Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques

Ahmed Kayad,Francesco Pirotti,Francesco Marinello,Marco Sozzi,Simone Gatto

doi:10.3390/rs11232873

Abstract

Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4–R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4–R6).

Highlights

Crop yield is defined as the total production per unit area and is commonly measured in tons per hectare [1]
Karimi et al [50] used support vector machines (SVM) and Artificial Neural Networks (ANN) to classify hyperspectral images based on nitrogen application rates and weed management practices over a corn field and the results showed that SVM provided very low misclassification rates compared with ANN
Tan et al [64] reported that Green Normalized Difference Vegetation Index (GNDVI) was the most positively correlated vegetation index with fraction of absorbed photosynthetically active radiation (FPAR) for corn canopies which subsequently describes the strong relation to corn biomass and yield

Summary

Introduction

Crop yield is defined as the total production per unit area and is commonly measured in tons per hectare [1]. Such yield changes spatially and temporally within-field zones based on local spatial variability in soil physical and chemical properties, management practices and localized damage due to pests and pathogens [2,3,4]. Satellite remote sensing (RS) offers a wide range of indications for crop and vegetation parameters, such as Leaf Area Index (LAI) [8,9], leaf nitrogen accumulation [10], the fraction of absorbed photosynthetically active radiation (FPAR) [11] and crop biomass [8,12,13,14] Most of these applications were on the large scale of a region or country compared with few applications at a field scale. The most common reasons for this trend are the high cost of obtaining and processing RS data on a field scale, research funders such as governments and organizations are focusing on total production, and a lack of ground truth data and measurement accuracy [5]

Objectives

Methods

Results

Discussion

Conclusion