LS5_GM_NBART_3_-17_19880101.nc
dataset of product ls5_nbart_geomedian_annual
Indexed by dra547
,
created
Archived
for
1st January 1988
Fields 🔗
creation_time
2018-07-05 17:17:15 🔍
format
NetCDF 🔍
id
a83bf760-5a13-4c17-b206-6f4f8b3318ff 🔍
instrument
TM
label
• 🔍
lat
-15.957 to -15.024 🔍
lon
134.766 to 135.715 🔍
platform
LANDSAT_5
product_type
surface_reflectance_statistical_summary
time
1988-01-01 00:00:00 to 1988-01-01 00:00:00 🔍
Location 🔗
Metadata Document 🔗
id:
a83bf760-5a13-4c17-b206-6f4f8b3318ff
product_type:
surface_reflectance_statistical_summary
creation_dt:
2018-07-05T17:17:15.516234
platform:
code:
LANDSAT_5
instrument:
name:
TM
format:
name:
NetCDF
extent:
coord:
ll:
lat:
-15.957121434393194
lon:
134.78651891586563
lr:
lat:
-15.933997405893452
lon:
135.7149004545405
ul:
lat:
-15.047157472075321
lon:
134.76640909288315
ur:
lat:
-15.024132478938641
lon:
135.6880972018651
to_dt:
1988-01-01T00:00:00
from_dt:
1988-01-01T00:00:00
center_dt:
1988-01-01T00:00:00
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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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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period:
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