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

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

3.0K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
3.0K
Bandpass Sampling01:17

Bandpass Sampling

265
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
265
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.2K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Polyvinyl-based hole-transporting materials processed with non-destructive and green solvents for tin-lead perovskite solar cells and all-perovskite tandems.

Chemical science·2026
Same author

Prescribing Trajectories in Type 2 Diabetes in the United States, 2019-2024.

Diabetes, obesity & metabolism·2026
Same author

Diagnostic Performance of <sup>68</sup>Ga-FAPI PET/CT Versus <sup>18</sup>F-FDG PET/CT in Evaluating Neoadjuvant Therapy Pathological Response in Breast Cancer.

NPJ breast cancer·2026
Same author

Divergent host cell protein profiles during special purification of biologics from prokaryotic versus eukaryotic systems dictate the need for tailored ELISA assay development.

Journal of pharmaceutical sciences·2026
Same author

Grazing intensity drives above-belowground productivity trade-offs and reveals belowground dominance in temperate herbaceous marsh wetlands.

Frontiers in plant science·2026
Same author

TCR Repertoire Analysis Unveils the Link Between Kawasaki Disease and Viral Infection.

Biomedicines·2026

Related Experiment Video

Updated: Sep 17, 2025

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
13:01

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

Published on: April 10, 2016

34.1K

Improving spatiotemporal data fusion method in multiband images by distributing variates.

Yihua Jin1, Zhenhao Yin2, Weihong Zhu3

  • 1College of Agriculture, Yanbian University, Yanji, 133002, China.

Scientific Reports
|July 2, 2025
PubMed
Summary

This study introduces a new Residual Distribution-based Spatiotemporal Data Fusion Method (RDSFM) for generating high-resolution satellite imagery. RDSFM improves accuracy by addressing spatial and temporal variations, especially for vegetation analysis.

Keywords:
IR-MADLandsatMODISSatellite image fusionTime-seriesUnmixing based method

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

2.5K
Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and &lambda; Hyperspectral FRET Imaging and Analysis
08:22

Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis

Published on: October 27, 2020

4.0K

Related Experiment Videos

Last Updated: Sep 17, 2025

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
13:01

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

Published on: April 10, 2016

34.1K
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

2.5K
Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and &lambda; Hyperspectral FRET Imaging and Analysis
08:22

Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis

Published on: October 27, 2020

4.0K

Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Image Processing

Background:

  • Spatiotemporal data fusion is crucial for generating continuous fine-resolution satellite imagery.
  • Existing methods face challenges in accurately capturing seasonal variations and handling landscape heterogeneity.

Purpose of the Study:

  • To introduce the Residual Distribution-based Spatiotemporal Data Fusion Method (RDSFM) for enhanced fusion accuracy.
  • To address residuals from spatial and temporal variations in satellite imagery.
  • To minimize data requirements by using only one high-resolution reference image.

Main Methods:

  • Utilized the IR-MAD algorithm to estimate subpixel distribution weights.
  • Incorporated multivariate data collected over time to address residuals.
  • Benchmarked RDSFM against unmixing-based data fusion (UBDF) using real satellite images.

Main Results:

  • RDSFM accurately predicts seasonal variations in red and NIR bands, vital for vegetation analysis.
  • The method effectively handles heterogeneous landscapes and dynamic land cover changes.
  • Visual and quantitative assessments confirmed RDSFM's strong performance.

Conclusions:

  • RDSFM offers significant advantages over existing spatiotemporal data fusion techniques.
  • The method enhances fusion accuracy, particularly for vegetation monitoring in complex environments.
  • RDSFM provides a robust solution for generating high-quality, fine-resolution satellite imagery with reduced data input.