Estimating rainfed groundnut’s leaf area index using Sentinel-2 based on Machine Learning Regression Algorithms and Empirical Models

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2024-02-21 DOI:10.1007/s11119-024-10117-0

Michael Chibuike Ekwe, Oluseun Adeluyi, Jochem Verrelst, Angela Kross, Caleb Akoji Odiji

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Abstract

The leaf area index (LAI), a crucial biophysical indicator, is used to assess and monitor crop growth for effective agricultural management. This study assessed the LAI at the seedling stage after conducting a field experiment with rainfed groundnut. The study tests the performance of multiple machine learning regression algorithms (MLRAs) and empirical vegetation indices (VIs) in retrieving groundnut's LAI using freely available Sentinel-2 data. The bands at 665 nm, 705 nm, 842 nm, and 2190 nm are the most sensitive for retrieving groundnut's LAI, according to an analysis of its band spectrum. Results suggest that VIs computed with wavebands centered at red (665 nm), red edge (705 nm), and near-infrared (842 nm) exhibited optimal R² with Sentinel-2 data. Normalized difference vegetation index (NDVI), red edge normalized difference vegetation index (NDVIre), simple ratio (SR), red edge simple ratio (SRre), and green normalized difference vegetation index (gNDVI) were utilized as predictors for LAI. Regarding the results of the validation between estimated and measured LAI, SR demonstrated the highest accuracy for groundnut LAI prediction (r² = 0.67, RMSE = 0.89). Ten MLRAs were tested, and results indicate from the perspective of the accuracy of models, the Gaussian processes regression, GPR (r² = 0.73 and RMSE = 0.81), Kernel ridge regression, KRR (r² = 0.72 and RMSE = 0.82) and Support vector regression, SVR (r² = 0.70 and RMSE = 0.85) demonstrated to be the most suitable for LAI estimation for rainfed groundnut at the seedling stage. The systematic analysis based on the regression approaches tested here revealed that the GPR outperformed other models combined, therefore, most suitable for estimating rainfed groundnut LAI at the seedling stage. These findings serve as a benchmark for obtaining crop biophysical parameters in the framework of groundnut traits monitoring in a tropical West Africa.

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基于机器学习回归算法和经验模型，利用哨兵 2 号估算雨浇花生的叶面积指数

叶面积指数（LAI）是一项重要的生物物理指标，用于评估和监测作物生长情况，以便进行有效的农业管理。本研究在对雨浇花生进行田间试验后，评估了苗期的叶面积指数。研究利用免费提供的哨兵-2 数据，测试了多种机器学习回归算法（MLRA）和经验植被指数（VI）在检索花生 LAI 方面的性能。根据对花生波段光谱的分析，665 nm、705 nm、842 nm 和 2190 nm 波段对检索花生的 LAI 最为敏感。结果表明，以红色（665 nm）、红边（705 nm）和近红外（842 nm）为中心的波段计算的植被指数与哨兵-2 数据的 R2 值最佳。归一化差异植被指数（NDVI）、红边归一化差异植被指数（NDVIre）、简单比率（SR）、红边简单比率（SRre）和绿色归一化差异植被指数（gNDVI）被用作 LAI 的预测因子。估算的 LAI 与测量的 LAI 之间的验证结果表明，SR 对花生 LAI 预测的准确度最高（r2 = 0.67，RMSE = 0.89）。结果表明，从模型准确性的角度来看，高斯过程回归 GPR（r2 = 0.73，RMSE = 0.81）、核脊回归 KRR（r2 = 0.72，RMSE = 0.82）和支持向量回归 SVR（r2 = 0.70，RMSE = 0.85）最适合用于苗期雨浇花生的 LAI 估算。根据本文测试的回归方法进行的系统分析显示，GPR 优于其他综合模型，因此最适合用于估算苗期雨浇花生的 LAI。这些发现可作为在西非热带地区花生性状监测框架内获取作物生物物理参数的基准。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.