LS5_TMAD_NBART_-13_-30_20030101.nc

dataset of product ls5_nbart_tmad_annual
Indexed by dra547 , created
Archived
for 1st January 2003

Fields 🔗

creation_time 2018-12-24 18:54:17 format NetCDF id adaba026-eaa0-440a-b717-24a3fd2dfe40 instrument TM label • lat -27.138 to -26.162 🔍 lon 118.71 to 119.823 🔍 platform LANDSAT_5 product_type surface_reflectance_triple_mad time 2003-01-01 00:00:00 to 2003-01-01 00:00:00 🔍

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LS5_TM_PQ_3577_-13_-30_2003_v1501871971.nc#part=41
LS5_TM_PQ_3577_-13_-30_2003_v1501871971.nc#part=8
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LS5_TM_NBART_3577_-13_-30_2003_v1493738744.nc#part=12
LS5_TM_PQ_3577_-13_-30_2003_v1501871971.nc#part=30
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LS5_TM_PQ_3577_-13_-30_2003_v1501871971.nc#part=24
LS5_TM_NBART_3577_-13_-30_2003_v1493738744.nc#part=33
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LS5_TM_PQ_3577_-13_-30_2003_v1501871971.nc#part=26
LS5_TM_NBART_3577_-13_-30_2003_v1493738744.nc#part=44
LS5_TM_NBART_3577_-13_-30_2003_v1493738744.nc#part=42
LS5_TM_NBART_3577_-13_-30_2003_v1493738744.nc#part=3
LS5_TM_NBART_3577_-13_-30_2003_v1493738744.nc#part=9
LS5_TM_PQ_3577_-13_-30_2003_v1501871971.nc#part=34
LS5_TM_PQ_3577_-13_-30_2003_v1501871971.nc#part=33
LS5_TM_NBART_3577_-13_-30_2003_v1493738744.nc#part=4
LS5_TM_PQ_3577_-13_-30_2003_v1501871971.nc#part=22
LS5_TM_NBART_3577_-13_-30_2003_v1493738744.nc#part=20
LS5_TM_NBART_3577_-13_-30_2003_v1493738744.nc#part=15
LS5_TM_NBART_3577_-13_-30_2003_v1493738744.nc#part=36
LS5_TM_NBART_3577_-13_-30_2003_v1493738744.nc#part=19
LS5_TM_NBART_3577_-13_-30_2003_v1493738744.nc#part=41
LS5_TM_PQ_3577_-13_-30_2003_v1501871971.nc#part=3
LS5_TM_NBART_3577_-13_-30_2003_v1493738744.nc#part=40
LS5_TM_PQ_3577_-13_-30_2003_v1501871971.nc#part=12
LS5_TM_PQ_3577_-13_-30_2003_v1501871971.nc#part=5
... 64 more
LS5_TMAD_NBART_-13_-30_20030101.nc
ga_ls_landcover_class_cyear_2_1-0-0_au_2003-01-01

Location 🔗

file:///g/data/fk4/datacube/002/stats/tmad/annual/ls5_mad/-13_-30/LS5_TMAD_NBART_-13_-30_20030101.nc

Metadata Document 🔗

RAW As Stac
id: adaba026-eaa0-440a-b717-24a3fd2dfe40
product_type: surface_reflectance_triple_mad
creation_dt: 2018-12-24T18:54:17.088144
platform:
code: LANDSAT_5
instrument:
name: TM
format:
name: NetCDF
extent:
coord:
ll:
lat: -27.049059630561565
lon: 118.71049614489176
lr:
lat: -27.138098267617266
lon: 119.72622817660617
ul:
lat: -26.162259915179945
lon: 118.81561728601834
ur:
lat: -26.250624050843626
lon: 119.82341686713909
to_dt: 2003-01-01T00:00:00
from_dt: 2003-01-01T00:00:00
center_dt: 2003-01-01T00:00:00
grid_spatial:
projection:
valid_data:
type: Polygon
coordinates: [
[
[
-1300000.0
-2940360.380411633
]
[
-1300000.0
-2900000.0
]
[
-1200000.0
-2900000.0
]
[
-1200000.0
-3000000.0
]
[
-1300000.0
-3000000.0
]
[
-1300000.0
-2940360.380411633
]
]
]
geo_ref_points:
ll:
x: -1300000.0
y: -3000000.0
lr:
x: -1200000.0
y: -3000000.0
ul:
x: -1300000.0
y: -2900000.0
ur:
x: -1200000.0
y: -2900000.0
spatial_reference: EPSG:3577
image:
bands:
edev:
path:
layer: edev
sdev:
path:
layer: sdev
bcdev:
path:
layer: bcdev
lineage:
algorithm:
name: datacube-stats
version: 0.9b4
repo_url: https://github.com/GeoscienceAustralia/datacube-stats.git
parameters:
configuration_file:
sources: [
masks: [
flags:
cloud_acca: no_cloud
contiguous: True
cloud_fmask: no_cloud
nir_saturated: False
red_saturated: False
blue_saturated: False
green_saturated: False
swir1_saturated: False
swir2_saturated: False
cloud_shadow_acca: no_cloud_shadow
cloud_shadow_fmask: no_cloud_shadow
product: ls5_pq_albers
group_by: solar_day
fuse_func: datacube.helpers.ga_pq_fuser
measurement: pixelquality
]
product: ls5_nbart_albers
group_by: solar_day
measurements: [
blue
green
red
nir
swir1
swir2
]
]
storage:
crs: EPSG:3577
driver: NetCDF CF
chunking:
x: 256
y: 256
time: 1
tile_size:
x: 100000.0
y: 100000.0
resolution:
x: 25
y: -25
dimension_order: [
time
y
x
]
location: /g/data/u46/users/bt2744/work/data/mad-annual/prod/ls5/
computation:
chunking:
x: 1000
y: 200
date_ranges:
end_date: 2020-01-01
step_size: 1y
start_date: 1986-01-01
stats_duration: 1y
input_region:
tiles: [
[
-13
-30
]
]
filter_product:
var_attributes:
output_products: [
name: ls5_nbart_tmad_annual
metadata:
format:
name: NetCDF
platform:
code: LANDSAT_5
instrument:
name: TM
statistic: external
product_type: surface_reflectance_triple_mad
output_params:
zlib: True
fletcher32: True
statistic_args:
impl: model.TernaryMAD
num_threads: 3
file_path_template: ls5_ternary_mad-{epoch_start:%Y%m%d}-{epoch_end:%Y%m%d}_{x}_{y}.nc
]
global_attributes:
title: LS5 Surface Reflectance Triple Median Absolute Deviation 2.1.0
cmi_id: SR_TMAD_25_2.1.0
source: datacube-2nd-order-stats
license: CC BY Attribution 4.0 International License
summary: This product provides 'second order' statistical techniques that follow from the geometric median, useful for environmental characterisation and change detection. The Median Absolute Deviation (MAD) is a measure of variance in a dataset through comparison to the median. It is similar in concept to the way that the standard deviation in statistics can be used to understand variance compared to the mean.
keywords: AU/GA,NASA/GSFC/SED/ESD/LANDSAT,TM,EARTH SCIENCE
references: Roberts, D., Dunn, B. and Mueller, N. (2018). Open Data Cube Products Using High-Dimensional Statistics of Time Series. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.
institution: Commonwealth of Australia (Geoscience Australia)
cdm_data_type: Grid
product_suite: Surface Reflectance Triple Median Absolute Deviation
publisher_url: http://www.ga.gov.au
publisher_name: Section Leader, Operations Section, NEMO, Geoscience Australia
acknowledgments: The high-dimensional statistics algorithms incorporated in this product is the work of Dr Dale Roberts, Australian National University.
product_version: 2.1.0
publisher_email: earth.observation@ga.gov.au
keywords_vocabulary: GCMD
machine:
uname: Linux r4405 3.10.0-862.14.4.el6.x86_64 #1 SMP Thu Sep 27 07:25:17 AEST 2018 x86_64
hostname: r4405
software_versions:
python:
version: 3.6.6 | packaged by conda-forge | (default, Oct 12 2018, 14:08:43) [GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]
datacube:
version: 1.6.1+72.gdfc6b47a
repo_url: https://github.com/opendatacube/datacube-core.git
source_datasets:
statistics:
step: 1y
period: 1y