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MODIS Data Product Non-Technical Description - MOD 35

Cloud Masking Improves MODIS Products’ Accuracy

One of the biggest hindrances to satellite remote sensing of hydrological, ecological, and even atmospheric conditions is our very cloudy Earth. To a cloud microphysicist, routine satellite observations of clouds might seem terrific. But what if you are a climatologist measuring snow pack in the Arctic? Or an ecologist mapping forest cover in Madagascar? Or an oceanographer, measuring ocean temperatures during El Niño events? In those cases, clouds are simply a nuisance. From a satellite’s perspective, they can look like snow, they can block the view, and they can make ocean temperatures seem colder than they are.

To help scientists decide if MODIS’ measurements of a particular location at a specific point in time were based on a cloud-free view of the Earth’s surface, Paul Menzel and his colleagues at the University of Wisconsin-Madison have developed the MODIS Cloud Mask. The product is called a mask because it gives scientists the opportunity to block out, or mask, pixels within a data set that might be contaminated by clouds. (See inset for a discussion of pixels).

MODIS collects data by pixels. In that sense, then, it is similar to a digital camera, where an image is made up of row upon row of thousands, sometimes millions, of tiny little boxes filled with patterns of color. These little boxes are called pixels. MODIS collects data (makes images) of the Earth using pixels of different sizes. The smallest pixels represent an area on the Earth 250m by 250 m—the largest, 1km by 1km.

All MODIS scientists, whether they ultimately want to know about vegetation, temperature, phytoplankton, or snow cover, start with the same basic measurements: radiances, or the amount of light or heat energy registered by MODIS’ photon detectors for a particular Earth scene pixel.

Scientists convert radiances to other land, ocean, or atmospheric characteristics using what they already know about how different objects interact with different kinds of energy. For example, scientists know that vegetation absorbs a lot of red light, and reflects a lot of infrared light. So if MODIS passed over a dense rainforest, the detectors would likely report low radiance values for red light, and high radiance values for infrared light, and biologists could be confident that the area was vegetated.

But what if the area over the rainforest was cloudy? Then the radiance measured by MODIS wouldn’t be the true radiance of the forest. If a biologist based a calculation of the amount of forest cover present on the cloud-contaminated radiance value, the estimate could have serious error. Similar cloud-contamination problems arise for other MODIS products, such as snow and sea ice detection, and sea and land surface temperature.

The MODIS Cloud Mask aims to minimize the potential errors resulting from cloud contamination by labeling every pixel of data as either confident clear, probably clear, uncertain, or confidently cloudy. In general, when MODIS receives energy coming from clouds, that energy is both brighter and colder than it would be if MODIS had a clear view of the Earth. So initial cloud detection begins with these electromagnetic characteristics.

However, there are many situations when the “colder-brighter” rule doesn’t hold up: clouds occurring over snow and ice, thin cirrus clouds, and nighttime stratus clouds that are very close to the surface, just to name a few. So Menzel’s team takes advantage of the fact that MODIS is designed to measure a large number of spectral bands, or ranges of wavelengths of radiation. Those wavelengths interact with water or ice droplets in clouds in different ways, giving the scientists wide variety of tests they can use to look for cloud contamination.

Menzel’s team uses 17 of MODIS’ 36 spectral bands for their tests, which they translate into a computer program that screens the MODIS data stream, testing every pixel of data for cloud contamination. For each of the spectral tests, the program assigns a probability between 1 and 0, where 1 indicates a near 100% probability that the pixel is clear, and 0 indicates almost zero probability that it is clear. Depending on how the pixel performed on the various tests, the program then combines the results of all the tests to classify the pixel as either confident clear, probably clear, uncertain, or cloudy. To be most useful of course, most pixels would fall into the first and last categories, and the team’s initial results indicate that on an average day 90% of pixels are labeled as confidently clear, or confidently cloudy.

The program isn’t perfect; it still makes occasional mistakes over snow and ice and sometimes the desert, whose bare ground can look as bright as a cloud. But as MODIS continues to collect data, the Cloud Mask tests will become more and more refined, yielding increasingly accurate and precise cloud detection, ultimately improving the quality of all MODIS products that depend on it.

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Curator: Brandon Maccherone
NASA Official: Shannell Frazier

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