Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling
Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems ar...
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ftpubmed:oai:pubmedcentral.nih.gov:4467079 2023-05-15T18:40:34+02:00 Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling Stoy, Paul C. Quaife, Tristan 2015-06-12 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4467079/ http://www.ncbi.nlm.nih.gov/pubmed/26067835 https://doi.org/10.1371/journal.pone.0128935 en eng Public Library of Science http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4467079/ http://www.ncbi.nlm.nih.gov/pubmed/26067835 http://dx.doi.org/10.1371/journal.pone.0128935 http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited CC-BY Research Article Text 2015 ftpubmed https://doi.org/10.1371/journal.pone.0128935 2015-07-05T00:17:18Z Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes. Text Tundra PubMed Central (PMC) Lagrange ENVELOPE(-62.597,-62.597,-64.529,-64.529) PLOS ONE 10 6 e0128935 |
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Research Article Stoy, Paul C. Quaife, Tristan Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling |
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Research Article |
description |
Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes. |
format |
Text |
author |
Stoy, Paul C. Quaife, Tristan |
author_facet |
Stoy, Paul C. Quaife, Tristan |
author_sort |
Stoy, Paul C. |
title |
Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling |
title_short |
Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling |
title_full |
Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling |
title_fullStr |
Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling |
title_full_unstemmed |
Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling |
title_sort |
probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling |
publisher |
Public Library of Science |
publishDate |
2015 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4467079/ http://www.ncbi.nlm.nih.gov/pubmed/26067835 https://doi.org/10.1371/journal.pone.0128935 |
long_lat |
ENVELOPE(-62.597,-62.597,-64.529,-64.529) |
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Lagrange |
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Lagrange |
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Tundra |
genre_facet |
Tundra |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4467079/ http://www.ncbi.nlm.nih.gov/pubmed/26067835 http://dx.doi.org/10.1371/journal.pone.0128935 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1371/journal.pone.0128935 |
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PLOS ONE |
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10 |
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