Date of Award

January 2022

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Atmospheric Sciences

First Advisor

Jianglong Zhang

Abstract

Accurate planting dates are a critical component of several processes in agricultural research, such as crop modeling, agricultural monitoring, and yield forecasts. Yet, field-scale estimations of yearly planting dates on a larger spatial domain are still a challenging task. Using Normalized Difference Vegetation Index (NDVI) values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) coupled with Growing Degree Days (GDD) calculated from the North American Regional Reanalysis (NARR) dataset, methods for estimating planting dates over individual fields are attempted over corn fields across North Dakota. Through these methods, mean statewide planting dates from the National Agricultural Statistics Service (NASS) crop progress reports across North Dakota were used for calibration of the developed algorithm for 2012. The coupled satellite-based/GDD method for calculating planting date was then further evaluated against weekly NASS data for the years 2003-2020 with a maximum r2 of 0.96 between the two variables. A calculated median planting date mean difference of just 1.3 days and root-mean-square-error (RMSE) of 3.6 days was achieved. This contrasts with using a set number of calendar days as a calibration, which resulted in a mean difference of 4.7 days and RMSE of 7.6 days. The results of this study suggest that satellite-based remotely sensed data coupled with high resolution meteorological data has the potential of being applied in estimating field-scale planting dates for agricultural evaluations and modeling efforts.

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