A Multi-band statistical restoration of the Aqua MODIS 1.6 micron band Irina Gladkova a , Michael Grossberg a , George Bonev a , and Fazlul Shahriar b , a City College of New York, 160 Convent Avenue, New York, NY 10031 b CUNY Graduate Center, 365 Fifth Avenue, New York, NY 10016 ABSTRACT Currently, the MODIS instrument on the Aqua satellite has a number of broken detectors resulting in unreliable data for 1.6 micron band (band 6) measurements. Damaged detectors, transmission errors, and electrical failure are all vexing but seemingly unavoidable problems leading to line drop and data loss. Standard interpolation can often provide an acceptable solution if the loss is sparse. Interpolation, however, introduces a-priori assumptions about the smoothness of the data. When the loss is significant, as it is on MODIS/Aqua, interpolation creates statistically or physically implausible image values and visible artifacts. We have previously developed an algorithm to recreate the missing band 6 data from reliable data in the other 500m bands using a quantitative restoration. Our algorithm uses values in a spectral/spatial neighborhood of the pixel to be estimated, and proposes a value based on training data from the uncorrupted pixels. In this paper, we will present extensions of that algorithm that both improve the performance and robustness of the algorithm. We compare with prior work that just restores band 6 from band 7, and present statistical evidence that data from bands 3, 4, and 5 are also pertinent. We will demonstrate that the increased accuracy from our multi-band statistical estimate has significant consequences at the product level. As an example we show that the restored band 6 has potential benefit to the NASA snow mask for MODIS/Aqua when compared with using band 7 as a replacement for the damaged band 6. Keywords: MODIS, 1.6 micron band, statistical regression, snow-cover mapping 1. INTRODUCTION The MODerate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua and Terra provides crucial earth science data which is used widely. MODIS on Aqua has a particularly unique role because it is part of a constellation of satellites known as the A-Train. Unfortunately the 1.6 micron channel (band 6) of MODIS/Aqua suffers from severe damage. In fact only 5 of the 20 detectors are fully functional, resulting in a severe striping pattern and large gaps in the data, as shown in figure 1. While usually not so severe, damaged detectors resulting in periodic line drop and striping are common. Other classic examples include damaged imagers on Landsat 4 and 5 and more recently the water vapor (WV) 6.2 micron channel on SEVIRI. Before one can use image processing software and higher level retrieval algorithms on striped or damaged images, the missing data must be first estimated in some principled way. While providing masks for missing or damaged data is critical, leaving the task of estimating the missing data estimation to end users is far from ideal. End users may have little or no knowledge of the best practices with which to do this estimation. This is particularly problematic because end users may only download the bands directly related to their target application even though information from other bands may help in estimation of the missing information, and removing stripes. NASA’s current algorithm for filling in the missing values of band 6 MODIS/Aqua is based on a column-wise spatial interpolation. Simple interpolation methods can create statistically or physically implausible image values and visible artifacts. Structural artifacts may not even be apparent through root mean square error (RMSE) metrics. The corruption of the image by naive interpolation, however, becomes obvious from image gradients Further author information: (Send correspondence to Irina Gladkova) E-mail: gladkova@cs.ccny.cuny.edu, Telephone: 212-650-6295