A multiband statistical restoration of the Aqua MODIS 1.6 micron band

摘要

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.