The fraction of green canopy cover (CC) is an important feature commonly used to characterize crop growth and for calibration of crop and hydrological models. It is well accepted that there is a relation between CC and NDVI through linear or quadratic models, however a straight-forward empirical approach, to derive CC from NDVI observations, is still lacking. In this study, we conducted a meta-analysis of the NDVI-CC relationships with data collected from 19 different studies (N = 1397). Generic models are proposed here for 13 different agricultural crops, and the associated degree of uncertainty, together with the magnitude of error were quantified for each model (RMSE around 6–18% of CC). We observed that correlations are adequate for the majority of crops as $R^2$ values were above 75% for most cases, and coefficient estimates were significant for most of the linear and quadratic models. Extrapolation to conditions different than those found in the studies may require local validation, as obtained regressions are affected by non-sampling errors or sources of systematic error that need further investigation. In a case study with wheat, we tested the use of NDVI as a proxy to estimate CC and to calibrate the AquaCrop model. Simulation outcomes were validated with field data collected from three growing seasons and confirmed that the NDVI-CC relationship was useful for modelling research. We highlight that the overall applicability of these relationships to modelling is promising as the RMSE are in line with acceptable levels published in several sensitivity analyses.

绿冠层覆盖率（CC）是一个重要特征，通常用于表征作物生长以及校准作物和水文模型。通过线性或二次模型在CC和NDVI之间存在关系是众所周知的，但是仍然缺乏直接的经验方法来从NDVI观测中得出CC。在这项研究中，我们对NDVI-CC关系进行了荟萃分析，并从19个不同的研究（N = 1397）中收集了数据。在此提出了针对13种不同农作物的通用模型，并对每种模型的相关不确定性程度和误差幅度进行了量化（RMSE约为CC的6-18％）。我们观察到，对于大多数作物而言，相关性是足够的，因为${\mathrm{[R}}^2$在大多数情况下，该值大于75％，并且对于大多数线性和二次模型，系数估计值均显着。外推到与研究中发现的条件不同的条件可能需要本地验证，因为获得的回归受非抽样误差或需要进一步研究的系统误差来源的影响。在小麦的案例研究中，我们测试了使用NDVI作为代理来估算CC和校准AquaCrop模型。通过从三个生长季节收集的实地数据验证了模拟结果，并证实了NDVI-CC关系对于建模研究很有用。我们着重指出，由于RMSE符合在几项敏感性分析中公布的可接受水平，因此这些关系对建模的总体适用性是有希望的。