LS5_GM_NBART_3_-25_20060101.nc
dataset of product ls5_nbart_geomedian_annual
Indexed by dra547
,
created
Archived
for
1st January 2006
Fields 🔗
creation_time
2018-07-06 11:53:48 🔍
format
NetCDF 🔍
id
afea6393-7c89-4852-a382-d372596ae13e 🔍
instrument
TM
label
• 🔍
lat
-23.159 to -22.241 🔍
lon
134.936 to 135.944 🔍
platform
LANDSAT_5
product_type
surface_reflectance_statistical_summary
time
2006-01-01 00:00:00 to 2006-01-01 00:00:00 🔍
Location 🔗
Metadata Document 🔗
id:
afea6393-7c89-4852-a382-d372596ae13e
product_type:
surface_reflectance_statistical_summary
creation_dt:
2018-07-06T11:53:48.342419
platform:
code:
LANDSAT_5
instrument:
name:
TM
format:
name:
NetCDF
extent:
coord:
ll:
lat:
-23.15949630427679
lon:
134.95857063827393
lr:
lat:
-23.135340964238043
lon:
135.94421257083937
ul:
lat:
-22.26495328845012
lon:
134.93591136060894
ur:
lat:
-22.24095160798584
lon:
135.91401269906405
to_dt:
2006-01-01T00:00:00
from_dt:
2006-01-01T00:00:00
center_dt:
2006-01-01T00:00:00
grid_spatial:
projection:
valid_data:
type:
Polygon
coordinates:
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location:
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computation:
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y:
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name:
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metadata:
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platform:
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instrument:
name:
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statistic:
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product_type:
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file_path_template:
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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:
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platform:
LANDSAT_5,LANDSAT_7,LANDSAT_8
instrument:
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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
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hostname:
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software_versions:
python:
version:
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datacube:
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repo_url:
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source_datasets:
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step:
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period:
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