Thesis - Open Access
Master of Science (MS)
Electrical Engineering and Computer Science
Change point detection, PICS, satellite imagery, Sequential Mann-Kendall test, Time Series, Virtual Constellation
The remote sensing community has extensively used Pseudo-Invariant Calibration Sites (PICS) to monitor the long-term in-flight radiometric calibration of Earth-observing satellites. The use of the PICS has an underlying assumption that these sites are invariant over time. However, the site’s temporal stability has not been assured in the past. This work evaluates the temporal stability of PICS by not only detecting the trend but also locating significant shifts (change points) lying behind the time series. A single time series was formed using the virtual constellation approach in which multiple sensors data were combined for each site to achieve denser temporal coverage and overcome the limitation of dependence related to a specific sensor. The sensors used for this work were selected based on radiometric calibration uncertainty and availability of the data: operational land imager (Landsat-8), enhanced thematic mapper (Landsat-7), moderate resolution imaging spectroradiometer (Terra and Aqua), and multispectral instrument (Sentinel-2A). An inverse variance weighting method was applied to the Top-of- Atmosphere (TOA) reflectance time series to reveal the underlying trend. The sequential Mann–Kendall test was employed upon the weighted TOA reflectance time-series recorded over 20 years to detect abrupt changes for six reflective bands. Statistically significant trends and abrupt changes have been detected for all sites, but the magnitude of the trends (maximum of 0.215% change in TOA reflectance per year) suggest that these sites are not changing substantially over time. Hence, it can be stated that despite minor changes in all evaluated PICS, they can be used for radiometric calibration of optical remote sensing sensors. The new approach provides useful results by revealing underlying trends and providing a better understanding of PICS' stability.
Number of Pages
South Dakota State University
In Copyright - Non-Commercial Use Permitted
Khadka, Neha, "Detection of Change Points in Pseudo-Invariant Calibration Sites Time Series Using Multi-Sensor Satellite Imagery" (2021). Electronic Theses and Dissertations. 5708.