Winter Wheat Maturity Prediction via Sentinel-2 MSI Images

Author:

Yue Jibo1ORCID,Li Ting1,Shen Jianing1,Wei Yihao1,Xu Xin1,Liu Yang2,Feng Haikuan345,Ma Xinming1,Li Changchun5,Yang Guijun45,Qiao Hongbo1,Yang Hao4ORCID,Liu Qian1

Affiliation:

1. College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China

2. Key Lab of Smart Agriculture System, Ministry of Education, China Agricultural University, Beijing 100083, China

3. College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China

4. Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

5. Institute of Quantitative Remote Sensing and Smart Agriculture, Henan Polytechnic University, Jiaozuo 454000, China

Abstract

A timely and comprehensive understanding of winter wheat maturity is crucial for deploying large-scale harvesters within a region, ensuring timely winter wheat harvesting, and maintaining grain quality. Winter wheat maturity prediction is limited by two key issues: accurate extraction of wheat planting areas and effective maturity prediction methods. The primary aim of this study is to propose a method for predicting winter wheat maturity. The method comprises three parts: (i) winter wheat planting area extraction via phenological characteristics across multiple growth stages; (ii) extraction of winter wheat maturity features via vegetation indices (VIs, such as NDVI, NDRE, NDII1, and NDII2) and box plot analysis; and (iii) winter wheat maturity data prediction via the selected VIs. The key findings of this work are as follows: (i) Combining multispectral remote sensing data from the winter wheat jointing-filling and maturity-harvest stages can provide high-precision extraction of winter wheat planting areas (OA = 95.67%, PA = 91.67%, UA = 99.64%, and Kappa = 0.9133). (ii) The proposed method can offer the highest accuracy in predicting maturity at the winter wheat flowering stage (R2 = 0.802, RMSE = 1.56 days), aiding in a timely and comprehensive understanding of winter wheat maturity and in deploying large-scale harvesters within the region. (iii) The study’s validation was only conducted for winter wheat maturity prediction in the North China Plain wheat production area, and the accuracy of harvesting progress information extraction for other regions’ wheat still requires further testing. The method proposed in this study can provide accurate predictions of winter wheat maturity, helping agricultural management departments adopt information-based measures to improve the efficiency of monitoring winter wheat maturation and harvesting, thus promoting the efficiency of precision agricultural operations and informatization efforts.

Funder

the Henan Province Science and Technology Research Project, China

the National Natural Science Foundation of China

the National Key Research and Development Program of China

the Science and Technology Program of Ministry of Public Security

Publisher

MDPI AG

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