Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Light Acquisition

Light Acquisition

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.

You might also read

Related Articles

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

Sort by

Same author

Alternative splicing: from tumorigenesis to neoantigen-mediated cancer immunotherapy.

Biomarker research·2025

Same author

Synergistic toxicity of cadmium and triadimefon on the microbiota and health of Rana dybowskii tadpoles.

Comparative biochemistry and physiology. Toxicology & pharmacology : CBP·2024

Same author

PSMA7 promotes the malignant proliferation of esophageal cancer.

Heliyon·2024

Same author

Associations of executive function and age of first use of methamphetamine with methamphetamine relapse.

Frontiers in psychiatry·2022

Same author

Predictive Role of Executive Function in the Efficacy of Intermittent Theta Burst Transcranial Magnetic Stimulation Modalities for Treating Methamphetamine Use Disorder-A Randomized Clinical Trial.

Frontiers in psychiatry·2021

Same author

High-Quality Boron Nitride Nanosheets and Their Bioinspired Thermally Conductive Papers.

ACS applied materials & interfaces·2019

Search research articles

Related Experiment Video

Updated: Jun 4, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

[LAI-based regional winter wheat yield estimation by remote sensing].

Jian-qiang Ren¹, Zhong-xin Chen, Qing-bo Zhou

¹Key Laboratory of Resources Remote-Sensing & Digital Agriculture, Ministry of Agriculture, Beijing, China. heb-jqren1975@126.com

Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology

|March 3, 2011

Summary

This summary is machine-generated.

This study developed a novel method for winter wheat yield estimation using Leaf Area Index (LAI) derived from satellite data. The approach accurately predicts crop yields with high precision, offering early harvest estimates.

More Related Videos

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

Related Experiment Videos

Last Updated: Jun 4, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

Area of Science:

Agricultural Science
Remote Sensing
Crop Modeling

Context:

Accurate winter wheat yield estimation is crucial for food security in the Huanghuaihai Plain.
Remotely sensed data, particularly Leaf Area Index (LAI), offers a scalable approach for regional crop monitoring.
Improving the quality of satellite data is essential for reliable yield prediction.

Purpose:

To develop and validate a robust model for regional winter wheat yield estimation using remotely sensed LAI.
To enhance the accuracy of yield prediction by employing data smoothing and crop growth simulation techniques.
To provide an early warning system for winter wheat harvest, aiding agricultural management.

Summary:

A Savitzky-Golay filter was applied to smooth MODIS-NDVI time series, reducing cloud contamination and anomalies.
A Gaussian model simulated daily crop LAI, corrected with interpolated measured LAI for key phenological stages.
Relationships between LAI and winter wheat yield were established, leading to an optimized estimation model.

Impact:

The developed model achieved a mean relative error of 1.21% and an RMSE of 257.33 kg/hm².
This method enables accurate winter wheat yield estimation 20-30 days prior to harvest.
The findings support improved agricultural planning and management through timely yield predictions.