LS5_GM_NBART_12_-12

creation_dt 2018-07-06T20:30:04.509651 creation_time 2018-07-06 20:30:04 format NetCDF id 0d55e0cd-b890-4c8d-b3ab-ef7d56f057a9 instrument TM label • lat -10.94 to -9.935 lon 142.657 to 143.621 platform LANDSAT_5 product_type surface_reflectance_statistical_summary time 2007-01-01 00:00:00 to 2007-01-01 00:00:00

Location 🔗

file:///g/data/fk4/datacube/002/stats/geomedian/annual/ls5_gm/12_-12/LS5_GM_NBART_12_-12_20070101.nc

Metadata Document 🔗

RAW As Stac

id: 0d55e0cd-b890-4c8d-b3ab-ef7d56f057a9

product_type: surface_reflectance_statistical_summary

creation_dt: 2018-07-06T20:30:04.509651

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to_dt: 2007-01-01T00:00:00

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image:

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metadata:

format:

platform:

code: LANDSAT_5

instrument:

statistic: new_geomedian

product_type: surface_reflectance_statistical_summary

output_params:

zlib: True

fletcher32: True

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num_threads: 3

file_path_template: ls5_gm/{x}_{y}/LS5_GM_NBART_{x}_{y}_{epoch_start:%Y%m%d}.nc

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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: 2.0

publisher_email: earth.observation@ga.gov.au

keywords_vocabulary: GCMD

coverage_content_type: modelResult

machine:

uname: Linux r2693 3.10.0-862.3.3.el6.x86_64 #1 SMP Wed Jun 27 18:22:35 AEST 2018 x86_64

hostname: r2693

software_versions:

python:

version: 3.6.5 | packaged by conda-forge | (default, Apr 6 2018, 13:39:56) [GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]

datacube:

version: 1.6rc2+2.g6aa9d16

repo_url: https://github.com/opendatacube/datacube-core.git

source_datasets:

statistics:

step: 1y

period: 1y