Wheat Yield Estimation Using Remote Sensing Indices Derived from Sentinel-2 Time Series and Google Earth Engine in a Highly Fragmented and Heterogeneous Agricultural Region

Abstract

In Morocco, monitoring and estimation of wheat yield at the regional and national scales are critical issues for national food security. The recent Sentinel-2 imagery offers potential for managing grain production systems on a field and regional level. The present study was planned based on a time series of six remote sensing indices and Multiple Linear Regression (MLR) methods for real-time estimation of wheat yield using the Google Earth Engine (GEE) platform in a highly heterogeneous and fragmented agricultural region, such as the Tadla Irrigated Perimeter (TIP). First, the spatial distribution of wheat in the TIP region was mapped by performing Random Forest (RF) classification of Sentinel 2 images. Following that, using MLR models, the wheat yield of nine sampled fields was estimated for the different phenological stages of wheat. The yield measured in-situ was the independent variable of the regressions. The dependent variables included the remote sensing indices derived from Sentinel-2. The remote sensing index and the phenological period of the greatest model were investigated to estimate and map the wheat yield in the entire study area. The RF generated the wheat mapping of the study area with an overall accuracy (OA) of 93.82%. Furthermore, the coefficient of determination (R2) of the tested MLR was from 0.53 to 0.89, while the Root Mean Square Error (RMSE) varied from 4.29 to 7.78 q ha−1. The best model was the one that uses the Green Normalized Difference Vegetation Index (GNDVI) in the tillering and maturity stages.

Keywords

• Google Earth Engine
• Multiple Linear Regression
• remote sensing indices
• Sentinel-2
• wheat yield