Next Article in Journal
Biosensing Using Microring Resonator Interferograms
Previous Article in Journal
Optimization of ERK Activity Biosensors for both Ratiometric and Lifetime FRET Measurements
Sensors 2014, 14(1), 1155-1183; doi:10.3390/s140101155
Article

A Methodology to Assess the Accuracy with which Remote Data Characterize a Specific Surface, as a Function of Full Width at Half Maximum (FWHM): Application to Three Italian Coastal Waters

1,* , 2
,
2
,
3
,
4
,
2
 and
3
Received: 5 October 2013 / Revised: 23 December 2013 / Accepted: 24 December 2013 / Published: 10 January 2014
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [923 KB, uploaded 21 June 2014]   |   Browse Figures

Abstract

This methodology assesses the accuracy with which remote data characterizes a surface, as a function of Full Width at Half Maximum (FWHM). The purpose is to identify the best remote data that improves the characterization of a surface, evaluating the number of bands in the spectral range. The first step creates an accurate dataset of remote simulated data, using in situ hyperspectral reflectances. The second step evaluates the capability of remote simulated data to characterize this surface. The spectral similarity measurements, which are obtained using classifiers, provide this capability. The third step examines the precision of this capability. The assumption is that in situ hyperspectral reflectances are considered the “real” reflectances. They are resized with the same spectral range of the remote data. The spectral similarity measurements which are obtained from “real” resized reflectances, are considered “real” measurements. Therefore, the quantity and magnitude of “errors” (i.e., differences between spectral similarity measurements obtained from “real” resized reflectances and from remote data) provide the accuracy as a function of FWHM. This methodology was applied to evaluate the accuracy with which CHRIS-mode1, CHRIS-mode2, Landsat5-TM, MIVIS and PRISMA data characterize three coastal waters. Their mean values of uncertainty are 1.59%, 3.79%, 7.75%, 3.15% and 1.18%, respectively.
Keywords: accuracy; characterization capability; FWHM; in situ hyperspectral reflectance; spectral similarity measurements; coastal water reflectance; CHRIS data; Landsat5-TM data; MIVIS data; PRISMA data accuracy; characterization capability; FWHM; in situ hyperspectral reflectance; spectral similarity measurements; coastal water reflectance; CHRIS data; Landsat5-TM data; MIVIS data; PRISMA data
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
MDPI and ACS Style

Cavalli, R.M.; Betti, M.; Campanelli, A.; Cicco, A.D.; Guglietta, D.; Penna, P.; Piermattei, V. A Methodology to Assess the Accuracy with which Remote Data Characterize a Specific Surface, as a Function of Full Width at Half Maximum (FWHM): Application to Three Italian Coastal Waters. Sensors 2014, 14, 1155-1183.

View more citation formats

Related Articles

Article Metrics

Comments

Citing Articles

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert