Extraction of Maize Distribution Information Based on Critical Fertility Periods and Active–Passive Remote Sensing

Xiaoran Lv, Xiangjun Zhang (), Haikun Yu, Xiaoping Lu, Junli Zhou, Junbiao Feng and Hang Su
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Xiaoran Lv: Key Laboratory of Spatio-Temporal Information and Ecological Restoration of Mines of Natural Resources of the People’s Republic of China, Henan Polytechnic University, Jiaozuo 454003, China
Xiangjun Zhang: Henan Remote Sensing and Mapping Institute, Zhengzhou 450003, China
Haikun Yu: Henan Remote Sensing and Mapping Institute, Zhengzhou 450003, China
Xiaoping Lu: Key Laboratory of Spatio-Temporal Information and Ecological Restoration of Mines of Natural Resources of the People’s Republic of China, Henan Polytechnic University, Jiaozuo 454003, China
Junli Zhou: Henan Remote Sensing and Mapping Institute, Zhengzhou 450003, China
Junbiao Feng: Key Laboratory of Spatio-Temporal Information and Ecological Restoration of Mines of Natural Resources of the People’s Republic of China, Henan Polytechnic University, Jiaozuo 454003, China
Hang Su: Henan Remote Sensing and Mapping Institute, Zhengzhou 450003, China

Sustainability, 2024, vol. 16, issue 19, 1-21

Abstract: This study proposes a new method for integrating active and passive remote sensing data during critical reproductive periods in order to extract maize areas early and to address the problem of low accuracy in the classification of maize-growing areas affected by climate change. Focusing on Jiaozuo City, this study utilized active–passive remote sensing images to determine the optimal time for maize identification. The relative importance of features was assessed using a feature selection method combined with a machine learning algorithm, the impact of both single-source and multi-source features on accuracy was analyzed to generate the optimal feature subset, and the classification accuracies of different machine learning classification methods for maize at the tasseling stage were compared. Ultimately, this study identified the most effective remote sensing features and methods for maize detection during the optimal fertility period. The experimental results show that the feature set optimized for the tasseling stage significantly enhanced maize recognition accuracy. Specifically, the random forest (RF) method, when applied to the multi-source data fusion feature set, yielded the highest accuracy, improving classification accuracy by 24.6% and 4.86% over single-source features, and achieving an overall accuracy of 93.38% with a Kappa coefficient of 0.91. Data on the study area’s maize area were also extracted for the years 2018–2022, with accuracy values of 93.83%, 98.77%, 97%, and 98.05%, respectively.

Keywords: critical fertility periods; active and passive remote sensing; machine learning; phenological characteristics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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