Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

373
A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
373
Errors in Global Positioning System01:26

Errors in Global Positioning System

307
Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
307
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.0K
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

395
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
395

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A New Matrix Certified Reference Material for Measurement of Chlormequat Chloride and 2,4-Dichlorophenoxyacetic Acid Residues in Cucumber.

Foods (Basel, Switzerland)·2026
Same author

Assessment of PlanetScope Spectral Data for Estimation of Peanut Leaf Area Index Using Machine Learning and Statistical Methods.

Sensors (Basel, Switzerland)·2026
Same author

Enhanced oil/water separation using superhydrophobic nano SiO<sub>2</sub>-modified porous melamine sponges.

Chemosphere·2024
Same author

Weakening of global terrestrial carbon sequestration capacity under increasing intensity of warm extremes.

Nature ecology & evolution·2024
Same author

Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling.

European journal of remote sensing·2024
Same author

Spatial and Temporal Analysis of Water Quality in High Andean Lakes with Sentinel-2 Satellite Automatic Water Products.

Sensors (Basel, Switzerland)·2023

Related Experiment Video

Updated: Jan 9, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

4.3K

Bridging Gaps in Aquatic Remote Sensing Reflectance Validation: Pixel Boundary Effect and Its Induced Errors.

Shuling Xiao1, Chunguang Lyu1, Chi Zhang1,2

  • 1College of Resources and Environment, Linyi University, Linyi 276000, China.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary

Ocean color remote sensing accuracy is improved by a new pixel-level spatial mismatch index (PSMI). This index quantifies errors from the pixel boundary effect (PBE), enhancing marine biogeochemical monitoring.

Keywords:
ocean colorpixel boundary effectpixel-level spatial mismatch indexremote sensing reflectanceuncertainty analysis

More Related Videos

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.8K
Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
13:35

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

1.3K

Related Experiment Videos

Last Updated: Jan 9, 2026

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

4.3K
Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.8K
Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
13:35

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

1.3K

Area of Science:

  • Oceanography
  • Remote Sensing
  • Geophysics

Background:

  • Ocean color remote sensing is vital for monitoring marine biogeochemical processes.
  • Accuracy of remote sensing reflectance (Rrs) is crucial but limited by scale mismatch between point measurements and pixel observations.
  • Pixel boundary effects (PBE) introduce poorly quantified uncertainty in Rrs products.

Purpose of the Study:

  • To introduce and validate the pixel-level spatial mismatch index (PSMI) for assessing spatial representativeness errors caused by PBE.
  • To quantify the impact of PBE on Rrs accuracy across different sensors and bands.
  • To develop a framework for measuring spatial deviation peaks and defining PBE windows.

Main Methods:

  • Developed the pixel-level spatial mismatch index (PSMI).
  • Utilized AERONET-OC data with MODIS/Aqua and OLCI/Sentinel-3A observations.
  • Proposed a Riemann Stieltjes integral-based index and a baseline method for PBE window definition.

Main Results:

  • PSMI effectively identified systematic Rrs deviation peaks at pixel edges.
  • Observed sensor- and band-dependent characteristics of these deviation peaks.
  • PBE was confirmed as an independent error source interacting with atmospheric and geometric errors.

Conclusions:

  • PBE significantly modulates overall uncertainty in Rrs products through multifactor interactions.
  • Incorporating pixel-scale effects into validation protocols is essential.
  • The PSMI framework offers a valuable tool for assessing and mitigating Rrs uncertainties.