Development of an Empirical Model for Estimating Quinoa Canopy Cover from NDVI under Different Irrigation and Fertilization Stress Conditions

Canopy cover (CC) is a critical parameter for characterizing crop growth and calibrating crop models. While the relationship between CC and the Normalized Difference Vegetation Index (NDVI) has been established through linear and quadratic models, a robust empirical approach for deriving CC from NDVI observations remains elusive. This study aims to develop an adequate equation relating NDVI to CC for quinoa crops that can be applied across different irrigation and fertilization stress conditions. A field trial was conducted from March to June 2023 in Chichaoua, a water-scarce region in central Morocco. Both NDVI and CC were determined through field measurements, with NDVI obtained using a handheld NDVI sensor and CC determined through hemispherical photography analysis. The NDVI and CC were evaluated for four treatment combinations: T1 (100% irrigation, 100% fertilization), T2 (80%, 100%), T3 (60%, 25%), and T4 (40%, 25%). Percentages were relative to optimal levels. Strong correlations between NDVI and canopy cover were observed across all treatments, with correlation coefficients ranging from 0.77 to 0.98. Multiple linear and quadratic models were derived for each of the four plots (T1-T4). Each plot-specific equation was then cross-validated by applying it to predict canopy cover in the remaining three plots. The linear model derived from the T3 treatment data emerged as the most representative equation: CC (%) = 141.75 × (NDVI) - 30.913. When applied to predict CC values across all plots, this model demonstrated good performance between predicted and observed CC values with R² (RMSE) values of 0.83 (14.99%), 0.96 (9.63%), 0.60 (8.70%), and 0.69 (8.89%) for T1, T2, T3, and T4, respectively. The developed linear model provides a practical tool for estimating quinoa canopy cover from NDVI measurements under varying irrigation and fertilization conditions, contributing to the improvement of crop monitoring and model calibration in water-scarce environments.