A Bayesian Approach for Interpolating Clear-Sky MODIS Land Surface Temperatures on Areas With Extensive Missing Data

Abstract

The MODIS land surface temperature (LST) products contain large areas of missing data due to cloud contamination. Interpolating clear-sky equivalent LSTs on those areas is a first step in a stepwise approach toward fully recovering missing data. A previous study (viz. the Yu method) has implemented an effective clear-sky interpolation method, especially targeting large-area missing data. The Yu method postulates several global reference LST images that contain over 90% of valid pixels and that are assumed to have a close statistical relationship to the interpolated images. However, in practice, such reference images are rarely available throughout a one-year cycle, and the time gaps between the available reference images and the interpolated images are often huge, resulting in compromised interpolation accuracy. In this study, we intended to address those weaknesses and propose a novel clear-sky interpolation approach. The proposed approach uses multiple temporally proximate images as reference images, with which multiple initial estimates are made by an empirically orthogonal function method and then fused by a Bayesian approach to achieve a best estimate. The proposed approach was compared through two experiments to the Yu method and two other widely used methods, i.e., harmonic analysis of time series and co-kriging. Both experiments demonstrate the superiority of the proposed approach over those established methods, as evidenced by higher spatial correlation coefficients (0.90-0.94) and lower root-mean-square errors (1.19-3.64 °C) it achieved when measured against the original data that were intentionally removed.