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.