LS_PQ_COUNT_3577_0_-30_20150101_20160101.nc
dataset of product pq_count_annual_summary
Indexed by dra547
,
created
Region
0_-30
for
1st January 2015
Fields 🔗
creation_time
2017-10-27 21:00:13
format
NetCDF
id
1100384f-17ae-4201-ad6c-e1247092c5a9
instrument
•
label
•
lat
-27.652 to -26.757 🔍
lon
132.0 to 133.026 🔍
platform
•
product_type
pq_count
time
2015-01-01 00:00:00 to 2015-01-01 00:00:00 🔍
Location 🔗
Metadata Document 🔗
id:
1100384f-17ae-4201-ad6c-e1247092c5a9
product_type:
pq_count
creation_dt:
2017-10-27T21:00:13.283619
format:
name:
NetCDF
extent:
coord:
ll:
lat:
-27.652325063256658
lon:
131.99999999999997
lr:
lat:
-27.648745929399436
lon:
133.02595073544552
ul:
lat:
-26.760893742362725
lon:
131.99999999999997
ur:
lat:
-26.757342516766744
lon:
133.01777758292212
to_dt:
2015-01-01T00:00:00
from_dt:
2015-01-01T00:00:00
center_dt:
2015-01-01T00:00:00
grid_spatial:
projection:
valid_data:
type:
Polygon
coordinates:
[
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]
,
[
0.0
-2905951.4576705107
]
,
[
]
0.0
-2905949.1482947553
]
,
geo_ref_points:
ll:
x:
0.0
y:
-3000000.0
lr:
x:
100000.0
y:
-3000000.0
ul:
x:
0.0
y:
-2900000.0
ur:
x:
100000.0
y:
-2900000.0
spatial_reference:
EPSG:3577
image:
bands:
clear_observation_count:
path:
layer:
clear_observation_count
total_observation_count:
path:
layer:
total_observation_count
lineage:
algorithm:
name:
datacube-stats
version:
0.9a6
repo_url:
https://github.com/GeoscienceAustralia/agdc_statistics.git
parameters:
configuration_file:
sources:
[
]
product:
ls5_pq_albers
group_by:
solar_day
fuse_func:
datacube.helpers.ga_pq_fuser
mask_nodata:
False
measurements:
[
pixelquality
]
product:
ls7_pq_albers
group_by:
solar_day
fuse_func:
datacube.helpers.ga_pq_fuser
mask_nodata:
False
measurements:
[
pixelquality
]
product:
ls8_pq_albers
group_by:
solar_day
fuse_func:
datacube.helpers.ga_pq_fuser
mask_nodata:
False
measurements:
[
pixelquality
]
storage:
crs:
EPSG:3577
driver:
NetCDFCF
chunking:
x:
200
y:
200
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/ia1511/Work/data/PQ-stats/annual/
computation:
chunking:
x:
4000
y:
4000
date_ranges:
end_date:
2017-01-01
step_size:
12m
start_date:
1986-01-01
stats_duration:
12m
var_attributes:
clear_observation_count:
long_name:
Number of clear observations for a pixel
coverage_content_type:
qualityInformation
total_observation_count:
long_name:
Total number of observations for a pixel
coverage_content_type:
qualityInformation
output_products:
[
]
name:
pq_count_annual_summary
statistic:
masked_multi_count
product_type:
pq_count
output_params:
zlib:
True
fletcher32:
True
statistic_args:
vars:
[
]
name:
clear_observation_count
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
name:
total_observation_count
flags:
contiguous:
True
simple:
True
nodata_flags:
contiguous:
False
file_path_template:
LS_PQ_COUNT/{x}_{y}/LS_PQ_COUNT_3577_{x}_{y}_{epoch_start:%Y%m%d}_{epoch_end:%Y%m%d}.nc
global_attributes:
title:
Pixel Quality Statistics 25m 2.0.0
source:
Remotely observed surface reflectance and modelled cloud/shadow
license:
CC BY Attribution 4.0 International License
summary:
The Pixel Quality (PQ) product is an assessment of whether an image pixel represents an un-obscured unsaturated observation of the Earth's surface and whether the pixel is represented in each spectral band. The PQ product allows users to produce masks which can be used to exclude affected pixels from which don't meet their quality criteria from further analysis. The capacity to automatically exclude such pixels from analysis is essential for emerging multi-temporal analysis techniques that make use of every quality assured pixel within a time series of observations. The PQ-STATS is a countof how many times a pixel contains a clear observation of the surface (land or sea). The count of clean observations provides users with an understanding of how many observations are available for analysis at a particular location for a particular period of time. PQ-STATS is available as a per year summary and as a 'whole of archive' summary (1987 to present).
keywords:
AU/GA,NASA/GSFC/SED/ESD/LANDSAT,ETM+,TM,OLI,EARTH SCIENCE
platform:
LANDSAT-5,LANDSAT-7,LANDSAT-8
instrument:
TM,ETM+,OLI
references:
- Berk, A., Anderson, G.P., Acharya, P.K., Hoke, M.L., Chetwynd, J.H., Bernstein, L.S., Shettle, E.P., Matthew, M.W., and Adler-Golden, S.M. (2003) Modtran 4 Version 3 Revision 1 User s manual. Airforce Research Laboratory, Hanscom, MA, USA. - Chander, G., Markham, B.L., and Helder, D.L. (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 113, 893-903. - Edberg, R., and Oliver, S. (2013) Projection-Independent Earth-Solar-Sensor Geometry for Surface Reflectance Correction. Submitted to IGARSS 2013, Melbourne. - Forrest, R.B. (1981) Simulation of orbital image-sensor geometry, Photogrammetric Engineering and Remote Sensing 47, 1187-93. - GA and CSIRO (2010) 1 second SRTM Derived Digital Elevation Models User Guide. Version 1.03. GA, Canberra. - Irish, R. (2000) Landsat 7 Automatic Cloud Cover Assessment, sourced: http://landsathandbook.gsfc.nasa.gov/pdfs/ACCA_SPIE_paper.pdf, last accessed 12/11/2012. - Irons, J.R., Dwyer, J.L., and Barsi, J.A. (2012) The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sensing of Environment (2012), doi:10.1016/j.rse.2011.08.026 - Kalnay, E. Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R. Jenne, R., Joseph, D. (1996) The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society 77, 437-71. - Li, F., Jupp, D.L.B., Reddy, S., Lymburner, L., Mueller, N., Tan, P., and Islam, A. (2010) An Evaluation of the Use of Atmospheric and BRDF Correction to Standardize Landsat Data. IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing 3, 257-70.;Li, F. (2010) ARG25 Algorithm Theoretical Basis Document. GA, Canberra. - Li, F., Jupp, D.L.B., Thankappan, M., Lymburner, L., Mueller, N., Lewis, A., and Held, A. (2012) A physics-based atmopheric and BRDF correction for Landsat data over mountainous terrain. Remote Sensing of Environment 124, 756-70. - Lubke, M. (2012) Landsat Geometry Calibration/Validation Update. Presentation at LTWG #21, 25 September 2012, Sioux Falls. USGS, USA. - OGC (2006) OpenGIS Web Map Server Implementation Specification (Ed: Jeff de la Beaujardiere) Ref. OGC 06-042. - OGC (2010) OGC WCS 2.0 Interface Standard - Core. (Ed: Peter Baumann) Ref. OGC 09-110r3. - OGC (2013) CF-netCDF3 Data Model Extension Standard (Eds: Ben Domenico and Stefano Nativi) Ref. OGC 11-165r2. - Roy, D.P., Ju, J., Kline, K., Scaramuzza, P.L., Kovalskyy, V., Hansen, M., Loveland, T.R., Vermote, E., & Zhang, C. (2010). Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sensing of Environment, 114, 35-49. - Sixsmith, J., Oliver, S., & Lymburner, L. (2013). A hybrid approach to automated Landsat pixel quality. In, Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International (pp. 4146-4149). - Strahler, A.H., and Muller, J.-P. (1999) MODIS BRDF/Albedo Product: Algorithm Theoretical Basis Document Version 5.0. http://modis.gsfc.nasa.gov/data/atbd/atbd_mod09.pdf - TM World Borders vector file: http://thematicmapping.org/downloads/world_borders.php - USGS (2012a) Landsat Thematic Mapper (TM) Level 1 (L1) Data Format Control Book (DFCB). LS-DFCB-20 Version 4.0. USGS, USA. http://landsat.usgs.gov/documents/LS-DFCB-20.pdf - USGS (2012b) Landsat 7 ETM+ Level 1 Product Data Format Control Book (DFCB). LS-DFCB-04 Version 15.0. http://landsat.usgs.gov/documents/LS-DFCB-04.pdf - Vincenty, T. (1975) Direct and Inverse Solutions of Geodesies on the Ellipsoid with Application of Nested Equations. Survey Review 23, 88-93. - Zhu, Z. and Woodcock, C. E. (2012) Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment 118, 83-94. - http://dx.doi.org/10.4225/25/55EF55788BAC1
institution:
Geoscience Australia - Client Services
cdm_data_type:
Grid
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
publisher_type:
institution
product_version:
Version 2.0, April 2015
publisher_email:
earth.observation@ga.gov.au
keywords_vocabulary:
GCMD
machine:
uname:
Linux r140 3.10.0-693.5.2.el6.x86_64 #1 SMP Tue Oct 24 10:37:43 AEDT 2017 x86_64
hostname:
r140
software_versions:
python:
version:
3.6.2 | packaged by conda-forge | (default, Jul 23 2017, 22:59:30)
[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]
datacube:
version:
1.5.3
repo_url:
https://github.com/opendatacube/datacube-core.git
source_datasets:
statistics:
step:
12m
period:
12m