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

The Calvin Benson Cycle01:46

The Calvin Benson Cycle

4.5K
Ribulose 1,5- bisphosphate carboxylase/oxygenase (RuBisCo) is a critical enzyme that catalyzes carbon dioxide assimilation during photosynthesis. However, it is an inefficient enzyme, having an extremely slow catalytic rate. A typical enzyme can process about a thousand molecules per second; however, RuBisCo fixes only around three-carbon dioxides per second. Photosynthetic cells compensate for this slow rate by synthesizing very high amounts of RuBisCo, making it the most abundant single...
4.5K
Light Acquisition02:16

Light Acquisition

8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K
Adaptations that Reduce Water Loss01:57

Adaptations that Reduce Water Loss

25.4K
Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.
25.4K

You might also read

Related Articles

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

Sort by
Same author

Integrating citizen science and spatial machine learning for mapping recreational cultural ecosystem services in rural regions.

Journal of environmental management·2026
Same author

CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021-2060.

Open research Europe·2025
Same author

Using biochar for environmental recovery and boosting the yield of valuable non-food crops: The case of hemp in a soil contaminated by potentially toxic elements (PTEs).

Heliyon·2024
Same author

Estimating annual GHG and particulate matter emissions from rural and forest fires based on an integrated modelling approach.

The Science of the total environment·2023
Same author

Modeling Phenological Phases across Olive Cultivars in the Mediterranean.

Plants (Basel, Switzerland)·2023
Same author

A Systematic Review on the Impacts of Climate Change on Coffee Agrosystems.

Plants (Basel, Switzerland)·2023

Related Experiment Video

Updated: Jun 17, 2025

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

7.9K

Artificial intelligence and Eddy covariance: A review.

Arianna Lucarini1, Mauro Lo Cascio2, Serena Marras2

  • 1Department of Agricultural Sciences, University of Sassari, Viale Italia 39A, 07100 Sassari, Italy; University School for Advanced Studies IUSS Pavia, Palazzo del Broletto, Piazza della Vittoria 15, 27100 Pavia, Italy.

The Science of the Total Environment
|August 10, 2024
PubMed
Summary

Artificial Intelligence (AI) is increasingly used with the Eddy Covariance (EC) method for monitoring Earth

Keywords:
Climate changeFlux monitoringMachine learningPRISMAScoping review

More Related Videos

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
09:55

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Published on: December 12, 2013

8.6K
Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy
06:28

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Published on: July 29, 2021

3.3K

Related Experiment Videos

Last Updated: Jun 17, 2025

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

7.9K
Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
09:55

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Published on: December 12, 2013

8.6K
Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy
06:28

Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Published on: July 29, 2021

3.3K

Area of Science:

  • Environmental Science
  • Climate Science
  • Data Science

Background:

  • The Eddy Covariance (EC) method measures carbon, water, and energy fluxes between Earth's surface and atmosphere.
  • EC data streams are complex and abundant, making them suitable for Artificial Intelligence (AI) applications.
  • AI and EC integration is vital for achieving climate change mitigation and adaptation goals outlined in Agenda 2030's Sustainable Development Goals (SDGs).

Purpose of the Study:

  • To conduct a scoping review of AI techniques used with the EC method over the past two decades.
  • To identify trends, common AI models, and challenges in AI-EC research for flux monitoring.
  • To propose future directions for AI and EC collaboration in climate change research.

Main Methods:

  • A scoping review methodology was employed to collect and analyze research on AI techniques in EC flux monitoring.
  • Seventy-one distinct AI algorithms were identified and categorized.
  • The review focused on the novelty and application of AI techniques over the last 20 years.

Main Results:

  • A significant increase in research quantity is observed, especially in the last five years.
  • A lack of uniformity exists in AI techniques due to diverse environmental conditions, ecosystems, and variables.
  • Popular Machine Learning (ML) models include Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), Support Vector Regression (SVR), and K-Nearest Neighbor (KNN).

Conclusions:

  • Future advancements require international collaboration between computer scientists and ecologists.
  • Investigating modern Deep Learning (DL) techniques like Transformers and generative AI is recommended.
  • A strategic approach is needed for optimal AI-EC utilization in flux monitoring for climate change.