Using NDVI for the assessment of canopy cover in agricultural crops within modelling research

• Published in: Computers and electronics in agriculture Vol. 182; p. 106038
• Main Authors: Tenreiro, Tomás R., García-Vila, Margarita, Gómez, José A., Jiménez-Berni, José A., Fereres, Elías
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.03.2021; Elsevier BV
• Subjects: Canopies; Canopy cover; Crop growth; Crop modelling; Crops; Empirical analysis; Error analysis; Hydrologic models; Hydrology; Meta-analysis; Modelling; NDVI; Root-mean-square errors; Sampling error; Systematic errors; Crop modelling; NDVI; Canopy cover; Meta-analysis
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2021.106038

•One increasingly important application for the spatial analysis of cropping systems, is the estimation of CC via remote sensing NDVI to calibrate simulation models.•In order to explore how correlations between NDVI and CC relate to specific crop species and groups of crop types, we conducted a meta-analysis in which we compiled information that correlated remote and proximal sensing NDVI with field observations of CC for different crop species and types.•Our study proposes generic correlations, which are computationally undemanding, for canopy data assimilation into modelling applications for 13 different agricultural crops.•The proposed models may be considered adequate for many applications, as the RMSE are in line with acceptable levels published in several sensitivity analyses, but there is an effect of irregular sampling sizes that must be considered, as well as sources of systematic error that affect extrapolation of results. 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 R2 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.