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Prediction of Corn Yield in the USA Corn Belt Using Satellite Data and Machine Learning: From an Evapotranspiration Perspective

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  • Zhonglin Ji

    (State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100875, China
    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China)

  • Yaozhong Pan

    (State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100875, China
    Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China)

  • Xiufang Zhu

    (State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100875, China
    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China)

  • Dujuan Zhang

    (State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100875, China
    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China)

  • Jiajia Dai

    (State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100875, China
    Institute of Remote Sensing Science and Engineering, Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China)

Abstract

The reliable prediction of corn yield for the United States of America is essential for effective food and energy management of the world. Three satellite-derived variables were selected, namely enhanced vegetation index (EVI), leaf area index (LAI) and land surface temperature (LST). The least absolute shrinkage and selection operator (LASSO) was used for regression, while random forest (RF), support vector regression (SVR) and long short-term memory (LSTM) methods were selected for machine learning. The three variables serve as inputs to these methods, and their efficacy in predicting corn yield was assessed in relation to evapotranspiration (ET). The results confirmed that a high level of performance can be achieved for yield prediction (mean predicted R 2 = 0.63) by combining EVI + LAI + LST with the four methods. Among them, the best results were obtained by using LSTM (mean predicted R 2 = 0.67). EVI and LST provided extra and unique information in peak and early growth stages for corn yield, respectively, and the usefulness of including LAI was not readily apparent across the whole season, which was consistent with the field growing conditions affecting the ET of corn. The satellite-derived data and the methods used in this study could be used for predicting the yields of other crops in different regions.

Suggested Citation

  • Zhonglin Ji & Yaozhong Pan & Xiufang Zhu & Dujuan Zhang & Jiajia Dai, 2022. "Prediction of Corn Yield in the USA Corn Belt Using Satellite Data and Machine Learning: From an Evapotranspiration Perspective," Agriculture, MDPI, vol. 12(8), pages 1-23, August.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1263-:d:892760
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    References listed on IDEAS

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    1. Zhou, Li & Wang, Yu & Jia, Qingyu & Li, Rongping & Zhou, Mengzi & Zhou, Guangsheng, 2019. "Evapotranspiration over a rainfed maize field in northeast China: How are relationships between the environment and terrestrial evapotranspiration mediated by leaf area?," Agricultural Water Management, Elsevier, vol. 221(C), pages 538-546.
    2. Unkovich, Murray & Baldock, Jeff & Farquharson, Ryan, 2018. "Field measurements of bare soil evaporation and crop transpiration, and transpiration efficiency, for rainfed grain crops in Australia – A review," Agricultural Water Management, Elsevier, vol. 205(C), pages 72-80.
    3. Yulin Shen & Benoît Mercatoris & Zhen Cao & Paul Kwan & Leifeng Guo & Hongxun Yao & Qian Cheng, 2022. "Improving Wheat Yield Prediction Accuracy Using LSTM-RF Framework Based on UAV Thermal Infrared and Multispectral Imagery," Agriculture, MDPI, vol. 12(6), pages 1-13, June.
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    1. Pankaj Das & Girish Kumar Jha & Achal Lama & Rajender Parsad, 2023. "Crop Yield Prediction Using Hybrid Machine Learning Approach: A Case Study of Lentil ( Lens culinaris Medik.)," Agriculture, MDPI, vol. 13(3), pages 1-13, February.

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    Keywords

    EVI; LAI; LST; LSTM; MODIS;
    All these keywords.

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