A Parametric Approach to Unmixing Remote Sensing Crop Growth Signatures

Colin Lewis-Beck (), Zhengyuan Zhu, Anirban Mondal, Joon Jin Song, Jonathan Hobbs, Brian Hornbuckle and Jason Patton
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Colin Lewis-Beck: Iowa State University
Zhengyuan Zhu: Iowa State University
Anirban Mondal: Case Western Reserve University
Joon Jin Song: Baylor University
Jonathan Hobbs: California Institute of Technology
Brian Hornbuckle: Iowa State University
Jason Patton: University of Wisconsin

Journal of Agricultural, Biological and Environmental Statistics, 2019, vol. 24, issue 3, No 7, 502-516

Abstract: Abstract Remote sensing data are often measured or reported over wide spatial footprints with heterogeneous ground cover. Different types of vegetation, however, have unique signatures that evolve throughout the growing season. Without additional information, signals corresponding to individual vegetation types are unidentifiable from satellite measurements. In this paper, we propose a parametric mixture model to describe satellite data monitoring crop development in the US Corn Belt. The ground cover of each satellite footprint is primarily a mixture of corn and soybean. Using auxiliary data from multiple sources, we model the aggregate satellite signal, and identify the signatures of individual crop types, using nonlinear parametric functions. Estimation is performed using a Bayesian approach, and information from auxiliary data is incorporated into the prior distributions to identify distinct crop types. We demonstrate our parametric unmixing approach using data from the European Space Agency’s Soil Moisture and Ocean Salinity satellite. Lastly, we compare our model estimates for the timing of key crop phenological stages to USDA ground-based estimates. Supplementary materials accompanying this paper appear online.

Keywords: SMOS; Unmixing; Bayesian estimation; Maize; Remote sensing (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13253-019-00368-0

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