Estimating Farm Wheat Yields from NDVI and Meteorological Data

Astrid Vannoppen,Anne Gobin

doi:10.3390/agronomy11050946

Astrid Vannoppen, Anne Gobin

Open Access

https://doi.org/10.3390/agronomy11050946

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Abstract

Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.

Highlights

Agronomy 2021, 11, 946. https://The availability of reliable crop yield data at scales ranging from the field level to the global level is imperative
The aim of this paper is to evaluate the integral normalized difference vegetation index (NDVI), peak NDVI, monthly precipitation, and monthly temperature for setting up an empirical winter wheat (Triticum aestivum) yield model for northern Belgium
When the weather predictors of precipitation and temperature and information on the location of the field were added to the models, the variance explained for winter wheat yield in Latvia reached 84% and 96% using a linear regression method and random forest model, respectively [4]

Summary

Introduction

The availability of reliable crop yield data at scales ranging from the field level to the global level is imperative. Knowledge of crop yield at the field level helps farmers to monitor yield effects of certain management choices, identify potential threats (e.g., consequences of increasing drought occurrence during the growing season) and enhance potential opportunities [1]. For insurance purposes, knowledge of average yield and yield variability is essential [1]. Crop yield data are needed for decision making and strategic planning [2,3]. To identify regions appropriate for setting up a specific agricultural development program, regional crop yield data are indispensable for policy and decision makers

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