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

Colin Lewis-Beck, Zhengyuan Zhu (), Victoria Walker and Brian Hornbuckle
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Colin Lewis-Beck: University of Iowa
Zhengyuan Zhu: Iowa State University
Victoria Walker: University of Montana
Brian Hornbuckle: Iowa State University

Journal of Agricultural, Biological and Environmental Statistics, 2020, vol. 25, issue 4, No 11, 657-675

Abstract: 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.

Keywords: Bayesian estimation; Remote sensing; SMOS; Spatial model; Stan; Vegetation indices (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13253-020-00419-x

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