Reconstructing MODIS LST Based on Multitemporal Classification and Robust Regression

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product can offer accurate LST with high temporal and spatial resolution, but the quality is often degraded by cloud. To improve the usability of the MODIS LST, this letter proposes a reconstruction method based on multitemporal data. First, a multitemporal classification is employed to distinguish the different land surface types. The invalid LST values can then be predicted using a robust regression with the multitemporal information from the other LSTs. Finally, postprocessing is proposed to eliminate outliers. Simulated and actual experiments show that the method can accurately reconstruct the missing values.