Search ga_s2am_ard_3 across October 2019

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels. For these ARD products, this value comes from the Fmask algorithm. from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels. For these ARD products, this value comes from the Fmask algorithm. 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 (e.g. ‘epsg:32654’) :

One of - final|interim|nrt (near real time):

ID of a dataset type:

Ground sampling distance of the sensor’s best resolution band in metres; represents the size (or spatial resolution) of one pixel. from:

Ground sampling distance of the sensor’s best resolution band in metres; represents the size (or spatial resolution) of one pixel. to (exclusive):

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north from:

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north to (exclusive):

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon. from:

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon. 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 from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm 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 from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm 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 from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm 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 from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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) 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. For Sentinel it is MGRS code. :

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to s3cloudless to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud land pixels according to s3cloudless to (exclusive):

Unique identifier for a datastrip relative to a given Datatake. (e.g. ‘S2A_OPER_MSI_L1C_DS_SGS__20161214T040601_S20161214T005840_N02.04’) :

ESA product URI, with the '.SAFE' ending removed. (e.g. 'S2A_MSIL1C_20220303T000731_N0400_R073_T56LNM_20220303T012845') :

Granule ID according to the ESA naming convention (e.g. ‘S2A_OPER_MSI_L1C_TL_SGS__20161214T040601_A007721_T53KRB_N02.04’) :

Acquisition time range From

Acquisition time range to (exclusive)

Dataset URI:

Add field

First 100 results (query limit)

Time (local)	Label
2019-10-01 10:03:40	ga_s2am_ard_3-2-1_54KZU_2019-10-01_final
2019-10-01 11:42:29	ga_s2am_ard_3-2-1_51KVA_2019-10-01_final
2019-10-01 11:44:39	ga_s2am_ard_3-2-1_50JPT_2019-10-01_final
2019-10-02 11:12:19	ga_s2am_ard_3-2-1_52KEF_2019-10-02_final
2019-10-02 11:12:22	ga_s2am_ard_3-2-1_52KDF_2019-10-02_final
2019-10-02 11:14:11	ga_s2am_ard_3-2-1_51KZP_2019-10-02_final
2019-10-03 10:42:12	ga_s2am_ard_3-2-1_53KNA_2019-10-03_final
2019-10-04 10:10:55	ga_s2am_ard_3-2-1_55LBE_2019-10-04_final
2019-10-04 10:10:57	ga_s2am_ard_3-2-1_54LZK_2019-10-04_final
2019-10-04 10:13:35	ga_s2am_ard_3-2-1_54KZU_2019-10-04_final
2019-10-04 10:13:58	ga_s2am_ard_3-2-1_54JXT_2019-10-04_final
2019-10-05 09:41:40	ga_s2am_ard_3-2-1_56KMF_2019-10-05_final.stac-item
2019-10-05 09:43:29	ga_s2am_ard_3-2-1_55KHP_2019-10-05_final
2019-10-05 11:22:14	ga_s2am_ard_3-2-1_52KDF_2019-10-05_final
2019-10-05 11:24:28	ga_s2am_ard_3-2-1_51JXN_2019-10-05_final
2019-10-06 10:52:00	ga_s2am_ard_3-2-1_53KLB_2019-10-06_final
2019-10-06 10:52:11	ga_s2am_ard_3-2-1_53KMA_2019-10-06_final
2019-10-06 10:53:59	ga_s2am_ard_3-2-1_52KHU_2019-10-06_final
2019-10-06 10:54:10	ga_s2am_ard_3-2-1_53JLN_2019-10-06_final
2019-10-06 10:54:23	ga_s2am_ard_3-2-1_52JFT_2019-10-06_final
2019-10-07 10:20:51	ga_s2am_ard_3-2-1_54LZK_2019-10-07_final
2019-10-07 10:21:43	ga_s2am_ard_3-2-1_54KXG_2019-10-07_final
2019-10-07 10:23:53	ga_s2am_ard_3-2-1_53KRP_2019-10-07_final
2019-10-08 11:32:14	ga_s2am_ard_3-2-1_51KXB_2019-10-08_final
2019-10-08 11:32:30	ga_s2am_ard_3-2-1_51KVA_2019-10-08_final
2019-10-08 11:32:35	ga_s2am_ard_3-2-1_51KWA_2019-10-08_final
2019-10-08 11:32:37	ga_s2am_ard_3-2-1_51KVA_2019-10-08_final
2019-10-08 11:34:18	ga_s2am_ard_3-2-1_51JXN_2019-10-08_final
2019-10-09 11:04:17	ga_s2am_ard_3-2-1_52JFT_2019-10-09_final
2019-10-10 10:31:48	ga_s2am_ard_3-2-1_54KUG_2019-10-10_final
2019-10-10 10:33:48	ga_s2am_ard_3-2-1_53KRP_2019-10-10_final
2019-10-10 12:14:48	ga_s2am_ard_3-2-1_49JFN_2019-10-10_final
2019-10-11 10:03:41	ga_s2am_ard_3-2-1_54KZU_2019-10-11_final
2019-10-11 11:42:30	ga_s2am_ard_3-2-1_51KVA_2019-10-11_final
2019-10-11 11:44:40	ga_s2am_ard_3-2-1_50JPT_2019-10-11_final
2019-10-12 11:12:20	ga_s2am_ard_3-2-1_52KEF_2019-10-12_final
2019-10-12 11:12:23	ga_s2am_ard_3-2-1_52KDF_2019-10-12_final
2019-10-12 11:14:12	ga_s2am_ard_3-2-1_51KZP_2019-10-12_final
2019-10-13 10:42:13	ga_s2am_ard_3-2-1_53KNA_2019-10-13_final
2019-10-14 10:10:56	ga_s2am_ard_3-2-1_55LBE_2019-10-14_final
2019-10-14 10:10:58	ga_s2am_ard_3-2-1_54LZK_2019-10-14_final
2019-10-14 10:13:36	ga_s2am_ard_3-2-1_54KZU_2019-10-14_final
2019-10-14 10:13:59	ga_s2am_ard_3-2-1_54JXT_2019-10-14_final
2019-10-14 11:54:36	ga_s2am_ard_3-2-1_50JPT_2019-10-14_final
2019-10-15 09:41:41	ga_s2am_ard_3-2-1_56KMF_2019-10-15_final.stac-item
2019-10-15 09:43:30	ga_s2am_ard_3-2-1_55KHP_2019-10-15_final
2019-10-15 11:22:15	ga_s2am_ard_3-2-1_52KDF_2019-10-15_final
2019-10-15 11:24:29	ga_s2am_ard_3-2-1_51JXN_2019-10-15_final
2019-10-16 10:52:00	ga_s2am_ard_3-2-1_53KLB_2019-10-16_final
2019-10-16 10:52:12	ga_s2am_ard_3-2-1_53KMA_2019-10-16_final
2019-10-16 10:54:00	ga_s2am_ard_3-2-1_52KHU_2019-10-16_final
2019-10-16 10:54:11	ga_s2am_ard_3-2-1_53JLN_2019-10-16_final
2019-10-16 10:54:24	ga_s2am_ard_3-2-1_52JFT_2019-10-16_final
2019-10-17 10:20:52	ga_s2am_ard_3-2-1_54LZK_2019-10-17_final
2019-10-17 10:21:44	ga_s2am_ard_3-2-1_54KXG_2019-10-17_final
2019-10-17 10:23:53	ga_s2am_ard_3-2-1_53KRP_2019-10-17_final
2019-10-18 11:32:14	ga_s2am_ard_3-2-1_51KXB_2019-10-18_final
2019-10-18 11:32:32	ga_s2am_ard_3-2-1_51KWA_2019-10-18_final
2019-10-18 11:32:35	ga_s2am_ard_3-2-1_51KVA_2019-10-18_final
2019-10-18 11:34:18	ga_s2am_ard_3-2-1_51JXN_2019-10-18_final
2019-10-19 11:04:18	ga_s2am_ard_3-2-1_52JFT_2019-10-19_final
2019-10-20 10:31:49	ga_s2am_ard_3-2-1_54KUG_2019-10-20_final
2019-10-20 10:33:49	ga_s2am_ard_3-2-1_53KRP_2019-10-20_final
2019-10-20 12:14:48	ga_s2am_ard_3-2-1_49JFN_2019-10-20_final
2019-10-21 10:03:41	ga_s2am_ard_3-2-1_54KZU_2019-10-21_final
2019-10-21 11:42:24	ga_s2am_ard_3-2-1_51KWA_2019-10-21_final
2019-10-21 11:42:31	ga_s2am_ard_3-2-1_51KVA_2019-10-21_final
2019-10-21 11:44:41	ga_s2am_ard_3-2-1_50JPT_2019-10-21_final
2019-10-22 11:12:20	ga_s2am_ard_3-2-1_52KEF_2019-10-22_final
2019-10-22 11:12:24	ga_s2am_ard_3-2-1_52KDF_2019-10-22_final
2019-10-22 11:14:12	ga_s2am_ard_3-2-1_51KZP_2019-10-22_final
2019-10-23 10:42:13	ga_s2am_ard_3-2-1_53KNA_2019-10-23_final
2019-10-24 10:10:57	ga_s2am_ard_3-2-1_55LBE_2019-10-24_final
2019-10-24 10:10:58	ga_s2am_ard_3-2-1_54LZK_2019-10-24_final
2019-10-24 10:13:36	ga_s2am_ard_3-2-1_54KZU_2019-10-24_final
2019-10-24 10:13:59	ga_s2am_ard_3-2-1_54JXT_2019-10-24_final
2019-10-24 11:54:31	ga_s2am_ard_3-2-1_50JPT_2019-10-24_final
2019-10-24 11:54:37	ga_s2am_ard_3-2-1_50JPT_2019-10-24_final
2019-10-25 09:41:41	ga_s2am_ard_3-2-1_56KMF_2019-10-25_final.stac-item
2019-10-25 09:43:30	ga_s2am_ard_3-2-1_55KHP_2019-10-25_final
2019-10-25 11:22:15	ga_s2am_ard_3-2-1_52KDF_2019-10-25_final
2019-10-25 11:24:29	ga_s2am_ard_3-2-1_51JXN_2019-10-25_final
2019-10-26 10:52:01	ga_s2am_ard_3-2-1_53KLB_2019-10-26_final
2019-10-26 10:52:12	ga_s2am_ard_3-2-1_53KMA_2019-10-26_final
2019-10-26 10:54:00	ga_s2am_ard_3-2-1_52KHU_2019-10-26_final
2019-10-26 10:54:11	ga_s2am_ard_3-2-1_53JLN_2019-10-26_final
2019-10-26 10:54:24	ga_s2am_ard_3-2-1_52JFT_2019-10-26_final
2019-10-27 10:20:52	ga_s2am_ard_3-2-1_54LZK_2019-10-27_final
2019-10-27 10:21:44	ga_s2am_ard_3-2-1_54KXG_2019-10-27_final
2019-10-27 10:23:54	ga_s2am_ard_3-2-1_53KRP_2019-10-27_final
2019-10-28 11:32:15	ga_s2am_ard_3-2-1_51KXB_2019-10-28_final
2019-10-28 11:32:32	ga_s2am_ard_3-2-1_51KWA_2019-10-28_final
2019-10-28 11:32:35	ga_s2am_ard_3-2-1_51KVA_2019-10-28_final
2019-10-28 11:34:18	ga_s2am_ard_3-2-1_51JXN_2019-10-28_final
2019-10-29 11:04:18	ga_s2am_ard_3-2-1_52JFT_2019-10-29_final
2019-10-30 10:31:49	ga_s2am_ard_3-2-1_54KUG_2019-10-30_final
2019-10-30 10:33:49	ga_s2am_ard_3-2-1_53KRP_2019-10-30_final
2019-10-30 12:14:48	ga_s2am_ard_3-2-1_49JFN_2019-10-30_final
2019-10-31 10:03:41	ga_s2am_ard_3-2-1_54KZU_2019-10-31_final
2019-10-31 11:42:31	ga_s2am_ard_3-2-1_51KVA_2019-10-31_final

Return to ga_s2am_ard_3 across October 2019 overview

ga_s2am_ard_3: Geoscience Australia Sentinel 2A MSI Analysis Ready Data Collection 3

Baseline satellite data

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

2019

October

All days