LS8_GM_NBART_15_-38_20160101.nc

dataset of product ls8_nbart_geomedian_annual
Indexed by dra547 , created
Archived
for 1st January 2016

Fields 🔗

creation_time 2018-03-12 03:50:07 🔍 format NetCDF 🔍 id 43f516c4-cbc2-4c2d-90bb-35e70272b268 🔍 instrument OLI label • 🔍 lat -33.938 to -32.932 🔍 lon 148.219 to 149.434 🔍 platform LANDSAT_8 product_type surface_reflectance_statistical_summary time 2016-01-01 00:00:00 to 2016-01-01 00:00:00 🔍

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LS8_OLI_PQ_3577_15_-38_20160417235538000000.nc
LS8_OLI_PQ_3577_15_-38_20160512234925000000.nc
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LS8_OLI_PQ_3577_15_-38_20160409000152500000.nc
LS8_OLI_NBART_3577_15_-38_20160104000220000000.nc
LS8_OLI_PQ_3577_15_-38_20160410234930000000.nc
LS8_OLI_PQ_3577_15_-38_20160519235540000000.nc
LS8_OLI_PQ_3577_15_-38_20160426234926000000.nc
LS8_OLI_PQ_3577_15_-38_20160425000145000000.nc
LS8_OLI_PQ_3577_15_-38_20160613234959000000.nc
LS8_OLI_PQ_3577_15_-38_20160613234935000000.nc
LS8_OLI_NBART_3577_15_-38_20160222235011000000.nc
LS8_OLI_PQ_3577_15_-38_20160706235558000000.nc
LS8_OLI_PQ_3577_15_-38_20160503235606000000.nc
LS8_OLI_PQ_3577_15_-38_20160629235007500000.nc
LS8_OLI_NBART_3577_15_-38_20160221000208500000.nc
LS8_OLI_PQ_3577_15_-38_20160325234937000000.nc
LS8_OLI_PQ_3577_15_-38_20160528234932000000.nc
LS8_OLI_PQ_3577_15_-38_20160325235001000000.nc
LS8_OLI_PQ_3577_15_-38_20160528234956000000.nc
LS8_OLI_PQ_3577_15_-38_20160629234944000000.nc
LS8_OLI_PQ_3577_15_-38_20160410234954000000.nc
LS8_OLI_PQ_3577_15_-38_20160519235604000000.nc
LS8_OLI_PQ_3577_15_-38_20160401235607000000.nc
LS8_OLI_PQ_3577_15_-38_20160426234950000000.nc
LS8_OLI_PQ_3577_15_-38_20160401235543000000.nc
... 128 more
LS8_GM_NBART_15_-38_20160101.nc
ga_ls_landcover_class_cyear_2_1-0-0_au_2016-01-01

Location 🔗

file:///g/data/fk4/datacube/002/stats/geomedian/annual/ls8_gm/15_-38/LS8_GM_NBART_15_-38_20160101.nc

Metadata Document 🔗

RAW As Stac
id: 43f516c4-cbc2-4c2d-90bb-35e70272b268
product_type: surface_reflectance_statistical_summary
creation_dt: 2018-03-12T03:50:07.054298
platform:
code: LANDSAT_8
instrument:
name: OLI
format:
name: NetCDF
extent:
coord:
ll:
lat: -33.938192607249526
lon: 148.3562370801783
lr:
lat: -33.819963025530406
lon: 149.4335801428971
ul:
lat: -33.049493989487345
lon: 148.2185574490684
ur:
lat: -32.932445880425675
lon: 149.28704823354514
to_dt: 2016-01-01T00:00:00
from_dt: 2016-01-01T00:00:00
center_dt: 2016-01-01T00:00:00
grid_spatial:
projection:
valid_data:
type: Polygon
coordinates: [
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lineage:
algorithm:
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parameters:
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y: 100000.0
resolution:
x: 25
y: -25
dimension_order: [
time
y
x
]
location: /g/data/u46/users/ia1511/Work/data/geomedian/output
computation:
chunking:
x: 2000
y: 2000
date_ranges:
end_date: 2019-01-01
step_size: 1y
start_date: 2016-01-01
stats_duration: 1y
output_products: [
name: ls8_nbart_geomedian_annual
metadata:
platform: LANDSAT-8
statistic: new_geomedian
product_type: surface_reflectance_statistical_summary
output_params:
zlib: True
fletcher32: True
statistic_args:
num_threads: 3
file_path_template: ls8_gm/ls8_gm_nbart_{x}_{y}_{epoch_start:%Y%m%d}.nc
]
global_attributes:
title: Surface Reflectance Geometric Median
source: geometric median v2017 of surface reflectance NBAR+T
license: CC BY Attribution 4.0 International License
summary: The surface reflectance geometric median (geomedian) is a pixel composite mosaic of a time series of earth observations. The value of a pixel in a geomedian image is the statistical median of all observations for that pixel from a period of time. For example the 2016 Landsat 8 geomedian image over an area will be the median of Landsat 8 pixels observed for that area in 2016. The geometric median products are available as annual datasets for years since 1987. Each annual composite is created from one calendar year of surface reflectance and for one sensor, i.e. Thematic Mapper (Landsat 5), Enhanced Thematic Mapper (Landsat 7) or Operational Land Imager (Landsat 8). The algorithm considers concurrent spectral measurements as a multi-dimensional set and outputs a multi-dimensional median that maintains the relationships among the measurements. This provides a median of the physical conditions measured by the earth observations used to create it, and provides a good representation of a typical pixel observation devoid of outliers. More information on the algorithm can be found in the article below: - Roberts, D., Mueller, N., & Mcintyre, A. (2017). High-Dimensional Pixel Composites From Earth Observation Time Series. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6254-6264. https://doi.org/10.1109/tgrs.2017.2723896
keywords: AU/GA,NASA/GSFC/SED/ESD/LANDSAT,REFLECTANCE,ETM+,TM,OLI,EARTH SCIENCE
platform: LANDSAT-5,LANDSAT-7,LANDSAT-8
instrument: TM,ETM+,OLI
references: - Roberts, D., Mueller, N., & Mcintyre, A. (2017). High-Dimensional Pixel Composites From Earth Observation Time Series. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6254-6264. https://doi.org/10.1109/tgrs.2017.2723896
description: Surface Reflectance Geometric Median 25 metre, 100km tile, Australian Albers Equal Area projection (EPSG:3577)
institution: Commonwealth of Australia (Geoscience Australia)
cdm_data_type: Grid
product_suite: Surface Reflectance Geometric Median 25m
publisher_url: http://www.ga.gov.au
acknowledgment: - Landsat data is provided by the United States Geological Survey (USGS) through direct reception of the data at Geoscience Australias satellite reception facility or download.
publisher_name: Section Leader, Operations Section, NEMO, Geoscience Australia
product_version: 2
publisher_email: earth.observation@ga.gov.au
keywords_vocabulary: GCMD
coverage_content_type: modelResult
machine:
uname: Linux r3532 3.10.0-693.17.1.el6.x86_64 #1 SMP Fri Feb 9 03:25:58 UTC 2018 x86_64
hostname: r3532
software_versions:
python:
version: 3.6.3 | packaged by conda-forge | (default, Nov 4 2017, 10:10:56) [GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]
datacube:
version: 1.5.1+421.g2306bf4
repo_url: https://github.com/opendatacube/datacube-core.git
source_datasets:
statistics:
step: 1y
period: 1y