH_2023-09-15_final
2023-09-15 10:50:46	ga_s2am_ard_3-2-1_53LMG_2023-09-15_final
2023-09-15 10:51:00	ga_s2am_ard_3-2-1_53LMF_2023-09-15_final
2023-09-15 10:51:15	ga_s2am_ard_3-2-1_53LME_2023-09-15_final
2023-09-15 10:51:29	ga_s2am_ard_3-2-1_53LMD_2023-09-15_final
2023-09-15 10:51:40	ga_s2am_ard_3-2-1_53LNC_2023-09-15_final
2023-09-15 10:51:44	ga_s2am_ard_3-2-1_53LMC_2023-09-15_final
2023-09-15 10:51:55	ga_s2am_ard_3-2-1_53KNB_2023-09-15_final
2023-09-15 10:51:58	ga_s2am_ard_3-2-1_53KMB_2023-09-15_final
2023-09-15 10:52:09	ga_s2am_ard_3-2-1_53KNA_2023-09-15_final
2023-09-15 10:52:13	ga_s2am_ard_3-2-1_53KMA_2023-09-15_final
2023-09-15 10:52:24	ga_s2am_ard_3-2-1_53KNV_2023-09-15_final
2023-09-15 10:52:27	ga_s2am_ard_3-2-1_53KMV_2023-09-15_final
2023-09-15 10:52:38	ga_s2am_ard_3-2-1_53KNU_2023-09-15_final
2023-09-15 10:52:42	ga_s2am_ard_3-2-1_53KMU_2023-09-15_final
2023-09-15 10:52:49	ga_s2am_ard_3-2-1_53KNT_2023-09-15_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