Quantitative Restoration for MODIS Band 6 on Aqua

Due to the harsh conditions of space, the detectors within satellite-based multispectral imagers are always at risk of damage or failure. In particular, 15 out of the 20 detectors that produce the 1.6-mu m band 6 of Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua are either dead or noisy. In this paper, we describe a quantitative image restoration (QIR) algorithm that is able to accurately estimate and restore the data lost due to multiple-detector failure. The small number of functioning detectors is used to train a restoration function that is based on a multivariate regression using the information in a spatial-spectral window around each restored pixel. The information from other spectral bands allows QIR to perform well even when standard image interpolation breaks down due to large contiguous sections of the image being missing, as is the case for MODIS band 6 on Aqua. We present a comprehensive evaluation of the QIR algorithm by simulating the Aqua damage using the working 1.6-mu m band of MODIS on Terra and then comparing the QIR restoration to the original (unbroken) Terra image. We also compare our results with other researchers' prior work that has been based on the assumption that band 6 could be approximated well solely as a function of the related band 7. We present empirical evidence that there is information in the other 500- and 250-m bands, excluding bands 6 and 7, that can inform the estimation of missing band 6 data. We demonstrate superior performance of QIR over previous algorithms as reflected by a reduced root-mean-square-error evaluation. The QIR algorithm may also be adapted to other cases and provides a powerful and general algorithm to mitigate the risks of detector damage in multispectral remote sensing.