Indirect, non-destructive methods to derive biophysical parameters, such as leaf area index (LAI), are of major importance for optimal grassland growth modelling and management. In this study, we compared different methods for the estimation of LAI in permanent grassland including (i) two direct methods, (ii) two indirect optical methods (AccuPAR and LAI-2200C), (iii) a proximal (field spectrometer) and a satellite remote sensing approach using Sentinel-2 (S-2) data, both based on radiative transfer modelling (RTM) of vegetation. To consider the seasonal variability of LAI sufficiently, we performed in situ measurements weekly during the entire growing season of 2018 and 2019. The RTM-based methods showed the lowest root-mean-square error (RMSE) when compared with direct green LAI measurements, which ranged from 1.68 to 7.85. The indirect optical methods resulted in higher RMSE values but in similar high correlation coefficients (r). The comparison between the indirect optical methods showed that the AccuPAR and the LAI-2200C highly correlate with a systematic underestimation of the AccuPAR in the upper range of LAI values. As expected, S-2 and the field spectrometer showed the highest correlation (RMSE: 0.40 and r: 0.95). Generally, we observed a significant influence of the seasonal changes of the canopy structure and morphology on the estimation accuracy.

间接的，非破坏性的方法来获取生物物理参数，例如叶面积指数（LAI），对于优化草地生长的建模和管理至关重要。在这项研究中，我们比较了估算永久性草地LAI的不同方法，包括（i）两种直接方法，（ii）两种间接光学方法（AccuPAR和LAI-2200C），（iii）近端（现场光谱仪）和使用Sentinel-2（S-2）数据的卫星遥感方法，两者均基于植被的辐射传递建模（RTM）。为了充分考虑LAI的季节变化，我们在2018年和2019年的整个生长季节每周进行一次原位测量。基于RTM的方法显示出最低的均方根误差（RMSE））与直接绿色LAI测量值（介于1.68到7.85之间）进行比较。间接光学方法导致较高的 RMSE 值，但相关系数（r）相似。间接光学方法之间的比较表明，在较高的LAI值范围内，AccuPAR和LAI-2200C与AccuPAR的系统性低估高度相关。不出所料，S-2和现场光谱仪显示出最高的相关性（RMSE：  0.40和 r：  0.95）。通常，我们观察到了冠层结构和形态的季节性变化对估计精度的重大影响。