IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i8p1263-d892760.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/8/1263/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/8/1263/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gustavo Castilho Beruski & Luis Miguel Schiebelbein & André Belmont Pereira, 2020. "Maize Yield Components as Affected by Plant Population, Planting Date and Soil Coverings in Brazil," Agriculture, MDPI, vol. 10(12), pages 1-20, November.
    2. Nandi, R. & Mondal, K. & Singh, K.C. & Saha, M. & Bandyopadhyay, P.K. & Ghosh, P.K., 2021. "Yield-water relationships of lentil grown under different rice establishments in Lower Gangetic Plain of India," Agricultural Water Management, Elsevier, vol. 246(C).
    3. Li, Cheng & Li, Zhaozhe & Zhang, Fangmin & Lu, Yanyu & Duan, Chunfeng & Xu, Yang, 2023. "Seasonal dynamics of carbon dioxide and water fluxes in a rice-wheat rotation system in the Yangtze-Huaihe region of China," Agricultural Water Management, Elsevier, vol. 275(C).
    4. Chin-Hung Kuan & Yungho Leu & Wen-Shin Lin & Chien-Pang Lee, 2022. "The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model," Agriculture, MDPI, vol. 12(8), pages 1-15, July.
    5. Mohammed, Ali T. & Irmak, Suat, 2022. "Maize response to irrigation and nitrogen under center pivot, subsurface drip and furrow irrigation: Water productivity, basal evapotranspiration and yield response factors," Agricultural Water Management, Elsevier, vol. 271(C).
    6. Yang, Wenjia & Yan, Naitong & Zhang, Jiali & Yan, Jiakun & Ma, Dengke & Wang, Shiwen & Yin, Lina, 2022. "The applicability of water-permeable plastic film and biodegradable film as alternatives to polyethylene film in crops on the Loess Plateau," Agricultural Water Management, Elsevier, vol. 274(C).
    7. Di Wang, & Wang, Li, 2023. "Characteristics of soil evaporation at two stages of growth in apple orchards with different ages in a semi-humid region," Agricultural Water Management, Elsevier, vol. 280(C).
    8. Peng, Manman & Han, Wenting & Li, Chaoqun & Li, Guang & Yao, Xiaomin & Zhang, Mengfei, 2021. "Diurnal and seasonal CO2 exchange and yield of maize cropland under different irrigation treatments in semiarid Inner Mongolia," Agricultural Water Management, Elsevier, vol. 255(C).
    9. Heba Alzaben & Roydon Fraser & Clarence Swanton, 2021. "The Role of Engineering Thermodynamics in Explaining the Inverse Correlation between Surface Temperature and Supplied Nitrogen Rate in Corn Plants: A Greenhouse Case Study," Agriculture, MDPI, vol. 11(2), pages 1-16, January.
    10. Jiang, Shouzheng & Zhao, Lu & Liang, Chuan & Hu, Xiaotao & Yaosheng, Wang & Gong, Daozhi & Zheng, Shunsheng & Huang, Yaowei & He, QingYan & Cui, Ningbo, 2022. "Leaf- and ecosystem-scale water use efficiency and their controlling factors of a kiwifruit orchard in the humid region of Southwest China," Agricultural Water Management, Elsevier, vol. 260(C).
    11. Fang, Heng & Li, Yuannong & Gu, Xiaobo & Yu, Meng & Chen, Pengpeng & Li, Yupeng & Liu, Fulai, 2022. "Optimizing the impact of film mulching pattern and nitrogen application rate on maize production, gaseous N emissions, and utilization of water and nitrogen in northwest China," Agricultural Water Management, Elsevier, vol. 261(C).
    12. Nyathi, M.K. & Mabhaudhi, T. & Van Halsema, G.E. & Annandale, J.G. & Struik, P.C., 2019. "Benchmarking nutritional water productivity of twenty vegetables - A review," Agricultural Water Management, Elsevier, vol. 221(C), pages 248-259.

    More about this item

    Keywords

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1263-:d:892760. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.