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Modeling Crop Phenology in the US Corn Belt Using Spatially Referenced SMOS Satellite Data

Author

Listed:
  • Colin Lewis-Beck

    (University of Iowa)

  • Zhengyuan Zhu

    (Iowa State University)

  • Victoria Walker

    (University of Montana)

  • Brian Hornbuckle

    (Iowa State University)

Abstract

Satellite measurements follow the growth and senescence of vegetation aid in monitoring crop development within and across growing seasons. For example, identifying when crops reach their peak growth stage or modeling the seasonal growing cycle is useful for agronomists and climatologists. In this paper, we analyze remote sensing data from an intensively cultivated agricultural region in the Midwest to provide new information about crop phenology. There is both a temporal and spatial dimension to the data as they are collected every 12 – 36 hours over regions approximately the size of a 45 km diameter circle. We represent the measurements using a functional data approach and account for spatial dependence between locations through the functional curve coefficients. Modeling across multiple growing years, and including growing degree days as a covariate, we estimate the timing for when crops reach their peak each season and make predictions at unobserved locations. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Colin Lewis-Beck & Zhengyuan Zhu & Victoria Walker & Brian Hornbuckle, 2020. "Modeling Crop Phenology in the US Corn Belt Using Spatially Referenced SMOS Satellite Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 657-675, December.
    • Handle: RePEc:spr:jagbes:v:25:y:2020:i:4:d:10.1007_s13253-020-00419-x
    DOI: 10.1007/s13253-020-00419-x
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    References listed on IDEAS

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    1. Zhang, Hao, 2004. "Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 250-261, January.
    2. Veerabhadran Baladandayuthapani & Bani K. Mallick & Mee Young Hong & Joanne R. Lupton & Nancy D. Turner & Raymond J. Carroll, 2008. "Bayesian Hierarchical Spatially Correlated Functional Data Analysis with Application to Colon Carcinogenesis," Biometrics, The International Biometric Society, vol. 64(1), pages 64-73, March.
    3. Li, Yingxing & Härdle, Wolfgang Karl & Huang, Chen, 2017. "Smooth principal component analysis for high dimensional data," SFB 649 Discussion Papers 2017-024, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    Cited by:

    1. Hans-Peter Piepho & Robert J. Tempelman & Emlyn R. Williams, 2020. "Guest Editors’ Introduction to the Special Issue on “Recent Advances in Design and Analysis of Experiments and Observational Studies in Agriculture”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 453-456, December.

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