Remote Sensing-based crop yield model at field and within-field scales in wheat and barley crops

Crop yield monitoring provides highly appreciated information by decision-makers and end-users, i.e., policymakers, insurance companies or professional farmers. Currently, the dense time series of remote sensing (RS) satellite images allow to accurately describe the spatial and temporal evolution of the canopy, providing valuable information for crop monitoring and yield estimation. In this paper, we present the basis of the integration of RS into the classical approaches for the estimation of biomass production and its partitioning. The proposed approach is based on the well-documented relationships among yield components, i.e. total aboveground biomass and harvest index, and accumulated biophysical variables (radiation absorption, transpiration and crop transpiration coefficient) estimated using widely accepted methodologies based on RS data. The model developed (MYRS: Mapping Yield Remote Sensing-based) provides a mechanistic and quantitative tool for the study of the impact of crop growth and development in the variables determining the final yield in grain crops. While the MYRS model relies on previous studies that demonstrated the feasibility of RS-based approaches to estimate the crop biomass accumulation (Campos et al., 2018a, Campos et al., 2018b) and harvest index (Campoy et al., 2020) in cereal crops, this paper described the operational implementation of these sub-models and the evaluation of the model at field and sub-field scales in commercial fields planted with wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) in Albacete, Ciudad Real, Cuenca, Córdoba and Sevilla (South of Spain). The results revealed the potential of the proposed MYRS model to capture the within and inter-field variability of yield in commercial fields under different environmental and management conditions and with limited requirements for input data. In addition, we discussed in this paper further applications of the model for the evaluation of management strategies and their application in precision agriculture.