Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 267

The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.

A Review of Distributed Fibre Optic Sensors for Geo-Hydrological Applications

Open access
Schenato, Luca·2017·Applied Sciences

Abstract

Distributed optical fibre sensing, employing either Rayleigh, Raman, or Brillouin scattering, is the only physical-contact sensor technology capable of accurately estimating physical fields with spatial continuity along the fibre. This unique feature and the other features of standard optical fibre sensors (e.g., minimal invasiveness and lightweight, remote powering/interrogating capabilities) have for many years promoted the technology to be a promising candidate for geo-hydrological monitoring. Relentless research efforts are being undertaken to bring the technology to complete maturity through laboratory, physical models, and in-situ tests. The application of distributed optical fibre sensors to geo-hydrological monitoring is here reviewed and discussed, along with basic principles and main acquisition techniques. Among the many existing geo-hydrological processes, the emphasis is placed on those related to soil levees, slopes/landslide, and ground subsidence that constitute a significant percentage of current geohazards.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/app7090896

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 29 references

Cited by

See all 267

Metadata

Authors
Schenato, L.
Venue
Applied Sciences
Publisher
MDPI AG
Published
2017-09-01
Volume
7
Issue
9
Pages
896
ISSN
2076-3417 (ISSN-L)
OpenAlex
W2752623846
Cited by
267
References
29
FWCI
6.82
Type
journal article
Language
en
Open access
gold

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A Review of Distributed Fibre Optic Sensors for Geo-Hydrological Applications. https://app.cochat.ai/papers/10.3390/app7090896
The paper
Schenato, L. (2017). A Review of Distributed Fibre Optic Sensors for Geo-Hydrological Applications. Applied Sciences. https://doi.org/10.3390/app7090896

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.