A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems
The Natural Environment Research Council (NERC) funded this work through the National Centre for Earth Observation (NCEO) and the Centre for Terrestrial Carbon Dynamics (CTCD). We present an approach for dealing with coarse-resolution Earth observations (EO) in terrestrial ecosystem data assimilatio...
Published in: | Journal of Geophysical Research |
---|---|
Main Authors: | , , |
Other Authors: | |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2012
|
Subjects: | |
Online Access: | http://hdl.handle.net/10023/2408 https://doi.org/10.1029/2010JD015268 |
id |
ftstandrewserep:oai:research-repository.st-andrews.ac.uk:10023/2408 |
---|---|
record_format |
openpolar |
spelling |
ftstandrewserep:oai:research-repository.st-andrews.ac.uk:10023/2408 2023-07-02T03:31:32+02:00 A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems Hill, T. C. Quaife, T. Williams, M. University of St Andrews. Earth and Environmental Sciences 2012-03-06T12:01:08Z 12 application/pdf http://hdl.handle.net/10023/2408 https://doi.org/10.1029/2010JD015268 eng eng Journal of Geophysical Research Hill , T C , Quaife , T & Williams , M 2011 , ' A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems ' , Journal of Geophysical Research , vol. 116 , D08117 . https://doi.org/10.1029/2010JD015268 0148-0227 PURE: 17179005 PURE UUID: 32a6a311-9aa0-4d5a-83a9-0fecd105bf02 WOS: 000290103300001 Scopus: 79955595740 http://hdl.handle.net/10023/2408 https://doi.org/10.1029/2010JD015268 Copyright 2011 by the American Geophysical Union Leaf-area index Kalman filter Models Water Flux Parameter Systems Fusion Energy Bias GE Environmental Sciences SDG 15 - Life on Land GE Journal article 2012 ftstandrewserep https://doi.org/10.1029/2010JD015268 2023-06-13T18:26:32Z The Natural Environment Research Council (NERC) funded this work through the National Centre for Earth Observation (NCEO) and the Centre for Terrestrial Carbon Dynamics (CTCD). We present an approach for dealing with coarse-resolution Earth observations (EO) in terrestrial ecosystem data assimilation schemes. The use of coarse-scale observations in ecological data assimilation schemes is complicated by spatial heterogeneity and nonlinear processes in natural ecosystems. If these complications are not appropriately dealt with, then the data assimilation will produce biased results. The "disaggregation" approach that we describe in this paper combines frequent coarse-resolution observations with temporally sparse fine-resolution measurements. We demonstrate the approach using a demonstration data set based on measurements of an Arctic ecosystem. In this example, normalized difference vegetation index observations are assimilated into a "zero-order" model of leaf area index and carbon uptake. The disaggregation approach conserves key ecosystem characteristics regardless of the observation resolution and estimates the carbon uptake to within 1% of the demonstration data set "truth." Assimilating the same data in the normal manner, but without the disaggregation approach, results in carbon uptake being underestimated by 58% at an observation resolution of 250 m. The disaggregation method allows the combination of multiresolution EO and improves in spatial resolution if observations are located on a grid that shifts from one observation time to the next. Additionally, the approach is not tied to a particular data assimilation scheme, model, or EO product and can cope with complex observation distributions, as it makes no implicit assumptions of normality. Publisher PDF Peer reviewed Article in Journal/Newspaper Arctic University of St Andrews: Digital Research Repository Arctic Journal of Geophysical Research 116 D8 |
institution |
Open Polar |
collection |
University of St Andrews: Digital Research Repository |
op_collection_id |
ftstandrewserep |
language |
English |
topic |
Leaf-area index Kalman filter Models Water Flux Parameter Systems Fusion Energy Bias GE Environmental Sciences SDG 15 - Life on Land GE |
spellingShingle |
Leaf-area index Kalman filter Models Water Flux Parameter Systems Fusion Energy Bias GE Environmental Sciences SDG 15 - Life on Land GE Hill, T. C. Quaife, T. Williams, M. A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems |
topic_facet |
Leaf-area index Kalman filter Models Water Flux Parameter Systems Fusion Energy Bias GE Environmental Sciences SDG 15 - Life on Land GE |
description |
The Natural Environment Research Council (NERC) funded this work through the National Centre for Earth Observation (NCEO) and the Centre for Terrestrial Carbon Dynamics (CTCD). We present an approach for dealing with coarse-resolution Earth observations (EO) in terrestrial ecosystem data assimilation schemes. The use of coarse-scale observations in ecological data assimilation schemes is complicated by spatial heterogeneity and nonlinear processes in natural ecosystems. If these complications are not appropriately dealt with, then the data assimilation will produce biased results. The "disaggregation" approach that we describe in this paper combines frequent coarse-resolution observations with temporally sparse fine-resolution measurements. We demonstrate the approach using a demonstration data set based on measurements of an Arctic ecosystem. In this example, normalized difference vegetation index observations are assimilated into a "zero-order" model of leaf area index and carbon uptake. The disaggregation approach conserves key ecosystem characteristics regardless of the observation resolution and estimates the carbon uptake to within 1% of the demonstration data set "truth." Assimilating the same data in the normal manner, but without the disaggregation approach, results in carbon uptake being underestimated by 58% at an observation resolution of 250 m. The disaggregation method allows the combination of multiresolution EO and improves in spatial resolution if observations are located on a grid that shifts from one observation time to the next. Additionally, the approach is not tied to a particular data assimilation scheme, model, or EO product and can cope with complex observation distributions, as it makes no implicit assumptions of normality. Publisher PDF Peer reviewed |
author2 |
University of St Andrews. Earth and Environmental Sciences |
format |
Article in Journal/Newspaper |
author |
Hill, T. C. Quaife, T. Williams, M. |
author_facet |
Hill, T. C. Quaife, T. Williams, M. |
author_sort |
Hill, T. C. |
title |
A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems |
title_short |
A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems |
title_full |
A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems |
title_fullStr |
A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems |
title_full_unstemmed |
A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems |
title_sort |
data assimilation method for using low-resolution earth observation data in heterogeneous ecosystems |
publishDate |
2012 |
url |
http://hdl.handle.net/10023/2408 https://doi.org/10.1029/2010JD015268 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
Journal of Geophysical Research Hill , T C , Quaife , T & Williams , M 2011 , ' A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems ' , Journal of Geophysical Research , vol. 116 , D08117 . https://doi.org/10.1029/2010JD015268 0148-0227 PURE: 17179005 PURE UUID: 32a6a311-9aa0-4d5a-83a9-0fecd105bf02 WOS: 000290103300001 Scopus: 79955595740 http://hdl.handle.net/10023/2408 https://doi.org/10.1029/2010JD015268 |
op_rights |
Copyright 2011 by the American Geophysical Union |
op_doi |
https://doi.org/10.1029/2010JD015268 |
container_title |
Journal of Geophysical Research |
container_volume |
116 |
container_issue |
D8 |
_version_ |
1770270911349391360 |