Date of this Version

Fall 10-2012


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Arts, Major: Geography, Under the Supervision of Professors Sunil Narumalani and Don Rundquist. Lincoln, Nebraska: October, 2012

Copyright (c) 2012 Nwakaku M. Ajaere


Measuring biomass in crops is important for yield prediction, nutrient management and analysis of carbon sequestration. Studying crop phenology via biomass can also provide insight into not only the state of the ecosystem but also environmental factors which may affect crop growth. Remote sensing techniques, as an alternative to traditional in-situ sampling methods for biomass assessment, provide potentially more efficient data acquisition and cost-effective procedures. Numerous vegetation indices (VI) have been developed which use spectral reflectance data to measure plant biophysical characteristics. The first objective of this research was to examine the correlation between crop biomass and selected environmental variables at multiple lag periods of 14, 28, 56, and 84 days prior to biomass measurement. Environmental variables studied were daily soil moisture (SM), growing degree days (GDD) and precipitation, and were correlated to field-measured biomass from 2002 – 2011. The second aim of this research was to compare three VIs for predicting the biomass of corn and soybeans in a rain-fed field. The VIs used were Normalized Difference Vegetation Index (NDVI), Red-Edge Chlorophyll Index (CIRed-Edge) and Wide Dynamic Range Vegetation Index (WDRVI).Canopy-level spectral reflectances acquired by a field spectroradiometer and digital aerial images acquired by the AISA-Eagle airborne hyperspectral sensor, during the 2002 – 2008 growing seasons, were analyzed in order to address this objective. Results from biomass correlation with environmental variables were more distinct in corn than soybean and showed that as lag periods increased, there was both increase and decrease in correlations with SM and GDD respectively. Prediction of biomass via VIs showed R2 values which ranged from 0.72 – 0.99, with NDVI having the highest overall.

Advisors: Sunil Narumalani and Don Rundquist