DIRS Laboratory 76-3215
December 7, 2018 at 8:00am
Ph.D. Thesis Defense

  Vegetation monitoring is one of the key applications of earth observing systems. Landsat data have spatial resolution of 30 meters, moderate temporal coverage, and reasonable spectral sampling to capture key vegetation features. These characteristics of Landsat make it a good candidate for generating vegetation monitoring products. Recently, the next satellite in the Landsat series has been under consideration and different concepts have been proposed. In this research, we studied the impact on vegetation monitoring of two proposed potential design concepts: a wider field-of-view (FOV) instrument and the addition of red-edge spectral band(s). Three aspects were studied in this thesis.

First, inspired by the potential wider FOV design, the impacts of a detector relative spectral response (RSR) central wavelength shift effect at high angles of incidence (AOI) on the radiance signal were studied and quantified. Results indicated that the RSR shift effect was more significant in green, red and SWIR2 bands, and will cause a radiance difference exceeding sensor noise specifications in all bands except SWIR1 band.

Second, the impacts of the potential new wider angular observations on vegetation monitoring scientific products were studied. Both crop classification and biophysical parameter retrieval applications were studied using the simulation code DIRSIG and the canopy radiative transfer model PROSAIL. Results show that for single view observation based analysis, the new higher angular observations have limited influence. However, for situations where two different angular observations are available potentially from two platforms, up to 4% and 2.9% improvement for crop classification and leaf chlorophyll content retrieval were found.

Third, the benefits of a potential new design with red-edge band(s) in future Landsat instruments on agroecosystem leaf area index (LAI) and canopy chlorophyll content (CCC) retrieval were studied and quantified using a real dataset. Three major retrieval approaches were tested and results show that retrieval performance were slightly improved.