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.

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，以实现有效的农业管理。本研究在对雨养花生进行田间试验后评估了苗期的 LAI。该研究使用免费的 Sentinel-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 LAI 光谱的分析，665 nm、705 nm、842 nm 和 2190 nm 的波段是检索花生 LAI 最敏感的波段。结果表明，使用以红色 (665 nm)、红边 (705 nm) 和近红外 (842 nm) 为中心的波段计算的 VI在 Sentinel-2 数据中表现出最佳的 R ² 。使用归一化植被指数（NDVI）、红边归一化植被指数（NDVIre）、简单比率（SR）、红边简单比率（SRre）和绿色归一化植被指数（gNDVI）作为LAI的预测因子。关于估计 LAI 和测量 LAI 之间的验证结果，SR 证明了花生 LAI 预测的最高准确度（r ²  = 0.67，RMSE = 0.89）。测试了 10 个 MLRA，结果从模型精度的角度来看，高斯过程回归、GPR（r ²  = 0.73 和 RMSE = 0.81）、核岭回归、KRR（r ²  = 0.72 和 RMSE = 0.82）和支持向量回归、SVR（r ²  = 0.70 和 RMSE = 0.85）被证明最适合雨养花生苗期的 LAI 估算。基于此处测试的回归方法的系统分析表明，GPR 优于其他模型组合，因此最适合估算苗期雨养花生 LAI。这些发现可作为在热带西非花生性状监测框架内获取作物生物物理参数的基准。