A long term record of the earth's vegetation is important in studies of global climate change. Over the last three decades, multiple data sets on vegetation have been collected using different satellite-based sensors. There is a need for methods that combine these data into a long term earth system data record.
The Advanced Very High Resolution Radiometer (AVHRR) has provided reflectance measures of the entire earth since 1978. Physical and statistical models have been used to improve the consistency and reliability of this record. The Moderated Resolution Imaging Spectroradiometer (MODIS) has provided measurements with superior radiometric properties and geolocation accuracy. However, this record is available only since 2000. In this thesis, we perform statistical calibration of AVHRR to MODIS. We aim to: (1) fill in gaps in the ongoing MODIS record; (2) extend MODIS values back to 1982.
We propose Bayesian mixed models to predict MODIS values using snow cover and AVHRR values as covariates. Random effects are used to account for spatiotemporal correlation in the data. We estimate the parameters based on the data after 2000, using Markov chain Monte Carlo methods. We then back-predict MODIS data between 1978 and 1999, using the posterior samples of the parameter estimates. We develop new Conditional Autoregressive (CAR) models for seasonal data. We also develop new sampling methods for CAR models.
Our approach enables filling in gaps in the MODIS record and back-predicting these values to construct a consistent historical record. The Bayesian framework incorporates multiple sources of variation in estimating the accuracy of the obtained data. The approach is illustrated using vegetation data over a region in Minnesota.