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

Flame Photometry: Overview01:02

Flame Photometry: Overview

786
Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
786

You might also read

Related Articles

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

Sort by
Same author

Photodissociation of state-selected hydrogen iodide molecules following excitation at vacuum ultraviolet wavelengths: a tuneable source of high velocity H atoms.

Physical chemistry chemical physics : PCCP·2026
Same author

Restoring immune homeostasis in the spinal microenvironment: targeting mechano-inflammation and immunometabolic reprogramming.

Frontiers in immunology·2026
Same author

Analysis of ElfBar Elfa vaping product aerosol compared with cigarette smoke and regulatory safety limits.

iScience·2026
Same author

UCMSC-Exo for chemotherapy-induced myelosuppression in acute myeloid leukemia: a phase I clinical trial protocol.

Nanomedicine (London, England)·2026
Same author

Quantum chemistry unifies phosphorus removal and recovery through environmental affinity.

Water research·2026
Same author

Photocatalytic radical addition/bicyclization of 1,7-enynes with <i>N</i>-sulfonylaminopyridinium salts/sulfamoyl chlorides: access to sulfonamide-containing benzo[<i>a</i>]fluoren-5-ones.

Organic & biomolecular chemistry·2026

Related Experiment Video

Updated: Sep 4, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.4K

A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms.

Xingan Chen1, Yuefei Huang1,2,3, Chong Nie4,5

  • 1State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China.

Scientific Data
|July 20, 2022
PubMed
Summary

Machine learning reconstructs solar-induced chlorophyll fluorescence (SIF) from TROPOMI satellite data, creating a long-term record. This high-resolution SIF dataset aids in understanding terrestrial photosynthesis and carbon budgets.

More Related Videos

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
14:09

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

7.0K
Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
12:24

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Published on: July 17, 2012

12.5K

Related Experiment Videos

Last Updated: Sep 4, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.4K
High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
14:09

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

Published on: November 16, 2019

7.0K
Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
12:24

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Published on: July 17, 2012

12.5K

Area of Science:

  • Earth System Science
  • Remote Sensing
  • Ecology

Background:

  • Solar-induced chlorophyll fluorescence (SIF) is a crucial proxy for terrestrial photosynthesis, linking carbon and water cycles.
  • Satellite observations of SIF, particularly from TROPOMI, offer high spatial and temporal resolution but suffer from limited data records.
  • Long-term SIF data are essential for studying global carbon budgets and water fluxes.

Purpose of the Study:

  • To reconstruct TROPOMI SIF data from 2001-2020 using machine learning, creating a high spatio-temporal resolution dataset (RTSIF).
  • To validate the reconstructed SIF (RTSIF) against existing satellite and ground-based SIF measurements.
  • To assess the potential of RTSIF for estimating gross primary production and understanding long-term terrestrial photosynthesis.

Main Methods:

  • Utilized machine learning algorithms to reconstruct TROPOMI SIF data for clear-sky conditions over a 20-year period (2001-2020).
  • Achieved high accuracy in the machine learning model with R² = 0.907 and a regression slope of 1.001 on training and testing datasets.
  • Validated the reconstructed SIF (RTSIF) against TROPOMI SIF, tower-based SIF, GOME-2 SIF, and OCO-2 SIF.

Main Results:

  • Developed a high spatio-temporal resolution (0.05°, 8-day) reconstructed TROPOMI SIF (RTSIF) dataset spanning 2001-2020.
  • Demonstrated high accuracy of the RTSIF dataset through rigorous validation against multiple SIF sources.
  • Showcased the potential of RTSIF for estimating gross carbon fluxes by comparing it with Gross Primary Production (GPP).

Conclusions:

  • The reconstructed TROPOMI SIF (RTSIF) dataset provides a valuable, long-term, high-resolution record of terrestrial photosynthesis.
  • RTSIF is a reliable proxy for photosynthesis and can be used to constrain global carbon budgets and water fluxes.
  • This dataset will advance research in long-term ecosystem monitoring and climate change impact assessments.