Prof. Kyle McDonald Profile

Prof. Kyle McDonald

at The City College of New York

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SPIE Journal Paper | 12 November 2020

Integrated crop growth and radiometric modeling to support Sentinel synthetic aperture radar observations of agricultural fields

Aaron Davitt, Jonathan Winter, Kyle McDonald

JARS, Vol. 14, Issue 04, 044508, (November 2020) https://doi.org/10.1117/12.10.1117/1.JRS.14.044508

KEYWORDS: Backscatter, Synthetic aperture radar, Radar, Scattering, Vegetation, Soil science, Agriculture, Data modeling, Systems modeling, Signal attenuation

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Crop monitoring using synthetic aperture radar requires an understanding of how dynamic crop features influence radar response. We use crop parameters from the decision support system for agrotechnology transfer (DSSAT) model, a dynamic crop growth model, as inputs to the Michigan microwave canopy scattering (MIMICS) model, a radiometric model, to simulate radar scattering from selected wheat, rice, and corn fields in Yolo County, California, during 2015. We compared DSSAT-MIMICS modeled backscatter to Sentinel-1A backscatter and conducted sensitivity analyses to examine crop features that influence backscatter. For each crop, DSSAT-MIMICS modeled VV (vertically transmitted and received) backscatter was correlated to Sentinel-1A σVV0 (mean R-value = 0.76, p < 0.05), root-mean-square error <2 dB, and a model bias between −0.23 and 0.99 dB. However, there were not sufficient Sentinel-1A VH (vertically transmitted and horizontally received) backscatter observations to robustly evaluate DSSAT-MIMICS modeled VH performance. The sensitivity analyses revealed modeled backscatter was most responsive to wheat and rice stems, and corn leaves. Using the analyses, we developed a crop growth index that normalizes Sentinel-1A backscatter to modeled backscatter and mapped corn, rice, and wheat variability, identifying high and low crop growth in fields. This research contributes to the potential application of Sentinel-1A for crop monitoring.

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