Research on Leaf Area Index Inversion Based on LESS 3D Radiative Transfer Model and Machine Learning Algorithms

Aug 4, 2026

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Leaf Area Index (LAI) is a key biophysical parameter for characterizing forest ecosystem structure and functioning. Efficient LAI retrieval supports ecological simulations and estimation of vegetation productivity, water-cycle dynamics, and carbon balance. This study integrates high-resolution GF-6 2 m satellite imagery with the LESS 3D RTM and multiple machine-learning algorithms to invert LAI for forest stands, using simulated reflectance and fused real inputs for training and measured LAI for validation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Leaf Area Index importance

Limitations of conventional LAI measurement

Remote sensing-based LAI inversion approaches

Empirical methods

Radiative Transfer Model (RTM) methods

remote sensing Article Research on Leaf Area Index Inversion Based on LESS 3D Radiative Transfer Model and Machine Learning Algorithms Yunyang Jiang 1, Zixuan Zhang 1, Huaijiang He 2, Xinna Zhang 1, *, Fei Feng 1, Chengyang Xu 1, Mingjie Zhang 3 and Raffaele Lafortezza 4,1 Citation: Jiang, Y.; Zhang, Z.; He, H.; Zhang, X.; Feng, F.; Xu, C.; Zhang, M.; Lafortezza, R. Research on Leaf Area Index Inversion Based on LESS 3D Radiative Transfer Model and Machine Learning Algorithms. Remote Sens. 2024, 16, 3627. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/rs16193627](10.3390/rs16193627) Academic Editor: Jochem Verrelst Received: 11 August 2024 Revised: 16 September 2024 Accepted: 25 September 2024 Published: 28 September 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Key Laboratory for Silviculture and Forest Ecosystem of State Forestry and Grassland Administration, Research Center for Urban Forestry, College of Forestry, Beijing Forestry University, 35 Tsinghua East Road, Haidian District, Beijing 100083, China; [jiangyunyang@bjfu.edu.cn](jiangyunyang@bjfu.edu.cn) (Y.J.); [forgetbear@bjfu.edu.cn](forgetbear@bjfu.edu.cn) (F.F.) 2 Faculty of Life Science, Jilin Provincial Academy of Forestry Sciences, Changchun 130033, China 3 Beidagou Forest Farm, Shunyi District, Beijing 102115, China 4 Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy * Correspondence: [zhangxinna@bjfu.edu.cn](zhangxinna@bjfu.edu.cn) Abstract: The Leaf Area Index (LAI) is a critical parameter that sheds light on the composition and function of forest ecosystems. Its efficient and rapid measurement is essential for simulating and estimating ecological activities such as vegetation productivity, water cycle, and carbon balance. In this study, we propose to combine high-resolution GF-6 2 m satellite images with the LESS threedimensional RTM and employ different machine learning algorithms, including Random Forest, BP Neural Network, and XGBoost, to achieve LAI inversion for forest stands. By reconstructing real forest stand scenarios in the LESS model, we simulated reflectance data in blue, green, red, and nearinfrared bands, as well as LAI data, and fused some real data as inputs to train the machine learning models. Subsequently, we used the remaining measured LAI data for validation and prediction to achieve LAI inversion. Among the three machine learning algorithms, Random Forest gave the highest performance, with an R2 of 0 .6164 and an RMSE of 0 .4109, while the BP Neural Network performed inefficiently (R2 = 0 .4022, RMSE = 0 .5407) . Therefore, we ultimately employed the Random Forest algorithm to perform LAI inversion and generated LAI inversion spatial distribution maps, achieving an innovative, efficient, and reliable method for forest stand LAI inversion. Keywords: Leaf Area Index; LESS model; machine learning; remote sensing inversion; GF-6 satellite images; forestry 1. Introduction The Leaf Area Index (LAI) is one of the critical parameters characterizing the growth and developmental status of vegetation. It has great significance in understanding the composition and functions of forest ecosystems [1,2] . LAI not only serves as an indicator of the status of biogeochemical cycles but also plays an indispensable role in evaluating the impact of global ecological changes [3] . In this study, LAI is defined as the total area of leaves on one side per unit of horizontal ground area [4–6], and it has been shown tobe essential for predicting light conditions, assessing total above-ground biomass, and calculating the primary pr

FAQs

What is the purpose of this study on LAI inversion?

To retrieve Leaf Area Index (LAI) efficiently for forest stands by combining high-resolution GF-6 imagery, the LESS 3D radiative transfer model, and machine learning algorithms.

Which machine learning algorithms are used for LAI inversion?

Random Forest, BP Neural Network, and XGBoost are used to perform LAI inversion, trained with reflectance data simulated by LESS and fused real inputs.

How do the algorithms compare in performance?

Random Forest achieves the highest performance (R2 = 0.6164, RMSE = 0.4109), while BP Neural Network performs worse (R2 = 0.4022, RMSE = 0.5407).

Aug 4, 2026

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