spatiotemporal variability of arctic soil moisture detected from high-resolution radarsat-2 sar data
Clicks: 148
ID: 209731
2018
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
30.0
/100
148 views
34 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #123 of 174 articles by views in The Journal of biological chemistry
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 174 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Various methods are used to determine soil moisture information from synthetic aperture radar (SAR) data, but none specific to High Arctic regions and their unique physical characteristics. This research presents a method for determining, at high spatial and temporal resolutions, surface soil moisture and its changes through time in the Canadian High Arctic. An artificial neural network (ANN) is implemented using input variables derived from RADARSAT-2 SAR data and previously modelled surface roughness information. The model is applied to SAR data collected at various incidence angles and acquisition dates across two study sites on Melville Island, Nunavut. The model results in absolute soil moisture errors of approximately 15% (r2 = 0.46) for the primary study sites and 12% (r2 = 0.26) for the verification study area. The ANN model is accurate for modelling (i) the spatial distribution of soil moisture and (ii) the changes in moisture through time across the study areas, two characteristics that are very important for inputs to hydrologic or climate models. In addition, the models appear to be scalable when applied at coarser spatial resolutions, showing potential for large-area mapping or modelling.
| Reference Key |
collingwood2018advancesspatiotemporal
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Adam Collingwood;François Charbonneau;Chen Shang;Paul Treitz |
| Journal | The Journal of biological chemistry |
| Year | 2018 |
| DOI |
10.1155/2018/5712046
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.