Electronic Theses and Dissertations

Availability

Open Access Thesis

Keywords

Evapotranspiration--Measurement; Drone aircraft; Corn--Water requirements; Soybean--Water requirements; Remote sensing;

Abstract

Evapotranspiration (ET) is a key hydrological variable and has been studies to plan irrigation schedule, understand surface-atmospheric interactions, assess crop sensitivity to droughts, etc. On top of that, ET is a proxy for evaluating water availability in trees canopies and assess soil moisture content. The recent advent of the Unmanned Aircraft Systems (UAS) has presented new opportunities and challenges in mapping ET at a much finer scale and under various atmospheric conditions. In this research, we integrate traditional remote sensing techniques with the novel UAS technology to estimate ET and surface energy fluxes for a corn and soybean field near Ames, Iowa, in five different stages of crop development: establishment, vegetative, flowering, yield formation and ripening. Multispectral and thermal cameras onboard the UAS were used to collect imagery that served as primary data for running the Surface Energy Algorithm for Land (SEBAL) model that estimates ET as a residual of the surface energy budget. Other data and materials used for the development of this research include eddy covariance flux towers, meteorological data, leaf area index measured in-situ, ground control points and surface reflectance and surface albedo measured with a field spectroradiometer. The eddy covariance flux towers are managed by the United States Department of Agriculture (USDA) and their data were utilized for calibrating and validating the model. Each tower has an approximated fetch of approximately 200 meters, and 24 tower footprints calculated for each tower using the Flux Footprint Predictions (FFP) model, that accounts for surface roughness, wind speed and friction velocity. The footprints were used to extract the mean value for each raster-energy flux that was compared with the observed values from the flux towers. Statistical methods for validating the energy fluxes produced by SEBAL include linear regression, residual plots, the root mean squared error, mean absolute error and confidence coefficient. The crosscomparison between observed and estimated values for the Net Radiation (Rn) showed an R squared of R2 = 0.71, for the Soil Heat Flux (G) an agreement of R2 = 0.17 for plate 1 and R2 = 0.22 for plate 2, for the Sensible Heat Flux (H) R2 = 0.50 and for the Latent Heat Flux (LE) an agreement of R2 = 0.82. The findings also indicate that ET rates are reliant upon the stage of crop development, where the corn plot had higher ET rates up until the appearing of the tassel, rapidly declining afterwards. The soybean field had a more consistent rate of ET from May through September, possibly due to its extended length of growth. This research concludes that the SEBAL model can be integrated with a a UAS platform for estimating ET and surface energy fluxes at very fine scale, however, atmospheric conditions still affect the accuracy and quality of remotely sensed data.

Year of Submission

2017

Degree Name

Master of Arts

Department

Department of Geography

First Advisor

Bingqing Liang

Date Original

2017

Object Description

1 PDF file (xii, 158 pages)

Language

EN

File Format

application/pdf

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