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 Experiment Video

Updated: Jun 6, 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

A cloud-computing framework for downscaled global 300 m SIF retrieval from Sentinel-3 and TROPOSIF.

Yuxin Zhang1, Pablo Reyes-Muñoz1, Jochem Verrelst1

  • 1Image Processing Laboratory (IPL) - University of Valencia, Catedrático Agustín Scardino Benlloch 9, Paterna, 46980, Spain.

International Journal of Applied Earth Observation and Geoinformation : ITC Journal
|June 5, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
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

PyEOGPR: A Python package for vegetation trait mapping with Gaussian Process Regression on Earth observation cloud platforms.

Ecological informatics·2025
Same author

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

Sensors (Basel, Switzerland)·2025
Same author

Tower-to-global upscaling of terrestrial carbon fluxes driven by MODIS-LAI, Sentinel-3-LAI and ERA5-Land data.

Ecological indicators·2025
Same author

Driving variables to explain soil organic carbon dynamics: páramo highlands of the Ecuadorian Real mountain range.

Journal of soils and sediments·2025
Same author

Crop type discrimination using Geo-Stat Endmember Extraction and machine learning algorithms.

Advances in space research : the official journal of the Committee on Space Research (COSPAR)·2024
Same journal

Detecting gaps between urban expansion and lighting infrastructure growth using daytime and nighttime satellite imagery.

International journal of applied earth observation and geoinformation : ITC journal·2026
Same journal

Predicting environmental suitability and future spread range of <i>An. stephensi</i> in the Greater Horn of Africa using remote sensing and ensemble modeling.

International journal of applied earth observation and geoinformation : ITC journal·2026
Same journal

How accurately does L band vegetation optical depth predict aboveground biomass?

International journal of applied earth observation and geoinformation : ITC journal·2025
Same journal

Geospatial impact evaluation of a low-cost agricultural intervention for enhancing environmental resilience.

International journal of applied earth observation and geoinformation : ITC journal·2025
Same journal

Optimizing the detection of emerging infections using mobility-based spatial sampling.

International journal of applied earth observation and geoinformation : ITC journal·2024
Same journal

Unraveling near real-time spatial dynamics of population using geographical ensemble learning.

International journal of applied earth observation and geoinformation : ITC journal·2024
See all related articles

This study introduces a new method to create high-resolution sun-induced chlorophyll fluorescence (SIF) maps from Sentinel-3 data. The downscaled SIF product (S3-SIF743) offers unprecedented insights into plant photosynthesis at the sub-kilometer scale.

Area of Science:

  • Earth and Planetary Sciences
  • Remote Sensing
  • Ecology

Background:

  • Sun-induced chlorophyll fluorescence (SIF) is a key indicator of photosynthetic activity.
  • Existing satellite SIF products have coarse spatial resolutions ( > 500 m), limiting fine-scale ecosystem studies.
  • There is a need for higher spatial resolution SIF data to understand vegetation health and function.

Purpose of the Study:

  • To develop a cloud-computing framework for generating a downscaled SIF product (S3-SIF743) with 300 m spatial resolution.
  • To integrate Sentinel-3 (S3) Ocean and Land Colour Instrument (OLCI) data with TROPOspheric Monitoring Instrument (TROPOMI) SIF (TROPOSIF743) using Google Earth Engine (GEE).
  • To provide a flexible and operational pathway for high-resolution monitoring of terrestrial photosynthesis.

Main Methods:

Keywords:
Google Earth EngineMachine learningSentinel-3 OLCISun-induced fluorescenceTROPOMI

More Related Videos

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Related Experiment Videos

Last Updated: Jun 6, 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

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

  • Utilized Google Earth Engine (GEE) for cloud-based processing.
  • Employed a Random Forest (RF) regression framework integrating TROPOMI SIF, S3 radiances, S3-derived vegetation traits, latitude, and longitude.
  • Trained and validated the model using reference data over Europe and ground-based observations globally.

Main Results:

  • Achieved robust performance in model training over Europe (R² = 0.767, RMSE = 0.137 mW m⁻² sr⁻¹ nm⁻¹).
  • S3-SIF743 successfully reproduced seasonal dynamics across diverse ecosystems and showed strong spatial consistency with TROPOSIF743 in agricultural regions.
  • The downscaled product revealed coherent patterns of photosynthetic activity globally and reduced retrieval noise compared to TROPOMI SIF.

Conclusions:

  • The developed framework successfully generates a high-resolution SIF product (S3-SIF743) at 300 m resolution.
  • S3-SIF743 provides valuable insights into sub-kilometer spatial heterogeneity of terrestrial photosynthesis.
  • This approach bridges the gap between existing coarse SIF products and future missions like FLEX, enabling enhanced ecosystem monitoring.