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Related Experiment Video

Updated: Jan 2, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Maize Crop Coefficient Estimated from UAV-Measured Multispectral Vegetation Indices.

Yu Zhang^1,2,3, Wenting Han^1,4, Xiaotao Niu^1,2

¹Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China.

Sensors (Basel, Switzerland)

|December 5, 2019

Summary

Unmanned aerial vehicle (UAV) multispectral imagery accurately estimates maize crop coefficients (Kc) under deficit irrigation. This precision agriculture approach improves farm-scale water management by assessing field variability.

Keywords:

crop coefficient (Kc)deficit irrigation regression model soil water balance stress coefficient vegetation indices

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Area of Science:

Agricultural Science
Remote Sensing
Water Management

Background:

Precision agricultural water management requires accurate, real-time crop coefficient (Kc) estimation.
Deficit irrigation strategies necessitate precise monitoring of crop water needs.

Purpose of the Study:

To map maize crop coefficients (Kc) with high accuracy under varying deficit irrigation levels.
To evaluate the effectiveness of unmanned aerial vehicle (UAV)-based multispectral data for Kc estimation.

Main Methods:

Utilized high-spatial-resolution multispectral images from a UAV.
Derived vegetation indices, including Normalized Difference Vegetation Index (NDVI), to estimate fraction of vegetation cover (f).
Developed two regression models (TCARI/RDVI and TCARI/SAVI) to retrieve Kc and compared them with field measurements.

Main Results:

NDVI showed significant changes in the maturation stage under deficit irrigation.
Fraction of vegetation cover (f) derived from NDVI correlated highly with field measurements (R² = 0.93).
The TCARI/RDVI model demonstrated a better correlation (R² = 0.68–0.80) for Kc estimation compared to TCARI/SAVI.

Conclusions:

UAV-based multispectral vegetation index approach enhances the assessment of field variability in soil and crops.
The proposed method improves the accuracy of crop coefficient estimation for precision agricultural water management.
This technology offers a valuable tool for optimizing irrigation strategies at the farm scale.