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
2023-04-04 11:17:03	ga_s2am_ard_3-2-1_51HYB_2023-04-04_nrt
2023-04-04 11:17:08	ga_s2am_ard_3-2-1_51HYA_2023-04-04_nrt
2023-04-05 10:41:29	ga_s2am_ard_3-2-1_53LND_2023-04-05_final
2023-04-05 10:41:30	ga_s2am_ard_3-2-1_53LRC_2023-04-05_nrt
2023-04-05 10:41:34	ga_s2am_ard_3-2-1_53LQC_2023-04-05_final
2023-04-05 10:41:37	ga_s2am_ard_3-2-1_53LPC_2023-04-05_final
2023-04-05 10:41:41	ga_s2am_ard_3-2-1_53LNC_2023-04-05_final
2023-04-05 10:41:42	ga_s2am_ard_3-2-1_54KTG_2023-04-05_final
2023-04-05 10:41:45	ga_s2am_ard_3-2-1_53KRB_2023-04-05_final
2023-04-05 10:41:48	ga_s2am_ard_3-2-1_53KQB_2023-04-05_final
2023-04-05 10:41:52	ga_s2am_ard_3-2-1_53KPB_2023-04-05_final
2023-04-05 10:41:54	ga_s2am_ard_3-2-1_54KTF_2023-04-05_final
2023-04-05 10:41:55	ga_s2am_ard_3-2-1_53KNB_2023-04-05_final
2023-04-05 10:41:59	ga_s2am_ard_3-2-1_53KRA_2023-04-05_final
2023-04-05 10:42:03	ga_s2am_ard_3-2-1_53KQA_2023-04-05_final
2023-04-05 10:42:06	ga_s2am_ard_3-2-1_53KPA_2023-04-05_final
2023-04-05 10:42:10	ga_s2am_ard_3-2-1_53KNA_2023-04-05_final
2023-04-05 10:42:12	ga_s2am_ard_3-2-1_53KRV_2023-04-05_final
2023-04-05 10:42:17	ga_s2am_ard_3-2-1_53KQV_2023-04-05_final
2023-04-05 10:42:21	ga_s2am_ard_3-2-1_53KPV_2023-04-05_final
2023-04-05 10:42:22	ga_s2am_ard_3-2-1_53KRU_2023-04-05_final
2023-04-05 10:42:24	ga_s2am_ard_3-2-1_53KNV_2023-04-05_final
2023-04-05 10:42:32	ga_s2am_ard_3-2-1_53KQU_2023-04-05_final
2023-04-05 10:42:36	ga_s2am_ard_3-2-1_53KPU_2023-04-05_final
2023-04-05 10:42:39	ga_s2am_ard_3-2-1_53KNU_2023-04-05_final
2023-04-05 10:42:43	ga_s2am_ard_3-2-1_53KMU_2023-04-05_final
2023-04-05 10:42:46	ga_s2am_ard_3-2-1_53KQT_2023-04-05_final
2023-04-05 10:42:48	ga_s2am_ard_3-2-1_53KPT_2023-04-05_final
2023-04-05 10:42:50	ga_s2am_ard_3-2-1_53KNT_2023-04-05_final
2023-04-05 10:42:51	ga_s2am_ard_3-2-1_53KMT_2023-04-05_final
2023-04-05 10:42:54	ga_s2am_ard_3-2-1_53KQT_2023-04-05_final
2023-04-05 10:42:54	ga_s2am_ard_3-2-1_53KQS_2023-04-05_final
2023-04-05 10:42:55	ga_s2am_ard_3-2-1_53KPT_2023-04-05_final
2023-04-05 10:42:55	ga_s2am_ard_3-2-1_53KPS_2023-04-05_final
2023-04-05 10:42:57	ga_s2am_ard_3-2-1_53KNT_2023-04-05_final
2023-04-05 10:42:58	ga_s2am_ard_3-2-1_53KMT_2023-04-05_final
2023-04-05 10:43:01	ga_s2am_ard_3-2-1_53KQS_2023-04-05_final
2023-04-05 10:43:04	ga_s2am_ard_3-2-1_53KPS_2023-04-05_final
2023-04-05 10:43:08	ga_s2am_ard_3-2-1_53KNS_2023-04-05_final
2023-04-05 10:43:11	ga_s2am_ard_3-2-1_53KMS_2023-04-05_final
2023-04-05 10:43:14	ga_s2am_ard_3-2-1_53KQR_2023-04-05_final
2023-04-05 10:43:19	ga_s2am_ard_3-2-1_53KPR_2023-04-05_final
2023-04-05 10:43:22	ga_s2am_ard_3-2-1_53KNR_2023-04-05_final
2023-04-05 10:43:24	ga_s2am_ard_3-2-1_53KQQ_2023-04-05_final
2023-04-05 10:43:26	ga_s2am_ard_3-2-1_53KMR_2023-04-05_final
2023-04-05 10:43:33	ga_s2am_ard_3-2-1_53KPQ_2023-04-05_final
2023-04-05 10:43:37	ga_s2am_ard_3-2-1_53KNQ_2023-04-05_final
2023-04-05 10:43:40	ga_s2am_ard_3-2-1_53KMQ_2023-04-05_final
2023-04-05 10:43:45	ga_s2am_ard_3-2-1_53KLQ_2023-04-05_final
2023-04-05 10:43:48	ga_s2am_ard_3-2-1_53KPP_2023-04-05_final
2023-04-05 10:43:51	ga_s2am_ard_3-2-1_53KNP_2023-04-05_final
2023-04-05 10:43:55	ga_s2am_ard_3-2-1_53KMP_2023-04-05_final
2023-04-05 10:43:58	ga_s2am_ard_3-2-1_53KLP_2023-04-05_final
2023-04-05 10:44:02	ga_s2am_ard_3-2-1_53JPN_2023-04-05_final
2023-04-05 10:44:06	ga_s2am_ard_3-2-1_53JNN_2023-04-05_final
2023-04-05 10:44:09	ga_s2am_ard_3-2-1_53JMN_2023-04-05_final
2023-04-05 10:44:12	ga_s2am_ard_3-2-1_53JLN_2023-04-05_final
2023-04-05 10:44:16	ga_s2am_ard_3-2-1_53JPM_2023-04-05_final
2023-04-05 10:44:20	ga_s2am_ard_3-2-1_53JNM_2023-04-05_final
2023-04-05 10:44:24	ga_s2am_ard_3-2-1_53JMM_2023-04-05_final
2023-04-05 10:44:27	ga_s2am_ard_3-2-1_53JLM_2023-04-05_final
2023-04-05 10:44:28	ga_s2am_ard_3-2-1_53JPL_2023-04-05_final
2023-04-05 10:44:35	ga_s2am_ard_3-2-1_53JNL_2023-04-05_final
2023-04-05 10:44:38	ga_s2am_ard_3-2-1_53JML_2023-04-05_final
2023-04-05 10:44:42	ga_s2am_ard_3-2-1_53JLL_2023-04-05_final
2023-04-05 10:44:47	ga_s2am_ard_3-2-1_53JKL_2023-04-05_final
2023-04-05 10:44:49	ga_s2am_ard_3-2-1_53JNK_2023-04-05_final
2023-04-05 10:44:53	ga_s2am_ard_3-2-1_53JMK_2023-04-05_final
2023-04-05 10:44:56	ga_s2am_ard_3-2-1_53JLK_2023-04-05_final
2023-04-05 10:45:00	ga_s2am_ard_3-2-1_53JKK_2023-04-05_final
2023-04-05 10:45:04	ga_s2am_ard_3-2-1_53JNJ_2023-04-05_final
2023-04-05 10:45:07	ga_s2am_ard_3-2-1_53JMJ_2023-04-05_final
2023-04-05 10:45:11	ga_s2am_ard_3-2-1_53JLJ_2023-04-05_final
2023-04-05 10:45:14	ga_s2am_ard_3-2-1_53JKJ_2023-04-05_final
2023-04-05 10:45:18	ga_s2am_ard_3-2-1_53JNH_2023-04-05_final
2023-04-05 10:45:22	ga_s2am_ard_3-2-1_53JMH_2023-04-05_final
2023-04-05 10:45:25	ga_s2am_ard_3-2-1_53JLH_2023-04-05_final
2023-04-05 10:45:29	ga_s2am_ard_3-2-1_53JKH_2023-04-05_final
2023-04-05 10:45:31	ga_s2am_ard_3-2-1_53JNG_2023-04-05_final
2023-04-05 10:45:36	ga_s2am_ard_3-2-1_53JMG_2023-04-05_final
2023-04-05 10:45:40	ga_s2am_ard_3-2-1_53JLG_2023-04-05_final
2023-04-05 10:45:41	ga_s2am_ard_3-2-1_53JNF_2023-04-05_final
2023-04-05 10:45:43	ga_s2am_ard_3-2-1_53JKG_2023-04-05_final
2023-04-05 10:45:47	ga_s2am_ard_3-2-1_52JGM_2023-04-05_final
2023-04-05 10:45:51	ga_s2am_ard_3-2-1_53JMF_2023-04-05_final
2023-04-05 10:45:55	ga_s2am_ard_3-2-1_53JLF_2023-04-05_final
2023-04-05 10:45:58	ga_s2am_ard_3-2-1_53JKF_2023-04-05_final
2023-04-05 10:46:00	ga_s2am_ard_3-2-1_52JGL_2023-04-05_final
2023-04-05 10:46:05	ga_s2am_ard_3-2-1_53HME_2023-04-05_final
2023-04-05 10:46:09	ga_s2am_ard_3-2-1_53HLE_2023-04-05_final
2023-04-05 10:46:12	ga_s2am_ard_3-2-1_53HKE_2023-04-05_final
2023-04-05 10:46:15	ga_s2am_ard_3-2-1_52HGK_2023-04-05_final
2023-04-05 10:46:20	ga_s2am_ard_3-2-1_53HMD_2023-04-05_final
2023-04-05 10:46:24	ga_s2am_ard_3-2-1_53HLD_2023-04-05_final
2023-04-05 10:46:27	ga_s2am_ard_3-2-1_53HKD_2023-04-05_nrt
2023-04-05 10:46:28	ga_s2am_ard_3-2-1_52HGJ_2023-04-05_nrt
2023-04-05 10:46:31	ga_s2am_ard_3-2-1_53HMC_2023-04-05_final
2023-04-05 10:46:33	ga_s2am_ard_3-2-1_53HLC_2023-04-05_nrt
2023-04-05 10:46:33	ga_s2am_ard_3-2-1_52HFJ_2023-04-05_nrt
2023-04-05 10:46:34	ga_s2am_ard_3-2-1_53HKC_2023-04-05_nrt

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