High-Resolution Mapping of Maize in Mountainous Terrain Using Machine Learning and Multi-Source Remote Sensing Data

Luying Liu, Jingyi Yang, Fang Yin () and Linsen He
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Luying Liu: Shaanxi Key Laboratory of Land Consolidation, School of Land Engineering, Chang’an University, Xi’an 710054, China
Jingyi Yang: Shaanxi Key Laboratory of Land Consolidation, School of Land Engineering, Chang’an University, Xi’an 710054, China
Fang Yin: Shaanxi Key Laboratory of Land Consolidation, School of Land Engineering, Chang’an University, Xi’an 710054, China
Linsen He: Shaanxi Key Laboratory of Land Consolidation, School of Land Engineering, Chang’an University, Xi’an 710054, China

Land, 2025, vol. 14, issue 2, 1-21

Abstract: In recent years, machine learning methods have garnered significant attention in the field of crop recognition, playing a crucial role in obtaining spatial distribution information and understanding dynamic changes in planting areas. However, research in smaller plots within mountainous regions remains relatively limited. This study focuses on Shangzhou District in Shangluo City, Shaanxi Province, utilizing a dataset of high-resolution remote sensing images (GF-1, ZY1-02D, ZY-3) collected over seven months in 2021 to calculate the normalized difference vegetation index (NDVI) and construct a time series. By integrating field survey results with time series images and Google Earth for visual interpretation, the NDVI time series curve for maize was analyzed. The Random Forest (RF) classification algorithm was employed for maize recognition, and comparative analyses of classification accuracy were conducted using Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Artificial Neural Network (ANN). The results demonstrate that the random forest algorithm achieved the highest accuracy, with an overall accuracy of 94.88% and a Kappa coefficient of 0.94, both surpassing those of the other classification methods and yielding satisfactory overall results. This study confirms the feasibility of using time series high-resolution remote sensing images for precise crop extraction in the southern mountainous regions of China, providing valuable scientific support for optimizing land resource use and enhancing agricultural productivity.

Keywords: machine learning; NDVI; time series; classification; remote sensing; Zea mays (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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