Rice Crop Yield Prediction from Sentinel-2 Imagery Using Phenological Metric

Abstract

Crop yield prediction at plot scale is a vitally important magnitude for farmers at the socio-economic level. This study aims to quantify rice yield using phenological metrics from a normalized difference vegetation index (NDVI) time series derived from Sentinel-2 imagery, with yield data collected from 32 plots with an area of 36 ha in the Ferreñafe District of the Lambayeque region, Peru. Three different rice yield models were obtained, the best linear regression models were obtained for the SVM classification, with R2 of 0.69, MAE = 1.01 and RMSE = 1.23 t ha−1; and MRL with R2 of 0.61, MAE = 1.10 and RMSE = 1.38 t ha−1; RF with R2 of 0.44, MAE = 1.23 and RMSE = 1.66 t ha−1. The models obtained open the possibility to generate more robust models using a larger number of samples, which would be useful for farmers as well as for management and planning decisions for food and economic security.

Keywords

• NDVI
• multispectral imaging
• machine learning
• time series
• precision agriculture