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 indicate: 1) the RSR shift effect is band-dependent and more significant in the green, red and SWIR 2 bands; 2) At high AOI, the impact of the RSR shift effect will exceed sensor noise specifications in all bands except the SWIR 1 band; and 3) The RSR shift will cause SWIR2 band more to be sensitive to atmospheric conditions.
Second, also inspired by the potential wider FOV design, the impacts of the potential new wider angular observations on vegetation monitoring scientific products were studied. Both crop classification and biophysical quantity retrieval applications were studied using the simulation code DIRSIG and the canopy radiative transfer model PROSAIL. It should be noted that the RSR shift effect was also considered. Results show that for single view observation based analysis, the higher view angular observations have limited influence on both applications. However, for situations where two different angular observations are available potentially from two platforms, up to 4% improvement for crop classification and 2.9% improvement for leaf chlorophyll content retrieval were found.
Third, to quantify the benefits of a potential new design with red-edge band(s), the impact of adding red-edge spectral band(s) in future Landsat instruments on agroecosystem leaf area index (LAI) and canopy chlorophyll content (CCC) retrieval were studied using a real dataset. Three major retrieval approaches were tested, results show that a potential new spectral band located between the Landsat-8 Operational Land Imager (OLI) red and NIR bands slightly improved the retrieval accuracy (LAI: R2 of 0.787 vs. 0.810 for empirical vegetation index regression approach, 0.806 vs. 0.828 for look-up-table inversion approach, and 0.925 vs. 0.933 for machine learning approach; CCC: R2 of 0.853 vs. 0.875 for empirical vegetation index regression approach, 0.500 vs. 0.570 for look-up-table inversion approach, and 0.854 vs. 0.887 for machine learning approach).
In general, for the potential wider FOV design, the RSR shift effect was found to cause noticable radiance signal difference that is higher than detector noise in all OLI bands except SWIR1 band, which is not observed in the current OLI design with its 15 degree
FOV. Also both the new wider angular observations and potential red-edge band(s) were found to slightly improve the vegetation monitoring product accuracy. In the future, the RSR shift effect in other optical designs should be evaluated since this study assumed the angle reaching the filter array is the same as the angle reaching the sensor. In addition to improve the accuracy of the off angle imaging study, a 3D vegetation geometry model should be explored for vegetation monitoring related studies instead of the 2D PROSAIL model used in this thesis.
Library of Congress Subject Headings
Remote-sensing images--Data processing; Landsat satellites; Plants--Remote sensing
Imaging Science (Ph.D.)
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Cui, Zhaoyu, "System Engineering Analyses for the Study of Future Multispectral Land Imaging Satellite Sensors for Vegetation Monitoring" (2018). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus