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the associated loss of carbon storage

Using the Moderate Resolution Imaging Spectroradiometer, or MODIS, instruments aboard NASA’s Terra and Aqua satellites, Hilker, Lyapustin and their colleagues developed a new method to detect and remove clouds and other sources of error in the data. It looks at the same location on Earth’s surface day after day over time and analysts pick out a pattern that is stable in contrast to the ever-changing clouds and aerosols. This knowledge of what the surface should look like from earlier observations is used later to detect and remove the atmospheric noise caused by clouds and aerosols. It’s as if the signal from the ground were a song on a static-y radio station, and by listening to it over and over again for long enough, the new method detects and removes the static. By reducing those errors, they increased the accuracy of the greenness measurements over the Amazon.

“We’re much more confident that this is a gap between clouds where we can measure greenness, but standard algorithms would call it a cloud,” said Lyapustin. “We can get more data about the surface, and we can start seeing more subtle changes.”

One of the subtle changes visible in the new data-set is how the Amazon’s greenness corresponds to one of the long-known causes of rainfall or drought to the Amazon basin: changes in sea surface temperatures in the eastern Pacific Ocean, called the El Nino Southern Oscillation. During warmer and dryer El Nino years, the Amazon appears browner. During cooler La Nina wet years, the Amazon appears greener.

In the past, with greenness data, “it’s been hard to tell an El Nino year from a non-El Nino year,” said Lyapustin.