fusion of landsat-8/oli and goci data for hourly mapping of suspended particulate matter at high spatial resolution: a case study in the yangtze (changjiang) estuary

Clicks: 452
ID: 220050
2018
Free PDF
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.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #5 of 499 articles by views in Journal of pharmacological sciences

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 499 in total.

Mint this article as an NFT
Not yet minted

Create 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
Sign in to mint
Abstract
Suspended particulate matter (SPM) concentrations ([SPM]) in the Yangtze estuary, which has third-order bifurcations and four outlets, exhibit large spatial and temporal variations. Studying the characteristics of these variations in [SPM] is important for understanding sediment transport and pollutant diffusion in the estuary as well as for the construction of port and estuarine engineering structures. The 1-h revisit frequency of the Geostationary Ocean Color Imager (GOCI) sensor and the 30-m spatial resolution of the Landsat 8 Operational Land Imager (L8/OLI) provide a new opportunity to study the large spatial and temporal variations in the [SPM] in the Yangtze estuary. In this study, [SPM] images with a temporal resolution of 1 h and a spatial resolution of 30 m are generated through the product-level fusion of [SPM] data derived from L8/OLI and GOCI images using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The results show that the details and accuracy of the spatial and temporal variations are maintained well in the [SPM] images that are predicted based on the fused images. Compared to the [SPM] observations at fixed field stations, the mean relative error (MRE) of the predicted SPM is 17.7%, which is lower than that of the GOCI-derived [SPM] (27.5%). In addition, thanks to the derived high-resolution [SPM] with high spatiotemporal dynamic changes, both natural phenomena (dynamic variation of the maximum turbid zone) and human engineering changes leading to the dynamic variability of SPM in the channel are observed.
Reference Key
pan2018remotefusion Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Yanqun Pan;Fang Shen;Xiaodao Wei
Journal Journal of pharmacological sciences
Year 2018
DOI
10.3390/rs10020158
URL
http://www.mdpi.com/2072-4292/10/2/158
Keywords
remote sensing data fusion landsat-8

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.