Leaf area index (LAI) is a key variable in understanding and modeling crop-environment interactions. With the advent of increasingly higher spatial resolution satellites and sensors mounted on remotely piloted aircrafts (RPAs), the use of remote sensing in precision agriculture is becoming more common. Since also the availability of methods to retrieve LAI from image data have also drastically expanded, it is necessary to test simultaneously as many methods as possible to understand the advantages and disadvantages of each approach. Ground-based LAI data from three years of barley experiments were related to remote sensing information using vegetation indices (VI), machine learning (ML) and radiative transfer models (RTM), to assess the relative accuracy and efficacy of these methods. The optimized soil adjusted vegetation index and a modified version of the Weighted Difference Vegetation Index performed slightly better than any other retrieval method. However, all methods yielded coefficients of determination of around 0.7 to 0.9. The best performing machine learning algorithms achieved higher accuracies when four Sentinel-2 bands instead of 12 were used. Also, the good performance of VIs and the satisfactory performance of the 4-band RTM, strongly support the synergistic use of satellites and RPAs in precision agriculture. One of the methods used, Sen2-Agri, an open source ML-RTM-based operational system, was also able to accurately retrieve LAI, although it is restricted to Sentinel-2 and Landsat data. This study shows the benefits of testing simultaneously a broad range of retrieval methods to monitor crops for precision agriculture.

叶面积指数 (LAI) 是理解和模拟作物与环境相互作用的关键变量。随着越来越高的空间分辨率卫星和安装在遥控飞机 (RPA) 上的传感器的出现，遥感在精准农业中的使用变得越来越普遍。由于从图像数据中检索 LAI 的方法的可用性也急剧扩大，因此有必要同时测试尽可能多的方法，以了解每种方法的优缺点。来自三年大麦实验的地面 LAI 数据与使用植被指数 (VI)、机器学习 (ML) 和辐射传输模型 (RTM) 的遥感信息相关，以评估这些方法的相对准确性和有效性。优化的土壤调整植被指数和加权差分植被指数的修改版本的表现略好于任何其他检索方法。然而，所有方法产生的确定系数都在 0.7 到 0.9 左右。当使用四个 Sentinel-2 波段而不是 12 个波段时，性能最佳的机器学习算法可以实现更高的准确度。此外，VI的良好性能和4波段RTM的令人满意的性能，有力地支持了卫星和RPA在精准农业中的协同使用。使用的方法之一是基于 ML-RTM 的开源操作系统 Sen2-Agri 也能够准确地检索 LAI，尽管它仅限于 Sentinel-2 和 Landsat 数据。