Advancing Leaf Area Index (LAI) Measurement for Climate-Smart Agriculture: Field-Based and UAV-Driven Approaches

Dear Colleagues,

Leaf area index (LAI) is a critical biophysical parameter that influences photosynthesis, evapotranspiration, and crop productivity. Accurate LAI measurement is essential for understanding plant health, modeling crop growth, and informing climate adaptation strategies. However, current methods vary widely in scale, precision, and accessibility.

This Special Issue aims to bring together cutting-edge research on both in situ and UAV-based LAI measurement techniques. By integrating ground truth data with remote sensing and machine learning approaches, we seek to advance scalable, accurate, and cost-effective solutions for agricultural monitoring under changing climate conditions. The research objectives include the following:

• To develop and validate high-resolution LAI estimation methods using UAV multispectral and hyperspectral imagery;
• To assess the performance of deep learning models versus classical regression techniques for LAI prediction;
• To explore LAI dynamics under abiotic stress (drought, nutrient deficiency) and link them to crop resilience;
• To integrate LAI data into crop growth models for improved yield forecasting and climate adaptation strategies.

We invite researchers to contribute novel and original articles, reviews, and case reports, etc., covering all related topics.

Dr. Klára Pokovai
Prof. Dr. Chengming Sun
Guest Editors

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