IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v9y2019i1p17-d196087.html
   My bibliography  Save this article

In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification

Author

Listed:
  • Md. Shahinoor Rahman

    (Center for Spatial Information Science and Systems (CSISS), George Mason University, Fairfax, VA 22030, USA)

  • Liping Di

    (Center for Spatial Information Science and Systems (CSISS), George Mason University, Fairfax, VA 22030, USA)

  • Eugene Yu

    (Center for Spatial Information Science and Systems (CSISS), George Mason University, Fairfax, VA 22030, USA)

  • Chen Zhang

    (Center for Spatial Information Science and Systems (CSISS), George Mason University, Fairfax, VA 22030, USA)

  • Hossain Mohiuddin

    (School of Urban and Regional Planning, The University of Iowa, Iowa City, IA 52242, USA)

Abstract

Crop type information at the field level is vital for many types of research and applications. The United States Department of Agriculture (USDA) provides information on crop types for US cropland as a Cropland Data Layer (CDL). However, CDL is only available at the end of the year after the crop growing season. Therefore, CDL is unable to support in-season research and decision-making regarding crop loss estimation, yield estimation, and grain pricing. The USDA mostly relies on field survey and farmers’ reports for the ground truth to train image classification models, which is one of the major reasons for the delayed release of CDL. This research aims to use trusted pixels as ground truth to train classification models. Trusted pixels are pixels which follow a specific crop rotation pattern. These trusted pixels are used to train image classification models for the classification of in-season Landsat images to identify major crop types. Six different classification algorithms are investigated and tested to select the best algorithm for this study. The Random Forest algorithm stands out among selected algorithms. This study classified Landsat scenes between May and mid-August for Iowa. The overall agreements of classification results with CDL in 2017 are 84%, 94%, and 96% for May, June, and July, respectively. The classification accuracies have been assessed through 683 ground truth data points collected from the fields. The overall accuracies of single date multi-band image classification are 84%, 89% and 92% for May, June, and July, respectively. The result also shows higher accuracy (94–95%) can be achieved through multi-date image classification compared to single date image classification.

Suggested Citation

  • Md. Shahinoor Rahman & Liping Di & Eugene Yu & Chen Zhang & Hossain Mohiuddin, 2019. "In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification," Agriculture, MDPI, vol. 9(1), pages 1-21, January.
  • Handle: RePEc:gam:jagris:v:9:y:2019:i:1:p:17-:d:196087
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/9/1/17/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/9/1/17/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Boryan, Claire G. & Yang, Zhengwei, 2012. "A new land cover classification based stratification method for area sampling frame construction," NASS Research Reports 234361, United States Department of Agriculture, National Agricultural Statistics Service.
    2. Ilya Gelfand & Ritvik Sahajpal & Xuesong Zhang & R. César Izaurralde & Katherine L. Gross & G. Philip Robertson, 2013. "Sustainable bioenergy production from marginal lands in the US Midwest," Nature, Nature, vol. 493(7433), pages 514-517, January.
    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. Md Shahinoor Rahman & Liping Di, 2020. "A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment," Agriculture, MDPI, vol. 10(4), pages 1-30, April.

    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. Stefan Arens & Sunke Schlüters & Benedikt Hanke & Karsten von Maydell & Carsten Agert, 2020. "Sustainable Residential Energy Supply: A Literature Review-Based Morphological Analysis," Energies, MDPI, vol. 13(2), pages 1-28, January.
    2. Yu, Ziyue & Zhang, Fan & Gao, Chenzhen & Mangi, Eugenio & Ali, Cheshmehzangi, 2024. "The potential for bioenergy generated on marginal land to offset agricultural greenhouse gas emissions in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    3. Liu, Guilin & Mai, Jianfeng, 2022. "Habitat shifts of Jatropha curcas L. in the Asia-Pacific region under climate change scenarios," Energy, Elsevier, vol. 251(C).
    4. Xiong, Wei & Balkovič, Juraj & van der Velde, Marijn & Zhang, Xuesong & Izaurralde, R. César & Skalský, Rastislav & Lin, Erda & Mueller, Nathan & Obersteiner, Michael, 2014. "A calibration procedure to improve global rice yield simulations with EPIC," Ecological Modelling, Elsevier, vol. 273(C), pages 128-139.
    5. Naseri, Hakim & Parashkoohi, Mohammad Gholami & Ranjbar, Iraj & Zamani, Davood Mohammad, 2021. "Energy-economic and life cycle assessment of sugarcane production in different tillage systems," Energy, Elsevier, vol. 217(C).
    6. Wu, Jy S. & Tseng, Hui-Kuan & Liu, Xiaoshuai, 2022. "Techno-economic assessment of bioenergy potential on marginal croplands in the U.S. southeast," Energy Policy, Elsevier, vol. 170(C).
    7. Ujjayant Chakravorty & Marie‐Hélène Hubert & Beyza Ural Marchand, 2019. "Food for fuel: The effect of the US biofuel mandate on poverty in India," Quantitative Economics, Econometric Society, vol. 10(3), pages 1153-1193, July.
    8. Chen, Xiaoguang & Huang, Haixiao & Khanna, Madhu & Önal, Hayri, 2014. "Alternative transportation fuel standards: Welfare effects and climate benefits," Journal of Environmental Economics and Management, Elsevier, vol. 67(3), pages 241-257.
    9. Niblick, Briana & Landis, Amy E., 2016. "Assessing renewable energy potential on United States marginal and contaminated sites," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 489-497.
    10. Weidong Huang, 2015. "An Integrated Biomass Production and Conversion Process for Sustainable Bioenergy," Sustainability, MDPI, vol. 7(1), pages 1-15, January.
    11. Amir Behzad Bazrgar & Aeryn Ng & Brent Coleman & Muhammad Waseem Ashiq & Andrew Gordon & Naresh Thevathasan, 2020. "Long-Term Monitoring of Soil Carbon Sequestration in Woody and Herbaceous Bioenergy Crop Production Systems on Marginal Lands in Southern Ontario, Canada," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    12. Hoekman, S. Kent & Broch, Amber & Liu, Xiaowei (Vivian), 2018. "Environmental implications of higher ethanol production and use in the U.S.: A literature review. Part I – Impacts on water, soil, and air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 3140-3158.
    13. Baka, Jennifer & Bailis, Robert, 2014. "Wasteland energy-scapes: A comparative energy flow analysis of India's biofuel and biomass economies," Ecological Economics, Elsevier, vol. 108(C), pages 8-17.
    14. Kiefer, Katharina & Kremer, Jasper & Zeitner, Philipp & Winkler, Bastian & Wagner, Moritz & von Cossel, Moritz, 2023. "Monetizing ecosystem services of perennial wild plant mixtures for bioenergy," Ecosystem Services, Elsevier, vol. 61(C).
    15. Ranjith P. Udawatta & Clark J. Gantzer & Timothy M. Reinbott & Ray L. Wright & Pierce A. Robert & Walter Wehtje, 2020. "Influence of Species Composition and Management on Biomass Production in Missouri," Agriculture, MDPI, vol. 10(3), pages 1-14, March.
    16. Perrin, Aurelie & Wohlfahrt, Julie & Morandi, Fabiana & Østergård, Hanne & Flatberg, Truls & De La Rua, Cristina & Bjørkvoll, Thor & Gabrielle, Benoit, 2017. "Integrated design and sustainable assessment of innovative biomass supply chains: A case-study on miscanthus in France," Applied Energy, Elsevier, vol. 204(C), pages 66-77.
    17. Liu, Kaimin & Fu, Jianqin & Deng, Banglin & Yang, Jing & Tang, Qijun & Liu, Jingping, 2014. "The influences of pressure and temperature on laminar flame propagations of n-butanol, iso-octane and their blends," Energy, Elsevier, vol. 73(C), pages 703-715.
    18. Frederik De Wieuw & Tom Pauwels & Christa Sys & Eddy Van de Voorde & Edwin van Hassel & Thierry Vanelslander & Jeffrey Willems, 2023. "Collection and Processing of Roadside Grass Clippings: A Supply Chain Optimization Case Study for East Flanders," Sustainability, MDPI, vol. 15(18), pages 1-24, September.
    19. Perdue, James H. & Stanturf, John A. & Young, Timothy M. & Huang, Xia & Dougherty, Derek & Pigott, Michael & Guo, Zhimei, 2017. "Profitability potential for Pinus taeda L. (loblolly pine) short-rotation bioenergy plantings in the southern USA," Forest Policy and Economics, Elsevier, vol. 83(C), pages 146-155.
    20. Baka, Jennifer & Bailis, Robert, 2014. "Wasteland energy-scapes: a comparative energy flow analysis of India's biofuel and biomass economies," LSE Research Online Documents on Economics 59896, London School of Economics and Political Science, LSE Library.

    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:9:y:2019:i:1:p:17-:d:196087. 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.