Jon Starr

Date of Award

January 2020

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Atmospheric Sciences

First Advisor

Jianglong Zhang

Abstract

Due to the rotational needs of crops, homogenous crop fields, and external influences such as market and policy changes, crop production generates significant changes to the landscape on annual and semi-annual basis. In this study we looked at two aspects of this change.

In the first aspect of the study, we attempt to account for market and policy driven producer’s decision making through a new model constructed by pairing an economics model with the ALMANAC crop simulation model via a two-way coupling. This coupled model approach integrated farmer’s land-use choices based on relative economic returns and produced dynamic land use probabilities for ALMANAC simulations through a feedback loop. The coupled model approach was inter-compared with static crop modeling through a historic acreage approach, and comparable accuracies were found from both modeling efforts for the 2014 growing season. Furthermore, as a proof-concept effort, the method was applied to evaluate the impact of two scenarios on crop simulations: major crops (maize, soybean, and wheat) intensification through price increases (e.g. market change), as well as incentivized grassland conservation (e.g. policy change). The results of this sensitivity study suggest that the coupled system has the capability of integrating economic factors into traditional crop simulation, allowing for insight into the impacts of changes in markets and policies on agricultural landscapes and crop yields.

In the second aspect of this study, changes to surface albedo driven by these landscape changes are investigated. Using collocated Moderate Resolution Imaging Spectroradiometer (MODIS) derived Bidirectional Reflectance Distribution Function (BRDF) with the Cropland Data Layer (CDL), we computed the daily albedo of homogenous agricultural fields across the United States for 55 crop types by wavelength, sky-type, day of year, crop, and hardiness zone over a four-year period (2015-2018). This study suggests that cropland spectral albedo is complicated by large variations over the course of the growing season, which can result in changes in reflectivity up to a factor of 2 at most wavelengths. This change was found to be unique per crop type, but predictable year-to-year for individual crops within specific regions, so generating a lookup table that incorporates these factors for use in remote sensing and atmospheric modeling applications is viable for albedo estimation. Additionally, impacts of crop types on broadband albedo were studied and found to be less conspicuous than the individual wavelength counterpart, but still significant over cropland. The results were used to evaluate the accuracy of a common method of albedo estimation, where NDVI is used as a proxy for albedo over cropland, and the NDVI method was found to have some significant limitations dependent on wavelength and day of year. Finally, a database of surface albedo variations as a function of growing period is constructed for 55 crops common to croplands across the United States. The constructed database can be used to aid both satellite remote sensing applications and long-term weather modeling efforts by providing a method for parameter adjustments based on crop driven albedo changes, including changes in cropland composition related to commodity markets and other external factors.

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